Tool/Resource Listing

nBEST: non-human primates Brain Extraction and Segmentation Toolbox

An accurate, adaptable, and automated AI pipeline called non-human primates Brain Extraction and Segmentation Toolbox (nBEST) for processing NHPs from multi-species, multi-site, and multi-developmental stages. This versatile toolbox supports a wide range of primate species, including macaques and chimpanzees, enabling robust cross-species analysis and facilitating research in comparative neuroimaging.

BabelBrain: Planning software for Transcranial Ultrasound Stimulation

BabelBrain is an open-source standalone graphic-user-interface application designed for studies of neuromodulation using transcranial ultrasound stimulation (TUS). It calculates the transmitted acoustic field in the brain tissue, taking into account the distortion effects caused by the skull barrier. The simulation is prepared using scans from magnetic resonance imaging (MRI) and, if available, computed tomography (CT) and zero-echo time MRI scans. It also calculates the thermal effects based on a given ultrasound regime, such as the total duration of exposure, the duty cycle, and acoustic intensity. The tool is designed to work in tandem with neuronavigation and visualization software, such as 3DSlicer. BabelBrain supports multiple GPU backends, including Metal, OpenCL, and CUDA, and works on all major operating systems, including Linux, macOS, and Windows. This tool is particularly optimized for Apple ARM64 systems, which are common in brain imaging research.

GenMIND: MRI-Derived Neuroanatomical Generative Models and Associated Dataset of 18,000 Samples

GenMIND is a dataset which consists of 18,000 synthetic neuroimaging data samples covering worldwide healthy population across human lifespan.

Dataset Details
The dataset has the following characteristics:

- Generative models were trained on 40,000 subjects from the iSTAGING consortium to synthesize 145 brain anatomical region-of-interest (ROI) volumes which are derived from structural T1-weighted magnetic resonance imaging (MRI).

- The dataset includes participants’ demographic information, such as sex, age and race, which are beneficial for research focusing on mitigating algorithmic bias and promoting fairness.

- Besides 18,000 samples in the dataset, we also share the pre-trained generative models for users to customize their needs for data synthesis at https://huggingface.co/spaces/rongguangw/neuro-synth

- Please check our paper for more details: https://arxiv.org/abs/2407.12897

Quebec Parkinson's Network Processed Data

Data sharing resources for the Quebec Parkinson's Network (QPN). This is for the processed and standardized data.

Atlas of Fetal Brain White Matter Tracts

This is a comprehensive spatiotemporal atlas detailing the development of white matter tracts. It includes a separate atlas for each gestational week between 23 and 36 weeks of gestation. For each gestational age, the atlas includes (1) The Diffusion tensor image, (2) A set of 60 distinct white matter tracts, including commissural, projection, and association fibers. This atlas can serve as a unique and useful resource for neuroscience research and clinical practice, improving our understanding of the fetal brain and potentially aiding in the early diagnosis of neurodevelopmental disorders. It had been constructed from a population of 59 fetuses.

fNIRS Social Interaction Dataset

You can find 2 fNIRS datasets here.
The first dataset consists of fNIRS hyperscanning dataset collected during a real social interaction experiment, along with a up-to-5-minute resting-state measurement.
The second dataset is a simulated fNIRS dataset representing two-person social interactions.

Diffusion-informed spatial smoothing atlas for white matter fMRI

If you are working with white matter fMRI, the diffusion-informed spatial smoothing (DSS) atlas is a valuable tool for enhancing your preprocessing pipeline. White matter BOLD signals are weaker in power and anisotropically oriented, so the typical isotropic Gaussian smoothing applied to gray matter fMRI averages out much of the signal in the white matter. Abramian et al. introduced a graph signal processing approach to smooth white matter fMRI using smoothing windows shaped by diffusion information (https://doi.org/10.1016/j.neuroimage.2021.118095), but this method needs paired diffusion MRI and fMRI data.

Here, we provide the DSS atlas to allow for anatomically-informed smoothing when diffusion information is not available, using information from the Human Connectome Project Young Adult population-averaged fiber orientation distribution functions. See https://dss_fmri_atlas.projects.nitrc.org/ for downloading the atlas files and https://github.com/MASILab/dss_fmri_atlas for more info on using the atlas.

Augmentation and Computation Toolbox for Brain Network Analysis

The Augmentation and Computation Toolbox for braIn netwOrk aNalysis (ACTION) is an open-source Python software, designed for functional MRI data augmentation, brain network construction and visualization, extraction of brain network features, and intelligent analysis of brain networks based on AI models pretrained on 3,800+ resting-state fMRI scans. Through a graphics user interface, the ACTION aims to provide users with comprehensive, convenient, and easy-to-use fMRI data analysis services, helping users simplify the processing pipeline and improve work efficiency.

Public nEUro

EU GDPR compliant data sharing
Thanks to the Open Neuro PET project, we have built a GDPR compliant repository to share brain imaging data as FAIRly as possibly. The principle is similar to OpenNeuro where each BIDS dataset is shared independently. As EU data have to be protected, we provide the necessary privacy protection.

OASIS3_AV1451_Longitudinal

This set is a subset of OASIS-3 subjects that have also undergone longitudinal TAU (AV1451) PET imaging. For access to this project email oasis-brains@nrg.wustl.edu for more instructions.

OASIS-3 TAU: OASIS-3 Flortaucipir F18 (AV1451) PET

In addition to OASIS-3 data, we introduce ‘OASIS-3_AV1451’ a cross-sectional TAU PET dataset for 451 subjects including 451 PET sessions and post-processed PUP.
This set is a subset of OASIS-3 subjects that have also undergone TAU (AV1451) PET imaging. For access to this project send detailed research statement to oasis-brains@nrg.wustl.edu

OASIS-4: Clinical Cohort

OASIS-3 and OASIS-4 are the latest releases in the Open Access Series of Imaging Studies (OASIS) that is aimed at making neuroimaging datasets freely available to the scientific community. By compiling and freely distributing this multimodal dataset generated by the Knight ADRC and its affiliated studies, we hope to facilitate future discoveries in basic and clinical neuroscience.
OASIS-4 contains MR, clinical, cognitive, and biomarker data for 663 individuals aged 21 to 94 that presented with memory complaints.
This is a unique dataset and not an update to the OASIS-3 Longitudinal Multimodal Neuroimaging dataset.
https://sites.wustl.edu/oasisbrains/home/oasis-4/

OASIS-3: Longitudinal Multimodal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer’s Disease

OASIS-3 is a retrospective compilation of data for 1378 participants that were collected across several ongoing projects through the WUSTL Knight ADRC over the course of 30years. Participants include 755 cognitively normal adults and 622 individuals at various stages of cognitive decline ranging in age from 42-95yrs. All participants were assigned a new random identifier and all dates were removed and normalized to reflect days from entry into study. The dataset contains 2842 MR sessions which include T1w, T2w, FLAIR, ASL, SWI, time of flight, resting-state BOLD, and DTI sequences. Many of the MR sessions are accompanied by volumetric segmentation files produced through FreeSurfer processing. PET imaging from different tracers, PIB, AV45, and FDG, totaling over 2157 raw imaging scans and the accompanying post-processed files from the Pet Unified Pipeline (PUP) are also available in OASIS-3.
https://sites.wustl.edu/oasisbrains/home/oasis-3/

A Multiple Session Dataset of NIRS Recordings From Stroke Patients Controlling Brain–Computer Interface

We provide the experimental records, optodes placement data, online BCI performance, and a MATLAB code for simple analysis of hemodynamic response. Each filename includes subject ID, day and session number. The file contains a tabulated configuration of the channels (source–detector pairs in the 10–10 system), the raw light intensity data for each channel on both wavelengths, HbO and HbR concentrations in mmol/l, labels of mental tasks for each time point, and a confusion matrix of online classification.

Details:
Associated publication: https://doi.org/10.1038/s41597-024-04012-6
MATLAB format
Stimuli type: imagined hand movements
15 stroke patients
7-24 sessions with online classification and visual feedback for each patient, 237 sessions in total
Session duration: 9 or 14 mins
50 hours of recordings in total
14 sources
8 detectors
28 channels on each wavelength, 56 in total
Sample Rate: 15.6 Hz
Wavelengths: 760/850 nm
Acquisition System: NIRScout

EEGExtract

A python package for extracting EEG features (detailed in the article "Unsupervised EEG Artifact Detection and Correction" in Frontiers in Digital Health, 2020). EEGExtract allows for rapid extractions of dozens of diverse types of EEG features, enabling rapid preprocessing and outlier detection, as well as application of ML classification algorithms for downstream tasks. EEGExtract has been used in emotion recognition, movement intention identification, epilepsy biomarkers discovery and much more.

eeg_pipelines

This repository contains 4 stand-alone pipelines (along with one test dataset), in EEGLAB, Fieldtrip, Brainstorm, and MNE. There is also a link to the HAPPE EEGLAB-based pipeline. The pipelines have been optimized to process event-related potential and are described in the associated manuscript. All pipelines run on the sample data provided although they do require a separate installation of the corresponding software packages.

Based on our scanning of the hyperparameter space for artifact rejection and preprocessing, these are the best automated EEG pipelines for EEGLAB, Fieldtrip, Brainstorm, and MNE to process ERP. Test them yourself by plugging in your data.

Dusk2Dawn

Dusk2Dawn is a plugin for the popular 'EEGLAB' MATLAB toolbox, which allows you to easily clean whole-night sleep EEG using Artifact Subspace Reconstruction (ASR). As ASR must be calibrated using relatively clean reference data, and there is considerable variability in signal-to-noise ratio across and within sleep-stages, a standard ASR applied to the whole-night would lack sensitivity during lower-amplitude N1 and REM stages, while inappropriately removing large-amplitude brain signals such as slow-waves in N2 and N3 stages. Dusk2Dawn therefore implements two methods to solve this problem: (1) Running ASR separately for each sleep stage, (2) Running ASR in a short sliding-window (e.g. 4 - 16 minutes). The other main benefit of Dusk2Dawn is that it allows you to easily and automatically run ASR several times with different sets of parameters (e.g. different levels of ASR severity) and easily test and visualize the effects of each run-through, before choosing which final dataset to use for your analysis.

PeriodAmplitudeAnalysis

Period Amplitude Analysis (PAA) for slow wave detection using eeglab

DetectSpindles

Method for detecting sleep spindles using EEGlab functions and datasets. A spindle detection method was developed that: (1) extracts the signal of interest (i.e., spindle-related phasic changes in sigma) relative to ongoing “background” sigma activity using complex demodulation, (2) accounts for variations of spindle characteristics across the night, scalp derivations and between individuals, and (3) employs a minimum number of sometimes arbitrary, user-defined parameters.

Counting Sheep PSG

EEGLAB-compatible manual sleep stage scoring, signal processing and event marking of polysomnographic (PSG) data for MATLAB. The "Event Marking Mode" allows users to create and manage events in an EEG analysis interface. An 'all channels' mode enables event marking without specifying a channel. Users can select or deselect existing events for deletion or merging with a single click. The system includes signal processing features using built-in EEGLAB functions, such as interpolating bad channels, downsampling, filtering, re-referencing, and channel editing, all in beta versions. It also offers Independent Component Analysis (ICA) and Power Spectral Analysis using Fast Fourier Transform (FTT), also in beta. The "Automatic Event Detection" feature includes automatic detection of movement artifacts, spindles (using EEGlab's 'detect_spindles' plugin), and slow waves (using EEGLAB's 'PAA' plugin).

EEG Deep Learning Library

EEG-DL is a Deep Learning (DL) library written by TensorFlow for EEG Tasks (Signals) Classification. The platform provides access to the most advanced deep learning algorithms, which are regularly updated to ensure their effectiveness.

Related Work:
1. A Novel Approach of Decoding EEG Four-class Motor Imagery Tasks via Scout ESI and CNN
Link: https://iopscience.iop.org/article/10.1088/1741-2552/ab4af6/meta
2. GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals
Link: https://ieeexplore.ieee.org/document/9889159
3. Deep Feature Mining via Attention-based BiLSTM-GCN for Human Motor Imagery Recognition
Link: https://www.frontiersin.org/articles/10.3389/fbioe.2021.706229/full
4. Attention-based Graph ResNet for Motor Intent Detection from Raw EEG signals
Link: https://arxiv.org/abs/2007.13484

BrainFlow

BrainFlow is a library intended to obtain, parse and analyze EEG, EMG, ECG, and other kinds of data from biosensors. It provides a uniform SDK to work with biosensors with a primary focus on neurointerfaces, all features available for free! BrainFlow offers a powerful and straightforward API with features for data acquisition, signal filtering, denoising, and downsampling, making development simpler. It supports multiple programming languages with a uniform API across all boards, facilitating ease of use, extension, and integration testing through its CI/CD system.

Locus Coeruleus Manual Labels for 20 HCP Subjects

This dataset contains locus coeruleus (LC) labels for the first 20 subjects of the "100 Unrelated Subjects" group of the Human Connectome Project, as described by Aganj et al (Front. Neurosci. 2024). The LC labels were manually delineated on preprocessed 3-Tesla T1-weighted MPRAGE magnetic resonance images based on geometry (rather than contrast).

OHSU Ferret Developmental Brain Atlas

This is an atlas of the developing ferret brain over the postnatal day (P)8 to P38 age range. It consists of T2-weighted MRI brain templates derived from 10 animals (5 male, 5 female), each scanned at ages P8, P14, P20, P26, P32, and P38. The templates have been annotated with atlas labels for 12-14 brain regions, depending on the age. The images used to generate templates were acquired using a 12T horizontal bore small-animal imaging system and a single-channel surface coil radiofrequency receiver. Image resolution consisted of isotropic 0.25 mm-sided voxels. Addition details are described in Cerebral Cortex, 34(4):bhae172 (2024). https://doi.org/10.1093/cercor/bhae172

ICH_PHE_Segmentation

ICH & PHE segmentation from non-contrast head CT v5
Input: non-contrast head CT
Output: ICH + PHE region

II) Run the application on Linux
1. Download the file "form*.zip"
2. Extract the file
3. Go to the folder: formV5
4. Run the command: ./formV5

III) Using the application
1. Segmentation
- Select "Load 3D head CT" and browse to the file. It will show the file information and we can select "Show brain window"
- Select "Segmentation". After running, it will show the status: error or successful.
- Select "Show results" to see brain+ICH+PHE
2. Batch segmentation
- Select "Load CT folders": browse to the folder containing *.nii.gz
- Select "Segmentation All": it will show the status of processing each patient.
All the file segmentation results are saved in the same folder of input files + "\output".
3. We uploaded the Lite version to run faster in your computer. Please contact if you need more information.

HELM - Hypothalamic ex vivo Label Maps

Here we present a dataset of synthetic, high-resolution MRI scans with labels for the hypothalamic subregions, which can be used to develop segmentation methods based on synthetic images. The dataset is composed of label maps built from publicly available ultra-high resolution ex vivo MRI from 10 whole hemispheres, using a combination of manual labels for the hypothalamic nuclei and automated segmentations for the rest of the brain, obtained with unsupervised clustering. The final label maps are mirrored to simulate an entire brain. We will also provide the pre-processed ex vivo scans, once the dataset supports an extension to other structures by manually tracing them.
The original ex vivo images are public available on DANDI Archive (https://dandiarchive.org/dandiset/000026)The codes to generate new label maps can be found at https://github.com/liviamarodrigues/hsynex, along with the codes for the automated hypothalamus segmentation method.
This dataset is under CC-BY license.

The Data Management and Sharing Project

This project is a clearinghouse and community meeting place to collect and disseminate resources and knowledge related to supporting researchers in preparing and executing their Data Management and Sharing Plans. An overview is provided in the MediaWiki of this project: https://www.nitrc.org/plugins/mwiki/index.php/dsmp:MainPage

We value and encourage the community's input and suggestions to add to this project.

RT-OSIRIS: Real-Time Optimal Synthetic Inversion Recovery Image Selection

RT-OSIRIS is a tool for real-time synthetic inversion recovery image calculation from T1 maps, including those of sub-millimeter spatial resolution (e.g., 7T MRI). Select the optimal inversion time to highlight structures of interest and export the result.

Google CoLab

Colaboratory is product from Google Research. Allows anybody to write and execute arbitrary python code through browser, and is especially well suited to machine learning, data analysis and education. Colab is hosted Jupyter notebook service that requires no setup to use, while providing free access to computing resources including GPUs.

This project is administratively added to NITRC for completeness; use with caution. The information was set from public content on October 6, 2023, and is not maintained. If you are or you know an appropriate administrator, contact moderator@nitrc.org.

VUIIS - 3T - Functional Contrasts - Resting/MotorStim/SensoryStim/Visual

Six sets of images sensitive to BOLD contrast (TR=3s, TE=45ms, SENSE=2, matrix=80×80, FOV=240×240mm2, 3 mm thick slice with zero gap, 145 volumes). The BOLD images contained a resting state acquisition (Resting), sensory stimulations to the right hand and the left hand (Sensory-R and Sensory-L), motor tasks by the subjects’ right hand and left hand (Motor-R and Motor-L), and visual stimulations (Visual).

All stimulations/tasks were prescribed in a block design format (30s on, 30s off), with five additional volumes acquired without stimulations or tasks at the beginning of each run.

The blocks of stimulation or task were repeated seven times for each of the functional acquisitions. The total acquisition time was 435s, yielding 145 volumes for each task/stimulation.

MRI acquired on a 3-T Philips Achieva CRX scanner at Vanderbilt University Institute of Imaging Science.

Data includes T1-weighted images, and diffusion data (2x2x2mm3, 32 DWIs, b=1000).

Synesthesia HCP (Human Connectome Project) Dataset

A neuroimaging database consisting of 102 synesthetic brains (and a smaller, N=25, sample of non-synesthetes) using 3T MRI protocols from the Human Connectome Project (HCP). Images processed through the HCP version 4.3.0 pipeline consist of Freesurfer surface reconstructions, rfMRI data (1952 timepoints), subject-specific parcellations (MSMAll), subject-specific node timeseries, and subject-specific parcellated connectomes. Raw data in HCP format are also included to allow re-running of HCP processing with future versions of the pipeline.

Statistical Power Tool for Whole Brain Connectome (BNPower)

The simulation-based procedure for the power calculation of data-driven network analysis. This procedure consists three steps: i) simulate M brain connectome data sets under H_1; ii) perform statistical inference; iii) calculate the power as the proportion of successfully rejecting the null hypothesis in ii) for all M datasets. The power calculation for the network outcome is determined by the sample size S, level of significance alpha, and effect sizes (Cohen's $d$ or $f^2$), which are the same as univariate cases. Additionally, users need to specify the network-specific parameters, such as $N, |V_c|, \rho_0, \rho_1$. In accordance with Helwegen et al. , these required parameters are used to characterize the ``network organization". In addition, to better approximate the statistical power, the number of repetitions $K$ and permutation tests $M$ also helps determine the quality of obtained power.

Neurodesk

Neurodesk provides a containerised data analysis environment to facilitate reproducible analysis of neuroimaging data.

The Neurodesk platform includes a browser-accessible Linux desktop environment integrated into Jupyterlab, a cross-platform desktop client and a command line interface. These mediate access to the containerised Neurodesk software libraries from a variety of systems, including personal computers, cloud computing, high-performance computers, and computational notebooks.

To find out how to get started visit: https://www.neurodesk.org/

INI-32 Orbit CT Atlas

The INI-32 CT atlas of the orbit provides an alignment target and reference space for morphometric imaging studies of the eye and surrounding anatomy.

NIDM-terms Community Terminology Browser

We have developed a JavaScript User Interface (UI) hosted on GitHub Pages to facilitate neuroimaging communities'' involvement in annotating and sharing terminologies for neuroimaging datasets. The UI allows community curators to interact with their own terminology and reuse terms from other neuroimaging communities. It is designed around Git and GitHub version control systems, enabling direct interaction with the NIDM-Terms GitHub repository. The UI uses JSON-LD files from the NIDM-Terms GitHub repository in the background. Its functionalities include browsing, searching, and suggesting edits to available terms and properties, suggesting new terms, and exporting selected terms and properties in various formats such as Markdown, JSON, JSON-LD, CSV, and N-Quads. Additionally, the UI allows for adding new neuroimaging communities to NIDM-Terms. Visit the NIDM-Terms UI at https://nidm-terms.github.io/.

ExploreASL

ExploreASL is a pipeline and toolbox for image processing and statistics of arterial spin labeling perfusion MR images. It is designed as a multi-OS, open-source, collaborative framework that facilitates cross-pollination between image processing method developers and clinical investigators.
The software provides a complete head-to-tail approach that runs fully automatically, encompassing all necessary tasks from data import and structural segmentation, registration, and normalization, up to CBF quantification. In addition, the software package includes quality control (QC) procedures and region-of-interest (ROI) as well as voxel-wise analysis of the extracted data. The ultimate goal of ExploreASL is to combine data from multiple studies to identify disease-related perfusion patterns that may prove crucial in using ASL as a diagnostic tool and enhance our understanding of the interplay of perfusion and structural changes in neurodegenerative pathophysiology.

BIDS Derivatives

BIDS Derivatives is a set of principles for organizing and describing outputs of computations performed on brain imaging data, enabling researchers and tools to understand and reuse those outputs in subsequent processing. BIDS Derivatives is an extension to the Brain Imaging Data Structure (BIDS), which is a standard for organizing magnetic resonance imaging (MRI), electrophysiological and behavioral data generated by a broad range of neuroscientific experiments.

Neurosynth Compose

Neurosynth compose allows you to perform an entire meta-analysis directly from the browser. It provides a centralized location to edit, organize, share, and keep provenance of meta-analyses.
Use neurosynth-compose for
Performing custom meta-analyses
Creating systematic reviews
Updating and replicating existing reviews
Analyzing neurosynth/neuroquery databases

BIDScoin

BIDScoin is a user-friendly open-source Python application that converts (“coins”) source-level (raw) neuroimaging data sets to standardized data sets that are organized according to the BIDS specification. BIDScoin uses a mapping approach to discover your repository's different source data types and convert them into BIDS data types. Different runs of source data are uniquely identified by their file system properties (e.g. file name or size) and their attributes (e.g. ProtocolName from the DICOM header). While this command-line procedure exploits all information available on disk, BIDScoin presents a Graphical User Interface (GUI) for researchers to check and edit these mappings. Data conversions are performed within plugins, such as plugins that employ dcm2niix, spec2nii or nibabel.

BIDScoin requires no programming knowledge in order to use it, but users can use regular expressions and plug-ins to further enhance BIDScoin’s power and flexibility, and readily handle a wide variety of source data types.

ANDA

The ANDA analysis workflow quantifies various aspects of neuronal morphology from high-throughput in-vitro live-cell imaging screens. Using ANDA, neuronal cell counts, neurite lengths, and neurite attachment points can be automated from phase-constrast neuronal differentiation images. This open-source tool is easy to use and can automatically process neuronal cell time-course measurements. ANDA can analyse images from rat, chicken and human in vitro nuronal models.

Osprey: Open-source processing, reconstruction & estimation of magnetic resonance spectroscopy data

Osprey is an all-in-one software suite for state-of-the art processing and quantitative analysis of in-vivo magnetic resonance spectroscopy (MRS) data.

### Features
- 1-file job definition system for reproducible data analysis
- Automated recognition of input file format and sequence origin
- Fully-automated loading and pre-processing pipeline for optimal SNR, linewidth, phasing, and alignment
- Integrated linear-combination modeling module
- Full density-matrix simulated basis sets
- Functions to create custom basis sets and import basis sets from LCModel or Tarquin
- Integrated voxel co-registration and segmentation module (requires SPM12)
- Quantification based on tissue fractions and (customizable) metabolite/tissue water relaxation times
- GUI to display data, quality assessment, and quantitative results at each step of the analysis
- Rich 'Overview' GUI panel for batched datasets to visualize distributions of metabolite estimates and mean +/- SD spectra

brainplotlib

brainplotlib is a Python package that plots data on cortical surface. It''s designed to have minimal requirements --- only NumPy and matplotlib.

Data for Research toolbox

This is the data for the python package 'research_toolbox'

Neuroscience Data Interface

Software tool as platform independent data interface and database for neuroscience physiology and imaging experiments. Platform independent standard that allows analyst to use and create software that functions independently from format of raw data or manner in which data are organized into files. Interface is rooted in simple vocabulary that describes common apparatus and storage devices used in neuroscience experiments. Results of analyses, and analyses of analyses, are stored as documents in scalable, queryable database that stores relationships and history among experiment elements and documents. Interface allows development of application ecosystem where applications can focus on calculation rather than data format or organization. This tool can be used by individual labs to exchange and analyze data, and it can serve to curate neuroscience data for searchable archives.

Cell Type Knowledge Explorer

The Cell Type Knowledge Explorer is a web application designed for exploration of individual cell types in motor cortex. In includes multimodal data from thousands of cells across 3 mammalian species.

Automated Fiber Quantification in Python

Software package focused on automated delineation of major fiber tracts in individual human brains, and quantification of tissue properties within the tracts.Software for automated processing and analysis of diffusion MRI data. Automates tractometry.

LocaliZoom - the companion web application for QuickNII and online counterpart for VisuAlign

LocaliZoom is a pan-and-zoom type viewer displaying high-resolution image series coupled with overlaid atlas delineations.

Three operating modes
- Display series with atlas overlay. Both linear and nonlinear alignments are supported (created with QuickNII or VisuAlign)
- Create or edit nonlinear alginments
- Create markup which can be exported as MeshView point clouds or to Excel for further numerical analysis.

LocaliZoom allows working with images in their original resolution, however the images need to be available in Deep Zoom format. While converting image data to Deep Zoom falls outside of the scope of LocaliZoom itself (and thus we offer suggestions only), LocaliZoom is part of the EBRAINS online analytical workflow (free for researchers) where we offer conversion too. Please visit https://ebrains.eu/register/ for details.

Demo deployment (also with GitHub and documentation links): https://object.cscs.ch/v1/AUTH_7e4157014a3d4c1f8ffe270b57008fd4/localizoom/!LocaliZoom/index.html

Mozak: Brain Builder

Mozak is a browser based tool that allows anyone to help reconstruct neurons, without the need to download any additional software or large datasets beforehand. Quickly jump in a view a 3D image of a neuron cell and start reconstructing the signal using intuitive click and drag based tools. All the work you and other users do on a cell are combined into a consensus trace that is viewable by everyone, that way you can see which areas other people have already traced and focus your efforts on unexplored areas of the cell. This final consensus is then sent to our partner labs that provided us the images for use in morphology related analysis.
Visit us here https://www.mozak.science

Data for 'High-frequency neuronal signal better explains multi-phase BOLD response'

Data for 'Zhang, Qingqing, et al. "High-frequency neuronal signal better explains multi-phase BOLD response." NeuroImage (2023): 119887.'

Data for 'Brain-wide ongoing activity is responsible for significant cross-trial BOLD variability'

Data for 'Brain-wide ongoing activity is responsible for significant cross-trial BOLD variability' (Zhang, Qingqing, et al. Cerebral Cortex 32.23 (2022): 5311-5329).

PET-CT mouse brain toolbox

The proposed toolbox provides multi-modality murine image templates (FDG-PET, CT and T2-MR), together with a ROIs atlas. All these images are spatially registered to the Allen Brain Atlas, but they have been optimized for in-vivo preclinical imaging in terms of resolution and ROI size. The ROIs have been derived from the Allen Brain Atlas. This toolbox has been described in Presotto et al (2022) in Scientific Reports: "Development of a new toolbox for mouse PET–CT brain image analysis fully based on CT images and validation in a PD mouse model", doi: https://doi.org/10.1038/s41598-022-19872-4

The package includes also few scripts to make it easier using this set of templates within SPM (version 12)

Unsupervised Cross-Domain Functional MRI Adaptation

This unsupervised functional MRI adaptation tool (called UFA-Net) is designed to model spatio-temporal fMRI patterns of labeled source and unlabeled target samples via an attention-guided graph convolution module, and also leverage a maximum mean discrepancy constrained module for unsupervised cross-site feature alignment between two domains. This facilitates unsupervised functional MRI adaptation/harmonization for multi-site research.

NIMH MIB MRS

This is a data repository used by researchers from the Molecular Imaging Branch of the National Institute of Mental Health, National Institutes of Health to share Magnetic Resonance Spectroscopy (MRS) data.

DomainATM: Domain Adaptation Toolbox for Medical Data Analysis

DomainATM is designed to facilitate researchers using different methods for feature-level and image-level biomedical data adaptation, data visualization, and analysis performance evaluation. It is implemented in MATLAB with a user-friendly graphical interface, and it consists of a collection of popular domain adaptation algorithms that have been extensively applied in the fields of biomedical data analysis and computer vision. It enables the users to develop and test their own data adaptation methods through scripting which greatly enhances its utility and extensibility.

C57BL anatomical mouse brain MRI atlases

In-vivo (N=11) and ex-vivo (N=10) C57BL anatomical mouse brain atlases are created by Brookhaven National Laboratory and National High Magnetic Field Laboratory research collaboration. 3D volumetric anatomical MRI reference data and corresponding manually delineated labels are shared in nifti image format.

The work has been published (PMID: 16165303 and 18958199) and the authors would like to acknowledge the financial support of the National Institute of Health through grants R01 EB 00233–04 and P41 RR16105 and the National High Magnetic Field Laboratory.

Human Connectome to BIDS

This python tool converts Human Connectome project data format into BIDS Structure.

frequently traveling human phantom (FTHP) MRI dataset

The dataset provides 557 images of the brain of a single healthy male volunteer.
scans were acquired on 116 different MRI machines
For some scanners, the volunteer was scanned in several imaging sessions with different MRI protocol setting.
Altogether the dataset includes data from 157 imaging sessions with 3-5 repeated scans in each session
Download at:
https://www.kaggle.com/datasets/ukeppendorf/frequently-traveling-human-phantom-fthp-dataset

ReproRehab

Data science provides an exciting opportunity to significantly improve the rigor of rehabilitation research by increasing the reproducibility and replicability of research. Data science methods, which rely on computer programming skills for data intake, management, storage, and analysis, can increase reproducibility, but clinical and basic rehabilitation researchers may have challenges adopting these practices due to a lack of fundamental training in computer programming.

Our new Reproducible Rehabilitation (ReproRehab) research education program aims to build a sustainable national workforce of rehabilitation researchers equipped with basic data science skills in five years.

Data Processing Assistant for Resting-state fMRI

NAcc, amygdala, hippocampus, mOFC and BA25 masks were created using the WFU_PickAtlas toolbox (Wake Forest University School of Medicine) as implemented in SPM12

NITRC Tool Performance

NITRC tool performance.

Effect of autobiographical false memory on the complexity of neural oscillations

Cerebral phonology of this research by Contact instruments psych lab
EEG 891 channels were monitored. This device is a product of Lehman country. The impedance of the electrode is below
1 kV was maintained. The signals are amplified with an EEG amplifier tuned to a filter
Passing down from 43 Hz and fixed time of 83 seconds, it is digitized by sampling vector 8314
Hz was performed and the male was reduced to 113 Hz for better analysis
However, analyses were performed only on the data acquired during the recognition phase. Electrodes were utilized in a 10-20 system of electrode placement (FPZ, FP1, FP2, F7, F3, Fz, F4, F8, FT7, FC3, FCZ, FC4, FT8, T3, C3, CZ, C4, T4, TP7, CP3, CPZ, CP4, TP8, T5, P3, Pz, P4, T6, O1, Oz and O2) with left mastoids (behind the ear) as the ground, and right lobule of auricle as the reference electrode. With a sampling frequency of 256Hz, the electrode impedance was less than 5kΩ for the duration of the experiment.

Eagle Atlas Compilation for Vestibular Research

This collection of atlases is provided by the Post-Concussion Vestibular Dysfunction (PCVD) research group at the Emory University School of Medicine, Atlanta, Georgia, United States. It consists of seven source atlases at 1mm isotropic resolution which are customized to facilitate functional and structural connectivity analyses of the vestibular system. Each source atlas has been processed to ensure that there are no overlaps among the ROIs. This atlas collection is intended for use with the CONN Toolbox (https://web.conn-toolbox.org). Simply import the GZipped NIfTI files and alert CONN that they are atlases by clicking the "Atlas" checkbox.

Source atlases include ROIs from Eickhoff et al. and Indovina et al., the Brainnetome Atlas, Diedrichsen/SUIT Cerebellar Atlas, Najdenovska Thalamic Atlas, brainstem and diencephalon ROIs from Brainstem Navigator, and the Neudorfer Anatomical Hypothalamus Atlas. If you leverage this atlas collection, you MUST cite the source references in any publications.

Multi-contrast PD126 and CTRL17 templates

Presenting PD126 and CTRL17: population-based multi-contrast templates for 126 Parkinson’s patients and 17 controls. These templates provide anatomical structural references for spatial normalization and structural segmentation of Parkinson's imaging data in the nine available contrasts: T1w (MPRAGE), T2*w, T1-T2* fusion, R2*, T2w, PDw, fluid-attenuated inversion recovery (FLAIR), neuromelanin-sensitive imaging, susceptibility-weighted imaging. The accompanying CTRL17 template facilitates group-wise analyses between Parkinson's and healthy controls. The templates are in stereotaxic space and are available in three different resolutions: 1×1×1 mm, 0.5×0.5×0.5 mm, and sectional 0.3×0.3×0.3 mm.

QCAlign - quality control for the QUINT workflow

Documentation: https://qcalign.readthedocs.io

QCAlign was developed to support the use of the QUINT workflow for high-throughput studies. The QUINT workflow supports spatial analysis of labelling in series of brain sections from mouse and rat based on registration to a reference atlas.

QCAlign provides information about:

1. The quality of the section images used as input to the QUINT workflow: detect regions that are affected by tissue damage, labelling defects, artifacts or errors in image acquisition.

2. The quality of the atlas-registration performed in the QUINT workflow: detect regions that are poorly registered, or where the registration cannot be verified.

3. QCAlign makes it easier for the user to explore the atlas hierarchy and decide on a customized hierarchy level for the investigation.

For user support: support@ebrains.eu

Machine Learning in NeuroImaging (MALINI)

Machine Learning in NeuroImaging (MALINI) is a MATLAB-based toolbox used for feature extraction and disease classification using resting-state functional magnetic resonance imaging (rs-fMRI) data. Eighteen different popular classifiers are presented. With slight modifications, it can also be used for any classification problem using any set of features.

Logitudinal research for social media

Social media is pervasively used in our life. There is a research hypothesis that the information in social media will cause readers' addiction, insomnia, and inability to pay attention, thus reducing the efficiency of learning and working. The aim of this research is to thoroughly investigate the effects of social media on the human brain using neuroimaging techniques and to predict these effects using genetic data.
Finally, 77 patients were enrolled, and to study this problem, we integrated multimodal MRI, longitudinal design, and genotype sampling. Cerebral blood flow (CBF), functional connectivity (FC), and white matter integrity were all reflected using multimodal MRI. The longitudinal design included short-term and long-term social media tasks as well as MRI scanning at baseline, after the short-term task, and after the long-term task. APOE and BDNF genotypes were sampled for genotypes. A group of participants were enrolled in a control group who read science fiction on their smartphones.

EEG Sleep Closed-loop TMR

The EEG Sleep Closed-loop TMR ( related preprint: https://doi.org/10.1101/2022.03.29.486197) is a freely available collection of EEG recordings on healthy volunteers.

Electrophysiology, resting state fMRI and respiration in rats

The dataset includes electrophysiology, resting state fMRI and respiration recorded from seven adult male Long-Evans rats (total 99 scans, 20min/scan) at light sedation and isoelectric state. Both raw and preprocessed data are included.

BrainVoyager EDU

The freely available educational version of BrainVoyager (“BrainVoyager EDU”) allows anyone to learn how to analyse structural and functional MRI datasets using the powerful computational tools and visualization features of the commercial BrainVoyager product.
BrainVoyager EDU is a fully functional version of BrainVoyager but operations are limited to specific source files, referred to as “enabled datasets”.
These enabled datasets are accompanied by extensive tutorials describing step-by-step how to analyse structural and functional MRI data with BrainVoyager. At present there are two bundled documents (“Basic Visualizations using Brain Tutor Data” and the “Getting Started Guide”) and more will be added over time. Processing of enabled datasets is not limited in any way, i.e. the data can be processed using any BrainVoyager tool, and nearly any plugin or script.

Turbo-Satori

Turbo-Satori is a real-time analysis program for functional near-infrared spectroscopy (fNIRS) data (at present only for hardware from NIRx company). While the also available Satori program is aimed at offline analysis, Turbo-Satori is optimised for real-time applications such as neurofeedback and brain computer interface (BCI) applications. The software supports online oxy/deoxy concentration value calculations from raw wavelength data and it provides advanced in-built neurofeedback options.
The program calculates and shows oxy and deoxygenated haemoglobin concentration values. These values will be used for time course displays and neurofeedback as default except in case that the “Show raw WL data” option is set explicitly. For a good channel, the oxy signal should move upward during activation while the deoxy channel should move downward; the deoxy signal (negative) amplitude should also be about 0.1-0.2 the amplitude of the oxy signal.

EEG2BIDS Wizard

EEG2BIDS can be downloaded from GitHub and installed within a few minutes. Its sequenced workflow walks users through data labelling and anonymization steps, and the embedding of supplemental metadata into a complete BIDS dataset. MNE-BIDS libraries provide standardization as well as anonymization functionality, and as a final step, the app runs the BIDS Validator locally to ensure data outputs are BIDS compliant.
The app’s GUI components are written using React and Node.js, supported by a Python server facilitated by a Socket.io library. The app can connect securely to any LORIS database instance to automate and validate data entry (Das 2012), and securely stores LORIS user credentials using the local system keychain. Its flexible open design optimizes customizability, extensibility and interoperability with other formats and data platforms.
This tool was released open-source in 2021 has been included in the BIDS Neuroimaging list of community tools.

FASTMAP

FASTMAP (Functional Analysis Software Tool for MRI and PET) is a free, open source, multi-platform tool developed in Qt/c++; visit https://www.nmr.mgh.harvard.edu/~jbm/fastmap for more information. Fastmap provides an intuitive GUI for 4-dimensional imaging display, preprocessing (smoothing, alignment, reslicing, partial volume correction, …), and time-series analysis for MRI (T1, T2, ADC, DTI, fMRI) and PET (reference tissue models, SUV). Use of the Qt libraries enables multi-platform support, multi-threading, high-level access to the file system, etc. Analysis can be performed interactively or automated from the command line for many common functions.

roiconnect

This toolbox allows performing connectivity analysis between regions of interest. Regions of interest are defined based on popular fMRI atlases, and source localization is performed through eLoreta and Beamforming. Connectivity analysis is performed between all pairs of brain regions using Granger Causality, Directed Transfer Entropy, and many other methods. Visualization is performed in 2-D and 3-D.

ICLabel

An automatic EEG independent component classifier plugin for EEGLAB. For more information, see the ICLabel website tutorial.

vbmeg

VBMEG (Variational Bayesian Multimodal EncephaloGraphy) is a Matlab toolbox for MEG/EEG current source imaging and connectome dynamics estimation. As its name stands, VBMEG is originally intended to obtain accurate source images on an individual brain basis by integrating currently available brain imaging modalities(MEG, EEG, fMRI, T1-MRI, dMRI).

VBMEG is based on the hierarchical Bayesian model and uses fMRI activity map as prior knowledge for source estimation and yields accurate source localization. Even without fMRI, VBMEG works and provides sparse current source imaging which is rather accurate for event-related brain activities.

The first version has been made available as open source software since 2011 and used in the several research projects in other groups. This new version provides additional functions including connectome dynamics identification method, connectome dynamics visualization method, and MEG+EEG simultaneous source imaging method.

NeuroPype

NeuroPype™ is a powerful platform for real-time brain-computer interfacing, neuroimaging, and bio/neural signal processing. The NeuroPype™ Suite is a collection of applications that, in addition to NeuroPype, includes an open-source visual pipeline designer and tools for interfacing with diverse sensor hardware, recording data, and other functions.

Chronux

Chronux is an open-source software package for the analysis of neural data. It was originally developed through a collaborative research effort based at the Mitra Lab in Cold Spring Harbor Laboratory. Chronux routines may be employed in the analysis of both point process and continuous data, ranging from preprocessing, exploratory and confirmatory analysis.

NeuroMark_Pipeline

NeuroMark automatically estimates brain functional network adaptable to each individual subject and comparable across datasets/studies/ disorders by taking advantage of the reliable brain network templates extracted from 1828 healthy controls as guidance. Four studies including 2442 subjects were conducted spanning six brain disorders (schizophrenia, autism spectrum disorder, mild cognitive impairment, Alzheimer’s disease, bipolar disorder, and major depressive disorder) to evaluate validity of the proposed pipeline from different perspectives (replication of brain abnormalities, cross-study comparison, identification of subtle brain changes, and multi-disorder classification using identified biomarkers).

NeuroMark

NeuroMark automatically estimates brain functional networks adaptable to each individual subject and comparable across datasets/studies/ disorders by taking advantage of the reliable brain network templates extracted from 1828 healthy controls as guidance.

References:
Y. Du, Z. Fu, J. Sui, S. Gao, and V. D. Calhoun, "NeuroMark: an automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders," NeuroImage: Clinical,2020
Yuhui Du*, Zening Fu, Ying Xing, Dongdong Lin, Godfrey Pearlson, Peter Kochunov, L. Elliot Hong, Shile Qi, Mustafa Salman, Anees Abrol, Vince D. Calhoun. Evidence of shared and distinct functional and structural brain signatures in schizophrenia and autism spectrum disorder. Communications Biology, 2021, 4(1): 1-16.

IABC

Using functional magnetic resonance imaging (fMRI) data to analyze human brain is an effective way to explore brain function and structure in medicine. We have developed a MATLAB toolbox called Intelligent Analysis of Brain Connectivity (IABC) Toolbox for extracting and analyzing brain functional networks and related measures from large-scale fMRI data. Our previous work group-information guided independent component analysis (GIG-ICA), NeuroMark and splitting-merging assisted reliable ICA (SMARTICA) make great progress in the analysis of fMRI data and generation of independent components among subjects. IABC integrates GIG-ICA ,NeuroMark and SMARTICA to estimate the brain functional network of individual subjects from fMRI data. IABC can also automatically determine an optimal number of spatial maps, visualize brain functions network and generate report of parameters information, pictures of independent components, function network connection and time courses.

NeuroDOT

NeuroDOT, a MATLAB- and Python- based self-contained toolbox, addresses common challenges in processing of functional near infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) data. NeuroDOT supports multiple common pre-processing and analytical pipelines for simulation, data-anatomy alignment and modeling, pre-processing, data quality control, reconstruction, spectroscopy, post-processing, and extensive data visualizations at all stages of processing. We also provide pre-assembled, anonymized, and published data samples from our lab to reflect common experimental paradigms in neuroimaging including visual, language, and resting tasks for human brain mapping. Visualization and analysis tools provide powerful and intuitive explorations of data and data quality. Register to receive development updates, news about workshops, and individual help from the NeuroDOT team: https://tinyurl.com/NeuroDOT .
The latest release version of NeuroDOT is v1.4 (October 17 2024).

MRI brain template for Chinese children from 1 to 6 years old

- Year-by-year T1w MRI brain templates for Chinese children aged 1-6.
- For each age bin, both mixed-sex (n = 30) and sex-specific (n = 15) brain templates are shared.
- We also share label maps for each age bin, which indicate ventricles, cerebral cortex, cerebral white matter, subcortical grey matter, brain stem, and cerebellum.
- The templates can be used as references for brain registration and segmentation.

UNC-BCP 4D Infant Brain Volumetric Atlas

UNC-BCP 4D infant brain volumetric atlases include atlases at 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 18, 21, 24, 36, 48, 60 months, which include the following components: 1) group-representative T1w and T2w images, 2) tissue probability maps, 3) tissue segmentation maps (manually revised based on the segmentation maps obtained by applying the iBEAT V2.0 on T1w and T2w templates), 4) cortical parcellation map, and 5) subcortical labels (manually labeled). These 4D atlases have a high spatial resolution, large age-range coverage (from birth to 5 years of age), and densely sampled time points. Specifically, 655 MRI scans with T1w and T2w images from 278 infants up to 60 months of age were utilized for our atlas construction. Please refer to the following article for the atlases: Chen, L. etc, (Neuroimage, 2022).

Waxholm atlas for HERBS and TRACER

The Neural Correlates of Amplitude of Low-Frequency Fluctuation (ALFF): A Multimodal Resting-state MEG and fMRI-EEG Study

This resource is a multimodel dataset including MEG and fMRI-EEG under eyes-open (8 mins) and eyes-closed (8 mins) resting states. Thirty-five healthy participants (22.4 ± 2.6 years, 6 females) were enrolled in this study. The acquisition information can be found in our paper listed below or in the readme file in the dataset.

Please cite: Zhang J, Liu D, Qian S, Qu X, Zhang P, Nai D, Zang Y-F. The Neural Correlates of Amplitude of Low-Frequency Fluctuation (ALFF): A Multimodal Resting-state MEG and fMRI-EEG Study. Cerebral Cortex, in press

Please let me know if you have any further questions (zhangjf111@gmail.com).

Chinese Adult Brain Atlas with Functional and White Matter Parcellation

This brain atlas, including T1, high angular resolution diffusion image (HARDI), and resting-functional MRI, were created based on large deformation diffeomorphic metric mapping (LDDMM) and Bayesian atlas estimation approaches. When creating the atlases, we combined the two approaches and integrated both T1 and HARDI information of adults aged from 22 to 79 years. The atlas includes structural, diffusion, and functional images as well as brain functional and deep white matter tract parcellation labels. The structural image (T1) and DTI show coherent gray and white matter structures. The label image includes both functional and white matter tract parcellation. All the image data used to generate this atlas are available under 'Downloads->bcas'

Population Averaged Diffusion MRI (DTI, NODDI) and Resting State FMRI (HRF)

Here, we provide a population-averaged template for diffusion MRI measures (DTI, NODDI) and resting state functional MRI measures (HRF)

Resting State Networks cortical and subcortical atlas

This atlas classifies each brain voxel (both subcortical and cortical) in 1 of 7 resting-state networks. 100 subjects from the GSP datasets [1] were used to construct the parcellation using Yeo 7 network parcellation [2]. Individual subject network maps were computed using Yeo regions as seeds and mean averaged between subjects. Each voxel was assigned to the network with the highest contribution. Networks:

1: Medial visual
2: Sensory motor
3: Dorsal attention
4: Ventral attention
5: FrontoParietal
6: Default Mode Network
7: Subcortical

Even if many voxels have overlapping networks we only provide the strongest network for each voxel.

This atlas was generated for:
Guzmán-Vélez E, et al. Amyloid-β and tau pathologies relate to distinctive brain dysconnectomics in preclinical autosomal dominant Alzheimer’s disease. doi: https://doi.org/10.1101/2021.07.03.450933

References:
[1] https://www.nature.com/articles/sdata201531
[2] https://journals.physiology.org/doi/full/10.1152/jn.00338.2011

NeuroInfo®

NeuroInfo® performs brain-wide analyses of cells and biochemical markers, and automatically categorizes them in neuroanatomical regions. The software uses advanced deep learning methods to detect cells and draw anatomic boundaries. NeuroInfo allows researchers to automatically identify and delineate brain regions within experimental mouse and rat brain sections.

BRAIN Initiative - Cell Census Network (BICCN)

The BICCN aims to provide researchers and the public with a comprehensive reference of the diverse cell types in human, mouse, and non-human primate brain. A network of integrated centers and laboratories including U01, RF1, and U19 data generating centers, R24 data archives, and a U24 Brain Cell Data Center (BCDC) are working collaboratively to generate, map, and share these data with the community.

A comprehensive understanding of brain cell types is essential to understand how neural circuits generate perception and complex behaviors. Identifying and characterizing brain cell types, with the means to target each cell type, will elucidate the functional interactions that give rise to the emergent properties of the central nervous system.

Each BICCN project contributes publicly accessible data to a multimodal classification of cell types based on transcriptomic and epigenetic, morphology and connectivity, and physiological signatures of cells.

Genotype-Expression-Imaging Data Integration model

Genotype-Expression-Imaging Data Integration (GEIDI) is a model to identify genetic and transcriptomic influences on brain sMRI measures. The relationships between brain imaging measures and gene expression are allowed to depend on a person’s genotype at the single-nucleotide polymorphism (SNP) level, making the inferences adaptive and personalized.

Brainstem Navigator

The Brainstem Navigator toolkit is a collection of in-vivo brainstem nuclei atlas labels, MRI templates and documentation. The atlas is in stereotactic space and includes gray matter brainstem regions involved in arousal/sleep, autonomic, motor, sensory and limbic function. The atlas labels of brainstem nuclei were created by semi-automatic and manual segmentations of multi-contrast MRI of living humans at 7 Tesla. This toolkit includes a tutorial of scripts with alignment routines of the brainstem nuclei atlas labels to structural, diffusion and functional MRI data of individual subjects. This package has been made available to enable researchers to identify the location of brainstem nuclei in both conventional and advanced MRI (e.g. 3 Tesla, 7 Tesla).

Multimodal Visual Consciousness Study Dataset

This 133-subject dataset (1.47TB) was acquired for and analyzed in Kronemer et al. https://doi.org/10.1038/s41467-022-35117-4 The primary aim of this study was to investigate the cortical and subcortical networks for visual conscious perception. The dataset consists of two adult healthy participants groups: (1) 3T MRI anatomical and BOLD images (TR = 1000ms; voxel size = 2x2x2mm) and (2) 256-channel scalp EEG (sampling rate = 1000Hz). Concurrent pupillometry and eye-tracking (EyeLink 1000 Plus; sampling rate = 1000Hz) is available for most participants. The dataset includes adult epilepsy patient participants with concurrent depth EEG and 20-channel scalp EEG recordings. All recordings were made concurrently with a custom visual perception task.

Citation: Kronemer, S.I., Aksen, M., Ding, J.Z. et al. Human visual consciousness involves large scale cortical and subcortical networks independent of task report and eye movement activity. Nat Commun 13, 7342 (2022). https://doi.org/10.1038/s41467-022-35117-4

Sleep data repository

Sleep MRI data

NeuroStack

NeuroStack builds AWS infrastructure to facilitate neuroimaging analysis using AWS cloud computing. It is designed to enable researchers to quickly transition to the cloud, and is ideal for AWS beginners or anyone working with neuroimaging at scale. NeuroStack is built with the NITRC compute environment (NITRC-CE), allowing access to all of the pre-installed software within the NITRC-CE.

UNC-LPBR 4D Cynomolgus Macaque Atlases from Birth to 48 Months

The constructed atlases comprise three major components: (1) the gray-scale average T1w and T2w images; (2) the tissue probability maps (TPMs) for each tissue type; (3) the anatomical parcellation maps. The paper has been published in Neuroimage (https://doi.org/10.1016/j.neuroimage.2021.118799).

Brain extraction and tissue segmentation models for macaque

TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models

www.templateflow.org - Group inference and reporting of neuroimaging studies require that individual's features are spatially aligned into a common frame where their location can be called standard.

FPS-fMRI paradigm analysis code with sample face-localizer data

Code and sample data for fast-periodic-stimulation (FPS) fMRI analysis, as in Gao, X., Gentile F., & Rossion, B. (2018). Fast periodic stimulation (FPS): a highly effective approach in fMRI brain mapping. Brain Structure and Function, 223(5), 2433-2454, doi: DOI: 10.1007/s00429-018-1630-4. The code runs in Matlab, you will need to add spm matlab functions and nifiti toolbox to your Matlab search path. The data contain two face-localizer runs (will need to unzip the files first, inside of the "preprocessed" folder). The "brainMask.nii" file is created based on the time averaged data of the first run. Run "FPSfMRIAnalysis.m" and find the output files in the "Results" folder. For any question, email: xiaoqinggao@zju.edu.cn. :)

Precision in Neuroimaging (PIN) Dataset

The Precision in Neuroimaging (PIN) Dataset is a collection of freely available structural MRI data. In Phase I, 20 healthy adults (11 females; aged 20-30 years) were scanned on two occasions three weeks apart on the same scanner using the ADNI-3 protocol. On each occasion, individuals were scanned twice (repetition), after re-entering the scanner (reposition) and after tilting their head forward. At one year follow-up, nine returning individuals and 11 new volunteers were recruited for Phase II (11 females; aged 22-31 years). Scans were acquired on two different scanners using the ADNI-2 and ADNI-3 protocols. The purpose of the dataset is to examine the effect of MRI parameters and physiological variables (such as blood pressure, sleep and time of day) with an ultimate aim of improving MRI processing software. Please see NeuroImage (2021), Hedges et al https://doi.org/10.1016/j.neuroimage.2021.118751. Raw MRI data is available from 24 subjects who consented for their data to be publicly available

Brain Aging in Detroit Longitudinal Studies

This is a 4-wave longitudinal study conducted in Detroit, MI. The baseline wave was an adult lifespan (age 18+) sample of healthy aging (no neurological, endocrinological or cardiovascular [except controlled hypertension] dysfunction), with subsequent waves being limited to persons 50 years of age and older at baseline. The subsample was intended as a pilot study for the use of Trasncranial Doppler Ultrasonography (TCD). MPRAGE, flair, sag, SWI, and DTI structural scans were collected on a 1.5T magnet and cognitive measures were collected at all four waves, with blood variables and TCD collected during the second and third waves. The first three waves were collected approximately 18 months apart, with the final wave being conducted approximately four years later.

Nine RS-fMRI Datasets Under Eyes Closed and Eyes Open Conditions

Dr. ZANG Yu-Feng’s research group acquired nine resting-state functional magnetic resonance imaging (RS-fMRI) datasets under eyes closed (EC) and eyes open (EO) conditions from 2007 to 2018. 3D T1 image were also shared.

The difference between EC and EO in RS-fMRI data is very consistent across studies which have used within-group design. Given that between-group comparisons are more prevalent in clinical studies and are not very reproducible, we divided the 9 datasets into 18 between-group design datasets with strictly matched for the gender, age and scanning order.

Please cite: Yue, J., Zhao, N., Qiao, Y., Feng, Z. J., Hu, Y. S., Ge, Q., Zhang, T. Q., Zhang, Z. Q., Wang, J., & Zang, Y. F. (2022). Higher reliability and validity of Wavelet-ALFF of resting-state fMRI: From multicenter database and application to rTMS modulation. Human brain mapping. https://doi.org/10.1002/hbm.26142

Please let me know if you have any further questions (yuejuan1016@foxmail.com).

Multiple Sclerosis Dataset

Magnetic Resonance Imaging scans (FLAIR sequences without gadolinium) of 20 patients affected by Multiple Sclerosis with hyperintense lesions were studied. The CAD system consisted of the following automated processing steps: images recording, automated segmentation based on the Watershed algorithm, detection of lesions, extraction of both dynamic and morphological features, and classification of lesions by Cluster Analysis. The investigation was performed on 316 suspect regions including 255 lesion and 61 non-lesion cases. The Receiver Operating Characteristic analysis revealed a highly significant difference between lesions and non-lesions; the diagnostic accuracy was 87% (95% CI: 0.83-0.90), with an appropriate cut-off of 192.8; the sensitivity was 77% and the specificity was 87%. In conclusion, we developed a CAD system by using a modified algorithm for automated image segmentation which may discriminate MS lesions from non-lesions.

Migraine Dataset

We analyzed brain morphologic images of migraine patients, 14 with aura (MwA) [the mean (SD) age was 42.36 (2.95) years (range, 37–47)] and 14 without aura (MwoA) [the mean (SD) age was 43.5 (3.25) years (range, 39–50)] during episodic attack compared with health subjects balanced (HS) [the mean (SD) age was 42.5 (5.17) years (range, 34–51)]. All subjects underwent a Magnetic Resonance Imaging (MRI) examination with a scanner operating at 3.0 T and voxel based morphometry (VBM) approach was used to examine the gray matter volume (GMV). The bilateral fusiform gyrus and the cingulate gyrus were increase in MwoA patients compared with HS. Our findings could provide an approach to understand possible differences in the pathogenesis of two type of migraine.

Macromolecular aging spectra 3T

This repository contains a set of MRS PRESS data collected with and without inversion pulses (TR/TI 2000/600 ms) at the centrum semiovale (CSO) and posterior cingulate cortex (PCC) brain regions, voxel size of 30 × 26 × 26 mm3, for mobile macromolecule using a 3T Philips MR scanner. 102 datasets were acquired at different age evenly. Demographic information are available upon request.

If using these data, please cite:

[1] Hui SCN, Gong T, Zöllner HJ, Song Y, Tapper S, Murali-Manohar S, Mikkelsen M, Porges E, Oeltzschner G, Saleh MG, Wang G, Edden RAE. The macromolecular MR spectrum does not change with healthy aging. Magnetic Resonance in Medicine 2021 Nov 28. doi: 10.1002/mrm.29093.

and acknowledge the following NIH grants: R01 EB016089, R01 EB023963, K99/R00 AG062230, K99 DA051315, P41 EB031771, K01 AA025306, and S10 OD021648.

In vivo symmetric multi-contrast MRI brain templates and atlas for spontaneously hypertensive rats

We have created the in vivo symmetric multi-contrast MRI brain templates and atlas for spontaneously hypertensive rats named the Heibei Medical University rat template set (HRT). Structural T2WI, DTI and resting-state functional MRI (BOLD) scans that were used to build the template set were obtained from 8 SHRs longitudinally scanned in vivo at 10, 24 and 52 weeks of age. HRT comprised T2WI, raw DTI with a b-value of 0 s/mm2 (B0), FA, MD and BOLD templates; tissue probability maps (TPMs) of gray matter, white matter and cerebrospinal fluid; and a whole-brain atlas with 163 labels. The cortical atlas was derived from the Tohoku atlas, and the subcortical regions were manually delineated. HRT was up-sampled to an isotropic resolution of 1 mm (after scaling 10). We hope that the HRT can serve as a beneficial tool for precise analysis of the SHR brain using structural and functional MRI, which can promote neuroimaging studies on essential hypertension.

Local Quantification of Extra-Axial Cerebrospinal Fluid (Local EACSF)

Local EACSF is an open source tool that quantifies locally the extra-axial cerebrospinal fluid(EA-CSF). It is an interface based tool implemented in C++ and Qt and it gives a measurement of EA-CSF at each vertex of the brain surface. Local_EACSF needs surfaces, a tissue segmentation of the brain and a CSF probability map to Compute the EA-CSF.

Yale Brain Atlas

The Yale Brain Atlas is a volumetric cortical atlas for localising multimodal data on the MNI152 brain to the nearest centimeter of cortex. The Atlas contains 690 parcels across the whole brain, each defined and named according to common neuroanatomy. The Atlas was created as an image segmentation in ITK-SNAP and has a nifti file with associated lookup table with the names of the cortical labels, and the RGB value of each label.

If you use the Atlas in any of your work, or wish to reference the Atlas for any reason, please cite the following paper and if possible include a link to the Yale Brain Atlas website https://yalebrainatlas.github.io/YaleBrainAtlas/

McGrath, H., Zaveri, H.P., Collins, E. et al. High-resolution cortical parcellation based on conserved brain landmarks for localization of multimodal data to the nearest centimeter. Sci Rep 12, 18778 (2022). https://doi.org/10.1038/s41598-022-21543-3

Ratlas-LH: An MRI template of the Lister hooded rat brain with stereotaxic coordinates

Ratlas-LH is a brain and skull template of the adult male Lister hooded rat designed to aid in the determination of stereotaxic coordinates for brain surgery in this rat strain. It was created from in vivo MRI data from adult male Lister hooded rats and ex vivo micro-CT skull data. The brain template is available separately to assist in the display of PET, SPECT and fMRI data.

Ratlas-LH can be used with free software such as MIPAV and ITK_SNAP to directly read stereotaxic coordinates values. A description of this process is provided in the two files available via the Documents link on this page. A set of delineations from the Waxholm space atlas of the Sprague Dawley rat brain were adapted by image registration to provide a guide to brain areas. These delineations and labels can be used when Ratlas-LH is viewed in ITK_SNAP. The brain-only template has the same coordinate framework as the combined skull and brain template.

Bottleneck configurations in the human brain white matter with diffusion MRI tractography

We characterized the prevalence of bottleneck regions, or where multiple white matter pathways of the brain converge and subsequently diverge. These are areas of the brain that present challenges for diffusion tractography, yet represent information highways where dense bundles flow together. Here, we make available bottleneck maps of the brain in a standard space, and provide a map of these junction areas to be used in future studies of the brain.

Prevalence of white matter pathways coming into a single diffusion MRI voxel orientation: the bottleneck issue in tractography
Kurt G Schilling, Chantal M.W. Tax, Francois Rheault, Bennett Landman, Adam Anderson, Maxime Descoteaux, Laurent Petit
bioRxiv 2021.06.22.449454; doi: https://doi.org/10.1101/2021.06.22.449454

Segmentation Labels for the REMBRANDT brain cancer MRI image collection

In this project, we took the raw MRI images from the REMBRANDT TCIA collection and processed them through a well-known image processing segmentation pipeline specialized for brain cancer MRI images. The raw images in DICOM file format were first cleaned to include only MRI scans from 4 modalities (T1, T1-C, T2, FLAIR) . After cleaning and pre-processing, automated volumetric segmentation was performed using tool GLISTRboost. This identified various subregions of the brain including necrotic core, edema, NET and ET, GM, WM, and Cerebrospinal Fluid (CSF) (not all segments were present for every patient). This dataset will now allow researchers to perform radiogenomics based analysis, integrate with gene expression and copy number data, and enable new discoveries and hypotheses.

ciftiTools: Tools for Reading, Writing, Viewing and Manipulating CIFTI Files

The `ciftiTools` R package provides a unified environment for reading, writing, visualizing and manipulating CIFTI-format data. It supports the "dscalar," "dlabel," and "dtseries" intents. It establishes the `xifti` object class, which encapsulates greyordinate data (from CIFTI or metric GIFTI files) with corresponding geometry (from surface GIFTI files). The `xifti` object is structured to allow for convenient access to the data and metadata, and the inclusion of surface geometry enables spatially-dependent functionality such as interactive & static visualizations and smoothing. This user-friendly suite of tools helps enable surfaced-based analysis of MR data, including the use and development of more advanced statistical techniques such as Bayesian methods.

mri_reface

mri_reface replaces identifiable face information in MRI/PET/CT scans to help prevent potential re-identification via face recognition. Faces are replaced with an average face, rather than removed, to better resemble a natural image and reduce effects on downstream brain measurement software. See our publications:
https://www.sciencedirect.com/science/article/pii/S1053811921001221
https://www.sciencedirect.com/science/article/pii/S1053811922004761

This software is under heavy active development to continually add features and improve performance. We highly recommend you sign up to be notified of new releases. It currently supports: T1, T2, and FLAIR MRI, Amyloid PET, tau PET, FDG PET, and CT. The current distribution is compiled matlab only and requires Linux with the (free) matlab runtime installed. Open source and containerized distributions are also in development.

This software was designed to be used only for research purposes, and it is made freely available only for non-commercial research use.

Brain Image Library (BIL)

The Brain Image Library (BIL) is a public resource that serves the neuroscience community by providing a persistent repository for microscopy data. The scope of data accepted by the library includes: whole brain microscopy image datasets and their accompanying secondary data such as neuron morphologies, targeted microscope-enabled experiments including connectivity between cells, spatial transcriptomics, and historical collections of value to the community. BIL is the NIH designated repository for BRAIN Initiative microscopy data.

There is no-charge to contribute data to BIL or access BIL services. Data deposited in BIL may be downloaded or interacted with in-place at BIL using high performance computing resources. Deposited data does not need to have associated NIH funding, but does need to be relevant to the BRAIN Initiative. For more information about BIL, its data and services visit https://www.brainimagelibrary.org/.

fNIRs PFC Memory and Deception

FNIRS dataset from DRM task involving truth-telling, false memories, and deception in prefrontal cortex using NIRScout system from NIRX.

R Analysis and Visualization of intracranial EEG (RAVE)

R Analysis and Visualization of intracranial EEG (RAVE) is free and open-source software for the analysis of intracranial electroencephalogram (iEEG) data, including data collected using strips and grids (electrocorticography, ECoG) and depth electrodes (stereotactic EEG). RAVE is easy to use and creates publication-ready figures with absolutely no programming. RAVE imports standard data formats, including Matlab and EDF, and is compatible with BIDS-iEEG. It runs on laptops, lab servers, or in the cloud. Since all user interactions take place through a web browser, the user experience is identical on Mac, Windows and Linux. Data from RAVE can be exported for analysis using other software. Outside results can be imported and visualized using RAVE's visualization engine. RAVE provides templates to make it easy to create GUI-based analyses using the streamlined application programming interface.
RAVE has been developed since 2017 with funding from NIH 1R24MH117529. doi: 10.1016/j.neuroimage.2020.117341

MRH network

training a neuro-network from MRI to histology in mouse brain voxel-by-voxel. The unique of our work is that all training or testing based on voxel-wise rather than large patch or slice method which used spatial informatoin traditionally, leading to all information generated principlly attribute to MRI protocals.

Acute-stroke Detection Segmentation (ADS)

We provide a DL based tool for detection and segmentation of ischemic acute/sub-acute strokes, trained and tested in 2,628 brain MRIs. Using the original DWI as input, this fully automated system outputs 3D digital infarct mask, volume, and the feature vectors of regions affected by the infarct in two parcellation schemes: structural anatomy and arterial territories. The method is fast (the lesion inference takes 20~30 seconds in CPU; the total processing, including image registration and generation of reports take 3-7 mins, depending on the choice for registration algorithm). ADSv1 includes outputs of the brains and infarct masks mapped to a common space (MNI), ASPECTS calculation, automated radiological reports, with interpretable descriptions of the models' predictions. This system is publicly available, real time, run on local computers, with minimal computational requirements, and accessible to non-expert users.
All software is under the JHU Academic Software License.

SCMAC templates

The Sun Yat-sen university cynomolgus macaque (SCMAC) MRI templates includes both T1w (SMAC_MRI63) and DTI (SMAC_DTI63) templates of the cynomolgus macaque brain. They were created based on 63 young male cynomolgus monkeys using the freely available softwares ANTs (http://stnava.github.io/ANTs/) and DTI-TK (http://dti-tk.sourceforge.net)in a common spaces. For more information on the template creation process or questions, contact Shihui Xing at xingshih@mail.sysu.edu.cn.

NBS-Predict: A Prediction-based Extension of the Network-based Statistic

NBS-Predict is a prediction-based extension of the Network-based Statistic (Zalesky et al., 2010). NBS-Predict aims to alleviate the curse of dimensionality, lack of interpretability, and problem of generalizability. By combining the powerful features of machine learning and graph theory in a cross-validation (CV) structure, it provides a fast and convenient tool to identify neuroimaging-based biomarkers with high generalizability. Unlike generic machine learning algorithms, results derived from the toolbox are straightforwardly interpretable.

NBS-Predict comes with a user-friendly graphical user interface (GUI) developed on MATLAB. Thus, it does not require any programming expertise. The toolbox provides an interactive viewer to visualize the results. The extensive user manual and usage tutorials are included in the toolbox.

Reference:
Serin, E., Zalesky, A., Matory, A., Walter, H., & Kruschwitz, J. D. (2021). NBS-Predict: A Prediction-based Extension of the Network-based Statistic. NeuroImage, 118625.

fMRS in pain

This dataset includes fMRS data for 15 participants undergoing a pain paradigm.
The data were collected using a 3 T Philips Achieva scanner (Best, Netherlands) with a single-channel Transmit-Receive (T/R) head coil, using the PRESS localization sequence.

Current available data include:

3D T1 (MPRAGE, TE/TR/TI=3.5/7.7/808 ms, shot interval=1800 ms, 1 mm3 isotropic resolution, FOV (ap/rl/f)=256/200/150 mm3, scan time=5:47).

H-MRS (PRESS, baseline: TE/TR = 22/4000 ms, NSA= 32, scan time = 3:12, and 16 non-water suppressed spectra were acquired; functional: TE/TR = 22/4000 ms, NSA= 16, scan time = 22:4; ACC, voxel size = 30/25/15 mm3 = 11.2 mL, 2nd order shimming, 16-step phase cycle with water suppression using the Excitation option.

bids-matlab-tools

bids-matlab-tools contains a collection of functions to import and export BIDS-formated experiments for EEG. The code is tailored for use as an EEGLAB plugin but may also be used independently of EEGLAB. Conversion of data format from non-supported BIDS binary format requires that EEGLAB be installed (supported formats are EEGLAB .set files, EDF files, BDF files, and Brain Vision Exchange Format files).

DIPFIT

DIPFIT is an EEGLAB plugin to perform inverse source localization.

A major obstacle to using EEG data to visualize macroscopic brain dynamics is the underdetermined nature of the inverse problem: Given an EEG scalp distribution of activity observed at given scalp electrodes, any number of brain source activity distributions can be found that would produce it. This is because there is any number of possible brain source area pairs or etc. that, jointly, add to the scalp data. Therefore, solving this EEG inverse problem requires making additional assumptions about the nature of the source distributions. A computationally tractable approach is to find some number of brain current dipoles (like vanishingly small batteries) whose summed projections to the scalp most nearly resemble the observed scalp distribution.

NeuroMatic

NeuroMatic is a collection of WaveMetrics Igor Pro software tools for acquiring, analyzing and simulating electrophysiological data. By merging a wide range of tools into a single package, NeuroMatic facilitates a more integrated style of research. Being open source and of modular design, NeuroMatic allows users to develop their own analysis functions that can be easily incorporated into NeuroMatic’s framework.

LESTWAVE

Letswave is a free, open-source Matlab toolbox to analyze EEG/MEG and other neurophysiological signals. The toolbox is hosted on Github.. As compared to other signal processing toolboxes, emphasis is placed on intuitive and streamlined tools to process and visualise EEG data, with a shallow learning curve. The new version provides advanced scripting possibilities. The project is managed by André Mouraux (Institute of Neuroscience, Université catholique de Louvain, Belgium), in collaboration with Gan Huang (Shenzhen University), Bruno Rossion (Institute of Neuroscience, Université catholique de Louvain), Li Hu (Southwest University, China) and Giandomenico Iannetti (University College London, UK).

Causality Challenge EEG Data

The data consists of 1000 examples of bivariate data for 6000 time points. Each example is a superposition of a signal (of interest) and noise. The signal is constructed from a unidirectional bivariate AR-model of order 10 with (otherwise) random AR-parameters and uniformly distributed input. The noise is constructed of three independent sources, generated with 3 univariate AR-models with random parameters and uniformly distributed input, which were instantaneously mixed into the two sensors with a random mixing matrix. The relative strength of noise and signal was set randomly. The data were generated with this [Matlab code]. (Of course, the seeds for the random number generators chosen for the challenge data are confidential.)

NAMIC

The National Alliance for Medical Image Computing (NA-MIC) is a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who develop computational tools for the analysis and visualization of medical image data. The purpose of the Center is to provide the infrastructure and environment for the development of computational algorithms and open-source technologies, and then oversee the training and dissemination of these tools to the medical research community.

Wistar Rat Multicontrast dMRI Atlas

A multicontrast dMRI atlas of the rat brain

Four-dimensional atlases of the rat brain based on magnetic resonance histology and code for 3D slicer that sets up an interactive multiplanar viewer designed to facilitate anatomic comparisons among the different volumes of each atlas and comparisons with new images imported to the viewer.

All atlas include multiple volumes in Paxinos Watson(7th edition) orientation.

Three sets of data:
a single specimen at 25 um and 50um
an average specimen at 50um
WHS3.0 data in affine alignment to Paxinos Watson(7th edition) orientation

Data+code bundle distributed via CIVM data sharing website(free registration required).

DeepBraTumIA

DeepBraTumIA is a deep learning based software tool for automated segmentation of glioblastoma multiform (GBM) multisequence MR images. As input the software requires the following MRI sequences: T1-weighted, T2-weighted, T1-contrast, FLAIR. The software enables automated skull stripping and voxel-wise segmentation of brain tumor and its main sub-compartments of necrotic, contrast-enhancing and edema. A batch mode is enabled for processing of many cases. DeepBraTumIA analyses hardware resources and adapts its memory and CPU/GPU requirements accordingly. Segmentations can be exported along with volumetric measurements of tumor compartments.
The tool can be used on pre- and post-operative cases, but better performance is known for pre-operative cases.

SIGMA EPI diffusion longitudinal data-set

This is a data-set containing Magnetic Resonance Imaging (MRI) rat brain data generated as part of the FCT/ANR co-financed SIGMA project, a collaboration between Neurospin (Saclay, France), the Life and Health Sciences Research Institute (Braga, Portugal) and the Institut de Psychiatrie et Neurosciences de Paris, INSERM (Paris, France).
All the data was acquired strictly following the recommendations of the European Community (2010/63/EU) and the French legislation (decree nº2013-118) for use and care of laboratory animals and were approved by the “Comité d’Éthique en Expérimentation Animale du Comissariat à l’Énergie Atomique et aux Énergies Alternatives – Direction des Sciences du Vivant”, Ile-de-France (CETEA/CEA/DSV IdF).
This data-set contains a total of 175 individual acquisitions, with 5 time points of 35 subjects. Each subject has three files, a base T2 weighted EPI image, a mask image and a semantic segmentation including gray and white matter and CSF.

Data Archive BRAIN Initiative (DABI)

Organize, Store, Disseminate, Analyze and Visualize Invasive Neurophysiology Data
Building on decades of experience creating widely-used, large-scale informatics solutions in the neurosciences, the team behind DABI has launched a new archive to ingest, harmonize, aggregate, store, visualize, and disseminate human invasive neurophysiology data, including EEG, ECoG, LFP, single unit activity, and more.

The repository, which will also house synchronized behavioral, imaging, demographic, and other key data, is specifically designed to help BRAIN Initiative researchers organize and analyze their own data while fulfilling data-sharing directives from federal agencies and their respective institutions. Investigators retain ownership and control of their data through a federated model.

Purdue Neurotrauma Group Adolescent Collision-Sport Athletes Brain Atlas

The Purdue Neurotrauma Group Adolescent Collision-Sport Athletes Brain Atlas contains population-specific anatomical templates (T1-weighted and DTI) and semantic labels (cortical and white matter parcellations) for early-to-middle adolescent (ages 13-19) collision-sport athletes, based on the MRI scans from the longitudinal database of Purdue Neurotrauma Group. The brain atlas was developed in an effort to facilitate multimodal neuroimaging studies, specifically to minimize bias introduced in spatial normalization, improve sensitivity of voxel-wise statistical analysis, and therefore better clarify the mechanisms that lead to traumatic brain injury in adolescent athletes, which is one of the commitments of Purdue Neurotrauma Group to this vulnerable population.

If using the resources of this atlas, please cite:

Zou, Y., Zhu, W., Yang, HC. et al. Development of brain atlases for early-to-middle adolescent collision-sport athletes. Sci Rep 11, 6440 (2021). https://doi.org/10.1038/s41598-021-85518-6

PyNIDM

A Python library to manipulate the [Neuroimaging Data Model](http://nidm.nidash.org).

Includes:
- NIDM-Experiment Tools
- BIDS MRI Conversion to NIDM
- CSV File to NIDM Conversion
- PyNIDM: REST API and Command Line Usage

Additional NIDM-related Tools
* NIDM-Terms <https://github.com/NIDM-Terms/terms>
* NIDM-Terms Scicrunch Interface <https://scicrunch.org/nidm-terms>
* Freesurfer stats -> NIDM <https://github.com/repronim/segstats_jsonld>
* FSL structural segmentation -> NIDM <https://github.com/ReproNim/fsl_seg_to_nidm>
* ANTS structural segmentation -> NIDM <https://github.com/ReproNim/ants_seg_to_nidm>

NeuroImaging Data Model (NIDM)

The Neuroimaging Data Model (NIDM) is a collection of specification documents that define extensions the W3C PROV standard for the domain of human brain mapping. NIDM uses provenance information as means to link components from different stages of the scientific research process from dataset descriptors and computational workflow, to derived data and publication.

NIDM is being developed by the INCF Neuroimaging Data Sharing (NIDASH) Task Force.

Aperture Neuro: An Open Publishing Platform of the Organization of Human Brain Mapping

The purpose of Aperture Neuro is to enable a diverse approach to sharing and communicating high quality, community-based, open neuroscience while bringing transparency and interactivity to the publishing process.

Our Goals - Share openly timely and relevant research results, data, and tools with the human brain mapping and neuroscience communities.
* Be a community-based publishing platform, offering a scientific society based governance.
* Beyond the PDF: Broaden the definition of a peer-reviewed publication to encompass novel Research Objects.

Scope of Submissions - Aperture Neuro is an open-access peer-reviewed online publishing platform. Aperture Neuro publishes articles (research reports and reviews) and other research objects, including tutorials, workshops, processing pipelines, software, simulations, registered report (protocol), interactive object, computational notebooks, and datasets.

Multi-site Frequency Drift MR Spectroscopy

This dataset consists of PRESS data acquired before and after running a heavy gradient duty fMRI sequence using standardized protocols for GE, Philips and Siemens scanners.

1) Pre-fMRI PRESS (TR/TE = 5000/35 ms; FA = 90°; 64 transients; no water suppression; voxel size = 2 × 2 × 2 cm3; second-order shim; scan duration = 5:20 min);
2) BOLD-weighted EPI based on the ADNI-3 protocol (Weiner et al., 2017) (TR/TE = 3000/30 ms; 197 dynamics; EPI factor/echo train length = 31/64, duration = 10 min);
-3) Post-fMRI PRESS (same parameters as for pre-fMRI PRESS except 360 transients and duration = 30 min).

This dataset is distributed freely under the Creative Commons Attribution-NonCommercial-ShareAlike license and consensus from co-authors.

Arterial Atlas

We present an atlas of brain arterial territories based on lesion distributions in 1,298 acute stroke patients. The atlas covers supra- and infra-tentorial regions and contains hierarchical segmentation levels created by a fusion of vascular and classical anatomical criteria. This deformable 3D digital atlas can be readily used by the clinical and research communities, enabling automatic and highly reproducible exploration of large-scaled data.
For reference, please cite our paper here https://www.nature.com/articles/s41597-022-01923-0

MRIcro

MRIcro is a medical image viewer for macOS. It can display popular medical imaging formats including DICOM and NIfTI. It is simple to use, in contrast to more powerful but necessarily more complicated tools. It supports statistical overlays, volume rendering, and 4D time series. It includes color schemes optimized for both MRI and CT scans.

SPM12 batch files/protocol for PPI connectivity analysis of all hippocampal voxels, including regional structural seed averages

SPM12 batch files are provided along with a protocol to generate PPI (psychophysiological interactions) connectivity maps from every voxel in the hippocampus, complete with average maps from “structural seeds” in different sectors of the hippocampus. This approach improves sensitivity in its connectivity maps (see https://doi.org/10.1101/2020.05.14.096339) while mapping task-specific connectivity from all regions of the hippocampus. This approach is particularly useful when the precise region of interest is unknown.

NEMAR

NEMAR will act as a portal or gateway to the OpenNeuro data archive. OpenNeuro is a free and open platform that allows researchers to upload and share neuroimaging data. Submitted datasets can then be analyzed by anyone who logs in. OpenNeuro has been designated by the NIMH as a repository for data collected from BRAIN Initiative projects as well as other types of human neuroimaging data; however, to date workflows only exist for fMRI data. This means that even if other types of brain scans were uploaded to OpenNeuro, there is no infrastructure in place for data analysis. NEMAR build the infrastructure and tools necessary to add human neuroelectromagnetic brain imaging to the archive, including EEG (electroencephalography) and its magnetic counterpart, MEG (magnetoencephalography). This brain data will be uploaded through the NEMAR portal at the San Diego Super Computer Center.

The DANDI Archive

DANDI: Distributed Archives for Neurophysiology Data Integration
DANDI is a platform for publishing, sharing, and processing neurophysiology data funded by the BRAIN Initiative. The platform is now available for data upload and distribution. If you would like to be kept informed of updates, please sign up for our newsletter (https://mailchi.mp/7c90915cdcc3/dandi-newsletter-signup)

The archive is available using the Data Portal link (https://dandiarchive.org/). For instructions on how to interact with the archive see this link (https://www.dandiarchive.org/handbook/).

GAT-FD: Graph Theoretical Analysis of Task-based Functional Dynamics Toolbox

Graph theoretical analysis of task-related functional dynamics (GAT-FD) is a systematically integrated user-friendly toolkit to characterize the functional dynamics for task-related fMRI data based on graph theoretical techniques. Researchers can use this toolkit to 1) implement slide-window approach for analyzing functional connectivity dynamics; 2) generate task condition files based on the experimental task, 3) calculate functional network topological measures and examine their dynamics, and 4) visually validate the results.

Multiple Sclerosis INO and MLF paradox

MRI images of multiple sclerosis (MS) cases with a paradox in presence of internuclear ophthalmoplegia (INO) and presence of a lesion in the medial longitudinal fasciculus (MLF).
See also published paper: https://onlinelibrary.wiley.com/doi/abs/10.1111/ene.14723

Fetal Tissue Annotation and Segmentation Dataset FeTA

The Fetal Tissue Annotation and Segmentation Dataset (FeTA) is a unique collection of super-resolution reconstructed fetal cerebral MRI scans and ground truth manual annotations of brain tissues. The dataset was created to foster machine learning algorithm development and testing. We provide fetal cerebral MRI and corresponding annotation of seven tissue types. The dataset serves the purpose of benchmarking fetal cerebral MRI segmentation methods, and therefore will be used in conjunction with upcoming challenges. The dataset is split into training (40 subjects) and an independent testing set (10 subjects). For the test set, we will not provide the ground truth labels, this way, the accuracy for new observations can be estimated in an unbiased way and compared across groups who wish to participate in the segmentation challenges. The dataset structure is compatible with BIDS 1.4.

White matter Hyperintensities Analysis Tools

WHAT (White matter Hyperintensities Analysis Tools) is a white matter hyperintensities (WMH) extraction pipeline based on deep learning, using T1-weighted and Fluid Attenuation Inversion Recovery (FLAIR) MRI sequences.The software calculates the volume of WMH (Lobe partition, AAL partition, periventricular and deep), and it also provides intermediate results for quality control.

High Resolution Multiple Image Co-Registration and Averaging (HR-MICRA)

This is a dataset of high resolution T2w 3D volumes of healthy control brain images created using the High Resolution Multiple Image Co-Registration and Averaging (HR-MICRA) approach. The goal of this project was to develop an imaging approach that demonstrates hippocampal internal architecture clearly and consistently in virtually all slices. A variable flip angle turbo spin echo sequence (BrainView) was used to acquire high resolution 3D volumes with a resolution of 0.5 mm in the coronal plane and 0.75 mm slice thickness in the A-P direction. The sequence parameters were tailored to acquire a single high-resolution 3D volume in a reasonable amount of time (~ 6 minutes) with good gray-white contrast at the expense of SNR.

Chinese Brain PET Template

In this database, Chinese population-specific brain [18F]-FDG PET templates and 116 normal Chinese PET images are provided, all of which have been converted to SUV images.
We provides a PET brain template specific to the Chinese population, and also includes programs for the building and applying of the template. This project was developed by Medical Imaging Research Group(https://biomedimg-dlut-edu.cn/), Dalian University of Technology based on 116 normal Chinese 18F-FDG PET brain images, and open source all data and programs. The dataset is stored on NITRC, please visit GitHub(https://github.com/DlutMedimgGroup/Chinese-Brain-PET-Template) for more information.
This project uses MIT license, please see Documents/LICENSE(https://www.nitrc.org/docman/view.php/1486/173511/) for details.

WANG H, TIAN Y, LIU Y, et al. Population-specific brain [18F]-FDG PET templates of Chinese subjects for statistical parametric mapping[J]. Scientific Data, 2021, 8(1): 305.

ARTS biomarker

ARTS is a fully automated biomarker that outputs a score linked to the likelihood a person suffers from arteriolosclerosis based on brain MRI data (3D T1w, T2w FLAIR, DTI data) and basic demographic information (age at MRI, sex). The higher the ARTS score, the higher the likelihood of arteriolosclerosis. A robust MRI data processing pipeline and the classifier are packaged into a software container; therefore, users do not need to perform any image processing, and are only required to install Singularity to run ARTS.

If you use ARTS please reference https://www.sciencedirect.com/science/article/pii/S2213158221002126

- The MRI acquisition protocol can be flexible. ARTS has been tested on data from GE, Siemens and Philips 3T MRI scanners.
- ARTS accepts images in both DICOM (preferred) and NIFTI formats.
- ARTS is compatible with Linux and Mac operating systems and can be used on High Performance Clusters.
- In addition to the detailed manual, a HowTo video is provided here: https://youtu.be/keA0TIDScJA

Concurrent Electrogastrography / Resting Brain fMRI highly-sampled-individual data resource (CERB)

A network of myenteric interstitial cells of Cajal in the corpus of the stomach continuously generates an electrical slow wave, which is transmitted to the brain chiefly by vagal afferents. In our study, we combined resting-state functional MRI (rsfMRI) with concurrent surface electrogastrography (EGG) to investigate whether fluctuations in brain resting state networks (RSNs) might be synchronized with the stomach. Here we share our resources (e.g., concurrently acquired EGG and preprocessed rsfMRI data set, and the code used to perform statistical analysis) associated with the study.

To generate the data set, an individual participant underwent 22 scanning sessions; in each, two 15-minute runs of concurrent EGG and rsfMRI data were acquired. EGG data from three sessions had weak gastric signals and were excluded; the other 19 sessions yielded a total of 9.5 hours of data.

(Please see the MediaWiki page for detailed information regarding the data acquisition parameters.)

Deep collaborative learning

Multi-modal functional magnetic resonance imaging has been widely used for brain research. Conventional data-fusion methods cannot capture complex relationship (eg, nonlinear predictive relationship) between multiple data. This paper aims to develop a neural network framework to extract phenotype related cross-data relationships and use it to study the brain development. We propose a novel method, deep collaborative learning (DCL), to address the limitation of existing methods. DCL first uses a deep network to represent original data and then seeks their correlations, while also linking the data representation with phenotypical information.

Joint-Label Fusion Brain Atlases for Dementia Research in Down Syndrome

The MRI Joint-Label Fusion brain atlases are specifically designed for PET amyloid accumulation in the dementia and Alzheimer's Disease research of the Down Syndrom population. They are high resolution brain atlases constructed using the Advanced Normalization Tool's (ANT's) joint-label-fusion algorithm (JLF). The JLF atlases were constructed from T1-weighted structural images of 83 adults Down Syndrome participants reflecting accurate shape, size and regional boundaries of their brains. Another JLF atlas was constructed from T1-weighted images of 56 cognitively stabled neurotypical (i.e. healthy) participants. The atlases are designed to be used based on the participant's diagnosis status (e.g. dementia, cognitively stables) and disease status (e.g. Down Syndrome).

AtlasTrack Fiber Tract Atlas

AtlasTrack Fiber Tract Atlas is a probabilistic atlas of white matter tracts. The atlas was created from manually selected fiber tract maps created using DTI Studio with dMRI data from a group of healthy controls and epilepsy patients (Hagler et al., 2009). This atlas is used to generate fiber tract segmentations for individual subjects who have been nonlinearly registered to the atlas. dMRI derived measures averaged within these fiber tract ROIs have been included in tabulated data publicly shared for the PING and ABCD studies (Jernigan et al., 2016, Hagler et al., 2019). This package includes atlas data, documentation, and some associated MATLAB functions. This package has been made available to enable researchers, particularly those using the PING and ABCD data resources, to visualize the atlas fiber tracts. Note that this package does not contain the complete set of software needed to apply the atlas and generate segmentations for individual subjects.
Estimate file storage is 758 MB.

Boutiques

Boutiques is a descriptive command-line framework to make tools Findable, Accessible, Interoperable and Reusable (FAIR).

Dictionary learning based functional atlases in four primate species (mouse lemurs, marmosets, macaques, and humans)

BOLD images were acquired in mouse lemurs, marmosets, macaques, and humans
Each functional atlas was made using a dictionary learning analysis with 7 components on pre-treated BOLD images.
Each component was concatenated and labeled to create each atlas.
The atlases were broken into functional regions automatically and manually.

For each species:
DL7cpts_DicL_yung.nii.gz ==> result of the dictionary learning

dict_learning_7compos_concat.nii.gz ==> functional atlas

dict_learning_7compos_concat_break.nii.gz ==> functional atlas breaks automatically into functional regions
or
dict_learning_7compos_concat_break_handseg.nii.gz ==> functional atlas breaks automatically and manually into functional regions

if you use these atlases in a publication please cite:

An evolutionary gap in primate default mode network organization
Apr 2022 Cell Report 39, 2, 110669 DOI: 10.1016/j.celrep.2022.110669 https://www.sciencedirect.com/science/article/pii/S2211124722004211

High-speed compressed-sensing fluorescence lifetime imaging microscopy of live cells

This is the data in paper "High-speed compressed-sensing fluorescence lifetime imaging microscopy of live cells"

Expected Label Value (ELV) Computation for Multi-Atlas Image Soft-Segmentation

This is the public Matlab implementation for medical image soft-segmentation using the atlas-based expected label value (ELV) approach proposed by Aganj and Fischl (IEEE TMI 2021; IEEE ISBI 2019). This approach considers the probability of all possible atlas-to-image transformations and computes the ELV, without relying only on the transformation chosen as "optimal" by a registration method. This is done without deformable registration, thereby avoiding the associated computational costs. A short tutorial is included in EXAMPLE.m.

o8t Labs: a machine learning image processing and functional connectomic pipeline

o8t Labs is a research tool offering a streamlined and standardized approach to image processing and anaysis. Requiring an anatomical T1, resting-state fMRI and DTI image series, Labs utilizes constrained spherical deconvolution to first perform a structural connectivity of the brains white matter tracts. Through the application of machine learning models, we then re-parcellate regions derived from the HCP Atlas onto realigned resting-state image series, creating an individualized brain atlas for further analyses. Functional connectomic analysis is then performed, correlating BOLD signal change at rest between each parcellation of the loaded subject’s brain. Correlations are compared against a bank of 250 healthy controls to determine typical and atypical functional connectivity. Multiple exports are currently available in Labs including processed image series, a structural atlas, as well as structural and functional connectivity matrices. Exports can be easily re-integrated into other analytic workflows.

Melbourne Subcortex Atlas

The Melbourne Subcortex Atlas is a hierarchical functional MRI atlas of the human subcortex (Tian et al 2020 Nat Neurosci). The atlas was derived from healthy young adults participating in the Human Connectome Project and represents a consensus among more than 1000 individuals.
Atlas features:
• Includes subdivisions of the striatum, globus pallidus, thalamus, amygdala and hippocampus.
• Available in four hierarchical scales as well as 3 and 7 Tesla versions.
• Each scale is bilaterally symmetric and defines a self-contained parcellation scheme.
• Delineated in MNI152 6th generation space but also available in MNI152 2009cAsym.
• Integrated into several existing cortex-only parcellation atlases including Glasser2016, Gordon2016 and Schaefer2018.
• Available in CIFTI and NIFTI formats.
• Analysis pipeline and code can be found at https://github.com/yetianmed/subcortex
• Read the manuscript: https://rdcu.be/b7N8K

Open access fNIRS dataset: painful stimuli with morphine vs placebo

Normal subjects were subjected to electrical pain stimulation while under the influence of morphine or a placebo in a double-blinded study. fNIRS recordings were acquired from the medial prefrontal cortex, right somatosensory cortex and left lateral prefrontal cortex, revealing attenuation in activation in the medial Brodmann Area 10 associated with lower reported pain levels.

ONPRC.18 Multimodal Macaque MRI Atlas

The ONPRC18 Multimodal MRI Atlas includes co-registered templates built from MR images used to characterize macroscopic brain structure (T2/SPACE and T1/MPRAGE), and a diffusion tensor imaging (DTI) template. The DTI template matches to the T1 and T2-weighted images (0.5 mm isotropic) and includes maps of derived DTI parameters (FA, AD, MD, RD, RGB). The ONPRC18 labelmaps delineate 57 bilateral gray matter regions of interest (ROIs; 36 cortical regions and 21 subcortical structures), as well as 74 bilateral white matter tracts. Importantly, the labelmaps overlay the structural and diffusion templates, enabling the same regions to be consistently identified across imaging modalities. A specialized condensed version of the labelmap with 17 larger ROIs is also included to further extend the usefulness of this tool for imaging data with lower spatial resolution, such as functional MRI (fMRI) or positron emission tomography (PET). https://www.sciencedirect.com/science/article/pii/S1053811920310028?via%3Dihub

CNN Labelling of Hippocampus and Amygdala using 700 µm isotropic 7T MP2RAGE MRI

This contains data related to our recent study that investigated the use of convolutional neural networks (CNNs) to label the hippocampus and amygdala on whole brain 7T MP2RAGE MRI scans in healthy subjects and individuals with epilepsy. The resource contains MRI scans, manual labels of the hippocampus and amygdala, code used to train the CNN, CNN-based predicted hippocampus and amygdala labels, and code/scripts used to analyze the output of the CNN.

fsbrain

GNU R library for structural neuroimaging. Provides high-level functions to visualize voxel-based and surface-based brain morphometry data (e.g. cortical thickness) for individual subjects and groups. The software can generate publication quality plots on brain surface meshes, and supports many different customization options like different view angles, colormaps, and material properties. The resulting plots can be saved as image files or integrated into R notebooks. The fsbrain package supports various standard neuroimaging file formats, including those used by FreeSurfer.

Brain Imaging Genetics Knowledge Portal (BIG-KP)

We have initialized the Brain Imaging Genetics Knowledge Portal (BIG-KP, https://bigkp.org/) project to accelerate genetic discoveries in the human brain. Through BIG-KP, we have made our imaging genetics GWAS summary statistics of more than 40,000 subjects publicly available. This portal is under active constructions. We will continue integrating genetics and neuroimaging data into our analyses and sharing research findings covering the full spectrum of structural and functional traits from different imaging modalities.

MVIWAS: R-package for Causal Brain Endophenotype Testing

The MVIWAS R package and corresponding resources on GitHub include 1) a function for implementing the Multivariate Imaging Wide Association Study for testing causal brain imaging endophenotypes using GWAS summary statistics and a reference panel and 2) a detailed analysis pipeline from the corresponding paper (https://doi.org/10.1016/j.neuroimage.2020.117347), which can be followed to test for causal brain imaging phenotypes in brain-related diseases using MV-IWAS.

ADIDP: Testing Genetically-Regulated Imaging Phenotypes in Neurodegenerative Disease

This resource provides R code for implementing association testing of imaging derived phenotypes (IDPs) in neurodegenerative diseases, such as Alzheimer's Disease, using Genome Wide Association Study summary statistics. This resource utilizes many existing packages for phenotype testing, including Mendelian Randomization, the Sum of Powered Score Tests, and the adaptive Sum of Powered Score Test. This code directly mirrors the analysis pipeline implemented in "Knutson, K.A., Pan, W. Integrating brain imaging endophenotypes with GWAS for Alzheimer’s disease. Quant Biol (2020). https://doi.org/10.1007/s40484-020-0202-9".

Multivariate Tensor-based Subcortical Morphometry System

Multivariate Tensor-based Subcortical Morphometry System (MTSMS) can model tube-like brain structures (Such as: hippocampus, thalamus, caudate, putamen, cerebellum) in sub-regional details. It includes MR images registration, brain structures segmentation, brain structure surface reconstruction, and surface-based multivariate morphometry statistics computing. It can produce detailed maps of point-wise correlations between subregional deformations and cognitive assessments or other biomarkers of disease.

Multi-modal imaging coupling analysis

We developed a convenient and time-saving software based on MATLAB 2014a platform, and could be used for the data preparation and analysis of across-voxel coupling between multimodal images.

Ventricular Morphometry Analysis System

Ventricular morphometry analysis system (VMAS) can generate a whole connected 3D brain ventricular shape model and encode a great deal of ventricular shape information that is inaccessible by ventricle volume measure. It is accessible here: http://gsl.lab.asu.edu/software/ventricle/.
VMAS contains an automated segmentation approach and surface-based multivariate morphometry statistics. The whole pipeline is special for server mode. That means you can parallel deal with individual MRIs. The terminate results of "VMAS/run.py" are smoothed bilateral ventricles and can be used to do group-wise morphometry analysis as hippocampus pipeline (http://gsl.lab.asu.edu/software/mtbm-sma/), directional analysis and effect size analysis.

In vivo Adult Yucatan micropig brain template

This is a T2-weighted MRI brain template constructed from 16 adult Yucatan micropigs aged 6 months to 2 years old, created using non-linear registration methods (Advanced Normalization Tools). Tissue probability maps for gray matter, white matter, and CSF are also included. Estimated whole brain tractography from one individual is included as a sample. Please refer to the following article when using this template: Chang SJ, Santamaria AJ, Sanchez FJ, Villamil LM, Pinheiro Saraiva P, Rodriguez J, et al. In vivo Population Averaged Stereotaxic T2w MRI Brain Template for the Adult Yucatan Micropig. Frontiers in neuroanatomy 2020;14(89).

CBICA: Federated Tumor Segmentation (FeTS)

The Federated Tumor Segmentation (FeTS) initiative, describes the on-going development of i) the largest international federation of healthcare institutions, and ii) an open-source toolkit with a user-friendly GUI, aiming at gaining knowledge for tumor boundary detection from ample and diverse patient populations without sharing any patient data.

The FeTS toolkit focuses on:

bringing pre- trained segmentation models of numerous deep learning algorithms and their fusion, closer to clinical experts and researchers, thereby enabling easy quantification of new radiographic scans and comparative evaluation of new algorithms.
allowing secure multi- institutional collaborations via federated learning to improve these pre-trained models without sharing patient data, thereby overcoming legal, privacy, and data-ownership challenges.

Open access fNIRS dataset: Electrical pain stimulus

This dataset features recordings of 11 subjects as they are subjected to innocuous and noxious electrical stimulus.

Assoc. publication: Yücel et al. https://doi.org/10.1038/srep09469
Year: 2015
Format: Snirf
Stimuli type: Pain (electrical)
Num. of Subj.: 11
Age: 28 ± 5
Sex (M/F): 11/0
Optode Coverage: Frontal, Motor, Somatosensory
Duration: 12.5 min
Sample Rate: 50 Hz
Wavelengths: 690/830
Acquisition System: TechEn CW6

Open access fNIRS dataset: observed versus executed motor tasks

This dataset features recordings of 18 subjects as they perform a set of motor tasks and then visually observe them.

Assoc. publication: Li, X. et al. https://doi.org/10.1038/s41598-020-67327-5
Year: 2020
Format: Snirf
Stimuli type: Motor tasks, visual
Num. of Subj.: 18
Age: 33.5 ±15.5
Sex (M/F): 9/12
Optode Coverage: Frontal, Motor, Parietal, Occipital
LS Channels: 52
SS Channels: 8
Duration: 6 min
Sample Rate: 25 Hz
Wavelengths: 690/830
Acquisition System: CW7 NIRS

Open access fNIRS dataset with induced motion artifacts

This dataset features resting state recordings interspered with various motor tasks. It is useful to evaluate the performance of methods which aim to detect and exclude motion artifacts from fNIRS data.

Assoc. publication: Yücel et al. https://doi.org/10.1016/j.neuroimage.2013.06.054
Year: 2014
Format: Snirf
Stimuli type: Motor tasks
Num. of Subj.: 7 (dataset1) | 5 (dataset 2)
Optode Coverage: Frontal
Duration: 12 min
Sample Rate: 50 Hz
Wavelengths: 690/830
Acquisition System: TechEn CW6

Open access multimodal fNIRS resting state dataset with and without synthetic hemodynamic responses

In order to objectively evaluate the power of methods which aim to remove confounding resting state physiological factors such as blood flow, low frequency oscillation, and breathing from fNIRS data, it is useful to have a dataset with known ground truth. In order to generate an fNIRS signal with confounding signals but also a known hemodynamic response function (HRF), real resting state fNIRS data is recorded, and a modeled HRF is added. The data also features auxilliary measurements such as accelerometer and photoplethysmography signals.

Year: 2020
Format: Snirf
Stimuli type: Resting state with synthetic HRF

DATASET 1 | DATASET 2
Aux Meas: PPG, RESP, BP, Accelerometer | Accelerometer
Num of Subj : 14 | 14
Num of Trial: 15 | 38
Age: 21 ± 2 | 32 ± 19
Sex (M/F): 11/3 | 7/5
Optode Coverage: Occipital | Fronto-parietal
LS Channel: 26 | 48
SS Channel: 2 | 8
Duration (min): 5 | 10
Sample Rate (Hz): 50 | 50
Wavelengths: 690/830 | 690/830
Acq. System: TechEn CW4 | TechEnCW5

freesurferformats: R Package For Reading and Writing Neuroimaging File Formats

The 'freesurferformats' package is an R library for reading and writing neuroimaging file formats. The focus is on structural MRI file formats from the FreeSurfer brain imaging software suite, but a range of other file formats are supported as well. The package can be used to read 3D and 4D volume files (brain models, segemntations, voxel-wise results), 2D surface-based morphometry data (e.g., cortical thickness maps), brain surface meshes, atlas-based parcellations and labels, surface patches, and spatial transformation matrices in various file formats. The vast majority of formats can also be written. The related 'fsbrain' package can be used to visualize neuroimaging data directly in R.

ABCD-ReproNim Course

The ABCD-ReproNim Course provides training for reproducible analyses of the Adolescent Brain Cognitive Development (ABCD) Study® data.

The ABCD Study is the largest long-term study of brain development and child health in the US. The ABCD-ReproNim Course was designed to provide a comprehensive background to #ABCD while delivering hands-on instruction on reproducible @ReproNim workflows and outcomes. Our 13-week Online Course starts Oct 16, 2020 and Project Week is scheduled for March 8-12, 2021. Participants may join as Observer or Enrolled students. There are no registration fees and, the course will be fully virtual and include both synchronous and asynchronous activities. All materials will be open and accessible.

Students will receive instruction on reproducible data analyses endorsed by ReproNim, a Center for Reproducible Neuroimaging Computation.

Timeline - https://www.abcd-repronim.org/index.html#timeline
Syllabus - https://www.abcd-repronim.org/syllabus.html

Open Neuroscience

Open Neuroscience (https://open-neuroscience.com) is a curated list of open source projects related to Neurosciences. The list is presented in the form of a website at https://open-neuroscience.com

It cover topics ranging from molecular biology to brain machine interfaces. The website is searchable and there are filtering tags to help users navigate the increasing number of projects listed!

The content is built collaboratively by members of the community and project developers. By simply filling out a form, people can send information about their project to be listed on the website. We check the content for spam as well its format. Afterwards, the projects are deployed on the website and we do a shout out about it using our social media accounts. More details and the links for the form and the project organization on GitHub can be found on the website's landing page https://open-neuroscience.com

MedSeg - free, online segmentation tool with AI-models

MedSeg is a free, online tool with manual and AI-based segmentation capabilities. The tool runs completely in your browser and requires no registration. Optimized for CT and MRI-images. Currently 20+ AI-models ready to use!

Imaging-Wide Association Study

We have created an online tutorial, which describes all the steps. Please see the following link for details: http://wuchong.org/IWAS.html

UNC Atlases for Macaque Brain Image Analysis

Macaque brain atlases generated at UNC-Chapel Hill.
Available atlases:
- DTIAtlas_6_12months: DTI atlas with white matter fiber tracts (6-12 months old). Works well with AutoTract.
- Tissue_multiAtlas: Multi-atlas for tissue segmentation in macaques, includes left-right mirrored images for symmetric analysis (6-12 months old). Works well with MultisegPipeline.
- PARC_multiAtlas: Multi-atlas for brain tissue lobar parcellation, includes left-right mirrored images for symmetric analysis (12 months old). Works well with MultisegPipeline.
- SubCorticals_multiAtlas: Multi-atlas for segmentation of subcortical structures, includes left-right mirrored images for symmetric analysis (12-24 months old). Works well with MultisegPipeline.

NeuroKit2

NeuroKit2 is an open-source, community-driven, and user-friendly Python package dedicated to neurophysiological signal processing. Its design philosophy is centred on user-experience and accessibility to both novice and advanced users. The package provides a consistent set of high-level functions that enable data processing in a few lines of code using validated pipelines, which we illustrate in two examples covering the most typical scenarios, such as an event-related paradigm and an interval-related analysis. The package also includes tools dedicated to specific processing steps such as rate extraction and filtering methods, offering a trade-off between efficiency and fine-tuned control to the user. Rather than focusing on specific signals, NeuroKit2 was developed to provide a comprehensive means for a simultaneous processing of a wide range of signals. Its goal is to improve transparency and reproducibility in neurophysiological research, as well as foster exploration and innovation.

ReproNim Training

Our Educational Objectives include:
* Topical training in the issues that affect the reproducibility of neuroimaging research (data acquisition, experimental methods, analyses, reusability, and sharing of data and methods)
* Development of a next-generation software developers that are versed in the techniques that promote reproducibility in the tools that promote reproducibility.
* Train the trainer: we adopted the software and data carpentry philosophy and work to train individuals who will themselves train others.

Our initial curriculum focuses on developing material that address reproducibility in four areas:
* FAIR data
* Computational basics
* Reproducible workflows
* Statistical tools
in order to initiate a set of tools and practices that are reproducibility-enabled. Future curricula will extend the reach of these materials to encompass a broader community of researchers and will feature more training material on the tools developed by the ReproNim project.

BrainSpace

BrainSpace is a lightweight cross-platform toolbox primarily intended for macroscale gradient mapping and analysis of neuroimaging and connectome level data. The current version of BrainSpace is available in Python and MATLAB, programming languages widely used by the neuroimaging and network neuroscience communities. The toolbox also contains several maps that allow for exploratory analysis of gradient correspondence with other brain-derived features, together with tools to generate spatial null models.

Dicomifier

Dicomifier is a set of tools to convert Bruker data to DICOM files, and DICOM files to NIfTI. It retains meta-data (e.g. MR parameters such as echo time or subject parameters such as weight or height) throughout the conversion process, and aligns the meta-data from Bruker on the DICOM dictionary for unified processing pipelines.

GTree

Global Tree Reconstruction System (GTree) is an cross-platform (Windows, Linux) and freely available neuronal image visualization & analysis software for neuroscience academic research. GTree can reconstruct neuronal population from single image or TB-size brain-wide image stacks precisely. GTree is also a powerful integrated software including soma localization, automatic/manual neuron reconstruction and manual revision functions, etc. A laptop and a removable mobile hard disk with the capacity of tens of TBs are only required in the brain-wide reconstruction with GTree.

Sycomore

Sycomore is an MRI simulation toolkit providing Bloch simulation, Extended Phase Graphs (EPG) (both regular and discrete), and Configuration Models. Sycomore is a Python packge in which all computationnaly-intensive operations are run by a C++ backend, providing a very fast runtime and further acceleration through OpenMP.

Pandora White Matter Atlas

This is a population-based collection of white matter atlases represented in both volumetric and surface coordinates in a standard space. These atlases are based on 2443 subjects, and include 216 white matter fascicles derived from 6 different state-of-the-art tractography techniques: Automated Fiber-tract Quantification, Automated Fiber-tract Quantification clipped, Recobundles, Tracula, TractSeg, and Xtract.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Odil

Odil is a DICOM library which provides a user-friendly C++11 and Python API for the different parts of the DICOM standard:
- Reading and writing data sets with any transfer syntax
- Standard JSON and XML representation of datasets
- Clients and servers for the various DICOM protocols (C-FIND, C-GET, C-MOVE, C-STORE)
- Implementation of the DICOM web-services (WADO-RS, QIDO-RS, STOW-RS)
- Conversion to and from DCMTK data structures.

Packages are provided on Anaconda, Debian, Ubuntu and Homebrew.

ReproMan

ReproMan aims to simplify creation and management of computing environments in Neuroimaging. While concentrating on Neuroimaging use-cases, it is by no means is limited to this field of science and tools will find utility in other fields as well.

Status: ReproMan is under rapid development. While the code base is still growing the focus is increasingly shifting towards robust and safe operation with a sensible API. There has been no major public release yet, as organization and configuration are still subject of considerable reorganization and standardization.

ReproNim/containers

This repository provides a DataLad dataset (git/git-annex repository) with a collection of popular computational tools provided within ready to use containerized environments. Versions of all images are tracked using git-annex with content of the images provided from a dedicated Singularity Hub Collection and http://datasets.datalad.org (AKA /// of DataLad) or other original collections.

The aims for this project are: 1) to be able to include this repository as a subdataset within larger study (super)datasets to facilitate rapid and reproducible computation, while adhering to YODA principles and retaining clear and unambiguous association between data, code, and computing environments using git/git-annex/DataLad; 2) to assist with containers execution in "sanitized" environments: no $HOME or system-wide /tmp is bind-mounted inside the containers, no environment variables from the host system made available inside; 3) make Singularity images transparently usable on non-Linux (OSX) systems via Docker.

VisualSpeech

Neural Correlates of Visual Speech in Familiar and Unfamliar Language

MR-TIM: MR-based head tissue modelling

MR-TIM is software for head tissue modelling from structural magnetic resonance (MR) images.
It performs automated segmentation of T1-weighted MR images in 12 tissues: brain gray matter (GM), cerebellar GM, brain white matter (WM), cerebellar WM, brainstem, cerebrospinal fluid, spongy bone, compact bone, muscle, fat, eyes and skin.

MR-TIM requires MATLAB 2016b (MathWorks) or later versions and it is intended as a toolbox for SPM12 software package.
User manual available at: https://github.com/gtaberna/mrtim/wiki

Please cite:
Taberna, G.A., Samogin, J. & Mantini, D. Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction. Neuroinform (2021). https://doi.org/10.1007/s12021-020-09504-5

Simulated MRI images and reference fiber tracts of 99 subjects

This archive contains simulated MRI images (T1, T2, dMRI) of 99 healthy subjects as well as corresponding reference fiber tracts used for the MRI simulation. The archive is primarily intended for the training and validation of fiber tractography methods but is well suited for many other tasks in the area o diffusion-weighted MRI image processing.The simulation was performed using the Fiberfox dMRI simulation tool included in MITK Diffusion. Fiberfox uses reference fiber tracts, generated using TractSeg and MITK Diffusion, and volume fraction maps, generated using MRtrix and FSL, as input to simulate the actual k-space acquisition. This enables highly realistic looking image contrasts and artifacts. The reference data was created on the basis of 99 unrelated subjects from the HCP young adult dataset. A detailed description of the data can be found in the archive, downloadable here: https://inrepo01.inet.dkfz-heidelberg.de/record/156611?ln=en

Volume and Surface-based Cortical Morphological Networks in Healthy Young Adults

The cortical morphological networks were generated by source-based morphometry using the T1-weighted MRI data from the Human Connectome Project. One volume-based (cortical volume) and four surface-based metrics (cortical thickness, gyrification index, sulcal depth, and fractal dimension) were used to quatify the structural covariance between different brain regions among the population.

A manuscript is accepted for publication: R. Ge, X. Liu, D. Long, S. Frangou, and F. Vila-Rodriguez, Sex Effects on Cortical Morphological Networks in Healthy Young Adults, NeuroImage, Accepted.

Human-Disease Phenotype Map

This is the phenotype connectivity map from one of the largest PheWAS using electronic health record (EHR)-derived phenotypes across 38,682 unrelated samples from the Geisinger’s MyCode Community Health Initiative genotyped through the DiscovEHR project. Click on each disease node to highlight other diseases found to be associated with this disease via SNPs. Anurag Verma, Lisa Bang, Jason E. Miller, Yanfei Zhang, Ming Ta Michael Lee, David J. Carey, Marylyn D. Ritchie, Sarah A. Pendergrass, Dokyoon Kim, on behalf of the DiscovEHR collaboration, Phenotype connectivity map across human diseases derived from PheWAS across 38,682 individuals, AJHG (https://www.cell.com/ajhg/fulltext/S0002-9297(18)30409-9)

CLIMB Lesion Symptom Mapping Software

CLSM is an Octave/Matlab package that performs several Multivariate and mass Univariate Lesion Symptom Mapping analyses. The software uses patient imaging lesion masks of brain insults and correlates them in multiple ways with patient behavioral and covariate data. Several permutation-based SPMs are computed along with power, variance explained, and lesion coverage maps.

Developmental Brain Functional Activity (DBFA) Maps

By integrating task-dependent fMRI data 548 young children (ages 7–12 year-old), we created a comprehensive set of developmental brain functional activity maps across 4 different domains, including attention, emotion, executive control and risky decision-making. Here we unify these unbiased, age-specific fMRI activation maps into a comprehensive toolbox, named Developmental Brain Functional Activity (DBFA) Maps. This DBFA toolbox can be freely downloaded by researchers worldwide for various needs. The number of participants in these atlases will gradually increase as the project progresses.
Any questions, please contact: hao1ei#foxmail-com

An in vivo 7T Probabilistic Atlas of the Human Locus Coeruleus

The 7T probabilistic LC atlas is created using magnetisation transfer images from 53 healthy volunteers (52 - 84 years) with ultra-high field 7T MRI. The atlas benefits from improved spatial resolution (0.4 x 0.4 x 0.5 mm) and SNR at 7T, advanced registration and segmentation methods, and consistent spatial features comparing to ex vivo findings.

The details for building the atlas and its derived images are described in: Rong Ye, Catarina Rua, Claire O'Callaghan, P Simon Jones, Frank Hubert Hezemans, Sanne S Kaalund, Kamen A Tsvetanov, Christopher Rodgers, Guy Williams, Luca Passamonti, James Rowe; An in vivo Probabilistic Atlas of the Human Locus Coeruleus at Ultra-high Field; bioRxiv 2020.02.03.932087; doi: https://doi.org/10.1101/2020.02.03.932087

Computational Network Analysis Toolbox

Our network analysis software provides an integrated platform for conducting network analysis on structural/functional brain networks. The general functions include:
(1) network construction,
(2) network hub/community detection,
(3) network display,
(4) support longitudinal network analyses, and
(5) machine learning for brain networks.

VisuAlign - Nonlinear adjustments after QuickNII

VisuAlign is a tool for applying user-guided nonlinear refinements (inplane) to an existing, affine 2D-to-3D registration (such as one created with QuickNII).
While linear registration tools are vital in bringing experimental image data to standardized coordinate spaces, for precise quantitative analysis one has to address the residual anatomical variability among test subjects after registration. VisuAlign is a tool designed for this task.

Rodent atlas delineations provided with VisuAlign:
- Waxholm Space Atlas of the Sprague Dawley rat brain, versions 2, 3, and 4 (at 39 μm resolution)
- Allen Mouse Brain Atlas reference atlas, CCFv3 (delineations from 2015 and 2017 at 25 μm resolution)

Output of VisuAlign can be directly used for numerical quantification with Nutil.
VisuAlign is available on GitHub under MIT license: https://github.com/HumanBrainProject/VisuAlign

hMRI-toolbox

The hMRI-toolbox is an easy-to-use open-source and flexible tool, for qMRI data handling and processing. It allows the estimation of high-quality multi-parameter qMRI maps (longitudinal and effective transverse relaxation rates R1 and R2*, proton density PD and magnetisation transfer MT saturation), followed by spatial registration in common space for statistical analysis.

NIRS-KIT: A MATLAB toolbox for both task and resting-state fNIRS data analysis

NIRS-KIT, a MATLAB toolbox for both task and resting-state fNIRS data analysis, covers the entire analysis pipeline. For task fNIRS, individual-level analysis is carried out to detect task-related neural activation . For resting-state fNIRS, individual-level functional connectivity (FC), ALFF/fALFF, and network metrics can be calculated. For group-level analysis, NIRS-KIT provides several popular parametric models and multiple comparison correction approaches. It also provides multiple result visualization functions. Please contact: houxin195776@mail.bnu.edu.cn or zhzong@mail.bnu.edu.cn for it.
NIRS-ICA, a toolbox for removing fNIRS noise and extracting neural activity-related sources, has been integrated into NIRS-KIT. Please contact: zhaoyang@cibr.ac.cn for NIRS-ICA.
Please Note: For convenience, we will be using GitHub for the update and management of the NIRS-KIT software in the future. If you wish to download the latest version, please visit the GitHub website (https://github.com/bnuhouxin/NIRS-KIT).

NeuralAct

NeuralAct is a tool to visualize cortical activity on a 3D model of the cortex. It runs under Matlab, is stable, robust, and well documented.
To visualize neural activations, NeuralAct takes as input the 3D coordinates of the recording sensors, a cortical model in the same coordinate system, and the activation data to be visualized at each sensor. It then aligns the sensor coordinates with the cortical model, convolves the activation data with a spatial kernel, and renders the resulting activations in color on the cortical model. NeuralAct can plot cortical activations of an individual subject as well as activations averaged over subjects. It is capable to render single images as well as activation videos.

MildInt: Deep learning-based multimodal longitudinal data integration framework

A deep learning-based python package for heterogeneous data integration is provided. The major functionality of our package is to integrate any numerical data generated from multiple domain regardless of time series or non-time series. The most significant advantage of this package is the flexibility in which irregular time series data can be processed. The package can incorporate any number of modalities by which combining multiple GRUs with simple concatenation-based vector integration. In fields where available samples are small and varied depending on modalities, our package can utilize every available samples including non-overlapping samples as well as overlapping samples.

MonkeyCBP: A toolbox for connectivity-based parcellation of monkey brain

MonkeyCBP (A toolbox for connectivity-based parcellation of monkey brain) is an integrated pipeline namedMonkeyCBP realizing tractography-based brain parcellation with automatic processing and massive parallel computing. The highly-automated process and high-throughput performance supported by GPU option makes the toolbox ready to be used by a wider research community.

Spherical U-Net For Infant Cortical Surface Parcellation

This package includes the codes and network model trained based on UNC datasets for infant cortical surface parcellation using Spherical U-Net architecture. Please refer to the following paper for the details of the Spherical U-Net architecture: F. Zhao et al., "Spherical u-net on cortical surfaces: Methods and applications," IPMI, 2019. The package provides the codes, manuals, and examples on using the Spherical U-Net to parcellate the infant cortical surface of each hemisphere into 36 regions based on FreeSurfer Desikan protocol.

Latent Gaussian Copula Model

This gives the code for the latent Gaussian copula model.
simdiscrete.R performs simulation for discrete data. simmix.R performs for mixed data.

Sparse Hidden Markov Model

matlab codes for sparse hidden Markov model for sparse precision matrix estimation
dependent toolbox includes:
glasso toolbox: http://statweb.stanford.edu/~tibs/glasso/
mixture gaussain HMM toolbox: https://www.cs.ubc.ca/~murphyk/Software/HMM/hmm.html
Ref: https://ieeexplore.ieee.org/document/8766884

Turone Sheep Brain Templates and Atlas

The Turone Sheep Brain Templates and Atlas (TSBTA) resources offer to the preclinical neuroimaging community a complete set of in vivo templates, tissues priors of the sheep brain for both sex as well as a mixed template and its associated priors. Additionnaly, we provide a complete anatomical atlas covering the entire brain. This new set of resources allows both linear and non-linear coregistration of multimodal acquisitions performed on the same animal (functional, anatomical and diffusion), leading to an accurate identification of the brain territories involved in a physiological or pathophysiological process. The TSBTA resources pave the way for standardization of preclinical MRI and neurosciences research in large animals. Due to space limitation a second version of this tool is avalaible on Zenodo (full description here : https://zenodo.org/records/10730961)

Turone Mouse Brain Template and Atlas

The Turone Mouse Brain Template and Atlas (TMBTA) resources offer to the preclinical neuroimaging community a complete set of ex vivo template, tissues priors as well as anatomical atlas of the mouse brain. This new set of resources allows both linear and non-linear coregistration of multimodal acquisitions performed on the same animal (functional, anatomical and diffusion), leading to an accurate identification of the brain territories involved in a physiological or pathophysiological process. The TMBTA is a support for the standardization of MRI preclinical research.

Awake Rat rsfMRI Database

The database contains resting-state fMRI data from 90 awake rats, obtained in a 7T scanner with a well-established awake imaging paradigm (Nanyin Zhang et al, 2010. J. Neurosci. Methods) that avoids anesthesia interference with rsfMRI data. Both raw and preprocessed data are included, as well as intermediate data from the preprocessing. ICA denoising similar to ICA-FIX was applied. A coregistered Swanson atlas, ROI seedmaps, and group ICA (n=28) are also included.

The database is acquired and maintained by the Translational Neuroimaging and Systems Neuroscience Lab (TNSNL) (https://sites.psu.edu/zhanglab1/) at the Penn State University.

The data is also available at https://psu.box.com/s/fgii5hofdh0fas9tzsze281m5oqqn06d

The updated preprocessing toolkit can be found at https://github.com/liu-yikang/rat_rsfmri_preprocessing

Citation:
Liu, Y., Perez, P. D., Ma, Z., Ma, Z., Dopfel, D., Cramer, S., Tu, W., and Zhang, N. (2020). An open database of resting-state fMRI in awake rats. NeuroImage (in press).

GIN: G-Node Infrastructure Services

The goal of the GIN project is to develop a free data management system designed for comprehensive and reproducible management of scientific data. It keeps track of changes to the contents and organization of the files and provides secure remote access to the data. With proper authorization, data stored on GIN can be accessed and changed remotely, making it easy to work from multiple workplaces while keeping all data at hand and in sync. The system handles any kinds of directory structures and file types, and tracks all changes. The service furthermore makes it straightforward to share any data within a lab or with off-site collaborators and to work on it in parallel.

Any GIN repository hosted on the official GIN service can be permanently archived (at a given point in time) and referenced by a unique Digital Object Identifier (DOI).

The GIN service is based on the Gogs Git service.

The data versioning, storage, and synchronisation part of the project is built on git and git-annex.

SUIT toolbox for cerebellar MRI analysis

SUIT is a Matlab toolbox dedicated to the analysis of imaging data of the human cerebellum. The toolbox allows you to...
...automatically isolate cerebellar structures from the cerebral cortex based on an anatomical image.
...achieve accurate anatomical normalisation of cerebellar structures into atlas space using the Dartel algorithm.
...display the functional data on a surface-based representation.
...normalize focal cerebellar lesions for lesion-symptom mapping.
...use Voxel-based morphometry (VBM) to determine patterns of cerebellar degeneration or growth.
...use a probabilistic atlas of cerebellar anatomy to assign locations to different cerebellar lobules and deep cerebellar nuclei.
...use a collection of functional atlases based on task-based data and task-free connectivity to define regions of interest.
...improve normalization of the deep cerebellar nuclei using an ROI-driven normalization.

MIITRA atlas

The Multichannel Illinois Institute of Technology & Rush university Aging (MIITRA) atlas (pronounced "mitra" from the Greek word "μήτρα" which means template) is specifically designed for studies of the older adult brain. The current version includes high-quality T1-weighted & DTI templates of the older adult brain, as well as a comprehensive set of labels and other resources. The MIITRA templates were constructed using advanced multimodal template construction techniques on data from a large, diverse, community cohort of 400 non-demented older adults and a) are well-matched in space, b) exhibit high image sharpness, c) are free of artifacts, d) provide high inter-subject and inter-modality spatial matching of older adult data, and e) are highly representative of the older adult brain. The MIITRA labels were generated by majority voting based on labels from all 400 older adult participants.

If you use MIITRA resources please reference:
https://www.sciencedirect.com/science/article/pii/S1053811923005384

Brain Imaging Data Structure (BIDS)

Neuroimaging experiments result in complicated data that can be arranged in many different ways. So far there is no consensus how to organize and share data obtained in neuroimaging experiments. Even two researchers working in the same lab can opt to arrange their data in a different way. Lack of consensus (or a standard) leads to misunderstandings and time wasted on rearranging data or rewriting scripts expecting certain structure. Here we describe a simple and easy to adopt way of organizing neuroimaging and behavioral data.

CANlab MediationToolbox

https://github.com/canlab/MediationToolbox/
This toolbox contains functions to aid in single- and multi-level mediation analyses of any kind of data. The main function, mediation.m, examines 3 timeseries to determine if one of them acts as a mediator between the other two. Works for both single-level and multi-level (multiple subjects/observations) data. There is a brain imaging specific mediation search function that takes two variables and searches functional MRI data for potential mediators. The toolbox also includes visualization and plotting functions for mediation analyses, and various computational support functions.

The Mediation_walkthrough folder contains a powerpoint presentation with a step-by-step example single-level mediation analysis of example brain data. See https://canlab.github.io/walkthroughs/ for more examples.

Non-Contrast Head CT of Stroke Patients with Brain Masks

We released 35 subject's non-contrast CT scans including manually segmented brain masks from 2 readers. The data is from "Validated automatic brain extraction of head CT images" (https://doi.org/10.1016/j.neuroimage.2015.03.074). The data are from stroke patients from multiple centers and can be used a validation data set for brain segmentation on CT.

CanlabCore object oriented Matlab toolbox for interactive analysis of neuroimaging data

https://github.com/canlab/CanlabCore
This repository contains core tools for MRI/fMRI/PET analysis from the Cognitive and Affective Neuorscience Lab (Tor Wager, PI) and our collaborators. The tools provide a high-level language for interacting with neuroimaging data. The idea is to take preprocessed data or even the results of single-subject analyses and import them into lightweight, flexible data objects specialized for neuroimaging visualization and analysis. These objects allow for interactive analysis with simple commands that allow you to view neuroimaging data with SPM's interactive brain viewer, perform multivariate predictions, independent components analysis, and other user friendly and robust neuroimaging data analyses. The repository also includes other useful toolboxes, including fMRI design optimization using a genetic algorithm, fMRI HRF estimation, fMRI analysis with Hierarchical Exponentially Weighted Moving Average change-point analysis, various fMRI diagnostics and more.

A naturalistic video for recognizing schizophrenia patients using fMRI

· A video to evoke synchronized brain activities across individuals, with subjective feeling ratings.
· The effectiveness of evoking synchronized brain activity has been validated in an empirical study (Yang et al., Neuroimage, 2019).
· Can be used in studies on brain synchronizations and individual differences of brain responses.
· Can be used to investigate individual/common brain responses to social-emotional events and can be applied in psychiatric imaging studies.

Knowing what you know (kwyk) - Bayesian Brain Parcellation

kwyk is a deep neural network for predicting FreeSurfer segmentations of structural MRI volumes, in seconds rather than hours. The network was trained and evaluated on an extremely large dataset (n = 11,148), obtained by combining data from more than a hundred sites. This tool is implemented as both Docker and Singularity containers.

RS-fMRI BOLD and ASL with eyes closed vs. eyes open

34 subjects were scanned under two states: eyes open (EO) and eyes closed (EC) conditions. Resting-state blood-oxygenation-level-dependent functional MRI (BOLD-fMRI),arterial spin labeling (ASL), and high-resolution 3D T1 imaging were performed under EO and EC conditions. Please see below for the original paper (Zou et al., 2015).
Zou Q, Yuan B-K, Gu H, Liu D, Wang DJJ, Gao J-H, et al. Detecting static and dynamic differences between eyes-closed and eyes-open resting states using ASL and BOLD fMRI. PloS One. 2015;10: e0121757. doi:10.1371/journal.pone.0121757

atlasBREX: Automated template-derived brain extraction in animal MRI

Due to optimization for the human brain, most common skullstripping/brain-extraction methods achieve insufficient results for non-human brains, which then require further manual intervention. Making use of the available brain-extraction from a template/atlas, this approach implements brain-extraction through reversal (rigid- and non-rigid) deformation of a template-derived mask.

-time-saving and straightforward (with various optional parameters for further optimization)
-multi-step registration (2- or 3-step) for improved registration to low resolution datasets
-robust FSL (FLIRT, FNIRT) or ANTs (SyN) registration frameworks
-compatible with T1-/T2-weighted datasets

Please cite: atlasBREX: Automated template-derived brain extraction in animal MRI
Scientific Reports, volume 9, Article number: 12219 (2019)
DOI: 10.1038/s41598-019-48489-3

Amygdala-Subregions-AutoSeg

Segmenting extremely small but important subcortical brain structures, such as the amygdala's subregions, would be useful in neuroimaging studies of many neurological disorders yet it remains very challenging. No reliable segmentation tools is currently available for amygdala subregions. We developed a 3D fully convolutional neural network to segment the amygdala and its subregions with high accuracy. Our method yields excellent segmentations and is much more efficient than multi-atlas based methods.

Acknowledgements
================
This work was supported by NARSAD: Brain and Behavior grant 24103 (to BN) and NIH grant funding NINDS R01 NS092870, NIMH P50 MH100031 and the Waisman Center U54 IDDRC from the Eugene Kennedy Shriver National Institute of Child Health and Human Development (U54 HD090256). NA was also supported in part by the BRAIN Initiative R01-EB022883-01, CPCP U54-AI117924-03, the Alzheimer's Disease Connectome Project (ADCP) UF1-AG051216-01A1 and R56-AG052698-01.

Automatic-Segmentation-Amygdala-Subregions

Segmenting extremely small but important brain regions such as the amygdala's subregions are useful in neuroimaging studies of many neurological disorders yet remains a challenging task. We developed a 3D fully convolutional neural network to segment the amygdala and its subregions with high accuracy and robustness.

Functional MRI dataset during passive visual stimulation and two motor conditions (sequence learning, paced repetitive tapping)

This fMRI dataset is ideal for studying mechanisms for cognitive control of simple movements without memory as a confound. The task used in this study includes two motor tasks under cognitive control, only one of which showed learning effects, and a passive visual condition.

Small effect size leads to reproducibility failure in resting-state fMRI studies

There were 5 datasets, including PD-off, PD-on, ASD, MF, and EOEC. The former 4 datasets were between-group design, and the EOEC was within-group design. Please see below for the preprint and its support information (Jia, Zhao et al. 2018).
References:
Jia, X.-Z., et al. (2018). "Small effect size leads to reproducibility failure in resting-state fMRI studies." 285171.

Brain Biomechanics Imaging Resources

Tools and data for the analysis of computer models of traumatic brain injury (TBI) and chronic traumatic encephalopathy (CTE).

Check out our documentation pages: https://www.nitrc.org/plugins/mwiki/index.php/bbir:MainPage

Greedy Projected Distance Correlation

Using projected distance correlation to build a conditional dependency graph among high-dimensional mixed data can reduce the computational cost, which is critical for analyzing large volume of imaging genomics data. The results from our simulations demonstrate a higher degree of accuracy with G-PDC than distance correlation, Pearson's correlation, and partial correlation, especially when the correlation is nonlinear. Finally, we apply our method to the Philadelphia Neurodevelopmental data cohort with 866 samples including fMRI images and SNP profiles. The results uncover several statistically significant and biologically interesting interactions, which are further validated with many existing studies.

FDRcorrectedSCCA

Here we propose a way of applying the FDR concept to sparse CCA, and a method to control the FDR. The proposed FDR correction directly influences the sparsity of the solution, adapting it to the unknown true sparsity level. Theoretical derivation as well as simulation studies show that our procedure indeed keeps the FDR of the canonical vectors below a user-specified target level.

Longitudinal survival analysis and two-group comparison for predicting the progression of mild cognitive impairment to Alzheimer

The characterization of the progression of Alzheimer's disease (AD) is important for both early detection and treatment. Longitudinal studies using structural magnetic resonance imaging (MRI) and neuropsychological measurements (NMs) allow a noninvasive means of following the subtle anatomical changes occurring during the evolution of AD.
This project compared two approaches for the construction of longitudinal predictive models, which were used here to estimate the conversion of mild cognitive impairment (MCI) to AD. These approaches were as follows:
a) two-group comparison between converter and nonconverter MCI subjects and b) model-based
survival analysis. Predictive models combined MRI-based markers (cortical thickness and volume of subcortical structures) with NMs and included demographic and clinical information as covariates. Both approaches employed linear mixed effect (LME) modeling to capture the longitudinal trajectories of the markers.

Chinese and Caucasian cortical surface templates

We use an unbiased cortical surface template construction algorithm to create Chinese (sCN200) and Caucasian (sUS200) cortical surface templates, which based on high-quality T1- and T2-weighted MR images from CHCP and HCP datasets aged 19 to 37 years. The Caucasian and Chinese cortical surface templates atlas comprise averaged anatomical cortical surfaces (white matter, pial and midthickness surface) and surface features (sulcal depth, curvature, thickness, T1w/T2w myelin, cortical labels).

OpenNeuro

OpenNeuro (https://openneuro.org/) is a data sharing repository for both researchers to seamlessly upload their datasets onto and download off of. We currently have over 250 publicly available datasets that contain many different psychological tasks and modalities, while following the BIDS (Brain Imaging Data Structure) standard. The repository is currently mostly MRI, but we accept EEG, iEEG, and MEG data. The repository is able to expand to accept and store other modalities as they are specified in the BIDS standard.

RT-NET

RT-NET (real-time non-invasive electrophysiology toolbox), is a new software which can be used for online neural activity reconstruction from hdEEG. The goal is to allow researchers and neuroscientists to easily make use of the analysis steps for real-time brain reconstruction using hdEEG. Our toolbox will contribute to source-based neurofeedback experiments and to development of novel BCI applications based on hdEEG. Through RT-NET, it is possible to enable source-based neurofeedback experiments in one or two brain areas, and to visualize in 2D and 3D (on a 3D cortical model) the online brain activity.

Cgrid-Toolbox

The Cgrid toolbox builds a rectangular representation of FreeSurfer ROI's, that can facilitate data processing and interpretation in various ways. The toolbox functions as an extension to FreeSurfer, but the output is compatible with all the major MRI/fMRI software analysis packages.

Joint CCA Model for Class-specific Correlation Analysis

JSCCA method estimates multiple pairs of canonical vectors with both shared and class-specific patterns, detecting abnormal interaction modules between genomic variants and brain activities.

Matlab codes and examples for JSCCA, joint detection of the associations between DNA methylation and gene expression from multiple cancers, learning with selected features, greedy projected distance correlation have been given below.

Flywheel

Imaging Data Informatics Platform for Research and Collaboration

The Flywheel imaging data platform captures, curates, and computes data for imaging researchers, scientists, and clinicians analyzing and collaborating on research data.
The platform is an open, extensible, and secure regulatory-compliant environment for sharing research data and algorithms ensuring productivity, accelerated discovery, and reproducibility.
Applications include large and small research projects involving machine learning, compute-intensive image processing, and multi-center projects at imaging research centers, clinical centers, as well as pharmaceutical and other commercial research organizations.
Automation of current and historical data - its organization, classification, and labeling - delivers reduced costs, ease-of-use, and collaboration. Flywheel achieves this by ensuring data and metadata, routine pre-processing and pipelines are automated and managed in a sharable, cloud-scalable, HIPAA compliant environment.

vini: A viewer for fMRI data

vini_main vini is a light-weight viewer for MR data. The strives to be fast and simple, yet powerful. Vini also features many practical keyboard shortcuts.

Currently, the following file formats are supported:

.nii, .nii.gz,.img/.hdr, .v (lipsia's vista format)

Furthermore, numpy arrays are *.npy (numpy arrays on disk)

The viewer is written in python and does not have any external dependencies.

BrainIAK (Brain Imaging Analysis Kit)

The Brain Imaging Analysis Kit is a package of Python modules useful for neuroscience, primarily focused on functional Magnetic Resonance Imaging (fMRI) analysis.

The package was originally created by a collaboration between Intel Labs and the Princeton Neuroscience Institute (PNI).

To reduce verbosity, we may refer to the Brain Imaging Analysis Kit using the BrainIAK abbreviation. Whenever lowercase spelling is used (e.g., Python package name), we use brainiak.

SIGMA Rat Brain Templates and Atlases

The SIGMA resources offer to the preclinical neuroimaging community a complete set of in vivo and ex vivo templates, tissues priors as well as functional and anatomical atlases of the rat brain. SIGMA allows both linear and non-linear coregistration of multimodal acquisitions for accurate identification of the brain territories involved in physiological or pathophysiological conditions. Since March 1, 2024, a second version of the SIGMA resources is available and in this release we :
- updated the previous SIGMA spaces,
- revised the GM/WM segmentation and created additional probabilistic maps (outbrain, skull)
- created diffusion templates (B0, FA, etc.) at both ex-vivo and in-vivo resolutions.
- created a CT/18FDG reference space for multimodal normalization data
- delivered a new SIGMA brain atlas obtained by the normalization of the Waxholm space published by Kleven, H. et al. Nat Met. (2023)

Due to space limitation this second version is available here : https://zenodo.org/records/10635831

The ERICA Toolbox for Event Related Independent Component Analysis

This toolbox performs Event-related independent component analysis [1], which:
• Is useful for event-related fMRI when the neuronal and/or haemodynamic response to events (the “fMRI response”) is not known a priori
• Assumes only that the peri-event fMRI time-course is similar for each event
• Requires fMRI time-series input data along with event timing
• Otherwise makes no assumptions about neuronal or hemodynamic response
• Step 1: At each voxel, estimate the fMRI response associated with the events of interest
• Step 2: Perform a spatial ICA on the resultant 4D spatio-temporal response map
• Results in a series of spatial maps and associated peri-event time-series
• Can detect neuronal responses even if they precede the events (e.g. anticipatory effects)
• Can detect subnetworks with different peri-event time-courses [2]

[1] DOI: 10.1016/j.neuroimage.2012.12.025
[2] DOI: 10.1111/epi.12163

HAMLET

The HAMLET (HArmonizing MuLti-sitE Traveling subjects) dataset is a multi-subject, multi-scanner, multi-contrast MRI dataset consisting of 5 subjects canned at 4 sites (multiple vendors) with no data usage agreement required to assess and utilize the data. This data includes structural, functional, and diffusion data, all acquired with standard clinical keys. This dataset can be utilized for scanner-to-scanner harmonization approaches, and intra-scanner, inter-scanner reproducibility, and inter-subject reproducibility studies of structure and function.

We encourage use of our data as an external dataset for validation of CDMRI submissions. If you would like to publicize the results of your work, please fill out the form below. We will post results to NITRC along with a dedicated summary page and provide a QR code to directly link to their data.

https://docs.google.com/forms/d/1jNdGazahoMp3TCmxn1kczQ32JGlZNIrCyNN5MLIju_k/edit?usp=sharing.

GSBSS

Imaging sessions on 30 healthy subjects. Imaging modalities include T1, DTI, HARDI and working memory fMRI. All data have been converted to NIFTI format. This is intended to be a resource for statisticians and imaging scientists to be able to quantify the reproducibility of gray matter surface based spatial statistics. Please cite: Prasanna Parvathaneni; Ilwoo Lyu; Yuankai Huo; Baxter P. Rogers; Kurt G. Schilling; Vishwesh Nath; Justin A Blaber; Allison E Hainline; Adam W Anderson; Neil D. Woodward; Bennett A Landman. "Improved gray matter surface based spatial statistics in neuroimaging studies." In MRI – Accepted.

Caucasian and Chinese Brain templates [US200 CN200]

We use a diffeomorphic template building framework (ANTs) to create Caucasian (US200) and Chinese (CN200) brain templates based on high-quality T1 weighted MR images from HCP and CHCP datasets aged 19 to 37 years. The Caucasian and Chinese templates include brain/head templates and tissue probability templates.
If you use this atlas in your research, please quote the article:
Yang, G., Zhou, S., Bozek, J., Dong, H. M., Han, M., Zuo, X. N., . . . Gao, J. H. (2020). Sample sizes and population differences in brain template construction. Neuroimage, 206, 116318.

HeuDiConv: a heuristic-centric DICOM converter

heudiconv is a flexible DICOM converter for organizing brain imaging data into structured directory layouts.

- it allows flexible directory layouts and naming schemes through customizable heuristics implementations
- it only converts the necessary DICOMs, not everything in a directory
- you can keep links to DICOM files in the participant layout
- using dcm2niix under the hood, it's fast
- it can track the provenance of the conversion from DICOM to NIfTI in W3C PROV format
- it provides assistance in converting to BIDS.
- it integrates with DataLad to place converted and original data under git/git-annex version control, while automatically annotating files with sensitive information (e.g., non-defaced anatomicals, etc)

ReproIn: The ReproNim image input management system (featuring DataLad)

ReproIn's goal is to provide a turnkey flexible setup for automatic generation of shareable, version-controlled BIDS datasets from MR scanners. To not reinvent the wheel, all actual software development is largely done through contribution to existing software projects:

* HeuDiConv: a flexible DICOM converter for organizing brain imaging data into structured directory layouts. ReproIn heuristic was developed and now is shipped within HeuDiConv, so it could be used independently of the ReproIn setup on any HeuDiConv installation (specify -f reproin to heudiconv call).
* DataLad: a modular version control platform and distribution for both code and data. DataLad support was contributed to HeuDiConv, and could be enabled by adding --datalad option to the heudiconv call.

Nutil - Neuroimaging utilities

User documentation: https://nutil.readthedocs.io/en/latest/

Nutil simplifies and streamlines the pre-and-post processing of 2D brain image data from mouse and rat. Nutil is developed as a stand-alone application and requires no experience to execute. The user specifies the input and output parameters in the GUI.

Nutil enables:
1. TiffCreator: convert JPEG, PNG and normal TIFF images to tiled TIFF format.
2. Transform: rename, rotate and resize extremely large tiled TIFF images.
3. Resize: for resizing JPEG/PNG images with output in PNG format.
4. Quantifier: for the batch extraction, quantification and spatial analysis of labelling in histological section images from mouse or rat brain. The spatial analysis is based on a reference atlas such as the Allen Mouse Brain reference atlas or the Waxholm Space Atlas of the Sprague Dawley rat brain.

For user support: support@ebrains.eu
Report issues: https://github.com/Neural-Systems-at-UIO/nutil/issues

Enumerated Auditory Response (EAR)

EAR is an automated auditory fMRI post processing method that provides consistent radiological interpretations for evaluating auditory response in TBI, carbon monoxide poisoning, and other neurological disease processes that affect auditory response. The tool was developed by an expert in auditory fMRI interpretation. Accuracy has been demonstrated with very high correlation to expert reads and other quantitative methods. A similar approach for fMRI analysis might be appropriate for both future research projects with constrained resources, as well as possible routine clinical use.

A novel method for extracting hierarchical functional subnetworks based on a multi-subject spectral clustering approach

Group-level Network Hierarchical Clustering (GNetHiClus) is a software toolbox that can be used to extract hierarchical brain network modules at group level. The software: (1) reads connectivity matrices from a group of subjects; (2) subsamples data to obtain subgroups; (3) extracts hierarchical brain subnetworks; (4) uses bootstrap method to estimate the most reliable subnetworks.
The method is described in the following paper:
Xiaoyun Liang, Chun-Hung Yeh, Alan Connelly, Fernando Calamante. A novel method for extracting hierarchical functional subnetworks based on a multi-subject spectral clustering approach. Brain Connectivity, 03/2019; DOI: 10.1089/brain.2019.0668.

Neurodevelopmental MRI Database

This is a database of average MRIs and associated MRI volumes for developmental MRI work. It consists of average MRI templates, segmented partial volume estimate volumes for GM, WM, T2W-derived CSF. The database is separated into head-based and brain-based averages. The data are separated by ages in months, years, 6-month, or 5-year intervals. The templates are grouped into first year (2 weeks through 12 months), early childhood (15 months through 4 years), childhood (4 years through 10 years), adolescence (10.5 years through 17.5 years) and adults (18 years through 89 years). Tools for cortical source analysis of EEG and ERP are provided. These tools are based on the average MRI templates, segmenting, and atlases.

PyNN

PyNN (pronounced 'pine') is a simulator-independent language for building neuronal network models.

In other words, you can write the code for a model once, using the PyNN API and the Python programming language, and then run it without modification on any simulator that PyNN supports (currently NEURON, NEST, and Brian), and on the SpiNNaker and BrainScaleS neuromorphic hardware systems.

The PyNN API aims to support modelling at a high-level of abstraction (populations of neurons, layers, columns and the connections between them) while still allowing access to the details of individual neurons and synapses when required. PyNN provides a library of standard neuron, synapse and synaptic plasticity models, verified to work the same on the different supported simulators. PyNN also provides a set of commonly-used connectivity algorithms (e.g. all-to-all, random, distance-dependent) but makes it easy to provide your own connectivity in a simulator-independent way.

MeshView for brain atlases

MeshView (please visit MediaWiki page for links) is a web application for real-time 3D display of surface mesh data representing structural parcellations from volumetric atlases, such as the Waxholm Space Atlas of the Sprague Dawley Rat Brain.

Key features:
- orbiting view with toggleable opaque/transparent/hidden parcellation meshes
- rendering user defined cut surface as if meshes were solid objects
- rendering point-clouds (simple type-in, or loaded from JSON)
- coordinate system is compatible with QuickNII (https://www.nitrc.org/projects/quicknii)

Two atlases are provided with MeshView:
- Waxholm Space Atlas of the Sprague Dawley rat brain, parcellation versions 2, 3, and 4 (generated from 39 μm resolution volume)
- Allen Mouse Brain Atlas reference atlas, CCFv3 parcellations from 2015, and 2017 (generated from 25 μm resolution volume)

A converter will be provided for importing STL meshes, like the ones generated by MeshGen.

SPOT3D: Spatial positioning toolbox for head markers using 3D scans

An accurate individual head model is fundamental in high-density EEG studies and reliable sensor positioning is necessary.
Using a colour-enhanced 3D scanner, it is possible to acquire the whole participant's head (with EEG cap), which can be post-processed using SPOT3D toolbox, which integrates a graphical user interface (GUI).
This software performs alignment of the 3D scan in individual magnetic resonance (MR) space, sensor detection and labelling on the aligned scan, and positioning of facial landmarks. The editing tools in the GUI allow the user to manually perform these steps or to adjust their outcome after automated processing.

Cite:
Taberna, G.A., Guarnieri, R. & Mantini, D. SPOT3D: Spatial positioning toolbox for head markers using 3D scans. Sci Rep 9, 12813 (2019). https://doi.org/10.1038/s41598-019-49256-0
Taberna, G.A., Marino, M., Ganzetti, M. & Mantini, D. Spatial localization of EEG electrodes using 3D scanning. J Neural Eng. 2019 Apr;16(2):026020. https://doi.org/10.1088/1741-2552/aafdd1

NeuroStars

A question and answer site for neuroinformatics.

Structural Asymmetries

The package of scripts available here enables users to measure structural asymmetries at different scales of resolution using deformation fields that are obtained at 3 increasingly finer scales of normalization resolution.

The Medical Image Computing and Computer Assisted Intervention Society

The Medical Image Computing and Computer Assisted Intervention Society (the MICCAI Society) is dedicated to the promotion, preservation and facilitation of research, education and practice in the field of medical image computing and computer assisted medical interventions including biomedical imaging and robotics, through the organization and operation of regular high quality international conferences and publications which promote and foster the exchange and dissemination of advanced knowledge, expertise and experience in the field produced by leading institutions and outstanding scientists, physicians and educators around the world. The MICCAI Society is committed to maintaining high academic standards and independence from any personal, political or commercial vested interests.

hippocampus subregional masks based on gray matter volume covariance

The masks were generated by parcellating the human hippocampus with two independent datasets, each of which consisted of 198 T1-weighted structural magnetic resonance imaging images of healthy young subjects. The method was based on gray matter volume (GMV) covariance, which was quantified by a bivariate voxel-to-voxel linear correlation approach, as well as a multivariate masked independent component analysis approach.

Small effect size leads to reproducibility failure in resting-state fMRI studies

The amplitude of low frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality (DC) images of 5 datasets: PD-off, PD-on, ASD, MF, and EOEC were shared. Please see The amplitude of low frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality (DC) images of 5 datasets: PD-off, PD-on, ASD, MF, and EOEC Please see Jia et al. 2018 for the details about preprocessing and metrics calculation.

Jia, Xi-Ze; Zhao, Na; Barton, Barek; Burciu, Roxana; Carriere, Nicolas; Cerasa, Antonio et al. (2018): Small effect size leads to reproducibility failure in resting-state fMRI studies.

Intra- and inter-scanner reliability of RS-fMRI BOLD and ASL with eyes closed vs. eyes open

Data information:
21 subjects were scanned 3 times (V1, V2, V3), with V1 and V2 on a scanner, V3 on another scanner in another site. Resting-state blood-oxygenation-level-dependent functional MRI (BOLD-fMRI), pseudo-continuous arterial spin labeling (pCASL), and high-resolution 3D T1 imaging were performed under eyes open (EO) and eyes closed (EC) conditions. Please see below for the original paper (Yuan et al., 2018) and another paper (Zhao et al. 2018).
Yuan, L.-X. et al. (2018) ‘Intra- and Inter-scanner Reliability of Scaled Subprofile Model of Principal Component Analysis on ALFF in Resting-State fMRI Under Eyes Open and Closed Conditions’, Frontiers in Neuroscience, 12, p. 311. doi: 10.3389/fnins.2018.00311
Zhao, N. et al. (2018) ‘Intra- and Inter-Scanner Reliability of Voxel-Wise Whole-Brain Analytic Metrics for Resting State fMRI’, Frontiers in Neuroinformatics, 12, p. 54. doi: 10.3389/fninf.2018.00054

Penn-CHOP Age-Specific Neonate Atlas

The Penn-CHOP age-specific neonate atlas package developed by Hao Huang lab (link1: http://www.haohuanglab.org/, link2: http://brainmrimap.org/penn-chop-age-specific-neonate-brain-atlas.html) includes volume image of atlas labels, DTI fractional anisotropy (FA) map, DTI orientation-encoded colormap (OEC) and mean diffusivity (MD) map for the human neonate brains at 33, 36 and 39 postmenstrual weeks (PMW) at scan. All maps and atlas labels are in the template space and have the same dimension and resolution with details below.

Image dimension of DTI maps and atlas labels of all PMW: 180x220x180;
Resolution: 0.6x0.6x0.6 mm3

Publication:
Feng L, Li H, Oishi K, Mishra V, Song L, Peng Q, Ouyang M, Wang J, Slinger M, Jeon T, Lee L, Heyne R, Chalak L, Peng Y, Liu S, Huang H. 2018. Age-specific gray and white matter DTI atlas for human brain at 33, 36 and 39 postmenstrual weeks. Neuroimage, 185: 685-698, 2019.

RNcut: functional brain networks parcellation

The RNcut software freely disseminated below can be used for parcellating functional brain networks with resting-state BOLD fMRI datasets. This software package represents the improved version of conventional Ncut with two added regularization terms, smoothing term Ev and small-patch removal term Ep. The software package can generate robust and homogeneous functional parcellation of not only adult but also neonate brain networks.

The paper titled 'Regularized -Ncut: Robust and homogeneous functional parcellation of neonate and adult brain networks' that describes details of the software has been published in Artificial Intelligence in Medicine (https://doi.org/10.1016/j.artmed.2020.101872).

movement_info

Software movement_info_2 is written to visualize movement parameters estimated during preprocessing of fMRI data. Its development was based on movement parameters estimated with the tool SPM, but it should work in general for all 6 parametric movement estimates (translations and rotations in axis x, y and z). The visualization is implemented in user-friendly GUI.
Main features of movement_info_2 are:
1) Visualization of 6 movement parameters and their differences
2) Framewise displacement (FD) –Computation and visualization
3) Setting threshold for scans based on FD value, visualization
4) Export of suprathreshold scans to Excel or mat file (button “Export suitable data”)
5) Setting threshold of affected scans for exclusion of datasets
6) Visualization of suitable and unsuitable datasets based on abovementioned thresholds. Export to Excel possible (button “Export suitable data”)

fMRIPrep

fMRIPrep is a functional magnetic resonance imaging (fMRI) data preprocessing pipeline that is designed to provide an easily accessible, state-of-the-art interface that is robust to variations in scan acquisition protocols and that requires minimal user input while providing easily interpretable and comprehensive error and output reporting. It performs basic processing steps (coregistration, normalization, unwarping, noise component extraction, segmentation, skull-stripping etc.) providing outputs that can be easily submitted to a variety of group-level analyses, including task-based or resting-state fMRI, graph theory measures, surface or volume-based statistics, etc.

CNN for 3D brain MRI classification

Functional organization of the mouse lemur Primate Microcebus murinus : from multilevel validation to comparison with humans

Brain network organization in the mouse lemur (Microcebus murinus) Primate.
(comparison with humans)
Archives contain:

- Dictionary learning analysis in mouse lemurs and humans showing networks identified in these two species.

- Cerebral templates from mouse lemurs and humans (MNI template). They can be used to localize networks.

- A functional atlas of the mouse lemur brain issued from resting fMRI. Resting-state functional MR images were recorded from 14 mouse lemurs at 11.7 Tesla (2 time point per animal).
- An atlas from human brain (issued from http://www.gin.cnrs.fr/fr/outils/aal-aal2/) that can be used to attribute human cerebral networks.

- Templates, atlases and networks can be easily observed together using ITK-SNAP (http://www.itksnap.org/).

QuickNII - Serial section aligner to volumetric atlases

QuickNII is a tool for user guided affine registration (anchoring) of 2D experimental image data, typically high resolution microscopic images, to 3D atlas reference space, facilitating data integration through standardized coordinate systems.

Key features:
- generate user defined cut planes through the atlas templates, matching the orientation of the cut plane of the 2D experimental image data, as a first step towards anchoring of images to the relevant atlas template
- propagate spatial transformations across series of sections following anchoring of selected images

Multi-modality atlasing datasets provided with QuickNII:
- Waxholm Space Atlas of the Sprague Dawley rat brain, versions 2, 3, and 4 (T2* MRI, DTI, and delineations at 39 μm resolution)
- Allen Mouse Brain Atlas reference atlas, CCFv3 (SST average template, grayscale Nissl volume reconstruction and delineations from 2015 and 2017 at 25 μm resolution)

Source code on GitHub under MIT license: https://github.com/HumanBrainProject/QuickNII

3-D Connective Field Modeling Toolbox

A framework for both fitting and describing connectivity patterns between regions that is a simple extension from the current population receptive field models in the visual neuroscience literature. This provides a description of the specific nature of connectivity from one region to another. The connectivity from each voxel in a designated seed region to a mapping region is modeled as a 3-dimensional Gaussian, providing location parameters and spread parameters. This allows the direct description of the relative mapping from one region to another.

Sinoplan

Sinoplan is a powerful software designed for planning of stereotactic implantation surgery, such as depth electrodes(SEEG) or DBS, It is also an useful neuroimaging tool.
Main functions:
Multi-model image fusion.
Automatic image registration;
3D reconstruction and visualization of Vessel, skin, cranial and bone;
Easily trajectory planning and viewing, 8-16pins electrode avaliable.
Calibration of the parameter of stereotactics frame, support both crw and leksell.

Working environment:
System: Windows 10 (64bits)
CPU: Intel Core i7
Memory: 8G
Graphic: Nvidia GeForce GTX1050M or higher is recommended
Network connection required.

Attention:
For intellectual property protection reason, this software must be worked in network environment and online verification may be required during operation. Users can use most of the software's features and register via email to access all functions.
If have any questions or want to report bugs, feel free to leave us messages through forum or contact lz@sinovationmed.com.

NRM2018 PET Grand Challenge Dataset

For many years PET centres around the world have developed and optimised their own analysis pipelines, including a mixture of in-house and independent software, and have implemented different modelling choices for PET image processing and data quantification. As a result, many different methods and tools are available for PET image analysis.

This dataset aims to provide a normative tool to assess the performance and consistency of PET modelling approaches on the same data for which the ground truth is known.
It was created and released for the NRM2018 PET Grand Challenge. The challenge aimed at evaluating the performances of different PET analysis tools to identify areas and magnitude of receptor binding changes in a PET radioligand neurotransmission study.

Quantification of Structural Brain Connectivity via a Conductance Model

This toolbox computes the structural connectivity of the brain (connectome) from diffusion-weighted MRI, using a conductance-based mathematical model introduced by Frau-Pascual et al (NeuroImage, 2019).

UNC Visual Motion QC Database

This project consists of a database of structural MRI images in the early postnatal phase. The intended use of this data is for training users in the visual assessment of motion artifacts in developmental structural MRI data. It allows the training of researchers to visually evaluate the degree of motion artifact present using the scoring system employed at UNC by the Neuro Image and Research Analysis Laboratories, the Early Brain Development Studies group, and the Autism Research group.

High Dynamic Range for Local Contrast Enhancement of MRI

An implementation of the high dynamic range (HDR) algorithm for MRI published in Magnetic Resonance in Medicine (MRM) as

Local Contrast Enhanced MR Images via High Dynamic Range Processing
Chandra SS, Engstrom C, Fripp J, Walker D, Rose S, Ho C, Crozier S.
Magnetic Resonance in Medicine, vol. 80, no. 3, pp. 1206–1218, 2018.

The HDR MRI algorithm allows for fusing the scans together to form a single omnibus image with enhanced definition of thin, complex anatomical structures, especially in the presence of variable or hyperintense signals. The algorithm takes as input coregistered MR images (preferrably of different contrasts), non-linearly combines them and outputs a single (contrast enhanced) HDR MR image. This library provides a single ITK class, a commandline executable and a GUI application built on the open source [SMILI Biomedical Imaging Framework](https://smili-project.sourceforge.io/).

HINT: Hierarchical Independent Component Analysis Toolbox

The Hierarchical Independent Component Analysis Toolbox (HINT) is a Matlab toolbox serving as a user-friendly platform for conducting analyses under the hierarchical ICA framework. At this time, the toolbox implements the hc-ICA technique of Shi and Guo, 2016 and the longitudinal technique of Wang and Guo, 2019.

Highlights:
Model based estimation and hypothesis testing of covariate effects
Visualization windows to examine covariate effects, contrasts, and to compare sub-populations

Shi, R., & Guo, Y. (2016). Investigating differences in brain functional networks using hierarchical covariate-adjusted independent component analysis. The Annals of Applied Statistics, 10(4), 1930–1957. http://doi.org/10.1214/16-AOAS946

Wang, Y., & Guo, Y. (2019). A hierarchical independent component analysis model for longitudinal neuroimaging studies. NeuroImage, 189, 380-400. https://doi.org/10.1016/j.neuroimage.2018.12.024

Video Tutorial: https://www.youtube.com/watch?v=lacy1bnKTYA

Mac/Windows/Linux/HPC

fNIRS during Stroop before and after acute bout of aerobic exercise

fNIRS data collected from prefrontal and motor cortices while human subjects perform Stroop task before and after thirty minutes of cardiovascular exercise. Associated preprint: https://www.biorxiv.org/content/early/2018/06/29/359307

Associated Dissertation:
https://digital.library.unt.edu/ark:/67531/metadc1157584/

Templates for Long-Evans Rat Brain

The average signal intensity, stereotaxic templates and TPMs of rat brain are intended for SPM normalization and segmentation (including skull-stripping) of rat head MRI data. Ex-vivo T2WI (an isotropic resolution of 118μm) of Long Evans (n=4, male, 8wo) rat head were acquired and DARTEL in SPM8 was used for the creation of templates and TPMs. The bounding box (a three dimensional space) of templates encompassed the followings x, y, z dimensions (-18,18; -20, 7; -14, 6) and the origin was (0, 0, 0) in mm. The horizontal slice (z=0) passed through bregma and lambda (the flat-skull position) and the coronal slice (y=0) lay perpendicular to the horizontal plane and passes through the bregma, and the sagittal slice (x=0) was located at the median sagittal plane. The locations of bregma and lambda were determined from the image contrasts of T2WI and CT data.

Authors: Keigo Hikishima, Susumu Setogawa, Hiroshi Mizuma, Yilong Cui, Kazuto Kobayashi, Hirotaka Onoe

Contact person: keigo.hikishima@oist.jp

Predicting Alzheimer’s conversion in mild cognitive impairment patients using longitudinal neuroimaging and clinical markers

Patients with mild cognitive impairment (MCI) have a high risk for conversion to Alzheimer’s disease (AD). Selecting a set of relevant markers from multimodal data to predict conversion from MCI to AD has become a challenging task.
The aim of this project is to quantify the impact of longitudinal predictive models with single- or multisource data for predicting MCI-to-AD conversion and identifying a very small subset of features that are highly predictive of conversion. We developed predictive models of MCI-to-AD progression that combine magnetic resonance imaging (MRI)-based markers (cortical thickness and volume of subcortical structures) with neuropsychological tests. A set of longitudinal features potentially discriminating between
MCI subjects who convert to AD and those who remain stable over a period of 3 years was obtained. The proposed approach was developed, trained and evaluated using the
Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset

Resting-state fMRI from healthy young subjects

This entry provides the original DICOM files of functional and structural MRI, obtained from resting-state healthy young subjects (100 males and 100 females), which were used in our research article (Donishi et al. 2017, https://doi.org/10.1002/brb3.890).

DensParcorr : Dens-Based Method for Partial Correlation Estimation in Large Scale Brain Networks

DensParcorr is a R package for estimating the direct functional connectivity in large scale brain networks based on partial correlation. Specifically, this package implements the statistical method proposed in Wang et al (2016) which utilized the constrained L1-minimization approach (CLIME) to estimate precision matrix and then applied the Dens-based tuning parameter selection method to help select an appropriate tuning parameter for sparsity control in the network estimation.

TRAFIC - Tracts Fibers Classification Using Deep Learning

We present TRAFIC, a fully automated tool for the labeling and classification of brain fiber tracts. TRAFIC classifies new fibers using a neural network trained using shape features computed from previously traced and manually corrected fiber tracts. It is independent from a DTI Atlas as it is applied to already traced fibers. This work is motivated by medical applications where the process of extracting fibers from a DTI atlas, or classifying fibers manually is time consuming and requires knowledge about brain anatomy. With this new approach we were able to classify traced fiber tracts obtaining encouraging results. In this report we will present in detail the methods used and the results achieved with our approach.

ManualWMH ImageJ Macros

ManualWMH is a set of ImageJ macros that facilitates the efficient manual segmentation of white matter hyperintensities (WMH / WMHI / leukoaraiosis) in T2 FLAIR images. It outputs volumetric 3D WMH masks and descriptive statistics.

Resting state MEG data used in Marek et al., 2018, Plos Biology

Raw resting state MEG data used in Marek et al., 2018 Plos Biology. Dataset includes both raw resting state data (5 minutes), as well as raw empty room MEG data (2 minutes) in 89 subjects aged 14-31 years.

Automatic Extra-Axial Cerebrospinal Fluid (Auto EACSF)

Automatic Extra-Axial Cerebrospinal Fluid (Auto EACSF) is an open-source, interactive tool for automatic computation of brain extra-axial cerebrospinal fluid (EA-CSF) in magnetic resonance image (MRI) scans of infants. Auto EACSF aims to automatically calculate the volume of EA-CSF. Auto EACSF is a user-friendly application that generates a Qt application to calculate the volume of EA-CSF. The application is run through a GUI, but also provides an advanced use mode that allows execution of different steps by themselves via Python and XML scripts.

ONPRC Fetal Macaque Brain Atlas

This is a multi-modal MRI (T2W and DTI) brain templates of fetal monkeys with atlas labels at three key time points over the second and third trimesters of the 168 day gestational term.. Specifically, template images are constructed for brains at gestational ages of 85 days (G85, N=18, 9 females), 110 days (G110, N=10, 7 females) and 135 days (G135, N=16, 7 females). T2W templates were able to characterize the development of fetal tissue zones and gyrification in cerebral cortex and DTI templates were able to infer the morphogenesis of cortical neurons and maturation of white matter tracts.
This work has been published and is available at:
"Anatomical and diffusion MRI brain atlases of the fetal rhesus macaque brain at 85, 110 and 135 days gestation", NeuroImage
Available online 24 October 2019, 116310,In Press, Journal Pre-proofWhat are Journal Pre-proof articles?
https://doi.org/10.1016/j.neuroimage.2019.116310

NeMo: NEuron MOrphological analysis tool

NEMO (NEuron MOrphological analysis tool) is a new freeware for semi automated quantitative and dynamic analysis of neuron morphometry.
NEMO was co-developed at the [Italian National Council of Research Institute of Clinical Physiology (IFC-CNR)] (https://www.ifc.cnr.it/index.php/it/) and the [Research Center "E. Piaggio", University of Pisa] (http://www.centropiaggio.unipi.it/).
It incorporates the most important microstructural quantification methods, such as fractal and sholl analysis with statistical and classification tools to provide an integrated image processing environment which enables fast and easy feature identification.
It includes:
Friendly interactive graphical user interface
Image pre-processing
Morphological analysis
Topological analysis
Cell counting
*3-way PCA analysis
Plot of variables
For additional information contact: [lucia.billeci@ifc.cnr.it] (mailto:lucia.billeci@ifc.cnr.it) or [chiara.magliaro@googlemail.com] (mailto:chiara.magliaro@googlemail.com)

User Friendly Functional Connectivity - UF²C

UF²C is an open source software developed at the Neuroimaging Laboratory at Unicamp that aims to simplify and organize functional studies in neuroimaging through clean and validated methodologies.
The graphical user interface makes the processing and analysis options accessible for neuroscientists, with reasonable choices of default settings. UF²C allows the user to study functional connectivity both through a quantitative view that provides detailed values of average connectivity and through a spatial view that provides statistical maps that can be directly used for further analyses. All results are carefully organized in a distinct folder for each subject, and a common folder is generated with a log file reporting the quantitative results of all the analyzed subjects.
Several UF²C modalities and tools runs combined with Statistical Parametric Mapping functions.
Coordinator and developer
Brunno M. de Campos, Ph.D.
Collaborator Developer
Raphael Fernandes Casseb, Ph.D.

Online EEG artifact removal by adaptive spatial filtering

The performance of electroencephalography (EEG) data depends on the effective attenuation of artifacts that are mixed in the recordings. To address this problem, we have developed a novel online EEG artifact removal method for online applications, which combines Independent Component Analysis (ICA) and regression (REG) analysis. ICA-REG method relies on the availability of a calibration dataset of limited duration for the initialization of a spatial filter using ICA. Online artifact removal is implemented by dynamically adjusting the spatial filter in the actual experiment, based on linear regression.

This software can be used to do a pseudo-online artifact removal (necessary for the validations in your project), or a real-time filtering, or to filter the whole signal offline.

For support: roberto.guarnieri@kuleuven.be

To cite this software: http://iopscience.iop.org/article/10.1088/1741-2552/aacfdf
Roberto Guarnieri et al 2018 J. Neural Eng. 15 056009

brainlife.io

brainlife.io is a platform that allows publishing reproducible code and datasets. The platform provides seamless access to national supercomputers, private clouds, and institutional high-performance computer systems. The goals of the platform are to promote data sharing in exchange for high-throughput computing and to contribute upcycling the long tail of neuroimaging data. To do so, brainlife.io makes available data and analyses software to pursuit gathering diverse neuroimaging datasets and redistribute them across multiple research communities such as psychology, engineering, computer science, and neuroscience. Brainlife.io combines modern web visualization, and database technologies as well as network neuroscience and image processing algorithms into a unique online platform. brainlife

ONPRC Infant Macaque Brain Atlas

This is a multi-modal (T1w and DTI) brain template of monkeys with atlas labels of a cohort of 6 rhesus macaques scanned longitudinally at ages 2, 4 and 6 months of age. All T1w and DTI templates are with the same coordinates. The reference publication is "The effects of breastfeeding versus formula-feeding on cerebral cortex maturation in infant rhesus macaques", Neuroimage, 2018, in press (https://doi.org/10.1016/j.neuroimage.2018.09.015).

Anima scripts

Anima scripts is a set of scripts that use the Anima software to perform complex preprocessing and core processing tasks on medical images such as diffusion imaging, atlasing, brain extraction. This repository is multi-platform for most scripts (as long as python is available for your system).

Wisconsin Rhesus Brain Parcellation

This dataset includes 286 of the Paxinos et al. rhesus brain regions aligned to the McLaren et al. rhesus MRI brain template. It also includes composite regions and regions drawn by the authors.

Big GABA

This repository contains a subset of a large-scale multi-vendor, multi-site collection of single-voxel MRS datasets that were acquired worldwide across 26 research sites on 3T MRI scanners from the three major vendors (GE, Philips, Siemens). Each site acquired up to 12 datasets.

Currently available data include (1) standard GABA+-edited MEGA-PRESS data, (2) macromolecule-suppressed GABA-edited MEGA-PRESS data, and (3) short-TE PRESS data.

If using these data, please cite:

Mikkelsen M et al. Big GABA: Edited MR spectroscopy at 24 research sites. NeuroImage 2017;159:32–45. doi: 10.1016/j.neuroimage.2017.07.021

Mikkelsen M et al. Big GABA II: Water-referenced edited MR spectroscopy at 25 research sites. NeuroImage 2019;191:537–548. doi: 10.1016/j.neuroimage.2019.02.059

Povazan M et al. Comparison of multivendor single-voxel MR spectroscopy data acquired in healthy brain at 26 sites. Radiology 2020;295:171–180. doi: 10.1148/radiol.2020191037

and acknowledge NIH grant R01 EB016089.

MMPS Docker

Docker image of the MMPS pipeline.

Rapid Permutation Testing Toolbox

RapidPT is an open-source MATLAB toolbox for fast and efficient permutation testing. RapidPT is available as a stand-alone toolbox and can be downloaded here (nitrc) or from its Github page: https://github.com/felipegb94/RapidPT. For ease of use, RapidPT has also been integrated into the development version of SnPM. For a description of RapidPT and the usage within SnPM please refer to the documentation page: http://felipegb94.github.io/RapidPT/

RapidPT is able to achieve substantial speedups over state of the art permutation testing implementations (e.g. SnPM) without compromising accuracy. For a thorough analysis of the scenarios were RapidPT performs best please refer to the paper:

Accelerating Permutation Testing in Neuroimaging through Subspace Tracking: A new plugin for SnPM.
F. Gutierrez-Barragan, V.K. Ithapu, C. Hinrichs, C. Maumet, S.C. Johnson, T.E. Nichols, V. Singh.
Neuroimage, 2017. https://doi.org/10.1016/j.neuroimage.2017.07.025

Sparse Sampling of Silence Database

Sparse Sampling of Silence

Nornir

Nornir’s takes large sets of overlapping images in 2D and produces registered (a.k.a. aligned) 2D mosaics and 3D volumes of any size and scale. Registered slices may be exported as a single large images or viewed/annoted with our Viking viewer.

Nornir has been used successfully on transmission electron microscopy, scanning electron microscopy images, and light microscopy images. Nornir supports interleaving different imaging methods into the same volume. Support for SerialEM, Objective Imaging, and Digital Micrograph (DM4) raw data is available. Adding formats is not complicated and the author will consider requests.

Nornir runs on fairly humble hardware for the task. A 32-core 64GB Xeon system built a ~60 TB 250um diameter 2.12nm/pixel volume from roughly 1400 slices. Nornir works incrementally, only updating data that has changed.

Installation is fairly simple and primarily uses Python's PIP installer.

For further information: http://nornir.github.io/

WFU_MMNET - Multivariate Modeling of Brain Networks Toolbox

This toolbox provides a framework for multivariate modeling of brain networks. It allows assessing brain network differences between study populations as well as assessing the effects of phenotypes such as aging, task performance, and disease states on the density and strength of connections in global and local brain networks while controlling for confounding variables. A variety of neuroimaging data such as fMRI, EEG, and DTI can be analyzed with this toolbox. The toolbox has been developed in MATLAB with a graphical user interface but uses SAS, R, or Python (depending on software availability) to perform the statistical modeling. New utilities for analyzing brain subnetworks and dynamic networks have been added to the new release, v1.1. For more detail, see the brief user manual for regional and dynamic networks. The fully updated user manual will be uploaded very soon.
We have made all efforts to avoid errors but users are strongly urged to post the bugs or suggested improvements on the provided forum.

NeuroML

NeuroML is a standardized model description language for computational neuroscience.

Resting State Hemodynamic Response Function Retrieval and Deconvolution (RS-HRF)

This toolbox is aimed to retrieve the onsets of pseudo-events triggering an hemodynamic response from resting state fMRI BOLD signal. It is based on point process theory, and fits a model to retrieve the optimal lag between the events and the HRF onset, as well as the HRF shape, using different shape parameters or combinations of basis functions.

Once that the HRF has been retrieved for each voxel/vertex, it can be deconvolved from the time series (for example to improve lag-based connectivity estimates), or one can map the shape parameters everywhere in the brain (including white matter), and use it as a pathophysiological indicator.

Input can be 2D GIfTI, 3D or 4D NIfTI images, but also on time series matrices/vectors.
The output are three HRF shape parameters for each voxel/vertex, plus the deconvolved time series, and the number of retrieved pseudo-events. All can be written back to GIfTI or NIfTI images.

Please refer to the links on the left for the different versions and relative documentation.

Anticorrelated Component Analysis

MATLAB code to compute the two most anticorrelated patterns in a multivariate dataset.

This technique was first applied to identify competing neuronal networks from the spontaneous activity of brain neural circuits in:

Nathan X. Kodama, Tianyi Feng, James Ullett, Hillel J. Chiel, Siddharth S. Sivakumar, and Roberto F. Galán (2018). Anti-correlated cortical networks arise from spontaneous neuronal dynamics at slow timescales. Scientific Reports, 8:666.

https://www.nature.com/articles/s41598-017-18097-0

Virtual Brain Generator

MATLAB code to reverse-engineer neural networks (Virtual Brains) whose dynamics reproduce large-scale activity patterns of real brains observed with EEG and MEG in health and disease. The model of the virtual brain was originally presented in:

G. Karl Steinke and Roberto F. Galán (2011). Brain rhythms reveal a hierarchical network organization. PLoS Computational Biology, 7(10): e1002207.
Key words: EEG, neural complexity, inverse-eigenvalue problem, network oscillations, brain connectivity.

http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002207

CBICA: Build system And Software Implementation Standard (BASIS)

The CMake Build system And Software Implementation Standard (BASIS) makes it easy to create sharable software and libraries that work together. This is accomplished by combining and documenting some of the best practices, utilities, and open source projects available. More importantly, BASIS supplies a fully integrated suite of functionality to make the whole process seamless!

NOTE: This tool has been deprecated and is no longer actively maintained. Details at https://cmake-basis.github.io/index.html

RecView

BrainVision RecView is an advanced solution designed for real-time analysis of data received over the Ethernet network via TCP-IP directly from BrainVision Recorder.

RecView at a glance
• widely used in the EEG/fMRI field to remove both the gradient and the ballistocardiogram artifact (permitting experimental control during the scan)
• The "Template Drift Compensation" algorithm remedies template jitter caused by imperfect synchronization between the EEG amplifier and the scanner clocks and thus ensures optimal data correction at any time.
• uses the same history tree concept already implemented in BrainVision Analyzer
• can also be used for FFT analysis, data filtering, mapping of the surface potentials as well as BCI and bio-/neurofeedback on the incoming data.

Recorder

BrainVision Recorder is a multifunctional recording software designed to provide our amplifier customers with a versatile and easy-to-use platform for recording setup and execution.

• A wizard menu guides you through hardware setup and hard/software filter configuration.
• Acquired data can be displayed in multiple ways.
• Channel montages (original, bipolar, average) can be switched on the fly to adjust the channel view.
• Channel by channel impedance check: Each electrode is placed at the topographic position and its impedance value is displayed with a color coding.
• Acquisition parameters and impedance values are automatically stored and accessable any time.
• An EP analysis can be performed in real-time; the segmented/averaged data can be stored together with the raw data.
• A video module allows the capture of images of the subject; the video is synchronized with the EEG.
• Data can be sent out to the network via the TCP/IP protocol (for real-time data analyses, e.g. in BrainVision RecView)

Adaptive Neuro-Fuzzy Inference System Granger Causality (ANFISGC)

The codes belong to the following article:
Farokhzadi, M., Hossein-Zadeh, G.A., Soltanian-Zadeh, H., 2018. Nonlinear Effective Connectivity Measure Based on Adaptive Neuro Fuzzy Inference System and Granger Causality. J Neuroimage 181, 382–394. https://doi.org/10.1016/j.neuroimage.2018.07.024
The shared MATLAB codes contain:
1) Generating three time series with 4000 samples (n=1,…,4000) based on the following equations:
x_1 (n)=3.4x_1 (n-1)(1-x_1^2 (n-1)) e^(-x_1^2 (n-1) )+ε_1 (n)
x_2 (n)=3.4x_2 (n-1)(1-x_2^2 (n-1)) e^(-x_2^2 (n-1) )+cx_2 (n-1) x_1 (n-1)+ε_2 (n)
x_3 (n)=3.4x_3 (n-1)(1-x_3^2 (n-1)) e^(-x_3^2 (n-1) )+0.3x_2 (n-1)+0.5x_1^2 (n-1)+ε_3 (n)
The signal-to-noise ratio (SNR) is 0 dB.
The parameter c, which indicates the coupling strength of (x1→x2) connection, is set to 0.5.
2) Implementation of ANFISGC criterion to detect both linear and nonlinear causal connections among brain regions.
In the Matlab functions, the inputs and outputs are described.

DCMTK - DICOM Toolkit

DCMTK is a collection of libraries and applications implementing large parts the DICOM standard. It includes software for examining, constructing and converting DICOM image files, handling offline media, sending and receiving images over a network connection, as well as demonstrative image storage and worklist servers. DCMTK is is written in a mixture of ANSI C and C++. It comes in complete source code and is made available as "open source" software.

DCMTK has been used at numerous DICOM demonstrations to provide central, vendor-independent image storage and worklist servers (CTNs - Central Test Nodes). It is used by hospitals and companies all over the world for a wide variety of purposes ranging from being a tool for product testing to being a building block for research projects, prototypes and commercial products.

The DCMTK software can be compiled under Windows and a wide range of Unix operating systems. All necessary configuration scripts and project makefiles are supplied.

DynamicBC

Dynamic brain connectome (DynamicBC) analysis toolbox is a Matlab toolbox to calculate Dynamic Functional Connectivity (d-FC) and Dynamic Effective Connectivity (d-EC). Sliding window analysis (Bivariate Pearson correlation and Granger causality) and time varying parameter regression method (Flexible Least Squares) are two dynamic analysis strategies for time-variant connectivity analysis in the DynamicBC. Granger causality density/strength (GCD/GCS) and functional connectivity density/strength (FCD/FCS) analysis would be performed in this toolbox.
You can download the toolbox with the following link: http://restfmri.net/forum/DynamicBC

Random forest for prediction of CI_WRS

To be added soon

Fitting Oscillations & One-Over F

FOOOF is a fast, efficient, and physiologically-informed tool to parameterize neural power spectra. FOOOF conceives of a model of the power spectrum as a combination of two distinct functional processes - a) an aperiodic 'background' component, reflecting 1/f like characteristics, modeled with an exponential fit, with b) a variable number of periodic components, that exhibit as band-limited peaks rising above this background, reflecting putative oscillations, modeled as Gaussians. For putative oscillations, the benefit of the FOOOF approach is that these peaks are characterized in terms of their specific center frequency, amplitude and bandwidth without requiring predefining specific bands of interest. It separates these peaks from a dynamic and independently interesting aperiodic background. This tool offers a way to parameterize both the aperiodic and periodic components together, explicitly dealing with the fact that they overlap in ways that makes estimating one or other in isolation difficult.

Surface-based processing and analysis of MRI

The SPAMRI (surface-based processing & analysis for MRI) aims to provide automatic and friendly-used GUI for analysis of structural MRI, such as cortical thickness analysis. Any questions or suggestions, please concact the author: longzhiliang@swu.edu.cn

HSNC

89 accurate masks of skull-stripped MR images

Chinese Pediatric Atlas (CHN-PD Atlas)

We use an unbiased template construction algorithm to create a set of age-specific Chinese pediatric (CHN-PD) atlases based on high-quality T1- and T2-weighted MR images from 328 cognitively normal Chinese children aged 6 to 12 years. The CHN-PD brain atlases include asymmetric and symmetric templates, sex-specific templates and tissue probability templates, and contain multiple age-specific templates at one-year intervals. We expect this set of age-specific Chinese brain atlases is useful for the image registration and spatial statistics in Chinese pediatric studies.

Unfold toolbox for regression-based EEG analysis

unfold is a MATLAB tool for regression-based EEG analysis that was written to facilitate the use of advanced deconvolution models and spline regression in event-related potential (ERP) research. In experiments that involve temporally overlapping brain responses, unfold can be used to isolate the responses to the individual events (linear deconvolution). Simultaneously, it can model influences of various linear or non-linear covariates on the neural response (using spline regression/generalized additive modeling). The toolbox is compatible with EEGLAB and offers built-in functions to visualize the model coefficients (betas) of each predictor as waveforms or scalp topographies ("regression-ERPs"). Alternatively, results can be easily exported as plain text or transferred to other toolboxes (e.g. EEGLAB or Fieldtrip). In addition to EEG, unfold can be used to analyze other overlapping biometric signals, such as electrodermal activity, or pupil responses.

SPD-rsfMRI-NEU-ZS

We provide MRI data of Schizotypal Personality Disorder(SPD) and Healthy controls. The total number of SPD subjects and HC are 36 (each 18). For each subject, T1 weighted and resting state functional MRI are included.

DEMON Deep Multi-Contrast Brain Extraction, Application to Human & Animal Imaging

DEMON (DEep Multi-cONtrast skullstripping) is a deep convolutional neural network (CNN) based software tool to generate brainmasks (or skullstrip) from multi-contrast MR brain images, such as T1, T2, PD, or FLAIR. Unlike most other skullstripping methods, DEMON can take multi-contrast inputs, and based on multiple atlases, it generates a brainmask of a given subject image. It is especially useful for images with pathology such as traumatic brain injury (TBI), where most of the traditional skullstripping algorithms perform poorly. DEMON has been shown to be robust on both normal and pathological human brains, as well as rodent (mice and rats) brains.

In addition to the software, we also provide 2 sets of atlas images, one human brains with TBI, and one normal mice . Each atlas contains multi-contrast MRI and their manually delineated brainmasks. If you use DEMON in your research, please cite the following paper,
https://ieeexplore.ieee.org/abstract/document/8363667/

Freesurfer fsaverage

Freesurfer fsaverage mesh files in gifti format

Neurodocker

Generate Dockerfiles and Singularity recipes for neuroimaging with a simple command-line interface. Neurodocker supports a growing list of software, including AFNI, ANTs, Convert3D, Dcm2niix, FreeSurfer, FSL, Matlab Compiler Runtime, MINC, Miniconda, MRtrix3, NeuroDebian, PETPVC, and SPM12.

Altered States Database

A variety of pharmacological and non-pharmacological methods were reported to induce consciousness alterations in humans. Within psychological experiments, the subjective experiences of ASCs are typically quantified with retrospective questionnaires. Here, we introduce a database, termed the Altered States Database (ASDB), comprised of questionnaire data extracted from original research articles. The database contains data from articles published in MEDLINE-listed journals from experimentally induced altered states that were assessed with a specified set of standardized questionnaires. The dataset at hand will allow direct comparisons of the psychological effects of different induction methods as well as meta-analyses to establish induction method specific dose-response relationships.

DWMRI Phantom Drift

This is a set of DWMRI acquisitions of two different diffusion phantoms with interspersed images with a b-value of zero. These were used to analyzed the affects of temporal scanner drift. There are 5 unique corrections (and associated corrected DWMRI data) that was applied to this data to correct for temporal/spatial drift over the course of each scan. The corrections were implemented in MATLAB.

resting-state fMRI of patients with pre-eclampsia and healthy pregnant women

resting-state fMRI data of patients with pre-eclapmsia and healthy pregnant women.

Functional-by-Strutural Hierarchical Mapping (FSH mapping)

Not every edge in the diffusion-MRI derived structural network is utilized in each specific brain status. Therefore, this tool, which we name functional by structural hierarchical (FSH) mapping, is to predict functional MRI (fMRI) correlation matrix using diffusion MRI-derived structural connectome. The output is a binary utilization matrix, which indicates the utilization of the structural network edges.

Personode

Personode is a user-friendly, open-source MatLab toolbox that semi-automatically guides through ICA components classification into resting-state networks (RSN), alleviating the selection process, and allows the robust definition of either group- or subject-specific ROIs derived from RSN, which could make network analysis more accurate and individually specific. This tool can produce higher activations than ROIs based on single-studies coordinates or meta-analysis studies for expected regions in a task-related ROI activation analysis and more accurate correlation values (either positive or negative) than clusters belonging to an atlas for expected interactions among networks in a resting state functional connectivity analysis.

TNS Lymphatics Dataset

This dataset provides deidentified imaging data for 5 subjects from an imaging study designed to image the lymphatic system non-invasively using MRI. The associated data was published as a part of an article in Elife ("Human and nonhuman primate meninges harbor lymphatic vessels that can be visualized noninvasively by MRI") and publicized by the NIH Director as a part of his blog (https://directorsblog.nih.gov/2017/10/17/new-imaging-approach-reveals-lymph-system-in-brain/).

TNS Data Repository

This project provides an umbrella for all releases of imaging data from the Translational Neuroradiology Section at the National Institutes of Health. This data will mainly focus on neuroimaging data collected on healthy controls and patients collect in various studies performed by the TNS.

Phenolyer

Phenolyzer is a software aimed at phenotype/disease specific gene prioritization and gene network visualization. It can be used alone to prioritize genes genome-widely, or from user-specified genomic regions or genes. It can also be used to further prioritize genes after the variant annotation and filtering step from other tools, like ANNOVAR. Phenolyzer has both a command line version and a web version: see http://phenolyzer.wglab.org/ and https://github.com/WGLab/phenolyzer.

InterVar

InterVar is a bioinformatics software tool for clinical interpretation of genetic variants by the ACMG/AMP 2015 guideline. The input to InterVar is an annotated file generated from ANNOVAR, while the output of InterVar is the classification of variants into 'Benign', 'Likely benign', 'Uncertain significance', 'Likely pathogenic' and 'Pathogenic', together with detailed evidence code. InterVar is a command-line-driven software written in Python and can be used as a standalone application on a variety of operating systems—includingWindows, Linux, and MacOS—where Python is installed. wInterVar is a web server, which offers a graphical user interface for InterVar.

wANNOVAR

ANNOVAR is a rapid, efficient tool to annotate functional consequences of genetic variation from high-throughput sequencing data. wANNOVAR provides easy and intuitive web-based access to the most popular functionalities of the ANNOVAR software.

Virtual digital brain

This software can be freely downloaded from the website: https://www.nitrc.org

NetPET: Covariance statistics and network analysis of brain PET imaging studies

The analysis of brain structural and functional imaging data using graph theory has increasingly become a popular approach since brain topological representations have proven to be helpful for visualising and understanding anatomical and functional relationships between different cerebral areas.

NetPET is a tool for graph-based analysis of brain PET studies which exploits population-based covariance matrices. NetPET aims to explore topological tracer kinetics differences in cross-sectional investigations. Simulations, test-retest studies and applications to cross-sectional datasets from three different tracers ([18F]FDG, [18F]FDOPA and [11C]SB217045) and more than 400 PET scans have been used to test the applicability of NetPET in healthy controls and patients. Results showed good reproducibility and general applicability of the methods within the range of experimental settings typical of PET neuroimaging studies.

NetPET is a Matlab-based software package.

DALAN: Disconnectivity Atlas for Lesion Analysis of Networks

DALAN is a HARDI diffusion streamline atlas of 41 older, healthy subjects to be used with lesion masks from clinical images to provide projected medium to long distance connectivity deficits implied by WM stroke lesion damage. The atlas uses the popular Harvard-Oxford (H-O) cortical and subcortical atlas as endpoint targets so that WM connectivity deficits can be analyzed in conjunction with corresponding GM deficits as well. Octave/MatLab functions are provided to illustrate how to use the streamline atlas.

Longitudinal Analysis Workspace

Welcome to the Longitudinal Analysis Workspace. In this NITRC Project we strive to aggregate information related to the unique issues related to longitudinal data and data analysis. As a 'community' we hope to encourage all interested individuals (users, data sharers, software developers, etc.) interested in this topic to participate. We will collect software and data related to this topic, and provide a resource-independent forum to discuss common topics.

Individual morphological connectivity using wavelet transform

The goal of this research is to build novel morphological connectivity in the single subject level based on anatomical MRI. To this end, we developed a package to compute individual morphological connectivity using wavelet transform. The packge included preprocessing of anatomical MRI, VBM, wavelet transform of 3D MRI datasets, and morphological connectivity computation. In addition, we also planned to release the prepocessed features of individual morphological connectivity.

Mapping population-based structural connectomes

Our developed PSC framework is a workflow which can simultaneously characterize a large number of white matter bundles within and across different subjects for group analysis. Given the DWI and T1 images it has three major components: (i) reliable construction of the structural connectome for the whole brain, including a robust tractography algorithm and streamline post-processing techniques, such as dilation of gray matter regions, streamline cutting, and outlier streamline removal are applied to improve the robustness of the extracted structural connectomes; (ii) low-dimensional representation of streamlines in each connection, including a shape analysis framework to separate the variation of streamlines in each cell of the streamlines, and an encoding and decoding procedure to efﬁciently compress the streamlines; (iii) multi-level connectome analysis, including the groupwise connectome analysis at three different levels, the streamline level, the weighted network level; and the binary network level.

Bravissima: Human Brain Artery NIfTI Atlas

Bravissima is a translation of the BraVa arterial vasculature database (nitrc.org/projects/breva) into the more useful NIFTI MRI file format that can be applied to stroke studies, fMRI resting state imaging studies and other clinical neuroscience studies. We have successfully converted BraVa artery locations in both hemispheres for 43 healthy subjects into standard NIFTI MRI format files, including normalizing artery locations into MNI152 stereotaxic space. We have also included subject NIFTI files of the distance each arterial location is from the Circle of Willis as well as group maps of the locations of major arterial branch junctions; frequent locations of arterial occlusion. Group artery region labels and arterial density maps are provided as well. Matlab scripts used to produce all files are included.

GAAIN toolset

The Alzheimer's Association created The Global Alzheimer's Association Interactive Network (GAAIN) to connect Alzheimer’s and dementia researchers and speed discovery by connecting potential collaborators to those sharing data (GAAIN Data Partners). GAAIN's powerful interactive tools allow users to explore data and create cohorts across multiple data sources and run immediate preliminary analysis while upholding the data control, security, and privacy policies of the data owners. These tools are freely accessible to all at www.gaaindata.org.

A wide and vastly varying range of metadata from nearly 500,000 subjects can be explored using these tools. Attributes include demographics, assessments, health factors, biologicals, imaging and genetics.

SCP_TaskfMRI

This repository holds the derived results of a task fMRI analysis software comparison project (Bowring et al, 2019). This project uses data from 3 carefully selected open source task fMRI datasets from OpenNeuro. Then, using the details in the published work, conducted three similar-as-possible parallel analyses in AFNI, FSL and SPM. The results are then all shared in the NeuroVault repository and all results are generated from freely available Python notebooks found https://github.com/NISOx-BDI/Software_Comparison_Analyses . The derived results comprise all first level and second results for AFNI, FSL and SPM parametric and nonparametric results.

Bowring, A., Maumet, C., & Nichols, T. E. (2019). Exploring the impact of analysis software on task fMRI results. Human Brain Mapping, 40(11), 3362–3384. https://doi.org/10.1002/hbm.24603

Connectopy Toolkits

While the topographic organization of brain pathways, i.e., connectopy, as a general principle has been known for a long time, it has yet to be quantitatively studied with in vivo neuroimaging. Using connectome imaging data, we have developed several novel algorithms to quantitively model the connectopy in human brains. These algorithms can be used to reconstruct topography-preserving pathways in visual, auditory, somatosensory, cortical-striatal, and fiber pathways in other brain networks. They can also improve the robustness of functional independent component analysis with regularization from topography-preserving fiber tracts. In this ConnectopyToolkits, we will publicly release the software tools for connectopy modeling.

Linear Fascicle Evaluation

This software implements a framework to encode structural brain connectomes into multidimensional arrays. These arrays are commonly referred to as tensors. Encoding Connectomes provides an agile framework for computing over connectome edges and nodes efficiently. We provide several examples of operations that can be performed using the framework. One major application of the tensor encoding is the implementation of the Linear Fascicle Evaluation method, in short LiFE. The tensor encoding method allows implementing LiFE with a dramatic reduction in storage requirements, up to 40x compression factors. Furthermore, connectome encoding allows performing multiple computational neuroanatomy operations such as tract-dissections, virtual lesions, and connectivity estimates very efficiently using the machine-friendly array operators.

TAPAS - Translational Algorithms for Psychiatry-Advancing Science

TAPAS is a collection of algorithms & software tools developed by the Translational Neuromodeling Unit, Zurich, & collaborators. The goal of these tools is to support clinical neuromodeling, particularly computational psychiatry, neurology, & psychosomatics.

Contents:

ceode: Robust estimation of convolution based DCMs for evoked responses
HGF: The Hierarchical Gaussian Filter; Bayesian inference on computational processes from observed behaviour
HUGE: Variational Bayesian inversion for hierarchical unsupervised generative embedding
MICP: Bayesian Mixed-effects Inference for Classification Studies
MPDCM: Efficient integration of DCMs using massive parallelization
PhysIO: Physiological Noise Correction for fMRI
rDCM: DCM based, efficient inference on effective brain connectivity for fMRI
SEM: SERIA Model for Eye Movements (saccades & anti-saccades) and Reaction Times
VBLM: Variational Bayesian Linear Regression
FDT: Filter Detection Task

TAPAS is written in MATLAB & distributed under GNU GPL V3.

ReproNim Simple Reexecutable Publication

In this project, we document a set of procedures, which include supplemental additions to a manuscript, that unambiguously define the data, workflow, execution environment and results of a neuroimaging analysis, in order to generate a verifiable re-executable publication. Re-executability provides a starting point for examination of the generalizability and reproducibility of a given finding.

This project accompanies Ghosh SS, Poline JB, Keator DB et al. A very simple, re-executable neuroimaging publication. F1000Research 2017, 6:124 (doi: 10.12688/f1000research.10783.2)

METH

METH is a collection of MATLAB functions to conduct data analysis of MEG and EEG data. A detailed documentation is contained in the download.
Its main features are:
• EEG and MEG forward calculation in realistic volume conductors based on expansions of the lead fields in spherical harmonics.
• Inverse Calculations: Dipole fitting for ERFs/ERPs and cross-spectra, MUSIC, RAP-MUSIC, SC-MUSIC, minimum norm source reconstruction, eLoreta, and beamformers.
• Power and coupling measures: cross-spectra coherency, cross-bispectra and bicoherence, 1:1 phase locking, power correlation including corrections for artifacts of volume conduction, lagged coherence, phase lag index, multivariate coupling measures robust to artifacts of volume conduction (MIM, MIC, GIM).
• Decomposition: MOCA for two or more source distributions
• Statistics: false discovery rate and permutation test
• Visualization: topo-plots, head-in-head plots and time-frequency plots, MRIs and surface plots (e.g. cortex) including results.

Adolescent Brain Cognitive Development (ABCD) Study

The Adolescent Brain Cognitive Development (ABCD) Study is the largest long-term study of brain development and child health in the United States. The ABCD Research Consortium consists of a Coordinating Center, a Data Analysis and Informatics Center, and 21 research sites across the country, which will invite approximately 10,000 children ages 9-10 to join the study. Researchers will track their biological and behavioral development through adolescence into young adulthood.

This study will determine how childhood experiences (such as sports, videogames, social media, unhealthy sleep patterns, and smoking) interact with each other and with a child’s changing biology to affect brain development and social, behavioral, academic, health, and other outcomes.

The results of the ABCD Study will provide families; school superintendents, principals, and teachers; health professionals; and policymakers with practical information to promote the health, well-being, and success of children.

Optil.io

OPTIL.io (http://www.optil.io) is the online platform that can be used to organize programming challenges. It is dedicated to optimization challenges. However, it can be adapted to other problem types. Optil.io receives algorithmic solutions of optimization problems in the form of source code from the crowd of contestants, compiles it, executes in a homogeneous run-time environment and objectively evaluates using the set of test cases. Problems that are solved can be provided by external scientists or companies. Solutions can be submitted in many programming languages, and they are then carefully assessed taking into account resource limitations set for the run-time environment, such as CPU time or virtual memory. Each participant can continuously observe how his solution is being compared to others and improve it further. The platform is free for scientific and educational, non-commercial use.

Gannet - Batch Analysis of Edited Magnetic Resonance Spectroscopy (MRS) Data

Gannet is a software package designed for the batch analysis of edited magnetic resonance spectroscopy (MRS) data.

Gannet runs in Matlab, and is distributed as code rather than executables, empowering users to make local changes.

Gannet is designed to run without user intervention, to remove operator variance from the quantification of edited MRS data.

Probability-Associated Community Estimation

Understanding the modularity of fMRI derived brain networks or ‘connectomes’ can inform the study of brain function organization. However, fMRI connectomes additionally involve negative edges, which may not be optimally accounted for by existing approaches to modularity that variably threshold, binarize, or arbitrarily weight these connections. Consequently, many existing Q maximization-based modularity algorithms yield variable modular structures. Here we present an alternative complementary approach that exploits how frequent the BOLD-signal correlation between two nodes is negative. This approach a) permits a dual formulation, leading to equivalent solutions regardless of whether one considers positive or negative edges; b) is theoretically linked to the Ising model defined on the connectome, thus yielding modularity result that maximizes data likelihood. Please refer to Zhan et. al. 2017 J Comp Neurol. 525(15):3251-3265. doi: 10.1002/cne.24274 for details.

Topographic Tract Filtering

The package contains the conference version of the TTF tool for filtering white matter tracts reconstructed from diffusion MRI based on a notion called topographic regularity.

DataLad

DataLad makes data management and data distribution more accessible. To do that it stands on the shoulders of Git and Git-annex to deliver a decentralized system for data exchange. This includes automated ingestion of data from online portals, and exposing it in readily usable form as Git(-annex) repositories, so-called datasets. The actual data storage and permission management, however, remains with the original data providers.
At the moment, DataLad provides access to over 11TB of data in a variety of datasets from different resources (see http://datasets.datalad.org). It allows for efficient search, dataset(s) installation, data modifications and their tracking within Git version control, publication of datasets to http websites, S3, figshare, etc; reproducible computation, and other features.

A novel group-fused sparse partial correlation method for simultaneous estimation of functional networks in group comparisons

Group-fused multiple-graphical lasso cobined with stability selection (GMGLASS) is a software toolbox that can be employed to simultaneously estimate both individual- and group-level functional networks from 2 groups.
The software:
(1) Reads time series from 2 groups of subjects;
(2) Randomly subsamples data 100 times to estimate stability with stability
selection;
(3) Group-fused multiple-graphical lasso is applied with two hyper-parameters: alpha and beta;
(4) Appropriate ranges of hyper-parameters are chosen for achieving stability selection;
(5) Both individual- and group-level functional networks can be estimated.

The method is described in the following paper:
Xiaoyun Liang, David N. Vaughan, Alan Connelly, Fernando Calamante. A novel group-fused sparse partial correlation method for simultaneous estimation of functional networks.in group comparison studies. Brain Topography, 12/2017; DOI:10.1007/s10548-017-0615-6.

Virtual Reality Brain Network Viewer

The Virtual Reality Brain Network Viewer provides a new and innovative way to interact with brain connectome data. By utilizing the Oculus Rift headset and the Oculus Touch controllers the software allows you to physically interact with your data sets. This includes the ability to pick up the brain model and move it with your hands, as well as the ability to slice open the brain mesh to further examine the nodes and connections within. The software also comes with analysis tools that allow control over the connection threshold, the ability to isolate connections on a given node, and the overlay of MRI data on the brain model.

masi_hardi_reproducibility

Massive single subject reproducibility data set acquired with HARDI suggested acquisition schemes

Local Label Learning for Image Segmentation

Local Label Learning for Multi-atlas based Image Segmentation Toolbox

ICN_Atlas

ICN_Atlas is an SPM compatible toolbox that is aimed at facilitating the interpretation of neuroimaging data in the context of intrinsic connectivity networks (ICNs, a.k.a. resting-state networks or RSNs) by describing fMRI maps or other statistical maps in function-oriented, objective and quantitative way using a set of 15 metrics conceived to quantify the degree of ‘engagement’ of ICNs for any given statistical map of interest. Besides functionally-derived atlasing (based on the BrainMap ICN data) the toolbox provides anatomy-based atlasing (based on the JHU and Brainnetome Atlas data), as well.

ICN_Atlas was developed by members of the former MR Research Center, and the Department of Neuroradiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary and the Institute of Neurology, University College London, UK.
Original idea: Louis Lemieux, software development: Lajos R Kozak, testing & validation: Louis Andre van Graan, Adam Szabo, Umair J Chaudhary, Azka Sohail and others.

Allen Brain Atlas: Data Portal

The Allen Institute aims to answer some of the biggest questions in neuroscience and accelerate research worldwide through public releases of new data, knowledge and tools. Beginning with our Allen Mouse Brain Atlas—a molecular map showing where all genes are expressed in all regions of the mouse brain—the Allen Institute has produced a collection of open science resources that give users a powerful way to explore gene expression data, neural connections, single cell characterization and neuroanatomy. All of our resources are openly accessible via the Allen Brain Atlas data portal at brain-map.org.

Resources include: ALLEN BRAIN OBSERVATORY, CELL TYPES DATABASE, MOUSE BRAIN CONNECTIVITY ATLAS, HUMAN BRAIN ATLAS, BRAINSPAN ATLAS OF THE DEVELOPING HUMAN BRAIN, MOUSE BRAIN ATLAS, DEVELOPING MOUSE BRAIN ATLAS, NIH BLUEPRINT NON-HUMAN PRIMATE (NHP) ATLAS, AGING, DEMENTIA AND TBI STUDY, IVY GLIOBLASTOMA ATLAS PROJECT, SPINAL CORD ATLAS, BRAIN ATLAS API and SOFTWARE DEVELOPMENT KIT.

Image Segmentation Based on the Local Center of Mass Computation

This is a Matlab toolbox for medical image segmentation, which uses an approach based on the local center of mass, as introduced by Aganj et al (Sci Rep, 2018). A short tutorial is included in EXAMPLE.m. If available, a GPU can be used to speed up the segmentation.

Electrophysiology Analysis Toolkit (Elephant)

The Electrophysiology Analysis Toolkit (Elephant) is an open-source library for the analysis of electrophysiological data in the Python programming language. The focus of Elephant is on generic analysis functions for spike train data and time series recordings from electrodes, such as local field potentials (LFP) or intracellular voltages. In addition to providing a common platform for analysis code from different laboratories, the Elephant project aims to provide a consistent and homogeneous analysis framework built on a modular foundation. Elephant is built on the Neo data object model for electrophysiological data.

brainSimulator

brainSimulator is a brain image synthesis toolkit written in python, intended to generate a new image set that share characteristics with an original one. The system focuses on nuclear imaging modalities such as PET or SPECT brain images. It analyses the dataset by applying PCA to the original dataset, and then model the distribution of samples in the projected eigenbrain space using a Probability Density Function (PDF) estimator. Once the model has been built, anyone can generate new coordinates on the eigenbrain space belonging to the same class, which can be then projected back to the image space.

p-[(123)I]iodo-L-phenylalanine (IPA)-SPECT template in MNI-coordinates

p-[(123)I]iodo-L-phenylalanine( IPA)-SPECT template in MNI-coordinates obtained from 12 human subjects

Multi-Modality Visualization Tool

The visualization and exploration of neuroimaging data are important for the analysis of anatomical and functional images and statistical parametric maps. While two-dimensional orthogonal views of neuroimaging data are used to display activity and statistical analysis, real three-dimensional (3d) depictions are helpful for showing the spatial distribution of a functional network, as well as its temporal evolution. For our best knowledge, currently, there is no neuroimaging 3d tool which can visualize both MEG, fMRI and invasive electrodes (ECOG, depth electrodes, DBS, etc.).

E39 Macaque Histology and MRI data

Macaque diffusion MRI dataset and corresponding histology for validating diffusion microstructure and tractography measures.

GIANT: Genome-scale Integrated Analysis of gene Networks in Tissues

GIANT leverages a tissue-specific gold standard to automatically up-weight datasets relevant to a tissue from a large data compendium of diverse tissues and cell-types. The resulting functional networks accurately capture tissue-specific functional interactions. Beyond questions pertaining to the role of single genes in single tissues, GIANT also enables examination of changes in gene function across tissues on a broad scale. Users can compare a gene's functional interaction in different tissues by selecting the relevant tissues in the dropdown menu. GIANT can effectively reprioritize functional associations from a genome-wide association study (GWAS) and potentially identify additional disease-associated genes. The approach, named NetWAS, can be applied to any GWAS study, and does not require that the phenotype or disease have any known associated genes.

Pattern Recognition based on Joint GAM-Sparsity Regression Model

This repository provides MATLAB toolbox for extracting meaningful patterns affected by a certain disease from a large data which are biased to other factors, e.g., age, gender, socioeconomic status, and scanner type. To find the meaningful patterns being able to distinguish group differences while suppressing the impact of other factors, we jointly parameterize a general additive model for desensitizing the image scores and a sparsity-constrained, logistic-regression model for classification by maximizing a likelihood. The software was developed by the Center for Health Sciences, SRI International.

If you use this code, please cite the following publication:

Park SH, Zhang Y, Kwon D, Zhao Q, Zahr N, Pfefferbaum A, Sullivan E, Pohl, KM: Alcohol use effect on adolescent brain development revealed by simultaneously removing confounding factors, identifying morphometric patterns, and classifying individuals, Scientific Reports, In press.

NPBayes-fMRI

This is a user-friendly matlab GUI that implements a spatio-temporal nonparametric Bayesian linear regression modeling approach for the analysis of task-related fMRI data from multi-subject experiments.

CHIASM - Combined Histology-MRI Integrated Atlas of the Squirrel Monkey

A web-based combined MRI-histology digital atlas of the squirrel monkey brain

EW_dmGWAS: Edge-weighted dense module search for genome-wide association studies and gene expression profiles

dmGWAS is designed to identify significant protein-protein interaction (PPI) modules and, from which, the candidate genes for complex diseases by an integrative analysis of GWAS dataset(s) and PPI network. The new algorithm, EW_dmGWAS, combines node weight, which are computed based on GWAS signals, and edge weight, which are computed based on gene expression data. Allow tissue-specific gene expression profile. For example, for breast cancer GWAS analysis, gene expression data from breast cancer patients can be used; for schizophrenia, gene expression data in brain tissues can be used.
All old versions are here: https://bioinfo.uth.edu/dmGWAS/dmGWAS_old.html.

SLEP: Sparse Learning with Efficient Projections

SLEP contains a collection of algorithms for solving sparse learning problems, including LASSO, group LASSO, sparse group LASSO, hierarchical tree structure, and trace norm regularization.

Fine Relational Spatial Topology

This tool takes resting state data and computes whole brain gradient maps describing the preferred connectivity across the spatial domains of your region of interest. This is a Matlab toolbox that depends on SPM, but primarily runs off of it's own GUI.

Neuroscience Gateway (NSG)

The Neuroscience Gateway (NSG) portal facilitates access and use of National Science Foundation (NSF) High Performance Computing (HPC) resources by neuroscientists. It will offer free computer time to neuroscientists acquired via the supercomputer time allocation process managed by the Extreme Science and Engineering Discovery Environment (XSEDE) Resource Allocation Committee (XRAC). The portal provides access to the popular computational neuroscience tools installed on various HPC resources. It also provides a community mailing list for neuroscientists to collaborate and share ideas.

Visually Evoked Potential EEG

An 18-subject EEG dataset from an experiment in which subjects performed a standard visual oddball task.

NIH Marmoset Brain Atlas

Philadelphia Neurodevelopmental Cohort (PNC)

The Philadelphia Neurodevelopmental Cohort (PNC) initiative focuses on characterizing brain and behavior interaction with genetics. This is a collaborative research effort between the Brain Behavior Laboratory at the University of Pennsylvania and the Center for Applied Genomics at the Children’s Hospital of Philadelphia.

The PNC includes a population-based sample of over 9500 individuals from the greater Philadelphia area, ages 8-21 years who received medical care at the CHOP network. The participants presented for diverse medical conditions, ranging from a well child visit and minor problems to chronic condition management to potentially life threatening health problems. Participants were genotyped during the time of their clinical visit and provided written permission to be recontacted for studies of complex pediatric disorders.

StartYourRecovery.org

StartYourRecovery.org offers people who are dealing with substance use issues a single source of reputable, objective information about signs, symptoms, conditions, treatment options, and resources — presented in a user-friendly format and in language that’s easy to understand.

DT_BOLD_calc

matlab script to calculate the sigmoidal delay function of Henson
dx.doi.org/10.1006/nimg.2001.0940
for the SPM.mat and condition specified in the input. Output as images of dt

CBICA: WhiteStripe

WhiteStripe is a user-friendly implementation of an algorithm to normalize conventional magnetic resonance images by detecting a latent subdistribution of normal tissue and linearly scaling the histogram of the images.

CBICA: Confetti

Confetti is a method for automated identification of white matter tracts of interest in a consistent and comparable manner over a large group of subjects without drawing the inclusion and exclusion regions of interest (ROI), facilitating an easy correspondence between different subjects, as well as providing a representation that is robust to edema, mass effect, and tract infiltration.
Confetti includes three main steps: (1) Connectivity signature generation for fibers, (2) Clustering of fibers using a mixture of multinomials (MNs) clustering method and Expectation-Maximization (EM) optimization framework, (3) Extraction of predefined white matter tracts.

Fast Lesion Extraction using Convolutional Neural Networks

FLEXCONN (Fast Lesion Extraction using Convolutional Neural Networks) is a toolbox for segmenting white matter lesions from multi-contrast MR images. Using T1-w and FLAIR images, a fully convolutional neural network (CNN) is trained using manually labeled training data. The trained CNN model can be applied to pre-processed pair of T1 and FLAIR images to generate a lesion membership as well as a hard segmentation. The algorithm is described in the following paper:
https://arxiv.org/abs/1803.09172

Python codes for training and prediction are provided. Trained models using manually labeled atlases from ISBI-2015 challenge (https://www.nitrc.org/projects/longitudinal_ms) are also provided. The CNN is implemented in Tensorflow and Keras (https://github.com/fchollet/keras).

This work was developed with funding support from the National Multiple Sclerosis Society (RG-1507-05243) and from the Center for Neuroscience and Regenerative Medicine in the Department of Defense.

Open Vanderbilt Archive of the Temporal Lobe

The Open Vanderbilt Archive of the Temporal Lobe is a tool for automated segmentation of the temporal lobe.

SpiCoDyn

SPICODYN is an automated and efficient open-source software for the analysis of multi-site neuronal spike signals. Accepted input data formats are HDF5, level 5 MAT and text files, containing recorded or generated time series spike signals data. SPICODYN processes such electrophysiological signals focusing on: spiking and bursting dynamics and functional-effective connectivity analysis. In particular, for inferring network connectivity, a new implementation of the transfer entropy (TE) method is presented dealing with multiple time delays (temporal extension) and with multiple binary patterns (high order extension). SPICODYN is specifically tailored to process data coming from different Multi-Electrode Arrays (MEAs) setups, guarantying, in those specific cases, automated processing. The optimized implementation of the Delayed Transfer Entropy (DTE) and the High-Order Transfer Entropy (HOTE) algorithms, allows performing accurate and rapid analysis on multiple spike trains from thousands of electrodes.

Neo

Neo is a package for representing electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats, including Spike2, NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt, and support for writing to a subset of these formats plus non-proprietary formats including HDF5.

The goal of Neo is to improve interoperability between Python tools for analyzing, visualizing and generating electrophysiology data (such as OpenElectrophy, Elephant, G-node, Helmholtz, PyNN) by providing a common, shared object model. In order to be as lightweight a dependency as possible, Neo is deliberately limited to represention of data, with no functions for data analysis or visualization.

Brainstem Connectome Atlas

This is a probabilistic atlas of 23 brainstem bundles using high-quality connectome imaging data and advanced analysis techniques. We performed rigorous quality control on connectome imaging data from the Human Connectome Project (HCP) and only accepted high-quality imaging data with minimal residual distortions for atlas construction. A systematic protocol is then developed to manually delineate 1300 ROIs on 20 HCP subjects (10 males; 10 females) for the reconstruction of brainstem fiber bundles and the probabilistic atlases in the MNI152 space. The 23 brainstem bundles can be divided into three groups: 1) Major motor tracts: the corticospinal tract (CST), the fronto-pontine tract (FPT), and the parieto-occipito-temporo-pontine tract (POTPT); 2) Major sensory tracts: the medial lemniscus (ML), the spinothalamic tract (STT), and the lateral lemniscus (LL); 3) Cerebellar peduncles: superior cerebellar peduncle (SCP), the middle cerebellar peduncle (MCP), and the inferior cerebellar peduncle (ICP).

Challenge Competitions Collection

This resource provides a guide to the many platforms available to conduct and compete in grand challenges, all in the name of improving science!

Challenges allow researchers to put their problems before other subject matter experts to be solved. The community then competes to craft the best algorithms and developers have an opportunity to evaluate novel algorithms by testing them against other competitors using real-world datasets. Everybody wins.

You can see a curated list of challenges by navigating to the Challenge Competitions Collection MediaWiki.

4D UNC Neonatal and Infant Cortical Surface Atlases

UNC infant surface atlases of cortical structures include 2 atlases, 1) The UNC 4D infant cortical surface atlas, which includes surface atlases at 1, 3, 6, 9, 12, 18, 24, 36, 48, 60 and 72 months, thus densely covering and well characterizing the critical stages of the dynamic infant brain development; The cortical parcellation maps based on the developmental patterns of cortical thickness is attached on this atlas. 2) The spatiotemporal neonatal cortical surface atlas, which mainly focuses on the neonatal cortical surface, includes surface atlases at 39, 40, 41, 42, 43, and 44 gestational weeks. Please refer to the following article for the atlases: Li, G. etc, (MedIA, 2015); Wu, Z. etc. (IEEE-ISBI, 2018; HBM, 2019); Wang, F. etc (PNAS, 2019)

MRI Brain Template and Atlas of the Mouse Lemur Primate Microcebus murinus

MRI template and 120-region atlas for the mouse lemur primate Microcebus murinus.

Generated from 34 animals aged 15-58 months old scanned at 7T using a T2-weighted sequence, resolution 115 × 115 × 230 µm. The code developed to create and manipulate the template has been refined into general procedures for registering small mammal brain MR images, available within a python module sammba-mri (SmAll-maMMals BrAin MRI; https://sammba-mri.github.io/). The template was up-sampled to 91 µm isotropic for hand-segmentation of structures, and also used to create probability maps of grey matter, white matter and cerebro-spinal fluid.

Note that this atlas is from July 2018, and will be regularly improved in future versions.

Nighres

Processing tools for high-resolution neuroimaging

PD RS-fMRI meta and validation

Part-1 was for coordinated-based meta-analysis. Its details were from published papers of RS-fMRI on PD. Part-2 was for validation purpose. It contains ALFF maps and ReHo maps of all subjects (n = 49 for each PD and control group).

CBICA: Chimera

CHIMERA is a probabilistic clustering approach that models the pathological process by a combination of multiple regularized transformations from normal control population to the patient population, while controlling the similarity in covariates (e.g. age, gender, height). Therefore, it seeks to identify multiple imaging patterns that relate to disease effects and to better characterize disease heterogeneity.

Tissue Thickness Estimation via Minimum Line Integrals

This open-source toolbox contains Matlab codes for the estimation of tissue (such as cortical) thickness from soft segmentation of 3D medical images, using minimum line integrals as described by Aganj et al (HBM 2009). A short tutorial is included in EXAMPLE.m.

ConWhAt - A library for Connectome-Based White Matter Atlas analysis

ConWhAt - a library for connectome-based white matter atlas analysis.

Conventional approaches to atlasing white matter structures follow a tract-based ontology: they assign locations in stereotaxic space to a relatively small number of gross white matter tracts from the classical neuroanatomy literature.

Problem is, they aren't particularly well-suited to network-based descriptions of brain organization.

Connectome-based white matter atlases take a different approach: they follow a connectivity-based ontology. The idea here is rather than following the classical anatomical tract nomenclature, to label white matter voxels according to the grey matter regions that their constituent fibers interconnect.

The benefit of this approach is that a scientist/clinician/citizen can take a set of (standard space) coordinates, or a nifti-format ROI mask such as a binary lesion map, and straightforwardly query which grey matter region pairs (i.e. connectome-edges) have fibers passing through those locations.

Segmentation-based Multimodal Rigid Image Registration

This is an open-source Matlab function that allows rigid registration of 3D multi-modal images based on simultaneous segmentation, as introduced by Aganj and Fischl (2017). It is a modification of spm_coreg, and requires the publicly-available SPM12 toolbox.

MIRCen Macaca fascicularis brain MRI segmentation dataset

This dataset comprises 10 T2-weighted MRIs acquired in healthy Cynomolgus macaques (Macaca fascicularis), aged 3 to 5 years, on a 7T scanner, along with manual segmentations into 17 anatomical regions of 15 sections per scan, along different incidences.

A set of python functions is also provided in order to compute F1/Dice scores between complete 3D segmentations and these 2D references.

It was used to validate the Primatologist segmentation pipeline and is intended to be used by any team or individual wishing to validate its algorithm or compare its results with ours. It is freely available for academic work upon citing the following Data in Brief article.

A thorough description of the material & methods can be found in:
- Balbastre et al., 2017. "A validation dataset for Macaque brain MRI segmentation."

Our results on this dataset can be found in:
- Balbastre et al., 2017. "Primatologist: a modular segmentation pipeline for macaque brain morphometry."

Primatologist

Primatologist is a BrainVISA toolbox dedicated to the processing of structural MRIs acquired in macaques (for now) and more generally in non-human primates (NHP, in the future).

Its main component is a modular segmentation pipeline for T1 or T2-weighted MRIs based on a generative model of intensities, with NHP-specific preprocessing tools. It allows segmenting the brain into 17 anatomical regions. The results can then easily be processed with Morphologist to extract meshes of the brain and sulci.

It takes advantages of the features of BrainVISA including an ontological organization of the data, the possibility to easily distribute processing on a multicore workstation or on a computing cluster, and easy quality checking with dedicated viewers.

QModeling

QModeling is a multiplatform toolbox for SPM to fit reference-region kinetic models (SRTM, SRTM2, Patlak Reference and Logan Reference plot are currently available in QModeling) to dynamic PET studies. The toolbox was developed in the bioengineering department of the Molecular Imaging Unit at CIMES (FGUMA) in collaboration with the department of Computer Science and Programming Languages at Malaga University (UMA).
More information: www.uimcimes.es

signalml

Example implementation of SignaML metadescription of formats of biomedical time series, allowing to read data in new formats base upon standardized XML description.

Also open platform for implementing advanced signal processing methods in user-friendly environment, at the moment interfacs for Java code, standalone executables and Matlab code via Matlab Builder for Java.

http://signalml.org/

UNC detail-preserved longitudinal 0-3-6-9-12 months-old atlas

We present a series of detail-preserved MRI longitudinal infant atlases. The atlases are 0-3-6-9-12 months-old respectively. For each time point, we built both T1-weighted & T2-weighted atlases, and the probabilistic segmentation maps. Data are in NIFTI format. Please see the related publication for further detials.
Zhang, Y., Shi, F., Wu, G., Wang, L., Yap, P.T. and Shen, D., 2016. Consistent spatial-temporal longitudinal atlas construction for developing infant brains. IEEE transactions on medical imaging, 35(12), pp.2568-2577.

NITRC Image Repository (NITRC-IR)

NITRC Image Repository offers a cloud-based federated neuroimaging data storage system for resource sharing of neuroimaging data in DlCOM and NIfTI formats. Available are thousands of subjects and imaging sessions searchable across over a dozen projects to promote re-use and integration of valuable NIH-funded data.

Search for and freely download publicly available data sets including thousands of DICOM and NIfTI normal subjects and those with diagnoses such as: schizophrenia, ADHD, autism, and Parkinson's disease. From the search results you may select and download images. NITRC's Image Repository leverages XNAT software: Extensible Neuroimaging Archive Toolkit.

UK Biobank - multimodal imaging

UK Biobank is a large prospective population-based study of half a million men and women aged 40-69. The cohort combines a large population with an extensive range of measurements, including lifestyle, environmental and health-related factors, cohort-wide genome-wide genotyping, biomarkers and linkage to longitudinal medical records.

UK Biobank is currently in the process of developing the worlds largest multimodal imaging resource which will feature 100,000 participants who have undergone brain, body and heart MRI, carotid ultrasound and DXA. This project started in 2015 and data is already available for >10,000 participants. The NITRC community might be particularly interested in the neuroimaging data which consists of T1 and T2 scans, diffusion imaging, resting and task fMRI in additional to susceptibility-weighted imaging.

Denoising of redundant MR series

Denoising of MR series (e.g. diffusion MRI, functional MRI, relaxometry, ...) by exploiting data redundancy in the PCA domain using the prior knowledge that the eigenspectrum of random covariance matrices is described by the universal Marchenko Pastur distribution.

Longitudinal neuroimaging hippocampal markers for diagnosing Alzheimer's disease

We introduce a longitudinal image analysis framework based on robust registration and simultaneous hippocampal segmentation and longitudinal marker classification of brain MRI of an arbitrary number of time points. The framework comprises two innovative parts: a longitudinal segmentation and a longitudinal classification step. We introduce a novel approach to the joint segmentation of the hippocampus across multiple time points; this approach is based on graph cuts of longitudinal MRI scans with constraints on hippocampal atrophy and supported by atlases.

Mayo Clinic Adult Lifespan Template and Atlases

The Mayo Clinic Adult Lifespan Template and its associated atlases were constructed and made publicly available to provide a template suitable for the analysis needs of aging and Alzheimer’s Disease population studies. Population-matched templates are known to allow more accurate quantitative MRI analysis, but most MRI standard templates are generated from scans of younger individuals. Unlike these, the MCALT is designed for analysis of MRI of adult subjects age 30+. MCALT was designed for use with SPM12 and integrates easily into its workflows, but is not specific to SPM12 and may be used easily with other segmentation software. MCALT also includes matlab source code for the complete SPM12 T1-weighted processing pipeline used in Dr. Jack's Aging and Dementia Research Lab at Mayo Clinic. Output volumes are not exactly identical but can be directly compared with those computed in-house.

TurboFIRE

Real-time task-based and resting-state fMRI analysis software

PREDiCT

PREDiCT is the Patient Repository for EEG Data + Computational Tools. It offers one-stop shopping for EEG tasks, data storage, and analytic tools for pattern classification of clinical groups.

NIAG Addiction Data

Addiction data

ENIGMA resting state fMRI processing pipeline

The ENIGMA rsFMRI pipeline provides tools for pre-processing of fMRI data and measurements of connectivity for stable functional networks, including default mode network. This is yet experimental pipeline that requires only EPI fMRI data and uses a population based template and tissue priors for regression of global connectivity signal. The pipeline is based on AFNI software and includes PCA-based denoising and group-based regularization approach for imputation of censored frames and removal of outliers. The pipeline can also be used for pre-processing of the task-based functional MRI studies in ENIGMA workgroups.The pipeline is distributed as a set of standard unix scripts and software and as a virtual machine's container for unix, mac and windows platforms.
To get involved in any ENIGMA group send us a message -- enigma@ini.usc.edu or support.enigmaDTI@ini.usc.edu.

MPI-Leipzig Mind-Brain-Body Dataset

The participants included in this dataset participated in one or two protocols. Each of these protocols included structural and resting-state fMRI data acquisition, as well as an extensive battery of behavioural tests.

PhysIO Toolbox

The general purpose of this Matlab toolbox is model-based physiological noise correction of fMRI data using peripheral measures of respiration and cardiac pulsation. The PhysIO Toolbox can be downloaded as part of the TAPAS software collection and integrates with SPM.

Core design goals for the toolbox were: flexibility, robustness, and quality assurance to enable physiological noise correction for large-scale and multi-center studies.

Some highlights:
1) Robust preprocessing via iterative peak detection and Hilbert-transform based estimation of respiratory volume per time, shown for noisy data and patients.
2) Flexible support of data formats (Siemens, Philips, GE, BIDS, Biopac, ...) and noise models (RETROICOR, RVHRCOR).
3) Fully automated noise correction and performance assessment for group studies.
4) Integration in fMRI processing pipelines as SPM Toolbox (Batch Editor GUI).

Relevant technical papers:
https://doi.org/10.1016/j.jneumeth.2016.10.019
https://doi.org/10.1016/j.neuroimage.2021.117787

ImageCite

The ImageCite Consortium is a group of image data host providers banded together with the objective to plan and implement an imaging unique identification (DOI) system across a broad set of imaging data sharing host providers. The consortium will review a proposed scheme of identification and attribution (https://doi.org/10.3389/fninf.2016.00034) in terms of its suitability and applicability to the needs of the community and feasibility of implementation in the various image hosting systems. Outcomes will be a conceptual plan of what can be accomplished by the repositories in the short term under current, existing development cycles (Phase 0); identification of additional future objectives that can be achieved with additional, potentially coordinated, support; and a ‘white paper’ document that documents this identifier vision that can be circulated to additional resource providers in order to attempt to gain even wider adoption.

nonfractal

It is a MATLAB toolbox for estimating "nonfractal connectivity" and "fractal connectivity" as well as Hurst parameters from a set of time series with long-range dependence such as resting state fMRI BOLD or EEG signals. Please refer to the following publication for the underlying theories.

Wonsang You, Sophie Achard, Joerg Stadler, Bernd Bruekner, and Udo Seiffert, "Fractal analysis of resting state functional connectivity of the brain," in 2012 International Joint Conference on Neural Networks, 2012.

For more details, please visit the wiki page:
https://www.nitrc.org/plugins/mwiki/index.php/nonfractal:MainPage

DCP：Diffusion Connectome Pipeline

The diffusion connectome pipeline is used for for building DTI networks.Firstly, it provides a friendly graphical user interface, and the users only need to use the mouse to set the parameters which are used in the whole process and click the button with label ‘RUN’. The DCP will process every participant’s data automatically, and generate DTI network of every participant. And the default parameters we provide are currently recognized as classic processing parameters. When the program is running, the user can accurately get which step the program is running through the monitor window. After the program is finished, a folder of quality control is generated, and the registration results of each subject are saved for quality checking.

calcFD - Calculate the fractal dimensionality of a 3D structure

A toolbox for MATLAB for calculating the fractal dimensionality of a 3D structure, designed to work with intermediate files from FreeSurfer analysis pipeline, but can also use other volumes.

iElectrodes

iElectrodes is an open source toolbox to semiautomatically localize and label intracranial electrodes in 2-3 minutes. The toolbox precisely localizes subdural grids (ECoG) and depth (SEEG) electrodes using MRI and CT images.

2D and 3D visualization simplify the comprehension of anatomical locations.

Also, it is possible to add anatomical information (brain parcellation images) and surfaces from Freesurfer. Data can be exported in multiple formats, including EEGLAB and Fieldtrip.

Additionally, grid electrodes can be corrected for brain shift or compression effects, and projected to brain surface.

Planning of depth electrode implantation is fast and easy.

More information in our paper Blenkmann et al., Front. Neuroinformatics, 2017 http://journal.frontiersin.org/article/10.3389/fninf.2017.00014/full

Arterial Territories of the Neonatal Brain: automated classification

This automated classification program is intended to facilitate a quick and precise classification of neonatal arterial ischemic strokes (NAIS) according to the arterial territories of the neonatal brain (ATNB). Precise stroke location, as well as the percentage of each arterial territory involved, are the main calculations performed by this script. An anatomical image of the subject(s), along with their lesion segmentation, are required.

Associated publication: https://www.nature.com/articles/s41390-019-0724-x

PVS_Enhance

PVS_Enhance is a toolbox for the enhancement of perivascular spaces (very thin and weak tubular structures) in MR image. Methods in the toolbox are based on Haar transformation of non-local cubes and 4D block-matching filtering. The software was implemented in MATLAB with cpp files (Mex compiling is required) and developed by the IDEA group at the University of North Carolina at Chapel Hill ( https://www.med.unc.edu/bric/ideagroup).

More detailed explanations can be found in the following paper. If you use this code, please include the following article as a reference.

[Yingkun Hou*, Sang Hyun Park*, Qian Wang, Jun Zhang, Xiaopeng Zong, Weili Lin, Dinggang Shen, "Enhancement of Perivascular Spaces in 7T MR Image using Haar Transform of Non-local Cubes and Block-matching Filtering," Scientific Reports 7, 8569, Aug. 2017.]

Multi Atlas Skull Stripping (MASS)

MASS [1] is a software package designed for robust and accurate brain extraction, applicable for both individual as well as large population studies. MASS is implemented as a Unix command-line tool. It is fully automatic and easy to use — users input an image, and MASS will output the extracted brain and the associated brain mask.

E-Prime Extensions for SMI

E-Prime® Extensions for SMI adds eye gaze and eye movement capabilities to existing and novel E-Prime experiments. Create new paradigms with E-Studio's graphical design interface and connect with flexible Remote Eye Tracking technologies by SensoMotoric Instruments.

Create interactive or passive eye tracking paradigms
Create paradigms which change based on participant eye gaze data, AOI hit tests, fixation and user interaction
Combine eye gaze data with E-Prime condition data for powerful analysis
Train participants to fixate and control eye movements
Give feedback on vigilance or attentive behaviors
Supports diagnostic and interactive studies:
Face perception and recognition
Infant studies
Reading (sliding window)
Attention patterns and behavior
Visual search
Scene recognition

E-Prime Extensions for Tobii

E-Prime® Extensions for Tobii combines the power of E-Prime with easy to use Tobii eye trackers. The E-Prime® Extensions for Tobii includes the script necessary to integrate E-Prime with Tobii eye tracking technology. E-Studio’s graphical design interface allows users to drag and drop eye tracking functionality into existing E-Prime® experiments or to easily create new E-Prime® eye tracking experiments.
Supports multiple types of studies: Diagnostic and Interactive
Face perception and recognition
Infant studies
Reading (sliding window)
Attention patterns and behavior
Visual search
Scene recognition
No head restraint necessary
Seamless E-Prime® integration

Cavernous Hemangioma: Epilepsy & ADD

This resource is populated with information related to a single subject with multiple cavernous hemangiomas with a diagnosis of epilepsy and ADD.

Chronos: A Multifunctional Response and Stimulus Device

Chronos allows the accurate collection and verification of tactile, auditory, visual, and analog responses along with the precise source of audio and generic analog output timing. Chronos features millisecond accuracy and consistent sound output latencies across machines. Chronos includes 16 digital inputs and 16 digital outputs, eliminating the need for a parallel port. The Photo Sensor accessory (included) can be used on CRT, LCD, and projection displays to detect stimulus onset events, refreshes, and measure rise and fall times. All responses collected are synchronous to the E-Prime® time domain.

SeeCAT

The Seed-based Connectivity Analysis Toolbox (SeeCAT) is developed in Matlab environment with GUI. The toolbox mainly includes four functional modules: 1) a preprocessing pipeline works with functional MRI data (work with SPM), which is enabled with fast parallel computing to improve processing speed; 2) a calculating module for seed-based structural or functional connectivity with various definitions of seeds; 3) a computation function for nodal degree in high-resolution functional brain networks (voxel-based) with flexible options, including selections for different thresholds, positive and negative connection, binary or weighted, anatomical distance, normalization, and smoothing; and 4) some useful tools for statistics, visualization and image calculation.

SVNTest

Test project for NITRC development..

SlicerSALT

SlicerSALT (Shape AnaLysis Toolbox, SALT) continues the maintenance, expansion, validation and dissemination of the SPHARM-PDM (SPherical HARMonics Point Distributed Models) shape analysis toolbox. It is distributed as a tailor-made lighter version of 3DSlicer.

SPHARM-PDM is a popular open-source tool that has been used by the biomedical research community to analyze the morphology of anatomical structures. SlicerSALT will maintain the core functionalities of SPHARM-PDM, as well as dramatically extending the toolbox into a general shape analysis toolbox. Novel, robust and validated methodologies will provide support for objects of non-spherical topology, skeletal models, four-dimensional models, and study-specific optimal model correspondence. SALT is being tested three application case scenarios to evaluate the efficacy of the proposed methods.

SlicerSALT will continue providing excellent documentation, community resources, and user support.

ToolConnect

ToolConnect is an open source software for the functional connectivity analysis of in vitro neural networks. In the current version, ToolConnect implements correlation- (cross-correlation, partial-correlation) and information theory (joint entropy, transfer entropy) based algorithms, as well as useful and practical add-ons to visualize functional connectivity graphs and extract some topological features derived from graph theory.

GWAS3D

Main Functions
Identify the most probable functional variants which affect transcriptional regulation;

Prioritize the leading variants when given a full list of GWAS result;

Evaluate the deleteriousness of genetic variants affecting the gene regulation when given a list of variants;

Annotate genetic variant from regulatory perspective.

NCAA-DOD Grand Alliance CARE Consortium

The CARE Consortium endeavors to provide necessary infrastructure and scientific expertise to study concussion. Together, we are united in our goal to gain a better understanding of the neurobiopsychosocial nature of concussive injury and recovery in order to ultimately enhance the safety and health of our student-athletes, service members, youth sports participants and the broader public.

The EBDS Infant DTI Fiber Atlases

Neonatal and 1-2 year old DTI fiber atlases for studies of brain development at birth to 2 years of age from the UNC Early Brain Development Studies (EBDS) group and the NIRAL (Neuro Image Research and Analysis Laboratory)

Infant DTI atlases with a comprehensive set of template fibers for semi-automatic tract based analysis that represents a typically developing human brain during the first few weeks up to 2 years of life. To the best of our knowledge, this the first such population atlases with this magnitude of quality and sample size.

These resources enable widespread application of a set of template fibers for atlas based along-tract analysis supporting an adequate and reliable analysis of DTI in newborns in both practice and in clinical research settings and address a critical gap in the current research community.

7T Probabilistic atlas of Subthalamic Nucleus for Use with 3T MRI

7T Probabilistic Atlas of STN for use with clinical 3T MRI

A Shimadzu to .nirs data converter

Shimadzu2nirs is a Matlab script which takes near-infrared spectroscopy data recorded by Shimadzu system(s) and converts it to a .nirs file format for use with the HOMER2 NIRS processing package.

Diffusion MRI Orientation Distribution Function in Constant Solid Angle (CSA-ODF) and Hough-Transform Tractography

This is a diffusion-weighted MRI processing Matlab toolbox (including binaries), which can be used to:
• Compute the Q-Ball Imaging Orientation Distribution Function in Constant Solid Angle (CSA-ODF) (Aganj et al, MRM 2010).
• Perform Hough-transform tractography (Aganj et al, MedIA 2011).
• Visualize ODFs and tracts, and export them for further analysis.
• Verify the correctness of the diffusion gradient table (Aganj, Sci Rep 2018).
• Compute and interactively visualize the connectivity matrix.
• Augment the connectivity matrix with indirect connections (Aganj et al, ISMRM 2014).

For short tutorials, see EXAMPLE.m and EXAMPLE_CLI.m. If available, GPUs can be used for speed-up.

MRI Predict

MRI Predict is an SPM toolbox, FSL-flavour lib and R package to easily predict an outcome (e.g. whether the patient will respond to a treatment, or whether the patient has one or another disorder) from sMRI scans. The model may include covariates, and the software conducts both cross-validation of the model and fitting for its use with new VBM data in other dimensions.

Framewise Integrated Real-time MRI Monitoring

[For Investigational Use Only]

Easy to set up and use, the FIRMM software suite provides MRI scanner operators with data quality metrics in real time. Using FIRMM to identify the ideal scan time for each person can reduce total brain MRI scan times and associated costs by 50% or more.

Features:

Accurate FD calculations as you scan. FIRMM’s framewise displacement (FD) calculations are not only fast, but also accurate, when compared to a standard, commonly utilized offline, post-hoc processing stream.

Prediction of remaining scan time needed. We built an algorithm that accurately predicts the required scan time until you have captured as much quality data as you need.

User-friendly, real-time feedback. FIRMM users have been able to share the percentage of quality data frames with participants or display the FIRMM GUI on the participant’s screen in the scanner room for feedback and training purposes.

CBICA: Datasets

Meta-project, providing datasets associated with various CBICA projects: Mouse Brain, Facial Expression/Schizophrenia, Prostate, etc.

Group Sparse Canonical Correlation Analysis

Group sparse Canonical Correlation Analysis is a method designed to study the mutual relationship between two different types of data.

S3DL Sparse Dictionary Learning based MR Image and Lesion Segmentation

S3DL (Subject Specific Sparse Dictionary Learning) is a software tool to generate whole brain segmentation as well as lesion segmentation from multi-contrast human brain MR images. It is a patch-based method, where similarities between patches from a subject and one or more atlases are exploited to create a segmentation of the subject.

There are two parts of the tool, one to create multi-class segmentation from T1-w MR images, another to segment MS lesions from T1-w and FLAIR images. They can be used independently. In addition to the software, some training atlases and test data are provided.

The toolbox is based on the following paper,
S. Roy, Q. He, E. Sweeney, A. Carass, D. S. Reich, J.L. Prince, and D. L. Pham, "Subject-Specific Sparse Dictionary Learning for Atlas-Based Brain MRI Segmentation", IEEE Journal of Biomedical and Health Informatics, 19(5):1598-1609, 2015.

Prospective Acquisition CorrEction (PACE) Resting State EPI data

This data release contains the 4D NifTi pre-processed images corresponding to resting-state functional magnetic resonance imaging (Rs-fMRI) data acquired from 47 healthy individuals using the EPI-PACE sequence on a Siemens 3T Verio scanner at the Auburn University MRI Research Center. Along with the 4D images, the release contains voxel-wise frame-wise displacement of the head (FDvox) for each subject, MATLAB files containing the motion metrics (3 translations and 3 rotations) and summary motion statistics.

We provide the 4D NifTi data for several combinations of nuisance signal regressors and retrospective motion correction approaches: (i) CSF (cerebro-spinal fluid) + WM (white matter) signal regression, (ii) CSF + WM + GS (global signal) regression, (iii) CSF + WM + Friston-24 motion regression, (iv) CSF + WM + GS + Friston-24 motion regression, (v) CSF + WM + Friston-24 motion regression + motion censoring, and (vi) CSF + WM + GS + Friston-24 motion regression + motion censoring.

GWASrap

GWASrap uses a one-stop solution, users can quickly fetch very comprehensive annotation when they are browsing the GWAS result in highly interactive Circos-style graph or dynamic Manhattan panel. System can perform independent variant prioritization based on additive effect principle by combining the original statistical value and variant prioritization score. The server also hosts a well-structured and up-to-date repositories which store all of significant TASs about hot cases frequently investigated by current GWAS to satisfy the increasing number of GWA studies.

GWASdb

The database provides following functions:
(i) In addition to all the TASs attained genome-wide significance (P-value < 5 x 10-8), we manually curated the TASs that are marginally significant (P-value < 10-3) by looking into the supplementary materials of each original publication;
(ii) Extensive functional annotations and predictions for those TASs across multiple domains;
(iii) Furthermore, we have manually mapped those TASs by phenotype according to Disease Ontology (DO), Human Phenotype Ontology (HPO) and Medical Subject Headings (MeSH) for easy access.
(iv) Besides the SNP-trait association, we also collect SNP-Drug Response data.
(v) Comprehensive and interactive user interface to facilitate the GWAS research.

Mixed Effect Model of Genetic-Set and Environment Interaction

Toolbox: Mixed Effect Model of Genetic-Set and Environment Interaction for Neuroimage applications

This MATLAB Toolbox provides a mixed effect model for gene-environment interaction (MixGE) on neuroimaging phenotypes, such as structural volumes and tensor-based morphometry (TBM). This model incorporates both fixed and random effects of genetic-set and environment interaction, to investigate homogeneous and heterogeneous contributions of sets of genetic variants and their interaction with environmental risks to phenotypes. To avoid direct parameter estimation in the MixGE model due to small sample size and high computational cost, score statistics were constructed for the terms associated with fixed and random effects of the genetic-set and environment interaction. They were combined into a single significance value.

CBICA: Brain Tumor Modeling - Coupled Solver

This is software package for tumor growth modeling employs a DiffusionSolver approach. It is a one-step further compared to the previous purely mechanical, pressure-based approach employed in ElasticSolver. In addition to the previous elasticity-based approach to simulate brain tissue deformations caused by growing tumors (mass-effect), a nonlinear reaction-advection-diffusion equation to describe the tumor spatio-temporal evolution is added. This equation has a two-way coupling with the underlying tissue elastic deformation. In this approach, the forces exerted by the tumor growth and infiltration onto the underlying brain parenchyma are local ones, proportional to local tumor density gradients. Curretnly BTMCS is used in GLioma Image SegmenTation and Registration (GLISTR) and Pre-Operative and post-Recurrence brain Tumor Registration (PORTR).

HippocampalUnfolding_ManualInitialization

Data here includes manually segmented hippocampal grey matter and dark band, as well as labels used in computational unfolding of grey matter using Laplace equation (1).

The goal of this work is to improve subfield segmentation and provide a standardized indexing system for hippocampal tissue that accounts for variability in ontological folding of this structure (e.g. digitation and curvature of the uncus in the anterior hippocampus).

Current atlas is v2.0.
Updates include addition and extension of labels surrounding the hippocampus to ensure easier integration with other neocortical and subcortical atlases, and some changes to labelling of hippocampal grey matter and dark band (mainly in subject V075 who originally had some tracing errors).

1. DeKraker, J., Ferko, K. M., Lau, J. C., Köhler, S., & Khan, A. R. (2017). Unfolding the hippocampus: An intrinsic coordinate system for subfield segmentations and quantitative mapping. bioRxiv, 146878.
code at https://github.com/jordandekraker/HippUnfolding

CBICA: Brain Anatomy Simulator

This package estimates the statistical properties of high-dimensional deformation fields[6], which are produced by deformable registration packages like HAMMER above, and then uses the estimates statistics to simulate brain images with very high degree of realism. The statistical properties of a family of deformations are estimated via a combination of wavelet packet decomposition on the deformations and their Jacobians, and PCA. This package can be used to better represent the statistical priors of deformation fields in deformable registration methods that use priors, such as active shape models. Moreover, SSD can be used to generate very realistic and rich deformations, which can be used for generation of gold standard against which different registration methods can be compared. Finally, a method for generation of simulated tissue atrophy is supplied as a separate package [7], which further enables simulation and validation studies.

CBICA: PREDICT

In our approach to simulate brain tissue atrophy, we adapt the strategy delineated by the well-known Occam's razor: solution to problems must be as simple as possible, but not simpler. In the case of brain tissue atrophy, in the absence of a priori knowledge of precisely how a specific medical condition causes tissue loss and in which pattern, the only information available is that the apparent volume of the tissue is reduced. We therefore proceed to find a deformation that corresponds to a reduction of tissue volume in a specified region of the brain. Specifically, given a prescribed level of tissue volume after atrophy over an image, we solve for a warping deformation that produces the desired volumetric changes using an energy minimization approach. The advantages of the method is that it is automated, easily generalizable to other instances of spatially varying volumetric change, and can readily be modified to incorporate a priori knowledge in the form of a statistical atlas of tissue loss.

CBICA: PHI Estimator

The Peritumoral Heterogeneity Index (PHI) Estimator is a lightweight tool built towards the following goals:
Perform quantitative pattern analysis of the spatial heterogeneity of peritumoral perfusion imaging dynamics, retrieved from Dynamic Susceptibility Contrast Magnetic Resonance Imaging (DSC-MRI) data.
Evaluate the imaging biomarker of the Epidermal Growth Factor Receptor variant III (EGFRvIII) mutation status, in individual patients diagnosed with Glioblastoma. After v1.0.0, this will get updated in the CaPTk project as "EGFRvIII Surrogate Index".

CBICA: GONDOLA

This software implements Generative-Discriminative Basis Learning (GONDOLA). Theoretical ideas are explained in the related papers, but as a brief explanation, GONDOLA provides a generative method to reduce the dimensionality of medical images while using class labels. It produces basis vectors that are useful for classification and also clinically interpretable. When provided with two sets of labeled images as input, the software outputs features in Weka Format (.arff files) and a MATLAB data file (.mat file). The program can also save basis vectors as NIfTI-1 images. Scripts are provided to find and build an optimal classifier using Weka. The software can also be used for semi-supervised cases in which a number of subjects do not have class labels.

CBICA: DTIDROID

This software implements a method for deformable registration of diffusion tensor (DT) images (DROID) that integrates geometry and orientation features into a hierarchical matching framework. The geometric feature is derived from the structural geometry of diffusion and characterizes the shape of the tensor in terms of prolateness, oblateness, and sphericity of the tensor. Local spatial distributions of the prolate, oblate, and spherical geometry are used to create an attribute vector of geometric feature for matching. The orientation feature improves the matching of the WM fiber tracts by taking into account the statistical information of underlying fiber orientations. These features are incorporated into a hierarchical deformable registration framework to develop a diffusion tensor image registration algorithm. Extensive experiments on simulated and real brain DT data establish the superiority of this algorithm for deformable matching of diffusion tensors, thereby aiding in atlas creation.

CBICA: PORTR

Pre-Operative and post-Recurrence brain Tumor Registration (PORTR) is a software package designed for determining the optimal deformation between pre-operative and post-recurrence scans by finding the minimum of an energy function, which is based on the concept of symmetric registration.

Some typical applications of PORTR include:
- Mapping post-recurrence scans to pre-operative scans
- Labeling entire brain regions of each scan.

PORTR is implemented as a command-line tool. It is semi-automatic and requires minimal user initializations. Users could use the visual interface called CaPTk to easily make initializations and a script for the execution. As a result, PORTR will output a label map of each scan, a mapping between two scans, etc.

CBICA: ODVBA

ODVBA provides a mathematically rigorous framework for determining the optimal spatial smoothing of structural and functional images, prior to applying voxel-based group analysis. In order to determine the optimal smoothing kernel, a local discriminative analysis, restricted by appropriate nonnegativity constraints, is applied to a spatial neighborhood around each voxel, aiming to find the direction best highlights the difference between two groups in that neighborhood. Since each voxel belongs to a large number of such neighborhoods, each centered on one of its neighboring voxels, the group difference at each voxel is determined by a composition of all these optimal smoothing directions. Permutation tests are used to obtain the statistical significance of the resulting Optimally-Discriminative VBM (ODVBA) maps.

CBICA: MOE

This software can be used for datasets where the reference group ("controls") and the affected group ("patients") cannot be separated by a single line ("hyperplane"). MOE combines multiple hyperplanes along with a clustering objective, to split the affected group into multiple sub-groups. This is done in such a manner that each of the resulting sub-group is separable from the reference group by a single line.

See Figure in test/All_test_results.png, where the reference group is denoted by circles, and the affected group by triangles. In Case1, the affected group is not heterogeneous, as it is seperable from the reference group with a single line. Cases 2, 3, 4 require multiple lines to separate the affected group from the reference group.

This software works with data saved in comma-separated value format. Input features can be based on any type of data - volumes, density, connectivity, diffusion, clinical scores, cognitive data etc.

MRTool

MRTool is a comprehensive collection of analysis tools for MR brain imaging data. It has been developed by Marco Ganzetti in the context of his doctoral degree in 2016. MRTool requires SPM12.

SPM-Workshop Hannover

In our workshops we offer a rich and pleasant environment to familiarize yourself with SPM and some useful extension under skilled supervision. We believe that the best way to learn how to operate SPM is by actually using it!

We will hone your data analysis skills from preprocessing to group statistics. You will also train how to arrive at high-class graphical presentations of your results for use in your publications. Moreover you will benefit both from your own learning experience as well as from those of your peers.

Go pro and learn how to analyze your own functional magnetic resonance imaging data from A to Z!

The Grid Code Analysis Toolbox (GridCAT)

The Matlab-based Grid Code Analysis Toolbox (GridCAT) allows researchers to carry out automated analysis of the putative firing of grid cells (i.e., the grid code) in human fMRI data. All analysis steps, from estimation and fitting of the grid code in the general linear model, to the generation of grid code metrics and plots, can be performed by means of a simple and user-friendly graphical user interface. Researchers confident with programming can also edit the open-source code and use example scripts accompanying the GridCAT to implement their own analysis pipelines. Moreover, an example dataset is provided together with a detailed manual, so that users can explore the GridCAT’s functionality.

Reference:

Stangl, M.*, Shine, J.*, & Wolbers, T. (2017). The GridCAT: A toolbox for automated analysis of human grid cell codes in fMRI. Frontiers in Neuroinformatics, 11:47. [* equal contribution]
https://doi.org/10.3389/fninf.2017.00047

VALiDATe29 squirrel monkey brain atlas

The VALiDATe29 squirrel monkey brain atlas was created from MRI scans of 29 squirrel monkey brains. The atlas is currently comprised of multiple anatomical templates (proton density, T1, T2* weighted), diffusion MRI templates (fractional anisotropy, mean diffusivity), and ex vivo templates (fractional anisotropy and a structural MRI). In addition, the templates are combined with histologically defined cortical labels, and diffusion tractography defined white matter labels. The atlas was created by researchers at Vanderbilt University Institute of Imaging Science.

Magnetic Resonance Angiography Atlas Dataset

Characterization of the complex branching architecture of cerebral arteries across a representative sample of the human population is important for diagnosing, analyzing, and predicting pathological states. Brain arterial vasculature can be visualized by magnetic resonance angiography (MRA). However, most MRA studies are limited to qualitative assessments, partial morphometric analyses, individual (or small numbers of) subjects, proprietary datasets, or combinations of the above limitations. Neuroinformatics tools, developed for neuronal arbor analysis, were used to quantify vascular morphology from 3T time-of-flight MRA high-resolution (620 μm isotropic) images collected in 61 healthy volunteers (36/25 F/M, average age=31.2 ± 10.7, range=19-64 years).

SigViewer

SigViewer is a viewing application for biosignals such as EEG or MEG time series. In addition to viewing raw data, SigViewer can also create, edit, and display event information (such as annotations or artifact selections). SigViewer supports many different biosignal data formats (including GDF, EDF, CNT, EEG, and many more). SigViewer also features basic signal processing modules such as computing the average over selected epochs (yielding event-related potentials) and power spectral densities of selected signals. SigViewer is written in standard C++ using the cross-platform graphical user interface (GUI) toolkit Qt. SigViewer is open source and available for all three major platforms (Windows, Mac OS X, and Linux). The application is licensed under the GNU GPL.

Organization for Human Brain Mapping

The Organization for Human Brain Mapping (OHBM) was created in 1995 and has since evolved in response to the explosion in the field of human functional neuroimaging and its movement into the scientific mainstream.

One of the primary functions of the organization is to provide an educational forum for the exchange of up-to-the-minute and groundbreaking research across modalities exploring Human Brain Mapping. It does this through a growing membership and an annual conference, held in different locations throughout the world.

TEMPORAL LOBE EPILPSY MULTIPLE MODALITY DATA

The data-set consists of multiple modalities from three temporal lobe epilepsy studies.

•Study 1- HFH_T1_HP_SEG_TLE_1

The purpose of this study was to develop a systematic and statistical approach using a large dataset for the evaluation of automated methods and establish a method that would achieve results better approximating those attained by manual tracing in the epileptogenic hippocampus.

•Study 2- HFH_FLAIR_T1_HP_SEG_TLE

The purpose of this study was to develop a systematic and statistical approach using a large dataset for the evaluation of automated methods and establish a method that would achieve results better approximating those attained by manual tracing in the epileptogenic hippocampus.

•Study 3- HFH_SPECT_T1_HP_SEG_TLE

This study assesses the utility of compartmental analysis of SPECT data in lateralizing ictal onset in cases of a putative mesial temporal lobe epilepsy (mTLE).

PS: CLICK ON EACH STUDY NAME ON THE DOWNLOAD PAGE FOR MORE INFO BEFORE DOWNLOADING.

Atlas3D

Multiplatform 3D visualization tool which allows import, linear transformation, cutting and export of various data formats in 3D atlas space.

Mid-Space-Independent and Intermediate Deformable Image Registration

This open-source toolbox contains Matlab codes (and some binaries) for 2D and 3D deformable image registration, using the mid-space-independent (MSI) approach introduced by Aganj et al (NeuroImage 2017) and the intermediate deformable image registration (IDIR) introduced by Aganj & Fischl (ISBI 2023). Short tutorials are included in EXAMPLE_MSI.m and EXAMPLE_IDIR.m. If available, a GPU can be used for speed-up.

Ultra-high field atlas for DBS planning

7T T1-w and T2-w average atlas (ANTS) from 12 healthy controls, with manual labelling of deep and mid brain structures.

SMAC: Spatial Multi-category Angle-based Classifier

We propose a novel Spatial Multi-category Angle-based Classifier (SMAC) for the efficient identification of biomarkers in the imaging data. The proposed SMAC not only utilizes the spatial structure of high-dimensional imaging data, but also handles both binary and multi-category classification problems. Besides, the SMAC also include the functions to deal with imaging based regression problems, with spatial and sparse penalty available.

MultiXplore: Visual Exploration Platform for Multimodal Neuroimaging Data

MultiXplore is a graphical user interface that has been implemented as a 3D Slicer plugin (scripted module). It serves to display corresponding set of cortical regions from functional connectivity matrix in an explorable 3D scene that represents brain anatomical environment. In addition to grey matter regions, MultiXplore automatically finds and extracts deterministic fiber bundles which exist between selected region(s) and adds them to the 3D environment. This is a helpful feature in generating region-based fiber bundles given a desired whole-brain tractography data. Please make sure to cite our paper if you find this tool useful in your own work:
Bakhshmand, S. M., Khan, A. R., de Ribaupierre, S., & Eagleson, R. (2017). "MultiXplore: Visual exploration platform for multimodal neuroimaging data". Journal of Neuroscience Methods.

ATPP: Automatic Tractography-based Parcellation Pipeline

ATPP (Automatic Tractography-based Parcellation Pipeline) is an integrated pipeline named ATPP realizing tractography-based brain parcellation with automatic processing and massive parallel computing.

ATPP offers a powerful CLI version (https://github.com/haililihai/ATPP_CLI) for parcellating multiple brain regions and a user-friendly GUI version (https://github.com/haililihai/ATPP_GUI) for parcellating a specific brain region.

StdpC

StdpC is dynamic clamp software that allows users to use dynamic clamp without in depth knowledge of real time interfaces or indeed programming. StdpC has a graphical user interface based on the popular QT framework and defines a good number of common synapse and ion channel models, including plasticity rules such as STDP. Advanced features include a comprehensive scripting mechanism that allows full experimental automation and the inclusion of spike generator and simulated neuron modules. StdpC also supports Active Electrode Compensation methods, as introduced by the Brette lab, for Dynamic Clamp with a single, high impedance electrode.

rapidtide

The rapidtide suite is a set of python tools that we've developed and tested over the last several years in the McLean Hospital Opto Magnetic Group to perform rapid time delay analysis on functional imaging data to find time lagged correlations between the voxelwise time series and other time series. This technique can be used to detect, quantify, and display blood-borne physiological noise signals, as a method for quantifying hemodynamic parameters, and/or as a way to remove them from datasets to improve fMRI data quality.

EEGBase

EEGBase portal enables community researchers to store, update, download and search data and metadata from EEG/ERP experiments. EEGbase advances electrophysiology research by enabling access to public data, tools and results.

TRENTOOL

TRENTOOL (TRansfer ENtropy TOOLbox) is an open source GPLv3 Matlab® toolbox for the analysis of information transfer in time series data.

Transfer entropy (TE) is an information-theoretic measure of directed information transfer; it offers an approach to the detection of neuronal interactions that is free of an explicit model of the interactions and thus offers the power to analyze linear and nonlinear interactions alike.

TRENTOOL provides user friendly routines for the estimation and statistical testing of TE in time series data. For the use with neural data TRENTOOL seamlessly integrates with the popular FieldTrip toolbox.

ViSAPy

ViSAPy (Virtual Spiking Activity in Python) is a tool for generation of biophysically realistic benchmark data for evaluation of spike sorting algorithms.

LFPy

LFPy is a Python package for calculation of extracellular potentials from multicompartment neuron models. It relies on the Python interface provided by the NEURON simulator.

G-3 / Neurospaces Simulator

The Neurospaces toolchain is being developed in the context of the GENESIS 3 project. Neurospaces software components serve the purpose of user-friendly computational model construction and simulation, morphology and topology analysis. A webinterface allows to define research projects in a database and browse models, simulation results and free text and other content related to a project.

Bayesian longitudinal low-rank regression

To perform a joint analysis of multivariate neuroimaging phenotypes and candidate genetic markers obtained from longitudinal studies, we develop a Bayesian longitudinal low-rank regression (L2R2) model. The L2R2 model integrates three key methodologies: a low-rank matrix for approximating the high-dimensional regression coefficient matrices corresponding to the genetic main effects and their interactions with time, penalized splines for characterizing the overall time effect, and a sparse factor analysis model coupled with random effects for capturing within-subject spatio-temporal correlations of longitudinal phenotypes. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm.

INSTAR (Infant Joint Segmentation and Registration)

INSTAR (Infant Joint Segmentation and Registration) provides a solution of accurate segmentation and registration for infant brain images, especially from birth to 1 year old, which plays an important role in many early brain development studies.
Specifically, infant brain image segmentation or registration often encounters much more challenges than segmentation/registration of adult brain images due to the dynamic appearance change with rapid brain development. Therefore, in this software package, we included out latest joint segmentation and registration framework [1] to tackle the challenges.
This software package was developed in the IDEA group at UNC-Chapel Hill (http://bric.unc.edu/ideagroup).
[1] P. Dong, L. Wang, W. Lin, D. Shen, and G. Wu, "Scalable joint segmentation and registration framework for infant brain images," Neurocomputing, In Press, 2016

Waxholm Space Atlas of the C57BL/6J Mouse Brain

Here you can download image volumes representing the canonical Waxholm Space (WHS) adult C57BL/6J mouse brain. These images include T1-, T2*-, and T2-Weighted MR volumes (generated at the Duke Center for In-Vivo Microscopy), Nissl-stained optical histology (acquired at Drexel University), and a volume of labels. All volumes are represented at 21.5μ isotropic resolution.

The label volume represents phase one delineations of the WHS mouse brain. The atlas contains 26 structures (+ inner ear) organized in accordance with the NeuroLex Brain Partonomy scheme found at: http://neurolex.org/wiki/Brain_Partonomy_(general_mammalian). The delineations are intended as a communication aid for finding brain locations. Delineations are based on visually distinguishable features and are not intended to impose a specific view of brain structure or function.

MeshGen - Surface meshes from volumetric segmentation data

MeshGen generates separate mesh files for each label present in the segmentation volume. The generation employs a variant of Constrained Elastic Surface Nets [CESN], which has been modified in order to avoid intersection between neighboring meshes.
Supported input formats:
- NIfTI for segmentation volume, with integer elements (8/16/32 bits, signed/unsigned)
- ITK label file (ITK-Snap compatible, does not contain hierarchy)
- ILF - Integrated Label File (MBAT-compatible, hierarchical)
- Brain-Map XML file (ontology from brain-map.org, hierarchical)
When label hierarchy is available, MeshGen can follow containment relations e.g. eliminates generation of extra inner contours around contained labels. The tool can optionally eliminate contained labels if their colour matches with the colour of their parent, e.g. when rendering or not rendering the meshes using their original colours would make no visual difference.

CutNII - Custom-angle slicer for brain atlas volumes

CutNII provides visualization and slicing of 3D isotropic image data (e.g. MRI) and atlases. The software features orthogonal views of the three standard planes (coronal, sagittal, horizontal) and a custom-angle slice cut through the volume. Slicing generates an arbitrarily positioned, oriented and sized rectangular sample from the dataset.

The main use of CutNII is to produce custom-angle atlas slices that match histological sections with non-standard cutting angles. In addition, custom slices allow inspection of anatomical features from non-conventional angles.

Multi-modality atlasing datasets to be provided with CutNII:

- Waxholm Space Atlas of the Sprague Dawley rat brain (T2* MRI, DTI, and delineations)
- Allen Mouse Brain Atlas reference atlas (grayscale Nissl volume reconstruction and delineations)

Time Encoding and Decoding Toolbox

Time Encoding Machines (TEMs) are asynchronous signal processors that encode analog information in the time domain. TEMs play a key role in modeling the sensing of the natural world by biological sensory systems.

Time Decoding Machines (TDMs) recover stimuli encoded as time (spike) sequences by TEMs. Assuming that Nyquist-type rate conditions are satisfied by the encoding architecture, arbitrarily precise stimulus recovery of time-encoded 1D and 2D (video) signals can be achieved.

The Time Encoding and Decoding Toolbox contains Python implementations of a selection of SISO, SIMO, and MIMO TEMs and corresponding TDMs that include encoding circuits utilizing classical neuron models (LIF, Hodgkin-Huxley, etc.), feedback, random thresholds, and/or Asynchronous Sigma/Delta Modulators. These can be used to develop new models of information encoding and processing in neural circuits.

ReproNim: A Center for Reproducible Neuroimaging Computation

ReproNim seeks to implement a shift in the way neuroimaging research is performed and reported. Through the development and implementation of technology that supports a comprehensive set of data management, analysis and utilization frameworks in support of both basic research and clinical activities, our overarching goal is to improve the reproducibility of neuroimaging science and extend the value of our national investment in neuroimaging research. Reproducibility is critical because the current literature is fraught with published results that are due to mistakes; or turn out to be false positive (contributed to by the lack of statistical power). More importantly, given the current publication system, it is exceedingly difficult to discern between false positive and true positive finding as data is hard to aggregate, and exact methods are hard to replicate.

ViMEAPy

Virtual MEA measurements in Python (ViMEAPy) is a Python toolbox for calculating the extracellular potential arising from a known current source distribution, in the in vitro slice setting, where a thin slice of neural tissue is immersed in saline on top of a Micro Electrode Array (MEA). For this experimental set-up, calculating the extracellular potentials arising from a known distribution of current sources is a non-trivial problem because of the heterogeneous conductivity of the system. We provide here a Python toolbox that facilitates such modelling by use of the Method of Images.

For specific details, validations, and usage, please go to the article "Modelling and Analysis of Electrical Potentials Recorded in Microelectrode Arrays (MEAs)" which is available here: http://link.springer.com/article/10.1007%2Fs12021-015-9265-6

Waxholm Space Atlas of the Sprague Dawley Rat Brain

Open access volumetric atlas of the Sprague Dawley rat brain. The delineations are defined in isotropic magnetic resonance (39 μm) and diffusion tensor (78 μm) images acquired ex vivo from an 80 day old male rat at the Duke Center for In Vivo Microscopy. Coordinates for navigating the volume are provided by the Waxholm Space coordinate system. The location of bregma and lambda are also identified as anchors towards stereotaxic space.

The atlas delineations and the MRI/DTI reference data are shared along with labels and configuration files for ITK-SNAP, the Mouse BIRN Atlasing Toolkit, and PMOD. The latest atlas version contains 222 structures. For more information, see MediaWiki.

The atlas has been adopted as the standard rat brain atlas for the EBRAINS infrastructure, see https://ebrains.eu/service/rat-brain-atlas/.

The atlas is shared under the CC BY 4.0 licence, see Citation for instructions on how to cite.

fMRI BOLD Signal Simulator

This library provides simulation tools for highly realistic resting state fMRI BOLD signal based on the scientific findings in a PhD dissertation. This library is intended to be used in scientific research, primarily in Neuroscience.

MicroDraw

MicroDraw (http://microdraw.pasteur.fr/) is a web application by NAAT to visualise and annotate collaboratively high resolution histology data. Annotations are vectorial, and you can use boolean operations to combine, subtract and split regions. Simply point MicroDraw to your own DeepZoom data!

brainspell

brainspell is the first open, human-curated, collaborative classification of the neuroimaging literature.

MetaSearch

MetaSearch (http://openneu.ro/metasearch/) is a search tool intended to help you find MRI data shared publicly on the Web. It is provided by the Open Neuroimaging Laboratory project as a companion to the BrainBox, a brain curation and annotation tool. If you want to see your data on MetaSearch, please let us know at https://github.com/OpenNeuroLab/metasearch/issues/new.

Open Neuroimaging Laboratory

OpenNeuroLab (http://openneu.ro) is a web framework for collaborative applications for neuroimaging. So far, we host BrainBox, MetaSearch, Brainspell and MindControl.

BrainBox

BrainBox is a web application developed by the Group of Applied and Theoretical Neuroanatomy – NAAT – for the Open Neuroimaging Laboratory Project. BrainBox allows you to visualise and segment collaboratively any brain MRI dataset available online. Segmentations are automatically saved and can be downloaded as Nifti files or triangular meshes. Point BrainBox to your own Nifti data, or try data catalogues created by the community. To access BrainBox, go to: http://brainbox.pasteur.fr/

Databrary

A video-based data library for behavioral science.

Multiscale Weighted Principal Component Regression

In MWPCR, we build an importance score weight matrix for the selection
of individual features at each location and a spatial weight matrix for the incorporation of the spatial pattern of feature vectors. We integrate the importance score weights with the spatial weights in order to recover the low dimensional structure of high dimensional features.

BluePyOpt

The Blue Brain Python Optimisation Library (BluePyOpt) is an extensible framework for data-driven model parameter optimisation that wraps and standardises several existing open-source tools.

It simplifies the task of creating and sharing these optimisations, and the associated techniques and knowledge. This is achieved by abstracting the optimisation and evaluation tasks into various reusable and flexible discrete elements according to established best-practices.

Further, BluePyOpt provides methods for setting up both small- and large-scale optimisations on a variety of platforms, ranging from laptops to Linux clusters and cloud-based compute infrastructures.

The main website of BluePyOpt is: https://github.com/BlueBrain/BluePyOpt

fNIRS Research Database

In the DOC tab, you can find 7 fNIRS datasets as well as the article that corresponds to them.

Umeå Brain Arteries

Umeå Brain Arteries (UBA24 and UBA167) are stereotactic atlases with probabilistic values describing the location of the main cerebral arteries.

Data collection is described in:
COBRA: A prospective multimodal imaging
study of dopamine, brain structure and function, and cognition, Nevalainen et al., Brain Research, 2015

neuroVIISAS

Functions of neuroVIISAS:
1) Generation of terminologies from lists of regions up to complex ontologies.
2) Linking external databases to nodes of ontologies
3) Define and work with competing, developing or variable terminologies
3) Defining stereotaxic coordinate systems
4) Import stacks of images: atlas contour images, histological, MRI, DTI, virtual slides
5) Definition of relations between ontologies and images
6) Import lists of neuronal connections
7) Visualizing sources and targets of connections in stereotaxic atlases
8) Nested 3D-atlas visualizations
10) Hierarchy based connectome navigation
11) Matrix visualizations of hierarchical and non-hierarchical connectomes
12) Interactive connectome exploration
13) Differential connectomics
14) Global and local connectome analysis
15) Multivariate and graph theoretical analysis and visualization
16) Phyton script generator for population simulations using atlas and connectome data.
17) Numerical and 2D-, 3D-simulation visualizations

FADTTSter

FADTTSter is a user-friendly version of FADTTS directed to users without advanced coding skills. FADTTS is Matlab (MathWorks Inc, MA, USA) based and needs Matlab coding knowledge to operate. FADTTSter was created to overcome this issue and make the statistical analysis accessible to any non-technical researcher. FADTTSter is a command line based module as well as an GUI based tool. Not only is FADTTSter practical but it enables any investigator to perform DTI analysis efficiently.

In addition, FADTTSter performs fiber profile QC as well as customized plotting of the FADTTS output

This tool can be divided in two main parts, each one working independently: a) Profile QC and Matlab script generation and matlab run for FADTTS, b) Statistical data plotting for FADTTS output.

Population-averaged diffusion tensor imaging atlas of the Sprague Dawley rat brain

The rat atlas can be downloaded in different data formats:
1. Mat-file: a single mat-file - ratatlas.m - contains all data, and is structured as follows:
DWI: Cell array containing all 13 diffusion weighted images
DT: Cell array containing the 6 independent diffusion tensor elements
FE: First eigenvectors
eigval: Three eigenvalues of the diffusion tensors
FA: Fractional anisotropy map
FEFA: FE map scaled by the FA map.
VDims: Voxel dimensions
g: Diffusion gradient directions
bval: Diffusion gradient strength
labels: Label field
T1: T1 image

2. Nifti: all diffusion weighted images, the T1 image, and label field are saved as individual nifti files (DWI_0..13.nii, T1.nii, and labels.nii). Diffusion gradient information has been stored in grad.txt, while the delineated anatomical structures are enumerated in labels.txt.

3. Amira: the label field is also provided in Amira format, labels.am. This file can be used for further segmentation, or (3D) visualization in Amira.

Ogles - OpenGL/OIV Stereotactic Tool

Ogles2 is an interactive slice and volume visualization and analysis tool based on Open Inventor/Coin3D. Ogles2 allows for reproducing the work flow of frame based stereotactic neurosurgery.
Ogles2 main focus is to combine slice derived volume data with surface meshes in the context of stereotactic neurosurgery. It allows for transformation of 3D brain atlas data to individual brain configurations and to estimate the effects of electrical fields of deep brain stimulation (DBS) electrodes.

Zebrafish heart segmentation

The package aims to perform image segmentation of the adult zebrafish cardiac ventricle obtained by light-sheet fluorescent imaging (also known as single plane illumination microscopy).

Please refer to the following article for the package:
Rene Packard, Kyung In Baek, Tyler Beebe, Nelson Jen, Yichen Ding, Feng Shi, Peng Fei, Bong Jin Kang, Po-Heng Chen, Jonathan Gau, Michael Chen, Jonathan Tang, Yu-Huan Shih, Yonghe Ding, Debiao Li, Xiaolei Xu, and Tzung Hsiai, "Automated Segmentation of Light-Sheet Fluorescent Imaging to Characterize Experimental Doxorubicin-Induced Cardiac Injury and Repair". Scientific Reports, in press, 2017.

SIVIC

SIVIC is an open-source, standards-based software framework and application suite for processing and visualization of DICOM MR Spectroscopy data. Through the use of DICOM, SIVIC aims to facilitate the application of MRS in medical imaging studies. Additional project information, binary downloads, documentations and tutorials are available at (https://sourceforge.net/projects/sivic/).

MONSTR Multi Contrast Brain Stripping

MONSTR (Multi-cONtrast brain STRipping) is a software tool to generate brainmasks (or skull-strip) from multi-contrast MR brain images, such as T1, T2, PD, or FLAIR. Compared to other T1-based skullstripping methods, MONSTR can take multi-modal inputs, and based on multi-modal atlases, it generates a brainmask of a given subject image.

It is a patch-based method. Multiple atlas images are first registered to a subject. Then based on the similarities between multiple atlas patches and a given subject patch, the corresponding label (i.e. brain or non-brain) of the subject patch is estimated.

In addition to the software, we also provide 3 sets of atlas images, having 17 subjects in total. Each atlas contains T1, T2, (FLAIR for one set), and their manually delineated brainmasks.

Elastic Net-based Parcellation (ENPA)

Parcellating brain regions into functionally homogeneous subdivisions is critical for understanding normal and abnormal brain functions. Hence, functional parcellation techniques have recently gained further research momentum.
The toolbox is for parcellation of brain regions with fMRI data using regularized sparse representation method, and consists of parcellation modules at group and individual levels.

SkullStrippingToolkit

A matlab-based code for skull stripping on infant and adult MR images.

Please refer to below papers for details:

"LABEL: Pediatric Brain Extraction Using Learning-based Meta-algorithm", Neuroimage 62(3):1975–1986, Sep. 2012. [Feng Shi, Li Wang, Yakang Dai, John H Gilmore, Weili Lin, Dinggang Shen]

AtlasConstructionToolkit

Atlas Construction Toolkit Using Sparse Representation

Please refer to below papers for details:

"Infant Brain Atlases from Neonates to 1- and 2-year-olds", PLoS ONE, 6(4): e18746, Apr. 2011. [Feng Shi, Pew-Thian Yap, Guorong Wu, Hongjun Jia, John H. Gilmore, WeiliLin, Dinggang Shen]

"Neonatal Atlas Construction Using Sparse Representation”, Human Brain Mapping 35(9):4663–4677, 2014. [Feng Shi, Li Wang, Guorong Wu, Gang Li, John H. Gilmore, Weili Lin, Dinggang Shen]

CBICA: Cancer Imaging Phenomics Toolkit (CaPTk)

The Cancer Imaging Phenomics Toolkit (CaPTk) is a comprehensive imaging analytics suite providing algorithms that produce extensive panels of quantitative imaging features. Applications in CaPTk use those imaging features in diagnostic and predictive models to support optimized, personalized treatment planning.

The software has been designed for research purposes only and has not been reviewed or approved by the Food and Drug Administration or by any other agency. It is not intended or recommended for clinical applications.

NOTE: Our NITRC page is only used to distribute binaries; for issues and other communication, please see our GitHub page at github.com/cbica/captk.

Lead-Connectome

Lead-Connectome is a fully equipped structural-functional connectome processing toolbox. It features several modern fiber-tracking algorithms that support single- and multishell (~DSI) data and state-of-the art nonlinear deformation algorithms. It operates in the Matlab development environment and is part of the Lead Neuroimaging Suite. As such, it seamlessly integrates with the Lead-DBS toolbox.

Deformable Template Model for Brain Analysis

The objective of the project is to develop computational shape models of brain structures for shape analysis. We develop a deformable template model framework based on “progressive surface deformation” method (Kim et al, IEEE-TMI: 34(6), 2015) to reconstruct target shapes with point-wise correspondence to template models robustly by minimizing the distortion of template models. Our toolbox contains a set of tools for constructing mean images, reconstructing individual surface models, computing average surface and finally computing local deformity.

This toolbox was developed by KAIST and University of Edinburgh (http://cgv.kaist.ac.kr/brain)

Brainance DTI Module

Brainance DTI Module is a web-based highly accurate and user friendly fiber tracking software created by Advantis Medical Imaging. The fiber tracking methodology developed and implemented achieves extremely high accuracy rates (90%) in the final 3D reconstruction of the nerve fibers, compared to the existing deterministic methodologies which do not exceed 65%. Create an account through our website (http://advantis.io/) and start using our one-month free trial!

Standard Amygdalar fMRI Probe Tasks

This resource provides MRI data on 32 control subjects who have been imaged with 4 standard amygdala mapping tasks.

UNC CEDARS INFANT ATLAS

We derived the first set of normative functional brain atlases for infants (neonates, 1- and 2-year olds) by parcellating the infant brain into functionally homogeneous regions based on a novel hybrid iterative normalized cut approach. Infant-specific structural (i.e., AAL) atlases were used as spatial constraint.

Please refer to the following article for the atlases:
Feng Shi, Andrew P. Salzwedel, Weili Lin, John H. Gilmore, Wei Gao, "Functional Brain Parcellations of the Infant Brain and the Associated Developmental Trends", Cerebral Cortex, 2017. https://doi.org/10.1093/cercor/bhx062

Axonal Beading and Microtubule Quantification

Interactive image analysis software for quantifying axonal beading morphology and focal microtubule loss using phase and fluorescent images of a neuron.

Purpose: Focal axonal beading and focal disruption of microtubule structure are characteristic to traumatic axonal injury. We reproduced these morphological and structural changes in our in vitro model system [Kilinc, Gallo, Barbee, 2008. Exp. Neurol. 212:422–430]. In order to measure bead formation objectively, an observer-independent quantification of beading was necessary. In addition, a quantitative measure for the extent of co-localization of axonal beads and microtubule disruptions was required to establish a causal relationship between focal cytoskeletal damage and bead formation. In this paper we describe Matlab-based, interactive image analysis programs for axonal beading quantification and co-localization analysis. Injury-induced increases in the axonal beading could be successfully detected using the bead analysis program.

CIVILITY : Cloub based Interactive Visualization of Tractography Brain Connectome

Cloud Based Interactive Visualization of Tractography Brain Connectome (CIVILITY) is an interactive visualization tool of brain connectome in the web.
CIVILITY is a web application and has mainly 2 components.

-CIVILITY-visualization ; front end of the application. This is a circle plot of the brain connectivity using the method of visualization : Hierarchical Edge Bundling. The graphic visualization of the brain connectivity is generated using Data Driven Documents (D3.js).

-CIVILITY-tractography ; analysis pipeline. The analysis of the brain connectome is computed with a probabilistic tractography method (FSL tools : bepostx and probtrackx2) using surfaces as seeds.

CIVILITY performs the brain connectivity analysis in remote computing grids where the CIVILITY-tractography pipeline is deployed. CIVILITY uses clusterpost (https://github.com/NIRALUser/clusterpost) to submit the jobs to the computing grid. The front end of CIVILITY submits tasks to clusterpost and retrieves results when they are finished.

A 3T probabilistic Locus Coeruleus atlas

This atlas takes advantage of in vivo high resolution TSE 3T structural MRI. The atlas includes a probability map of the Locus Coeruleus based on healthy young subjects. The subjects were scanned twice to test the test-retest reliability of the identification of the Locus Coeruleus.

A description of the MRI scan parameters, segmentation protocol, and registration protocol to MNI space can be found in:
Tona, K-D., Keuken, M.C., de Rover, M.,Lakke, E., Forstmann, B.U., Nieuwenhuis, S., van Osch, M.J.P. (accepted). In vivo visualization of the locus coeruleus in humans: Quantifying the test-retest reliability. Brain, Structure and Function

epi-example-dicom

Contains an example echoplanar session, in Dicom format, and a python script (extract_timings.py) to extract the times of slice acquisition.

ENIGMA-Viewer: Visualizing Effect Sizes in Meta-Analysis

ENIGMA-Viewer is an interactive visualization tool for neuroscientists to examine DTI data and compare effective sizes and associated genetics information in meta-analysis. The program supports the visualization of sub-cortical and cortical regions. The user may upload your own data for comparison.

Single-index Varying Coefficient Model for Functional Responses

The aim of this tool is to implement a functional analysis pipeline, for the joint analysis of functional data and clinical data, for example age, gender and disease status. SIVC consists of a functional single index for characterizing the association of functional response with covariates of interest by incorporating complex spatial-temporal correlation structure, an efficient method for spatially smoothing varying coefficient functions, an estimation method for estimating the
spatial-temporal correlation structure, a simultaneous confidence band for quantifying the uncertainty in the estimated coefficient functions.

Evaluation of automated brain extraction methods on multi-site multi-disorder data

This project was the largest evaluation ever conducted of automated brain extraction methods. The code, data, and results are accessible via Synapse: https://www.synapse.org/#!Synapse:syn3207674/wiki/160612

Brain registration evaluations (2008-2011)

This project contains four human brain MR image registration evaluation projects from 2008 to 2011, including the largest ever conducted. They are accessible via Synapse: https://www.synapse.org/#!Synapse:syn3207152/wiki/160605

A robust method for correcting partial volume effect in ASL data

Partial volume correction using modified least trimmed squares (PVC-mLTS) is a software toolbox that can be employed to correct partial volume effect for single inversion-time ASL data.

The software:
(1) Reading and writing images in nifti format;
(2) Implementing Modified least trimmed squares method (mLTS)
(3) Applying mLTS to ASL data for correcting partial volume effect
(3) Computing CBF maps for both GM and WM.

The method is described in the following paper:
Xiaoyun Liang, Alan Connelly, Fernando Calamante. Improved partial volume correction for single inversion time arterial spin labeling data. Magn Reson Med. 2013 Feb;69(2):531-7. doi: 10.1002/mrm.24279.

Measuring eye states in functional MRI

An automized tool to segment eye bulbs and measure movements and eye state.

Non redundant connectivity (NRC) maps

Non-Redundant Connectivity (NRC) maps are summary maps of functional connectivity levels in the brain, estimated from fMRI datasets. They are similar to weighted Global Brain Connectivity (GBC) maps (Cole et al. 2010). However, NRC is based on estimates of multiple correlations instead of bivariate correlations as in GBC, allowing the exploration of the multivariate nature of brain connectivity. To avoid dimensionality problems it applies the method of Supervised Principal Components (Bair et al. 2006).

To run a NRC analysis you can use nrcon, a 64 bit linux executable that can be downloaded from www.neuroimagen.es/webs/nrcon/

nrcon also calculates GBC and Degree Centrality (DC) images. A full description of the method is available in:

Non redundant functional brain connectivity in schizophrenia.
R. Salvador, R. Landín-Romero, M. Anguera, E. J. Canales-Rodríguez, J. Radua, A. Guerrero-Pedraza, S. Sarró, T. Maristany, P.J. McKenna, E. Pomarol-Clotet.
Brain Imaging Behav. 2016 Mar 21

Brainnetome DiffusionKit

This page is no longer maintained and please visit http://diffusion.brainnetome.org .

Statistical Methods for Manifold-valued Data

The aim of this package is to develop several statistical methods for manifold-valued data. Mixture of offset-normal shape factor analyzers (MOSFA) toolbox is a penalized model-based clustering framework to cluster landmark based planar shape data.

Statistical Methods for Heterogeneous Neuroimaging Data

The aim of this package is to present several statistical analysis pipelines for heterogeneous neuroimaging data. The Gaussian hidden Markov model (GHMM) toolbox is for dealing with the spatial heterogeneity of cartilage progression across both time and subjects. To estimate unknown parameters in GHMM, we employ a pseudo-likelihood function and optimize it by using an expectation-maximization (EM) algorithm. The proposed model can effectively detect diseased regions in each OA subject and present a localized analysis of longitudinal cartilage thickness within each latent subpopulation.

fNIRS-toolbox

This toolbox is a set of Matlab based tools for the analysis of functional near-infrared spectroscopy (fNIRS) developed by T. Huppert at the University of Pittsburgh

fMRI Results Comparison Library

Exported results from 28 variants of an fMRI analysis, conducted in AFNI, FSL, and SPM, carried out on the OpenfMRI BIDS-compliant ds000011 dataset.

HFH_DTI_TLE_TOOLS

The shared MATLAB codes contains:
1-Preprocessing Step including: Extraction of DWIs from Dicom images, calculation of tensor and diffusion maps, seed insertion or ROI depiction utility, MATLAB toolbox and instruction for segmentation of brain structures.
2-Uncertainty Analysis of hippocampal volume, FLAIR intensity, and MD; and cingulate and forniceal FA, for Lateralization of temporal lobe epilepsy.
3-DTI-based Response-Driven Modeling of mTLE Laterality using FA in Cingulum, Fornix, and Corpus Callosum.
Please cite the related articles for using the segmentation of Cingulum, Fornix, and Corpus Callosum, uncertainty analysis or DTI-based TLE modeling MATLAB code and its relevant image modalities (DTI, T1, and FLAIR).

HFH_DTI_DATA

The shared dataset consists of T1, FLAIR, and DTI images for 24 Temporal Lobe Epilepsy (TLE) patients along with their clinical description as described in the following:

1- T1
2- FLAIR
3- Diffusion Tensor Imaging (DTI)
4- Apparent Diffusion Coefficient (ADC)

The T1 folder includes the following image sets:
1- Skull-Stripped T1 images
2- Manually segmented hippocampi
3- Brain segmented regions by FreeSurfer

The FLAIR folder includes:
1- Skull-Stripped FLAIR
2- Skull-Stripped T1 co-registered to the FLAIR
3- Manually segmented hippocampi co-registered to the FLAIR

The DTI folder includes the following image sets:
1- Diffusion Weighted Images (DWIs)
2- Fractional Anisotropy (FA)
3- Apparent Diffusion Coefficient (ADC)
4- Axial and radial diffusivity maps (AD & RD)
5- Geometry Shapes and the Eigen Value images of the tensor

The ADC folder includes:
1- ADC map images
2- Skull-Stripped T1 co-registered to the ADC map.
3- Manually segmented hippocampi co-registered to the ADC map.

Progression Score Model Toolkit

Progression Score Model Toolkit is a Matlab-based cross-platform statistical software that allows for the characterization of the temporal trajectories of voxelwise imaging measures from longitudinal data. The method can be applied to a variety of images including PET radiotracer binding maps, thickness maps, functional connectivity in fMRI maps, FA/MD maps, and determinant of Jacobian maps.

The toolkit provides code for visualization of the estimated trajectories as a movie. The method also computes a summary score for each scan that indicates its position along the estimated trajectories. These summary scores can be used to explore associations with external variables such as genetic factors, disease risk scores, etc.

Please cite:
Murat Bilgel, Jerry L. Prince, Dean F. Wong, Susan M. Resnick, Bruno M. Jedynak, "A multivariate nonlinear mixed effects model for longitudinal image analysis: Application to amyloid imaging", NeuroImage (2016), DOI: 10.1016/j.neuroimage.2016.04.001.

SLIC: a whole brain parcellation toolbox

The SLIC toolbox contains five whole brain parcellation approaches that operates on resting-state fMRI data. Three of them are reproduced from the Ncut-based approaches in (Craddock et al., 2012, HBM) and (Shen et al., 2013, Neuroimage). The remaining two are the mean SLIC and two-level SLIC approaches that integrate Ncut and SLIC.

The release includes a demo which reproduces the experiments in the paper (https://doi.org/10.3389/fnhum.2016.00659), and atlases generated based on 190 subjects from the Beijing_Zang dataset. Matlab scripts, a set of test data and some instructions are included in the release.

CWL EEG fMRI Data Set

EEG/fMRI Data from 8 subject doing a simple eyes open/eyes closed task is provided on this webpage.

The EEG/fMRI data are six files for each subject, with two basic factors: recording during Helium pump On and Helium pump Off, and recording during MRI scanning and without MRI scanning. In addition 'outside' EEG data is provided, before as well as after the MRI session.

There are 30 EEG channels, 1 EOG channel, 1 ECG channel, as well as 6 CWL signals. A tool to use the CWL signals for regression of artifacts can be found here: https://www.nitrc.org/projects/cwl_eeg_fmri/

See also the “Eyes Open – Eyes Closed” EEG/fMRI data set including dedicated “Carbon Wire Loop” motion detection channels paper linked here: http://www.sciencedirect.com/science/article/pii/S2352340916301056

HistoloZee - 3D histology reconstruction and co-registration with MRI

HistoloZee is a tool that integrates histology reconstruction, MRI co-registration, and manual segmentation tools in an easy-to-use and intuitive interface. HistoloZee permits real-time interaction with complex and large (multi-GB) histology datasets during the co-registration steps of histology reconstruction. Please see our website (http://picsl.upenn.edu/software/histolozee/) and the series of tutorial videos on YouTube: https://www.youtube.com/playlist?list=PL68v8FP_IVlg2tCgJXrsO3UHve1q9wLjI

HistoloZee was developed by Daniel Adler and Paul Yushkevich. The current release of HistoloZee (v.03) runs only under Mac OS X, version 10.7 or later.

Computational Anatomy Toolbox - CAT

CAT is a an extension to SPM12 (Wellcome Department of Cognitive Neurology) to provide computational anatomy. This covers diverse morphometric methods such as voxel-based morphometry (VBM), surface-based morphometry (SBM), deformation-based morphometry (DBM), and region- or label-based morphometry (RBM).

Serotonin Transporter HighResolution PET Template

This PET template is the result of a recent study where we designed and validated an user-independent approach for a detailed in vivo quantification of serotonin transporter (5-HTT) availability in the human brainstem using a template-based approach.
The template was generated with 3T-MR images and parametric binding potential (BPND) [11C]MADAM images of ten healthy subjects. Afterward on this template 5 brainstem regions of interest (Dorsal, Median and Caudal Raphe, Ventral Midbrain and Superior Colliculi) were delineated with a 3D Erosion approach.

Epilepcy Neuroimaging Data

The shared image modalities and their relevant clinical information are for 12 TLE patients.

Please cite the following article if you use the uncertainty Analysis MATLAB code and its relevant image modalities (T1, FLAIR, and DTI):

Mohammad-Reza Nazem-Zadeh, Kost V Elisevich, Jason M Schwalb, Hassan Bagher-Ebadian, Fariborz Mahmoudi, Hamid Soltanian-Zadeh. Lateralization of Temporal Lobe Epilepsy by Multimodal Multinomial Hippocampal Response-Driven Models. Journal of the neurological sciences, 347 (1), 107-118, 2014.

Please cite the following article if you use T1, FLAIR, and SPECT modalities:

Mohammad-Reza Nazem-Zadeh, Jason M Schwalb, Kost V Elisevich, Hassan Bagher-Ebadian, Hajar Hamidian, Ali-Reza Akhondi-Asl, Kourosh Jafari-Khouzani, Hamid Soltanian-Zadeh. Lateralization of temporal lobe epilepsy using a novel uncertainty analysis of MR diffusion in hippocampus, cingulum, and fornix, and hippocampal volume and FLAIR intensity. Journal of the neurological sciences, 342 (1), 152-161, 2014.

A probabilistic subcortical atlas for PD

This atlas takes advantage of ultra-high resolution 7T MRI to provide unprecedented levels of detail on structures of the basal ganglia in-vivo. The atlas includes a disease-specific probability map of the subthalamic Nucleus based on Parkinson's Disease patients.

When using these masks please cite: Anneke Alkemade, Gilles de Hollander, Max C Keuken, Andreas Schäfer, Derek VM Ott, Johannes Schwarz, David Weise, Sonja A Kotz, Birte U Forstmann (2017). Comparison of T2* and QSM contrasts in Parkinson’s disease to visualize the STN with MRI. PLOS one

POS2SPM

POS2SPM is a MATLAB-based conversion and formatting tool that can be applied to a number of different probe arrays and channel configurations using either 1 or 2 POS measurement files from an electromagnetic 3D digitizer. This conversion tool outputs the appropriately formatted files necessary to complete spatial registration of functional near-infrared spectroscopy (fNIRS) data with the NIRS-SPM toolbox (SPM5 or SPM8), the SPM-fNIRS toolbox (SPM12), or both.

CBICA: Tutorials (Image Processing and Machine Learning) using CPP, ITK, etc.

DEPRECATED: Please visit https://github.com/CBICA/Tutorials

Welcome to CBICA’s C++ learning resource.

Here, we will be showcasing our seminar series “CPP for Image Processing and Machine Learning” including presentations and code examples.

There are image processing and machine learning libraries out there which use C++ as a base and have become industry standards (ITK for medical imaging, OpenCV for computer vision and machine learning, Eigen for linear algebra, Shogun for machine learning). The documentation provided with these packages, though extensive, assume a certain level of experience with C++. Our tutorials are intended for those people who have basic understanding of medical image processing and machine learning but who are just starting to get their toes wet with C++ (and possibly have prior experience with Python or MATLAB).

Lupus patients with white matter lesions

Data set consisting of 20 Lupus patients (T1w, FLAIR, FLAIR corrected, WML). The correction of the FLAIR images consists of the skull removal and the intensity correction (anisotropic filter and bias correction).

This data set has been used for experimental purposes evaluating an automated approach for the detection of Lupus WML in MRI images. Lupus WML appear as small focal abnormal tissue observed as hyperintensities in the FLAIR images. The quantification of these WML is a key factor for the stratification of lupus patients and therefore both lesion detection and segmentation play an important role. Therefore, this resource can be used either for reproducibility of the results presented in the work mentioned above, or for testing new approaches with similar purposes.

SegEditing

SegEditing is a segmentation editing tool using existing labels as references. It is useful when you would like to correct large errors with a few user interactions such as dots or rough scribbles using one or multiple reference labels of the target object. This toolbox includes an image viewer, graphical user interface, and semi-automatic algorithms for segmentation. The tool can support the following functions: Open medical images with various formats (such as mhd, hdr, nii), control intensity range in visualization, zoom in/out, show segmentation results, load and save user interactions and segmentation results, and so on. The software is developed by the IDEA group at the University of North Carolina at Chapel Hill (https://www.med.unc.edu/bric/ideagroup).

Windows operating system is required to run it. The executable file was compiled on Windows 7 (64 bit). The user interfaces and overall framework of the software were implemented in C++. Parallelization technologies were also used to speed up.

CBICA: Identification of Sparse Connectivity Patterns in rsfMRI (SCPLearn)

This software is used to calculate Sparse Connectivity Patterns (SCPs) from resting state fMRI connectivity data. SCPs consist of those regions whose between-region connectivity co-varies across subjects. This algorithm was developed as a complementary approach to existing network identification methods.

SCPLearn has the following advantages:

Does not require thresholding of correlation matrices
Allows for both positive and negative correlations
Does not constrain the SCPs to have spatial/temporal orthogonality/independence
Provides group-common SCPs and subject-specific measures of average correlation within each SCP
Can be run within a hierarchical framework to get "primary" (large spatial extent) and "secondary" level (small spatial extent) SCPs

Subject-level coefficients can be used for subsequent group-level analysis.

CBICA: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters (MUSE)

MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection

MUSE generates a large ensemble of candidate labels in the target image space using multiple atlases, registration algorithms and smoothness values for these algorithms. The ensemble is then fused into a final segmentation.

MUSE is a Linux command-line tool, with support for several HPC schedulers or the ability to run as multiple processes on a single workstation.

CBICA: Multi Atlas Skull Stripping (MASS)

Multi Atlas Skull Stripping (MASS) [ARAD2013], is a software package designed for robust and accurate brain extraction, applicable for both individual as well as large population studies.

MASS is implemented as a Unix command-line tool. It is fully automatic and easy to use — users input an image, and MASS will output the extracted brain and the associated brain mask.

CBICA: Laboratory for Individualized Breast Radiodensity Assessment (LIBRA)

The Laboratory for Individualized Breast Radiodensity Assessment (LIBRA) is a fully-automatic breast density estimation solution based on a published algorithm that works on either raw (i.e., “FOR PROCESSING”) or vendor post-processed (i.e., “FOR PRESENTATION”) digital mammography images from two vendors: GE Healthcare and Hologic. LIBRA has been applied to over 70,000 screening exams and is being increasingly utilized in larger studies.

LIBRA has two modes of operations:

1. An easy-to-use interactive mode with Graphical-User-Interface where the user is prompted to select either a single DICOM image or a folder of DICOM images, an output folder for the results, and whether they wish to save intermediate files.
2. A command-line interface amenable to batch processing and scripting, where the user can explicitly define the input and output paths.

For more info click Homepage in the left panel.

CBICA: Geodesic graph-based segmentation with shape priors (GraSP)

GraSP is a graph-based parcellation software.
GraSP was initially developed for parcellating the cortex into functionally coherent regions, based on their Pearson correlation, that presents different cortical parcellations of the left hemisphere. However, the software can handle any graph, and 3D parcellations can be obtained easily.

CBICA: GLioma Image SegmenTation and Registration (GLISTR)

GLioma Image SegmenTation and Registration (GLISTR) is a software package designed for simultaneously segmenting brain scans of glioma patients and registering these scans to a normal atlas.

Some typical applications of GLISTR include,

Labeling entire brain regions of glioma patients;
Mapping gliomas into the healthy atlas space;
Estimating parameters of the tumor growth model.

GLISTR is implemented as a command-line tool. It is semi-automatic and requires minimal user initializations. Users could use the visual interface called BrainTumorViewer to easily make initializations and a script for the execution. As a results, GLISTR will output a label map, a mapping between atlas and input, tumor parameters, etc.

CBICA: Brain Tumor Viewer (BTV)

BrainTumorViewer has been superceded by the Cancer Imaging Pheonmics Toolkit (CaPTk) Console: https://www.nitrc.org/projects/captk/

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BrainTumorViewer (BTV) is a lightweight viewer, built for fast and simple interaction with MRI image volumes.

BTV is primarily designed for visualizing multi-modal MRI brain volumes and initializing seed-points for the following software packages:

i) GLioma Image SegmenTation and Registration (GLISTR)
ii) Pre-Operative and post-Recurrence brain Tumor Registration (PORTR)

After initializing these seed-points, a user can generate execution scripts through BTV's graphical user interface for various platforms and custom input parameters.

Local Manifold Learning for Multi-atlas segmentation

LMLMS(Local Manifold Learning for Multi-atlas segmentation) employs manifold learning to determine spatially local weights for label fusion in a low-dimensional coordinate space. It's based on paper "Local manifold learning for multiatlas segmentation: application to hippocampal segmentation in healthy population and Alzheimer's disease"

Hadoop for data colocation

Data colocation via Hadoop and Hbase custom region split policy.

ICA Components from Kessler, et al, JAMA Psychiatry 2016

This dataset includes 15 unthresholded ICA connectomic components and the grid parcellation from Kessler et al “Growth Charting Brain Connectivity Networks and the Identification of Attention Impairment in Youth”. This release can be used to extract component expression scores for new datasets (see manuscript for methodological details). These expression scores can then be age-corrected and used to predict attention-related phenotypes.

MENGA: Multimodal Environment for Neuroimaging and Genomic Analysis

MENGA is a platform for the integration of imaging data and Allen Human Brain Atlas mRNA data. MENGA aims to provide a comprehensive environment to investigate correlation patterns between various imaging modalities and gene expression profiles based on the Allen Brain Atlas.

The project comes from a collaboration between the Department of Information Engineering (DEI), University of Padova with the Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College, London (UK).

For any enquiries, please contact Gaia Rizzo, Mattia Veronese or Paul Expert.

Software: http://fair.dei.unipd.it/require-software/

We have now published the reference paper of MENGA on PlosOne, in which all the aspects of the software and its functionalities are described in details: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0148744

Simulated Diffusion-Weighted Datasets

Simulated datasets generated using the framework described in the paper 'Realistic simulation of artefacts in diffusion MRI for validating post-processing correction techniques'. These can be used to validate your postprocessing correction techniques.

Data containing susceptibility artefacts from the paper 'Quantitative assessment of the susceptibility artefact and its interaction with motion in diffusion MRI' is now also available for download.

Code to simulate your own datasets can be found at:
https://github.com/marksgraham/DW-POSSUM

Available files:
-SimulationFiles.zip contains DWI data with eddy-current and motion artefacts
-Susceptibility datasets (static) contains DWIs with the susceptibility artefact
-Susceptibility datasets (dynamic) contains data with the susceptibility artefact and motion (NB motion affects the susceptibility field)
-Susceptibility datasets (other) contains a mask, T2-weighted images and a field-map for the static susceptibility data.

Estimating Functional Brain Network based on Modularity Prior

This is the source code of a new method for estimating functional brain networks. The method is based on sparse and low-rank representation for incorporating modularity prior into brain network construction.

Parallel Graph-theoretical Analysis Toolkit

PAGANI Toolkit is a complete software package which can implement all process including brain network construction and network analysis for fMRI data. A CPU-GPU hybrid framework is adopted to make PAGANI Toolkit faster in network analysis.Our PAGANI Toolkit has low hardware requirements for only a PC with a CUDA available GPU. Compared with PC clusters, it’s easier to set up and has comparable or better performance for human brain network analysis.

A joint sparse partial correlation method for estimating group functional networks

Joint graphical models combined with stability selection (JGMSS)is a software toolbox that can be employed to robustly estimate both individual- and group-level sparse networks. The software:
(1) Reads time series from a group of subjects;
(2) Subsample data 100 times to estimate stability (i.e. stability
selection);
(3) Group graphical-lasso constraints are applied, including 2
regularization parameters;
(4) Ranges of regularization parameters are chosen to implement stability
selection;
(5) The alternating direction method of multipliers (ADMM) approach is
employed to solve the problem.

The Method is described in the following paper:
Xiaoyun Liang, Alan Connelly, Fernando Calamante. A novel joint sparse partial correlation method for estimating group functional networks. Human Brain Mapping 12/2015; DOI:10.1002/hbm.23092.

brainGraph - Graph Theory Analysis of Brain MRI Data in R

"brainGraph" is an R package for performing graph theory analyses of brain MRI data. It is most useful in atlas-based analyses (e.g., using an atlas such as AAL, or one from Freesurfer); however, many of the computations (e.g., the GLM-based functions and the network-based statistic) will work with any graph that is compatible with igraph. The package will perform analyses for structural covariance networks (SCN), DTI tractography (I use probtrackx2 from FSL), and resting-state fMRI covariance (I have used the Matlab-based DPABI toolbox).

In addition to general network operations (available through the R package "igraph"), there is code to perform: bootstrapping, permutation tests, random graph generation, small-worldness/efficiency, robustness/attack analysis, rich-club analysis, and more. There is a GUI for quick data viewing and exploration.

The "Docs" link here on the NITRC page will take you to the User Guide (PDF link) which has detailed information and code blocks examples.

EyeBallGUI

EyeBallGUI is a tool for interactively viewing & marking multi-channel biosignals

Please cite using:
https://doi.org/10.1101/129437

[1] K. Mohr, B. Nasseroleslami, P. M. Iyer, O. Hardiman, and E. C. Lalor, ‘EyeBallGUI: A Tool For Visual Inspection And Binary Marking of Multi-Channel Bio-Signals’, bioRxiv, p. 129437, May 2017.

rda2nifti

rda2nifti - useful MATLAB script for conversion from Siemens RDA file to NIFTI mask
Dependencies: NIfTI_tools by Jimmy Sheen, MATLAB
Credits to the author of read_rda102 function which has been already included in the ZIP file.

3T DWI test-retest reliability dataset

This data-set contains all 3T MRI data used in Boekel et al., (submitted), in which we aimed to investigate the invivo test-retest reliability of several DTI derived measures.

34 healthy young subjects were scanned twice on the same day with a 3T MRI scanner in which the following data was acquired:
- T1 weighted whole brain scan (1mm isotropic).
- B0 weighted whole brain scan (2mm isotropic)
- Diffusion weighted whole brain scan (2mm isotropic, 32 directions, b-value=1000, 4 repetitions)

A subset of 15 subjects were also scanned a third session on the same day, as well as in a two-week follow-up session with the same MRI protocol.

The full reference of the article is:
Boekel, W., Forstmann, B. U., Keuken, M.C. (In press) A test-retest reliability analysis of diffusion measures of white matter tracts relevant for cognitive control. Psychophysiology.

Task-based fMRI atlases

The 200 ROI Task-based fMRI atlas and 222 ROI Task-based fMRI atlas are fully labeled, whole brain atlas derived via spatially constrained n-cut parcellation of both task-based and resting-state fMRI data. (James et al, Magn Reson Imaging, in press; PMID 26523655). Atlases is provided in NIFTI format. Anatomic labels are provided in BrainViewer format and comma-delimited text format.

Emory-UNC Developmental Macaque Structural and DTI Atlases

This is a set of cross-sectional structural atlases as well as cross-sectional and longitudinal DTI atlases of a cohort of 40 rhesus macaques scanned longitudinally at ages 2 weeks, 3 months, 6 months, 12 months, and 18 months.

Gibbsconnectome

The Gibbs' connectome is a set of ready-processed high-dimensional structural and functional connectivity matrices and group fibertract information of 169 subjects. The data is based on the openly available NKI enhanced Rockland sample (http://fcon_1000.projects.nitrc.org/indi/enhanced/) which represents a state-of-the-art community sample. To estimate a group connectome of fiber tracts, the Gibbs' tracking algorithm has been applied to each subjects dMRI data. Fiber tracts have been normalized into standard space using DARTEL.
Explicit methodology of the data set is described here:
http://www.ncbi.nlm.nih.gov/pubmed/26327244

The role of V6 in the interaction between dorsal and ventral streams in near and far space processing

Using functional MRI, aimming at investigated the neural interface which integrated information between the dorsal and the ventral streams. The results showed that the parietal-occipital junction (POJ) and bilateral superior occipital gyrus (SOG) showed relative increased activity when responded to a target presented in near space than in far space, which was independent of the retinotopic or perceived size of the target. Furthermore, the POJ showed enhanced functional connectivity with both the dorsal and ventral streams during far space processing irrespective of the target size, supporting the role of the POJ as an interface between the two streams. In contrast, the SOG showed enhanced functional connectivity only with the ventral stream if retinotopic sizes of the targets in near and far space were matched, which suggested the functional dissociation between the POJ and SOG.

ANIMA

ANIMA (the Archive of Neuroimaging Meta-analyses) is an online repository of meta-analytic neuroimaging results, using methods such as activation likelihood estimation and multi-kernel density analysis. The ANIMA platform consists of an intuitive online interface for querying, downloading, and contributing data from published meta-analytic studies. Additionally, to aid the process of organizing, visualizing, and working with these data, we present an open-source desktop application called Volume Viewer. Volume Viewer allows users to easily arrange imaging data into composite stacks, and save these sessions as individual files, which can also be uploaded to the ANIMA database. The application also allows users to perform basic functions, such as computing conjunctions between images, or extracting regions-of-interest or peak coordinates for further analysis.

Surf Ice

Surf Ice is a tool for surface rendering the cortex with overlays to illustrate tractography, network connections, anatomical atlases and statistical maps. While there are many alternatives, Surf Ice is easy to use and uses advances shaders to generate stunning images. It supports many popular mesh formats [3ds, ac3d, BrainVoyager (srf), ctm, Collada (dae), dfs, dxf, FreeSurfer (Asc, Srf, Curv, gcs, Pial, W), GIfTI (gii), gts, lwo, ms3d, mz3, nv, obj, off, ply, stl, vtk], connectome formats (edge/node) and tractography formats [bfloat, pdb, tck, trk, vtk].

UManitoba-JHU Functionally-Defined Human White Matter Atlas

This is a probabilistic atlas of human white matter tracts/regions underlying several well-known resting state brain networks (e.g., dorsal and ventral Default Mode, left and right Executive Control, anterior and posterior Salience, Basal Ganglia, Language, Sensorimotor, Visuospatial, etc.). These were derived by performing fMRI-guided DTI tractography on a group of 32 neurologically-healthy, adult control subjects. The atlas includes group probability maps for each network, as well as each individual tract (i.e., 1-mm isotropic NIFTI images) that are aligned to both the SPM and MRIStudio ICBM templates. In accordance with the CC BY-NC-ND 4.0 License, this atlas may be used for academic purposes, but users should cite the following two papers:

1. Figley TD, Bhullar N, Courtney SM and Figley CR (2015). Front. Hum. Neurosci. 9:585.

2. Figley TD, Mortazavi Moghadam B, Bhullar N, Kornelsen J, Courtney SM, and Figley CR (2017). Front. Hum. Neurosci. 11:306.

GAT : Graph Analysis Toolbox

GAT is a Matlab-based software that provides a GUI framework for conducting graph analysis with MR data. It integrates the Brain Connectivity Toolbox, REX toolbox, BrainNet Viewer, with original code for additional graph analyses and comparing networks between groups.

Using GAT, you can analyze structural/functional networks constructed from VBM, Free Surfer data, resting state/task fMRI, DTI, MRS, and behavioral data. GAT provides non-parametric statistics for comparing small-world parameters, regional topology, hubs, network resilience, and modularity between groups.

Please cite the following paper if you are using GAT:
Hosseini et al (2012). GAT: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks. PLoS ONE 7(7), e40709.

Please subscribe to GAT mailing list at
https://mailman.stanford.edu/mailman/listinfo/gat_user_forum to get updates and share questions.

Contact: Hadi Hosseini (hosseiny (at) stanford.edu)

ENIGMA-DTI pipeline

The ENIGMA DTI pipeline provides tools to extract whole-brain average and regional measurements from DTI images including FA, AD, RD and MD. The pipeline has been used successfully across sites worldwide and shown to extract reliable and heritable measures from diffusion imaging. We provide a FA-template created from multiple high resolution datasets to better accommodate registrations to a standard atlas than current existing tools. The ENIGMA-DTI analysis team is continuously working to bridge together pipelines to analyze DTI in reliable ways across multiple datasets and reduce inter-site inhomogeneities and inconsistencies between findings, so stay tuned for more updates and tools. Successful global studies of genetic or disease effects underlying variability in white matter neuroanatomy have been published through ENIGMA and many more are underway. To get involved in any ENIGMA group send us a message -- enigma@ini.usc.edu or support.enigmaDTI@ini.usc.edu.

MRBrainS Evaluation Framework / Challenge

The aim of the MRBrainS online evaluation framework is to compare automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T MRI scans. Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all 20 scans and used as the reference standard (trainingset: 5, testset: 15). The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, 95th percentile Hausdorff distance, and absolute volume difference). The results are published on the MRBrainS13 website. If you would like to join the MRBrainS challenge, please register a team on the MRBrainS website: http://mrbrains13.isi.uu.nl/register.php and sign the confidentiality agreement. You will be granted access to the data and can submit your automatic segmentation results for evaluation. Visit http://mrbrains13.isi.uu.nl

4D (Spatio-Temporal) DRAMMS Registration

4D (3D+time, or spatial-temporal) consistent deformable registration build on top of 3D pair-wise DRAMMS registration.

- to quantify tumor changes in multiple visits;
- to quantify voxel-wise neuro-development or structural growth.

N2A

A scalable object-oriented language for modeling neural systems, along with an IDE for writing models and controlling simulations.

dbSNP

The Single Nucleotide Polymorphism Database (dbSNP) is a free public archive for genetic variation within and across different species developed and hosted by the National Center for Biotechnology Information (NCBI) in collaboration with the National Human Genome Research Institute (NHGRI).

dbGaP

The database of Genotypes and Phenotypes (dbGaP) was developed to archive and distribute the results of studies that have investigated the interaction of genotype and phenotype.

Unbiased Atlas Construction via Population (group-wise) DRAMMS Registration

Unbiased atlas construction by group-wise dramms registration.

Can be used to construct a normal atlas from either a normal population or an abnormal population.

The tool will find a virtual space that represents the mean geometry, mean anatomy and voxel-wise mean intensity of the input population (hence unbiased).

If the input images are from an abnormality-bearing population, the above happens to normal regions only, and will create an unbiased normal-appearing atlas. The software can either take as input the manually annotated abnormality masks, or automatically detect and exclude abnormalities from atlas construction.

Haiko89: MRI in vivo Baboon Brain Templates Collection (Papio anubis)

A collection of open access MRI in vivo brain template images from average baboon brains (Papio anubis):

- Symmetric and Asymmetric population-average T1 brain templates from N=89 subjects (Haiko89 & Haiko89sym)

- Tissue (Grey matter, White matter & CSF) probability maps from symmetric and asymmetric Haiko89 brain templates

- Sex balanced Brain template created with an equal number of N=31 male and N=31 female images (Haiko62MF)

- Adult Brain Template created from N=67 adult baboons' (over 7 years old) images (Haiko67A)

When using these images please cite:
Love, S.A., Marie, D., Roth, M., Lacoste, R., Nazarian, B., Bertello, A., Coulon, O., Anton, J.-L., Meguerditchian, A., 2016. The average baboon brain: MRI templates and tissue probability maps from 89 individuals. NeuroImage. doi:10.1016/j.neuroimage.2016.03.018

Anima

Anima is a set of ITK/VTK based libraries and multi-platform command line tools for medical image analysis. It contains software for image registration (linear and non linear block matching registration, EPI distortion correction), statistical analysis (group comparison, patient to group comparison), diffusion imaging (model estimation, tractography, etc.), quantitative MRI processing (quantitative relaxation times estimation, MR simulation), image denoising and filtering, and segmentation tools (Graph cut segmentation and multiple sclerosis lesion segmentation).

Anima is available as a Github repository (see the source code link) and compiled binaries for various OS (OSX, Fedora, Ubuntu, Windows).

In vivo MEMRI-based Rat Brain Atlas

In vivo MEMRI-based Rat Brain Atlas provides a reference MEMRI data of Wistar rat brain and corresponding 40 structure labels. Using the reference MRI and the atlas data users can perform regional specific MRI parametric and volumetric analysis.

STAMP Atlases for Brain Abnormality Detection

STandardized Abnormality-free Multi-Parametric (STAMP) atlases can help detect various lesions (e.g., MS lesions, stroke lesions, primary/recurrent tumors, post-surgery cavity, etc).

STAMP atlases include 4 modalities (T1, T2, T1c, FLAIR) and 1 artificial modality (T1d:=T1c-T1, highlighting pure contrast). Here T1c is T1 MRI with gadolinium contrast. Multi-modal atlases help detect lesion components. The otherwise rarely available T1c/T1d atlases can help differentiate between normal vessels and enhancing tumor, both enhancing upon gadolinium injection.

The STAMP atlases quantify the normal intensity variations at the voxel level, which adds to those at the image or tissue level. This may help abnormality detection, because normal variations and hence abnormal detection may depend on anatomical locations.

The STAMP atlas can also be used to standardize intensity, to spatially normalize patient images, or to extract alignment-based features.

Publication pending.

Single-subject Resting state fMRI Reproducibility Resource

We have acquired a longitudinal single-subject dataset of a healthy volunteer (40 years of age at time of initial scan; male). A total of 156 sessions of MRI data was acquired on a weekly basis, over a span of 185 weeks (a little over 3 years).

The subject was scanned on a 3T Philips Achieva scanner (Philips HealthCare, Best, Netherlands). Rs-fMRI data of the subject was acquired using a multi-slice SENSE-EPI pulse sequence with TR/TE = 2000/30 ms, SENSE factor = 2, flip angle = 75°, 37 axial slices, nominal resolution = 3x3x3 mm3, 1 mm gap, 16 channel neuro-vascular coil, number of dynamics (frames) per run = 200. The subject was instructed to stay as still as possible with his eyes closed during the entire scan, and no other instruction was provided.

(Please see the MediaWiki page for additional information)

Multi-contrast and submillimetric 3-Tesla hippocampal subfield segmentation protocol and dataset (MNI-HISUB25)

The MNI-HISUB25 dataset contains manual hippocampal subfield labels and associated high-resolution MRI data (submillimetric T1- and T2-weighted images, detailed sequence information, and stereotaxic probabilistic anatomical maps) based on 25 healthy subjects. Data were acquired on a 3-Tesla MRI system using a 32 phased-array head coil. The protocol divided the hippocampal formation into three subregions: subicular complex, Cornu Ammonis 1, 2 and 3 (CA1-3), and CA4-dentate gyrus (CA4-DG). Segmentation was guided by consistent intensity and morphology characteristics of the densely myelinated molecular layer together with few geometry-based boundaries flexible to overall mesiotemporal anatomy, and achieved excellent intra-/inter-rater reliability (Dice index ≥90/87%). The dataset can inform neuroimaging assessments of the mesiotemporal lobe and help to develop segmentation algorithms relevant for basic and clinical neurosciences.

Developmental Resting State fMRI and Structrual Sample 10-26 yo

Eyes closed 5 min rest and structural 1.5 TR 3T Siemens Trio in developmenetal sample 10-26 years of age. 192 subjects. Data used in PLOS Bio paper, Contribution of Network Organization and Integration to the development of Cognitive Control
Download: http://devrsfmri_2015.projects.nitrc.org/devrsfmri_2015.tar.bz2

Cerebellar White Matter Atlas

The Cerebellar White Matter Atlas is based on probabilistic tractography of high resolution, high quality diffusion MR data of 90 healthy subjects from the Human Connectome Project database. The atlas comes as a 3D probabilistic map, as well as binary parcellations at different probability thresholds. MNI - as well as SUIT (Spatially Unbiased Infratentorial Template) - compatible versions of the atlas are provided.

SPM for fNIRS toolbox

The SPM for fNIRS toolbox is the SPM12-based software for statistical analysis of functional near-infrared spectroscopy (fNIRS) signal. The toolbox allows for inferences about regionally specific hemodynamic effects with superresolution, by applying the general linear model and random field theory to fNIRS data.

In the toolbox manual, we provide an illustrative statistical parametric mapping (SPM) analysis using fNIRS data acquired during Stroop task, and then describe detailed information about functions implemented in the toolbox.

Manual: https://www.nitrc.org/docman/view.php/965/1995/manual_spm_fnirs.pdf

Reference:
Sensor space group analysis for fNIRS data.
Tak, S, Uga, M, Flandin, G, Dan, I, Penny, WD, 2016. J. Neurosci. Meth. 264, 103-112.

Brain Hierarchical Atlas: A brain atlas where the regions of interest are relevant for both structure and function

This atlas results from a hierarchical clustering approach applied to a combination of functional (resting fMRI) and structural (DTI) datasets. The novelty of the atlas is based on the fact that ROIs are functionally coherent (i.e., the dynamics of voxels within regions have high similarity) and at the same time they are structurally wired (the voxels within regions are highly integrated by white-matter fibers).

This Project contains the following files:

-average_networks.mat: The population (N=12) functional and structural matrices, each one with dimensions 2514x2514

-functional.zip/structural.zip: Zip files containing the functional/structural atlas for all the stages in the hierarchical tree, from M=1 to 2514 ROIs

-test_crosmodularity.m: Code example on how to compute crosmodularity. It needs crossmodularity.m and modularity_index.m

For further information see:

Diez, P. et al, A novel brain partition highlights the modular skeleton shared by structure and function, Sci Rep 5: 10532, 2015

Using Make for Neuroimaging Workflow: Manual and Examples

Make Pipelines is a set of examples of neuroimaging workflows, implemented using the popular UNIX utility "make", documented in an extensive manual. These examples span structural MRI processing, fMRI processing (resting and task), diffusion tensor imaging, quantitative perfusion imaging, and reporting of fMRI results.

See documentation link for related paper and manual.

VANDPIRE

VANDPIRE is intuitive and comprehensive software for quantifying cerebral blood flow (CBF) and calculating CBF territories using arterial spin labeling (ASL) MRI data. CBF analysis, particularly CBF territory analysis, is time-consuming and requires advanced knowledge of scripting and up-to-date mathematical models. This toolbox addresses these concerns, and is available on multiple platforms as a graphical user interface.

Features:
- Available on Windows, Mac, and Linux platforms
- Runs from single folder and thus capable of being run online in scanning environment
- Comes with complete help tutorial in HTML format and example data
- Numerous customizable options that are responsive to user input
- Output data formats: Nifti, Dicom
- Includes modifiable motion correction and coregistration options using FSL or Elastix
- Currently tested with Philips Par/Rec and Philips Dicom pseudocontinuous ASL input data; however, support available for other data formats upon request

Created by Daniel F. Arteaga.

Advanced Connectivity Analysis (ACA): a large scale functional connectivity data mining environment

Using resting-state functional magnetic resonance imaging (rs-fMRI) to study functional connectivity is of great importance to understand normal development and function as well as a host of neurological and psychiatric disorders. Seed-based analysis is one of the most widely used rs-fMRI analysis methods. Here we describe a freely available large scale functional connectivity data mining software package called Advanced Connectivity Analysis (ACA). ACA enables large-scale seed-based analysis and brain-behavior analysis. It can seamlessly examine a large number of seed regions with minimal user input. ACA has a brain-behavior analysis component to delineate associations among imaging biomarkers and one or more behavioral variables.

EEG-FNIRS hybrid SMR BCI data

To be filled after registering

Self-threat and self-concept data

The content includes the fMRI data of all of the 46 participants of the study mentioned above. You will be able to download the raw data, for preprocessing and analysing by yourself. Please be aware of the different conditions, which are mentioned in the connected paper and attached as logfiles in the repository.

EEG Study data of Rapid Serial Visual Presentation (RSVP) in ESS format

Rapid Serial Visual Presentation (12Hz) Target (airplane) detection without immediate response.

The purpose of this study was to explore the neural basis of target detection in human brain and to compare the performance of brain-computer interface (BCI) methods in classification of target vs. non-target images. The data was acquired during a rapid serial visual presentation task which was composed of a sequential presentation of image clips in rapid succession (12/s) in 4.1-s bursts, to which subjects were to indicate whether or not the satellite image clips of London presented included a small target airplane image by making one of two button presses.

This study was collected at UCSD/Swartz Center and placed into ESS version 2.02, levels 1&2 by Syntrogi Inc.

EEG Study Schema (ESS)

EEG Study Schema (ESS) makes it easier for researchers in the field of EEG/BCI to package, share and automatize the analysis workflow of their study data. You can think of ESS as a "shipping container" for your EEG study data.

Using other people’s EEG data could be painful as often one has to do detective work to find out:

What happened in the experiment?
Which files are for which subjects/sessions?
What do these event codes mean?
To remove ‘EEG data sharing pain' we have created a set of standards (HED and ESS) and tools

ESS is designed from a user-centered viewpoint that emphasizes simplicity and ease of use. It is created to contain all the information a researcher unfamiliar with a particular EEG (or MEG) Study needs to further analyze the data. It is An XML-based specification
Holds all the information necessary to analyze an EEG study.

Please visit www.eegstudy.org for more information.

Hierarchical Event Descriptor (HED) Tags

HED tags are assigned to event codes (also known as triggers or event numbers) in EEG recordings and allow humans and computers to better understand what these codes represent (e.g. code #5 -> Target detection in an RSVP paradigm).

Why tags?

In the same way that we tag a picture on Flicker, or a video clip on Youtube (e.g. cat, cute, funny), we can tag EEG experimental event types used in event-related EEG research. Hierarchical Event Descriptors (HED) is a set of descriptor tags partially adopted from BrainMap/NeuroLex ontologies and organized hierarchically. HED tags can be used to describe many types of EEG experiment events in a uniform, extensible, and machine-readable manner.

Visit www.hedtags.org for more information.

minc-toolkit-v2

Whole set of MINC-based image processing tools packaged together and several ITKv4 tools Includes: MINC,N3,BICPL,EBKTS,ANIMAL,INSECT,BEaST,Register,Display,xdisp,ANTS, Elastix, ABC, C3D

Monthly Morphometry Report

The Monthly Morphometry Report (MMR) is designed to be a quick, high-level synopsis of the volumetric literature, compiled on a monthly basis. We seek to represent the corpus of published findings in a suscinct and common format, to ease human interpretation of ths vast set of measures that are being reported.

To start, monthly PUBMED reports of potential volumetric publications in humans are identified, and human curators then summarise these abstracts into a standard report format. In the future, we hope to add automation to the curation process and index more complete full-text content.

This project is HIGHLY alpha in its design and should be considered only a 'work in progress'. Enter at your own risk, or, volunteer to help!

The De-identification Toolbox - A data sharing tool for neuroimaging studies

The De-identification Toolbox (formerly DeID) is a Java program that users can use to remove identifying information in neuroimaging datasets. This software provides a series of user interaction wizards to allow users to select data variables to be de-identified, implements functions for auditing and validation of de-identified data, and enables the user to share the de-identified data in a single compressed package through various communication protocols. The software runs with Windows, Linux, and Mac operating systems.

PALM - Permutation Analysis of Linear Models

PALM — Permutation Analysis of Linear Models — is a tool that allows inference using permutation methods, offering a number of features not available in other analysis software. For complete details, including documentation and User Guide, please visit: http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/PALM

Longitudinal Rhesus Monkey Brain Templates

Age-specific rhesus monkey brain templates from T1-weighted structural MRI (3T Scanner). Templates created from 24 rhesus macaques using deformable image registration (ANTS), at 1week, 4weeks, 8weeks, 13weeks, 26weeks, 39weeks, 52weeks, 104weeks, and 260 weeks of age. Tissue segmentations and lobar parcellations are also provided.

AutoTract

AutoTract is an automatic tractography tool featuring advanced processing tools to clean tracts after the initial tractography. It uses the Slicer TractographyLabelMapSeeding, with tracts from a reference DTI atlas as label maps.

The 3 main steps of the tool are:
-Registration between the DTI image that we want to tract and the reference DTI atlas.
-Tractography of the DTI image using the TractographyLabelMapSeeding Slicer module.
-Process of each tract individually to clean and remove any unwanted fiber.

The processing includes (for each tract):
-cutting every fiber ends that are in the gray matter (cutting them until the fiber ends is in the white matter), based on a white matter mask created by the tool.
-ensuring every fiber is not in the CSF (removes them from the tract otherwise)
-several image comparisons with the reference tracts (such as fiber length matching, use of a distance map to ensure that the tract is close to the reference tract, and potentially to be added later on, shape comparison).

isc-toolbox: Inter-subject correlation analysis for fMRI in Matlab

This Matlab toolbox performs inter-subject correlation (ISC) based analysis of fMRI data and includes a GUI for the visualization of the resulting thresholded statistical maps.

The toolbox contains functions to carry out various ISC based analysis such as mean, frequency band, time window and phase synchronization ISC analysis. All analyses can be run from a GUI. Isc-toolbox includes also automated support for Slurm/SGE based grid environments.

A set of visualization tools - particularly designed for ISC analyses - are integrated to the GUI. Visualization tools contain functionalities to which standard functional neuroimaging packages are difficult to adapt.

Reference: J.-P. Kauppi, J. Pajula J and J. Tohka (2014). A Versatile Software Package for Inter-subject Correlation Based Analyses of fMRI. Frontiers in Neuroinformatics, 8:2.

Project in Research Gate: https://www.researchgate.net/project/Inter-subject-correlation-based-analysis-of-fMRI

Cloud Reproducibility Challenge

The use of standardizable computing environments offers a promise for enhancing the reproducibility of neuroimaging computational workflows. In this hackathon project we seek to develop a reproducibility assessment framework for testing and validating similar workflows run under different conditions. For a given workflow, which can be run on a local computer system, as a virtual machine, or in a cloud computing environment, we want to assess the potential variances introduced in workflow results as a function of the operating environment. This framework can then be extended to assessment of variations due to additional factors, such as software versioning, workflow parameters, and input data. The end result of this project would be a stand alone application that details the reproducibility of workflow/result pair in the context of the various execution variables.

This project hopes to leverage the NITRC-CE, nipype, testkraut, and existing shared data/shared results.

FVGWAS: Fast Voxelwise Genome Wide Association Analysis

The Fast Voxelwise Genome Wide Association analysiS (FVGWAS) framework to efficiently carry out whole-genome analyses of whole-brain data. FVGWAS consists of three components including a heteroscedastic linear model, a global sure independence screening (GSIS) procedure, and a detection procedure based on wild bootstrap methods. Specically, for standard linear association, the computational complexity is O(n*N_V*N_C) for voxelwise genome wide association analysis (VGWAS) method compared with
O((N_C+N_V)*n^2) for FVGWAS. Our FVGWAS may be a valuable statistical toolbox for large-scale imaging genetic analysis as the field is rapidly advancing with ultra-high-resolution imaging and whole-genome sequencing.

Corpus callosum and brain segmentations from ABIDE database

- NIFTI format corpus callosum segmentation data
- NIFTI format brain segmentations

Both derived from ABIDE database

Tractography based on Riemannian geodesics and tensor adjugates

This tool provides a way to trace fiber tracts based on Riemannian geodesics, the local cost function being defined as the adjugate (as opposed to the commonly used inverse) of the diffusion tensor. The minimization of the cost function is globally (not just locally) attained via dynamic programming, using the "Fast sweeping" algorithm for the sake of efficiency.

Phoneme-Syllable Tsinghua University CS&T 2011

FMRI data set for phoneme coding in the auditory cortex with 7 subjects and 9 Chinese CV syllables.

In vivo MEMRI-based Mouse Brain Atlas

In vivo MEMRI-based Mouse Brain Atlas provides a reference MEMRI data of NSG mouse brain and corresponding 41 structure labels. Using the reference MRI and the atlas, data users can perform regional specific MRI parametric and volumetric analysis.

Combining a patch-based approach with a non-rigid registration-based label fusion method for the hippocampal segmentation

We propose a patch-based labeling method, which cooperates
with a label fusion using non-rigid registrations. We present
a novel patch-based label fusion method, whose selected patches and their weights are calculated from a combination of similarity measures between patches using intensity-based distances and labeling-based distances, where a previous labeling of the target image is inferred by a label fusion method using non-rigid registrations. We compare several label fusion methods on publicly available MR images of human brains for segmenting the hippocampus.

Offline Processing MRI

This is structural and functional MRI data from 35 healthy volunteers that accompanies Bursley et al. (2015), "Awake, Offline Processing During Associative Learning." Subjects encoded paired associates and then performed a distractor task before being probed on associate pairs. Pattern analyses suggest that encoded memories were reactivated during the distractor task, and performance of the distractor task led to superior recall for the associate pairs, compared to a control condition in which no distractor task was performed.

PICASSO skull stripping tool

The Personalized Congregation of Algorithms for Skull Stripping Optimization (PICASSO) tool is a general skull stripping for T1-weighted brain MRIs
(a) in multiple imaging sites;
(b) by scanners of various vendors (GE/Siemens/Philips);
(c) by scanners of various magnetic field strengths (1.0T/1.5T/3T/7T);
(d) by various T1-weighted pulse sequences (MPRAGE, SPGR, etc);
(e) with various fields-of-view (even containing neck and shoulders);
(f) from subjects of various ages (infants, pediatrics, young adults, elderly adults);
(g) from subjects of different health conditions (normative, dementia, tumor-bearing);
(h) with various image resolutions (from 1x1x1mm to 1.5x1.5x6mm); and
(i) with various image contrasts (high or low SNRs).

The PICASSO tool runs in the unix command line. It is compatible with a single linux/mac computer, or preferably with high-performance parallel clusters (PBS or SGE).

Publication pending.

masked ICA (mICA) Toolbox

mICA Toolbox is a user-friendly toolbox to perform masked independent component analysis, i.e. ICA within a spatially restricted subregion of the brain. It is based on command line tools from FSL suite to perform the ICA and related analyses (e.g. specific data preprocessing, atlas-based mask generation, mICA-based parcellation, dual-regression and direct back reconstruction). Various options provide flexible control of the analysis.

mICA Toolbox is a helpful utility for researchers in the field of neuroimaging. It facillitates the study of intrinsic connectivity of brain (sub-)regions and the reconstruction of their extrinsic connectivity (i.e. connectivity with regions outside of the mask).

The toolbox furthermore offers an easy way to calculate ICA reproducibility over a range of decomposition dimensions, which can be used to overcome the common problem of dimensionality estimation.

Dual-tree complex wavelet combined with non-local means for ASL fMRI denoising

Dual-tree complex wavelet combined with non-local means ASL fMRI denoising is a software toolbox that can denoise MR images, especially ASL fMRI images. The software:

(1) Reads and writes images in analyze format;
(2) Pre-processes ASL fMRI images and provides denoised images for further analysis;
(3) Achieves variable denoising outcomes by changing certain parameters;
(4) Can be employed for denoising other MR images, such as diffusion MRI,etc.

The method is described in the following paper:
Voxel-wise functional connectomics using arterial spin labeling fMRI: the role of denoising.
Xiaoyun Liang, Alan Connelly, Fernando Calamante, 2015. Brain connectivity. DOI: 10.1089/brain.2014.0290.

Templates for In vivo Mouse Brain

The templates for in vivo mouse brain are intended for SPM normalization and segmentation (including skull-stripping). The population-averaged, stereotaxic, and tissue segmented template were created from in vivo T1WI (an isotropic resolution of 80μm) of C57Bl/6(n=30, male), BALB/cBy(n=10, male), C3H/He(n=10, male), and DBA/2 (n=10, male) mice. The voxel size of templates were multiplied by 10 to be able to use SPM directly.
Keywords: atlas, mouse, probability map, template, inbred strain

Please refer to the following article for the atlases:
Hikishima K, Komaki Y, Seki F, Ohnishi Y, Okano HJ, Okano H.
In vivo microscopic voxel-based morphometry with a brain template to characterize strain-specific structures in the mouse brain. Sci Rep. 2017 Dec;7(1):85. doi:10.1038/s41598-017-00148-1. PubMed PMID: 28273899.

Authors: Keigo Hikishima, Hideyuki Okano
Contact person: k-hikishima@aist.go.jp

NeuroImage Field-of-View Normalization Tool

Field-of-view mismatch in different subjects' images poses additional challenges to the streamline analysis of large-scale brain MRI datasets.

We release an atlas-based FOV normalization tool. This tool automatically truncates the FOV to that of the MNI152 atlas', which includes the whole brain, the skull, and ends 10-15mm inferior to the most inferior voxel in the cerebellum. This tool runs in linux platform (command line). It has been extensively validated in hundreds of MRIs, acquired from various scanners (Philips 1.0T, Siemens/GE 1.5T/3T), in various imaging sites, with various scanning FOVs, and from subjects of various ages (infants, neonates, adolescents, young adults, elderly).

We have quantitatively shown that this FOV normalization significantly improves the accuracy and success rate in streamline analysis of neuroimages in the very first steps (e.g., skull stripping, inter-subject registration, etc).

The software runs in unix command line, single machine or clusters.

Publication pending.

volBrain: online MRI segmentation

volBrain is an online MRI brain segmentation system. It is intended to help researchers all over the world to obtain automatically volumetric brain information from their MRI data without the need for any infrastructure in their local sites. volBrain works in a fully automatic manner and is able to provide brain structure volumes without any human interaction. We encourage you to use the system hoping you find it useful.

CWL-EEG-fMRI

This is a plugin for EEGlab that does Carbon-Wire Loops regression for getting rid of Ballistocardiac artifacts and other motion-related artifacts such as the Helium pump artifact (for the paper of its performance on the He pump artifact, see "Carbon-wire loop based artifact correction outperforms post-processing EEG/fMRI corrections-A validation of a real-time simultaneous EEG/fMRI correction method.", NeuroImage, 2016 Jan 15;125:880-94.

This EEGLAB plugin also comes with a (very) small example dataset for testing out the regression: this dataset has got 30 EEG channels, 1 EOG, 1 ECG and 6 CWL signals (the latter of which are used for the regression of the EEG and the EOG channel).

A full CWL-EEG/fMRI 8-subject eyes open/eyes closed data set (EEG and MR data ~ 25 Gig) is available at the NITRC website (see: https://www.nitrc.org/search/?type_of_search=group&q=cwl+eeg+fmri&sa.x=0&sa.y=0 ) See also the following NITRC Project for more information: https://www.nitrc.org/projects/cwleegfmri_data/

MOCA

MOCA stands for "Metric Optimization for Computational Anatomy". It is a collection of software tools for the computational analysis of brain anatomy with MRI data. It includes automated software tools from surface reconstruction to their mapping via metric optimization in the Laplace-Beltrami embedding space. It is general and can be applied to a wide range of anatomical structures including cortical, sub-cortical, and fiber bundle surfaces.

MGH Neonatal/Pediatric ADC atlases

Existing neonatal/pediatric atlases were mainly on structural MRI. We release here a set of Apparent Diffusion Coefficient (ADC) atlases, on 10 age groups: first 2 weeks of life, the rest of the 1st quarter, the 2nd, 3rd, and 4th quarters of life, year 1-2, 2-3, 3-4, 4-5 and 5-6). Atlases represent the mean geometry/anatomy of the age group, and the mean and standard deviation ADC values at the whole brain, regional (61 regions) and voxel levels.

The age-specific ADC atlases characterize normative ADC development in 0-6 years of life (in neuroscience) and can provide reference to identify abnormalities (in pediatric neuroradiology).

Refs:
Y Ou, et al, "Using Clinically-Acquired MRI to Construct Age-Specific ADC Atlases: Quantifying Spatiotemporal ADC Changes from Birth to 6 Years Old", 38(6): 3052-3068, Human Brain Mapping, (2017).

S Sotardi, et al, "Using Clinically-Acquired MRI to Construct Age-Specific ADC Atlases: Quantifying Spatiotemporal ADC Changes from Birth to 6 Years Old", under review.

ShapeComplexityIndex

ShapeComplexityIndex tool calculate the local complexity as the difference between the observed distributions of local surface topology to its best-fit basic topology model within a given local kernel.

The reference paper was published in http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=2211519&resultClick=1

CWx Optical Imaging System

CWx Optical Imaging System is a real-time data acquisition system used widely for functional NIRS measurements. Data from the CWx Systems is specific to fNIRS and directly compatible with Homer2 fNIRS analysis software.

The Current CW6 system offers up to 32 lasers and 32 detectors in any combination for maximum channel count with an adjustable output data sample rate. Typical sample rate is 50Hz for most configurations.

The CWx Software includes SDGui, used to create compatible source-detector probe geometry files designed by the user.

Bayesian Connectivity Analysis software (BCA)

Bayesian Connectivity analysis software (BCA) is a pipeline for connectivity matrix analysis. A general user is a researcher who is interested in brain network analysis and has basic knowledge of Linux/Unix, R, and Matlab.

BioEra

BioEra provides environment and tools to create various types of processing tasks. It can be used for research, games, self exploration, entrainment, sound processing and many others. Each task is contained in a design. To create a design no programming skill is required, only understanding of the process and its requirements. BioEra is used to create a design visually. A design represents data flow from input (e.g. biofeedback device) to output (e.g. visual or sound feedback). The flow can be customized with hundreds built-in objects (elements). For example an amplitude of alpha brainwaves can be filtered from input EEG signal, it can then trigger a MIDI, video or a computer task at a certain threshold level.

BrainBay

Brainbay supports Human-Computer-Interface functions and diverse Biosignal- and EEG amplifier hardware - including the OpenBCI Cyton and Ganglion devices - for live recording and processing of bioelectric signals.
BrainBay is a part of the OpenEEG project and provides graphical + musical feedback functions and feedback-games.

The project is hosted on Github:
https://github.com/ChrisVeigl/BrainBay

Advanced Segmentation Tools (ASeTs)

Advanced Segmentation Tools (ASETS) - Fast Continuous Max Flow variants in CUDA/C/Matlab

Attention: This is a beta version. We appreciate any bug reports and feedback on how to improve usability. For installation and usage please refer to the readme.md.

- Fast parallel continuous max flow solvers in 2D/3D
-- Binary max flow
-- Multi-region (Potts model, Ishikawa model, Hierarchical Max Flow)
-- In two different implementations (full flow and pseudo flow solvers)

- Implemented in multiple languages
-- Matlab/mex/C
-- Matlab/CUDA

- Application examples for (medical) image segmentation:
-- Interactive max flow graph cuts
-- Regularization of probabilistic label maps as in atlas-based segmentation
-- High-performance multi-phase levelsets
-- Post-processing of flawed manual segmentations with contrast sensitive regularization
-- L1 intensity segmentation

- Resources:
http://www.advancedsegmentationtools.org

Randomized Denoising Autoencoders for Neuroimaging

The toolbox provides Matlab codes for learning randomized denoisiging autoencoders (rDA) based imaging marker for neuroimaing studies. rDA is an ensemble of neural networks (based on denoising autoencoders) that take imaging data as inputs and produce single/multi dimensional summary score. Parameters are learned from training data. rDA can be used for any learning task (classification, regression), for designing imaging disease markers. The unbiased and low variance of rDA's outputs are highly relevant for designing efficient clinical trials. Further details about the model are in the following paper (please cite it if the codes are used).

Daikon

Daikon is a pure JavaScript DICOM reader:
- Works in the browser and Node.js environments.
- Parses DICOM headers and reads image data.
- Supports compressed DICOM data.
- Orders and concatenates multi-file image data.
- Supports RGB and Palette data.
- Supports Siemens "Mosaic" image data.
- Parses Siemens CSA header.

Supported Transfer Syntax:
1.2.840.10008.1.2 (Implicit VR Little Endian)
1.2.840.10008.1.2.1 (Explicit VR Little Endian)
1.2.840.10008.1.2.2 (Explicit VR Big Endian)
1.2.840.10008.1.2.1.99 (Deflated Explicit VR Little Endian)
1.2.840.10008.1.2.4.50 (JPEG Baseline (Process 1) Lossy JPEG 8-bit)
1.2.840.10008.1.2.4.51 (JPEG Baseline (Processes 2 & 4) Lossy JPEG 12-bit)
1.2.840.10008.1.2.4.57 (JPEG Lossless, Nonhierarchical (Processes 14))
1.2.840.10008.1.2.4.70 (JPEG Lossless, Nonhierarchical (Processes 14 [Selection 1]))
1.2.840.10008.1.2.4.90 (JPEG 2000 (Lossless Only))
1.2.840.10008.1.2.4.91 (JPEG 2000)
1.2.840.10008.1.2.5 (RLE Lossless)

https://github.com/rii-mango/Daikon

Diffusion MRI Tool

DMRITool is a free and open source toolbox for diffusion MRI data processing. It is written in C++ with Matlab interface. You can also use the released mex executables in matlab.

DMRITool website:
https://diffusionmritool.github.io/

Latest source code:
https://github.com/DiffusionMRITool/dmritool

Mailing list:
http://www.nitrc.org/mailman/listinfo/dmritool-discussion

DMRITool is currently supported in SQITS at NIH
https://science.nichd.nih.gov/confluence/display/sqits/Home

With DMRITool, you can:

* perform reconstruction/estimation of diffusion data, including diffusion weighted signal, ensemble average propagator (EAP), diffusion orientation distribution function (dODF), and some meaningful scalar maps, etc.
* generate spherically uniform sampling schemes for single or multiple shells.
* perform diffusion MRI data simulation.
* visualize spherical function field (e.g. dODF field, EAP profile field)

IXI Dataset

The IXI Dataset is a collection of nearly 600 MR images from normal, healthy subjects. The MR image acquisition protocol for each subject includes T1, T2 and PD-weighted images, MRA images, and diffusion-weighted images (15 directions). The data was collected at three different hospitals in London using 1.5T and 3T scanners.

mask_explorer

Tool mask_explorer is used to explore fMRI datasets and to find inconsistences in fMRI data, it is suitable especially for group analyses.

Main features of mask_explorer are:
1) Estimation of binary masks
2) Exploration and export of valid subject counts image and binary group mask
3) Estimation of outlier masks in dataset
4) Creating list of suitable/unsuitable data in relation of positions and ROIs (coverage by masks included) and export to Microsoft Excel or text file.
5) Creating and loading list of positions of interest
6) Compare valid subject counts image with NIFTI file
7) Use of user specified background template

Mask_explorer can compute subject’s masks. Loaded masks are used to create parametric map. Its values show number of datasets with valid information.

The best citations for this software is:

Gajdoš M, Mikl M, Mareček R. Mask_explorer: a tool for exploring brain masks in fMRI group analysis. Computer Methods and Programs in Biomedicine. 2016 Jul. DOI: 10.1016/j.cmpb.2016.07.015

MriCloud

MriCloud is a cloud-based platform that provides several advanced features including;

1) Fully automated cloud service for brain parcellation of MPRAGE images based on Multiple-Atlas Likelihood Fusion (MALF) algorithm (Tang, et al. PLoS ONE, 2013), JHU multi-atlas inventories with 286 defined structures, and our Ontology Level Control (OLC) technology (Djamanakova, et al. NeuroImage, 2014).

2) Fully automated cloud service for DTI tensor calculation and extensive quantitative reports for quality control (Li, et al. PLoS ONE, 2013).

K-space Domain Image Fusion

Matlab code that implements a K-space magnetic resonance image fusion technique.

Massively Expedited Genome-wide Heritability Analysis (MEGHA)

A MATLAB toolbox for fast and flexible high-dimensional heritability analysis using genome-wide single nucleotide polymorphism (SNP) data from unrelated individuals, developed in the article "Massively Expedited Genome-wide Heritability Analysis (MEGHA)".

age-ility

This data set consists of 136 subjects of which the following neuroimage data is acquired:
MRI (3T):
- T1 anatomical scan
- DWI scan (two different types of scan paramaters)
- Resting state fMRI

EEG (64 channels):
- Resting state EEG

All data have been quality checked. See for more information the following publication:
Karayanidis, F., Keuken, M.C., Wong, S.A., Rennie, J.L., de Hollander, G., Cooper, P.S., Fulham, W.R., Lenroot, R., Parsons, M.W., Philips, N., Michie, P.T., Forstmann, B.U. (2015). The Age-ility Project (Phase 1): Structural and functional imaging and electrophysiological data repository. Neuroimage.

Augmented Reality Mirror

The Augmented Reality Mirror is an open source rendering software to provide users with a virtual reality experience. With the provisions of a projection display, a laptop with multi-core GPU, and XBOX kinect, users can stand in front of the mirror and representative anatomical images will overlay the body. The images implemented are adapted to approximate the actual anatomy. The open source software will have an application programming interface (API) to allow users to develop additional content within the Augmented Reality Mirror.

[123I] FP-CIT SPECT brain template in MNI space

The FP-CIT SPECT brain template has been created using a fully automatic procedure involving posterization of the source image to three levels: background, brain and striatum.

We performed a spatial affine registration of these 40 posterized source images to a posterized reference image in the MNI space. The intensity values of the transformed images is normalized linearly, assuming that the histogram of the intensity values follows an alpha-stable distribution. Lastly, we built the [123I]FP-CIT SPECT template by the mean of the transformed and normalized images.

More info:

1) Salas-Gonzalez et al. Building a FP-CIT SPECT brain template using a posterization approach. Accepted in Neuroinformatics.

2) Salas-Gonzalez et al. Linear intensity normalization of FP-CIT SPECT brain images using the alpha-stable distribution. NeuroImage, Volume 65, 2013, pp. 449-455.
http://dx.doi.org/10.1016/j.neuroimage.2012.10.005

VarTbx – Variability Toolbox

VarTbx measures within-voxel time series variability in fMRI data. The VarTbx structure is intended to be similar to a standard SPM first-level analysis. You can then proceed to pass those first-level, variability-based images to a level-2 SPM analysis in order to model group effects of interest. However, the first-level NIFTI output files could also be used within other statistics programs of your choice (e.g., FSL, AFNI, PLS).

VarTbx currently supports modeling block designs with a boxcar model, and computes temporal variability using measures such as: detrended variance (VAR), detrended standard deviation (SD), mean squared successive difference (MSSD), and SQRT(MSSD). We plan to add additional modeling approaches and variability measures in future releases.

A label fusion using CRF

We present a label fusion method based on minimizing an energy function by using graph-cut techniques. We use a conditional random field (CRF) model that allow us to incorporate shape, appearance and context information efficiently. This model is characterized by a pseudo Boolean function defined on unary, pairwise and higher order potentials. To evaluate the performance and the robustness of the proposed label fusion method, we employ two available database of T1-weighted (T1W) magnetic resonance (MR) images of human brains. We compare our approach with other label fusion methods in the hippocampal automatic segmentation from T1W-MR images.

Brain Genomics Superstruct Project (GSP) Open Access Data Release

Instructions for accessing the GSP Open Access Data may be found at

http://www.neuroinfo.org/gsp/

AURA tools : AUtomated Retinal Analysis tools

The dissemination of software for the analysis of optical coherence tomography scans of the retina, including the macula.

Kurtosis Imaging Network - KIN

Sorry, but KIN has been shut down. Images from KIN are no longer available for download.

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Kurtosis Imaging Network (KIN) was an open source database for normal healthy controls as well as various pathologies in an attempt to establish a standard range of kurtosis values within each population.

Infant Macaque MRI And DTI Templates

This is a multi-mode (T1w and DTI) brain template of monkeys during late infancy based on longitudinal data (from 6 months to 16 months), which will help spatial normalization, voxel-based and tract-based analyses in non-human primates.

Lots efforts were made to ensure the quality:
1) we manually skull-striped brains for all T1w&DTI data (120+ brains). The time-consuming procedure guaranteed more accurate brain extraction than automatic methods (3dSkullStrip or BET).
2) We had tested different methods, and the best ones were chosen to build the T1w (by ANTS) and DTI (by DTI-TK) templates.
3) We also made high quality template-related files, including parcellation maps, tissue probability maps, inferior cingulum mask for TBSS, and templates for each time point.

Detailed information about monkeys and methods of building templates can be found in:
Liu,C, et al. "Rhesus monkey brain development during late infancy and the effect of phencyclidine: a longitudinal MRI and DTI study." NeuroImage (2015).

lop-DWI

For information about lo-DWI and the data please visit:
https://sites.google.com/site/lopdwi/home

Diatrack particle tracking software

Diatrack, powerful particle tracking (win64)

Benchmark object tracking software for 2D and 3D applications, such as biological imaging, rheology, or single molecule imaging. Reliably follow and characterize hundreds of objects simultaneously with a precision that reaches 0.01 pixels (depending on data). DiaTrack provides a compelling set of tools to extract quantitative information from image sequences.

Diatrack can be downloaded under www.diatrack.org/contact.html
zip file is about 30MB; additional Mathworks libraries are automatically downloaded and installed by Setup.

3T MRI data set on structural brain-behavior correlations

This data-set contains all 3T MRI and behavioural data used in Boekel, W., Wagenmakers, E. J., Belay, L., Verhagen, A.J., Brown, S. D., Forstmann, B. U. (2015) A purely confirmatory replication study of structural brain-behavior correlations. Cortex. and consists of:

- 36 T1 and DWI images acquired on a 3T MRI scanner
- Behavioral data for 2 computerized tasks, and 3 questionnaires.

The behavioral data occasionally contains a subset of the 36 subjects, due to questionnaires/computer-tasks not having been completed fully or properly. Subject numbers (e.g. 1244) are matched to MRI subject numbers (e.g. sc1244).

Note!
A mistake was discovered in the post-processing pipeline of the DWI data. See corrigendum for a description of the error and corrected results:

Keuken, M.C., Ly, A., Boekel W.E., Wagenmakers, E-J., Belay, L., Verhagen, J.A., Brown, S.D., Forstmann, B.U. (2017). Corrigendum A purely confirmatory replication study of structural brain-behavior correlations. Cortex.

Hierarchical Functional Networks in Resting State fMRI

We proposed a fully automatic, iterative reclustering framework in which a small number of spatially large, heterogeneous networks are initially extracted to maximize spatial reproducibility.
Then, the large networks of the brain are iteratively subdivided to create spatially reproducible subnetworks until the overall within-network homogeneity does not increase substantially.
Here, you may download the experimental results generated using the proposed clustering method. The results include a meaningful pattern for spatially hierarchical structure of the brain.
Shams et al., "Automated Iterative Reclustering Framework for Determining Hierarchical Functional Networks in Resting State fMRI". Human Brain Mapping, Accepted.

LEAD-DBS

LEAD-DBS is a MATLAB-toolbox facilitating the:
- reconstruction of deep-brain-stimulation (DBS) electrodes in the human brain on basis of postoperative MRI and/or CT imaging
- visualization of localization results in 2D/3D
- group-analysis of DBS-electrode placement results and their effects on clinical results
- simulation of DBS stimulations (calculation of volume of activated tissue – VAT)
- diffusion tensor imaging (DTI) based connectivity estimates and fiber-tracking from the VAT to other brain regions (connectomic surgery)

LEAD-DBS builds on SPM8/12, especially regarding warping and segmentation procedures.

Simple Medical Imaging Library Interface (SMILI)

The Simple Medical Imaging Library Interface (SMILI), pronounced 'smilie', is an open-source, light-weight and easy-to-use medical imaging viewer and library for all major operating systems. The main sMILX application features for viewing n-D images, vector images, DICOMs, anonymizing, shape analysis and models/surfaces with easy drag and drop functions. It also features a number of standard processing algorithms for smoothing, thresholding, masking etc. images and models, both with graphical user interfaces and/or via the command-line. See our YouTube channel for tutorial videos via the homepage.

The applications are all built out of a uniform user-interface framework that provides a very high level (Qt) interface to powerful image processing and scientific visualisation algorithms from the Insight Toolkit (ITK) and Visualisation Toolkit (VTK). The framework allows one to build stand-alone medical imaging applications quickly and easily, while having a multitude of processing and visualisations built-in.

SchizConnect: Large-Scale Schizophrenia Neuroimaging Data Mediation & Federation

Large-scale data sharing and integration is needed to further the state-of-the-art schizophrenia research, but is presently not possible due to practical limitations in the way in which data are being shared. SchizConnect is a data mediation and integration resource to overcome these limitations in a low-cost manner and deliver a web portal to interact with the federated databases. Access SchizConnect at http://schizconnect.org.

pBrain

Pbrain is a collection of applications for the analysis of EEG and medical image data. Currently there are two applications, eegview and loc3d. loc3d is a 3D image analysis application for localizing, identifying and labeling objects in image data, which we use primarily for locating electrodes in CT data. eegview is an eeg visualization and analysis application which has facilities for displaying the EEG mapped onto the spatial coordinates output from loc3d.

ProCTSeg

ProCTSeg is a prostate segmentation tool for the treatment CTs. This toolbox includes an image viewer, graphical user interface, and automatic algorithms for landmark detection and prostate segmentation. The tool can support the following functions: Open medical images with various formats (such as mhd, hdr, nii), control intensity range in visualization, zoom in/out, show prostate landmarks and segmentation results, load and save landmark positions and segmentation results, and so on. The software is developed by the IDEA group at the University of North Carolina at Chapel Hill (https://www.med.unc.edu/bric/ideagroup).

Windows operating system is required to run this software. The executable file was compiled on Windows 7 (64 bit). The graphical user interfaces and the overall framework of the software were implemented in C++. Parallelization technologies were also used to speed up landmark detection and prostate segmentation.

ORION I

ORION I is a software designed for automatic 3D morphological reconstruction of neurons. The input of a software is a 3D image stack (RAW format) representing the neuron or a sequence of 3D image stacks with the provide translation parameters to align the stacks. The output is the 3D morphological reconstruction of the neuron represented in a 3D acyclic graph as the standard SWC format.

XFSL: An FSL toolbox

A set of BASH scripts for MRI data management, FSL automation and web application

MRIcroGL

MRIcroGL allows you to view 2D slices and renderings of your brain imaging data. It can display many image formats and includes a graphical interface for dcm2nii to convert DICOM images to NIfTI format. It allows you to draw regions of interest which can aid lesion mapping and fMRI analysis. It provides sophisticated rendering.

BCI2000

BCI2000 is a general-purpose system for brain-computer interface (BCI) research. It can also be used for data acquisition, stimulus presentation, and brain monitoring applications. The mission of the BCI2000 project is to facilitate research and applications in these areas. Our vision is that BCI2000 will become a widely used software tool for diverse areas of real-time biosignal processing. The BCI2000 system is available for free for non-profit research and educational purposes. We have provided it to over 3000 users around the world.
BCI2000 development has been sponsored by a NIH (NIBIB/NINDS) Bioengineering Research Partnership, a NIH (NIBIB) R01 grant, and a NIH (NIBIB) Center grant.

Functional Mixed Processes Models

The aim of this tool is to implement a functional analysis pipeline, for the joint analysis of longitudinally measured functional data and clinical data, for example age, gender and disease status. FMPM consists of a functional mixed effects model for characterizing the association of functional response with covariates of interest by incorporating complex spatial–temporal correlation structure, an efficient method for spatially smoothing varying coefficient functions, an estimation method for estimating the spatial– temporal correlation structure, a test procedure with local and global test statistics for testing hypotheses of interest associated with functional response, and a simultaneous confidence band for quantifying the uncertainty in the estimated coefficient functions.

Longitudinal Multiple Sclerosis Lesion Imaging Archive

Longitudinal Multiple Sclerosis (MS) Lesion Imaging Archive provides Training data consisting of longitudinal images from 5 patients and Testing data from 14 patients.

Each longitudinal dataset includes T1-w, T2-w, PD-w, and T2-w FLAIR MRI with 3-5 time points acquired on a 3T MR scanner. With the multiple time points, this
constitutes 82 individual data sets. To minimize the dependency of the results on registration performance and brain extraction, all images are already rigidly co-registered to the baseline T1-w image with automatically computed skull stripping masks. Unprocessed data is also included. The Training data also contains manual segmentations of the MS lesions from two different raters for each of the time points provided.

The main page regarding the challenge is http://www.iacl.jhu.edu/MSChallenge and the data is available from the Smart Stats Website: https://smart-stats-tools.org/node/26

GazeReader

GazeReader is a toolbox for a point-process derived GLM analysis of eye tracking data in Matlab (Mathworks, Nattick, MA). It was developed with the following goals in mind:

1. Flexible yet well ordered integration of the steps involved in linear model specification, fitting, selection and review.
2. Ease of use and efficiency in specifying and comparing numerous alternate models through both a graphical interface and Matlab scripting.
3. Simple error checking and review of model fitting and procedures.
4. The ability to display model data side by side with raw eye-tracking data along with any appropriate stimulus related background images in order to facilitate interpretation.
5. Efficiency of numerical model fitting.

Data loading, model specification, fitting and review are organized into a sequence of events, each of which is handled by a separate module in the toolbox. The graphical interface was created using the Matlab graphical user interface development environment (GUIDE).

Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX)

The Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX) is a Matlab tool for simulating extracellular potential recordings in spiking neural network (SNN) models. VERTEX is designed to facilitate the simulation of extracellular potentials generated by activity in SNNs; in particular, spatially-organised networks containing thousands or hundreds of thousands of neurons. VERTEX’s interface and model specification options were designed with this particular task in mind. It is therefore less flexible than other neural simulators, but the limited scope has allowed us to simplify the user interface so that a simulation can be specified simply by setting some parameters and run using a few function calls.

Functional Connectivity Analysis Tool for near-infrared spectroscopy data

FC-NIRS is a Functional Connectivity Analysis Tool for near-infrared spectroscopy dataThe package’s functions include preprocessing, quality control, FC calculation and network analysis.

PLINK

PLINK is a free, open-source whole genome association analysis toolset, designed to perform a range of basic, large-scale genetic analyses.

Clinical Toolbox for SPM

The clinical toolbox is useful for normalizing data from individuals with brain injury and/or modalities popular in the clinical environment (CT), as described by Rorden et al. (2012, PMID: 22440645). The latest version supports either enantiomorphic (PMID: 18023365) or lesion-masked (PMID: 11467921) normalization. It can be either scripted or used with SPM's simple graphical interface.

dcm2nii

dcm2nii is a popular tool for converting images from the complicated formats used by scanner manufacturers (DICOM, PAR/REC) to the simple NIfTI format used by many scientific tools. dcm2nii works for all modalities (CT, MRI, PET, SPECT) and sequence types. Note: dcm2nii has traditionally been included with MRIcron downloads. This project describes the next generation version which we refer to as dcm2niix. Future releases will supersede the (legacy) dcm2nii distributed with MRIcron.

NeuriteTracer

NeuriteTracer is a set of ImageJ plugins for automated measurement of neurite outgrowth in fluorescence microscopy images of cultured neurons.

ERPwavelab

The open source toolbox 'ERPWAVELAB' is developed for multi-channel time- frequency analysis of event related activity of EEG and MEG data. The toolbox provides tools for data analysis and visualization of the most commonly used mea- sures of time-frequency transformed event related data as well as data decomposition through non-negative matrix and multi-way (tensor) factorization. The decomposi- tions provided can accommodate additional dimensions like subjects, conditions or repeats and as such they are perfected for group analysis. Furthermore, the toolbox enables tracking of phase locked activity from one channel-time-frequency instance to another as well as tools for artifact rejection in the time-frequency domain.

MRIcroS

When performing computational neuroscience-based analysis, it is often beneficial to view the data. Also, as Matlab is a common tool for such analysis, it is beneficial to visualize the data without having to open an external application that does not run in the Matlab environment.

MRIcroS, a Matlab-based tool, provides:
1) surface mesh visualization (PLY, PIAL, NV, STL,VTK, GIFTI formats)
2) Convert NIfTI voxel images to surface meshes (and save as PLY or VTK)
3) track (TRK files) visualization
4) connectome data (BrainNet Viewer .node and .edge files) visualization
5) intuitive GUI
6) all functions available in the GUI are available through scripting. Therefore, automated scripts can be created
7) can export rendered image as bitmap

NiiStat

NiiStat is a set of Matlab scripts for analyzing neuroimaging data from clinical populations.

NeuroMorpho.Org

NeuroMorpho.Org is a centrally curated inventory of digitally reconstructed neurons associated with peer-reviewed publications. It contains contributions from over 100 laboratories worldwide and is continuously updated as new morphological reconstructions are collected, published, and shared. To date, NeuroMorpho.Org is the largest collection of publicly accessible 3D neuronal reconstructions and associated metadata.

The goal of NeuroMorpho.Org is to provide dense coverage of available reconstruction data for the neuroscience community. Data sharing through NeuroMorpho.Org enables the full and continuing research potential of existing digital reconstruction data.

Stereo Investigator

A Stereo Investigator system for stereology gives you accurate, unbiased estimates of the number, length, area, and volume of cells or biological structures in a tissue specimen. It is a key research tool that has helped lead advances in numerous areas of neuroscience including neurodegenerative diseases, neuropathy, memory, and behavior, as well as other research fields including pulmonary research, spinal cord research, and toxicology.

In addition to stereology software, a Stereo Investigator system includes any hardware you need, such as a microscope, computer, motorized stage, camera, etc., and comes with MBF’s technical support for the entire system as well as research support to optimize your experimental design.

Neurolucida

Neurolucida is a powerful tool for creating and analyzing realistic, meaningful, and quantifiable neuron reconstructions from microscope images. Perform detailed morphometric analysis of neurons, such as: the number of dendrites, axons, nodes, synapses, and spines the length, width, and volume of dendrites and axons, area and volume of the soma, and the complexity and extension of neurons. www.mbfbioscience.com/neurolucida

Researchers have reconstructed and analyzed tens of thousands of neurons using Neurolucida, leading to advances in areas of neuroscience including neurodegenerative diseases, neuropathy, memory, and behavior, and advances in research fields such as ophthalmology.

Hippocampome.org

The Hippocampome is a curated knowledge base of the circuitry of the hippocampus of normal adult, or adolescent, rodents at the mesoscopic level of neuronal types. Knowledge concerning dentate gyrus, CA3, CA2, CA1, subiculum, and entorhinal cortex is distilled from published evidence and is continuously updated as new information becomes available. Each reported neuronal property is documented with a pointer to, and excerpt from, relevant published evidence, such as citation quotes or illustrations.

The goal of the Hippocampome is dense coverage of available data characterizing neuronal types. The Hippocampome is a public and free resource for the neuroscience community, and the knowledge is presented for user-friendly browsing and searching and for machine-readable downloading.

University of Illinois Cognitive Neuroimaging Laboratory Optical Brain Imaging Software

“CNL Optical Brain Imaging Software” is a suite of tools for designing and analyzing whole head optical data, both "slow (NIRS) and "fast" (EROS). These tools are designed to work together to simplify, as much as possible, the co-registration of digitized source and detector locations (“OCP”), correction of pulse artifacts, filtering and averaging (“p_pod”), motion artifacts (kbWF) and 3D reconstruction and statistical analysis (“opt3d”). Most of these steps are implemented as Matlab subroutines, which can be called by batch controllers or used independently. Currently, the final stages (“opt3d”) are implemented in Lahey FORTRAN.

NEURON

NEURON is a simulation environment for modeling individual neurons and networks of neurons. It provides tools for conveniently building, managing, and using models in a way that is numerically sound and computationally efficient. It is particularly well-suited to problems that are closely linked to experimental data, especially those that involve cells with complex anatomical and biophysical properties. For a more detailed description see http://www.neuron.yale.edu/neuron/what_is_neuron

neuroConstruct

neuroConstruct is being developed in the Silver Lab in the Department of Neuroscience, Physiology and Pharmacology at UCL. neuroConstruct has been designed to simplify development of complex networks of biologically realistic neurons, i.e. models incorporating dendritic morphologies and realistic cell membrane conductances. It is implemented in Java and generates script files for the NEURON and GENESIS simulators, with support for other simulation platforms (including PSICS, MOOSE and PyNN) in advanced stages of development. It uses the latest NeuroML specifications, including MorphML, ChannelML and NetworkML, and supports NeuroML v2.0.

Neuron-C

Neuron-C is a simulation language for modeling biophysically realistic neural circuits.

MGA - Multimodal Glioma Analysis

MGA is an MRI preprocessing pipeline built with HOF (Heterogeneous Optimization Framework) methodology. MGA prepares neuro-oncology clinical imaging studies for scientific analysis such as multispectral ROI analysis, in both longitudinal and cross-sectional studies. MGA works on DICOM images from a single MRI study. Includes perfusion (DSC sequence based) analysis and DTI analysis, and spatially co-registers all study images to an atlas template and to a template image within the study. MGA is designed to reasonably minimize user configuration steps and to accept a broad range of MRI submodalities, including support for clinical image quality. The distribution is available as a stand-alone docker.io virtual appliance for 64-bit Linux environments (see detailed instructions in the installation package):
https://bitbucket.org/mmilch01/mga_docker_install

L-Measure

L-Measure is a robust and web-accessible neuroinformatic tool for quantitative and qualitative analysis of three-dimensional neuromorphological reconstructions. Visit LM home at http://cng.gmu.edu:8080/Lm/ for downloading the tool. Or, you may also analyze your vector form reconstructions by directly uploading them to the server via web-interfaced application available at the same URL.

NeuronJ

NeuronJ is an ImageJ plugin for computer-aided delineation of elongated image structures and the measurement of basic length statistics. It was developed primarily to facilitate the tracing and quantification of neurites in 2D fluorescence microscopy images.

libSBML

LibSBML is a free, open-source programming library to help you read, write, manipulate, translate, and validate SBML files and data streams. It's not an application itself (though it does come with example programs), but rather a library you embed in your own applications.

GENESIS neural simulator

GENESIS (the GEneral NEural SImulation System) is a software platform for the simulation of neural systems ranging from subcellular components and biochemical reactions to complex models of single neurons, large networks, and systems-level processes.

Features:

Object oriented modeling paradigm facilitates:
- modification, reuse, and exchange of models or model components
- extension of simulator functionality by adding new classes or commands
"hsolve" solver object allows fast implicit matrix methods and delivery of spike events, while preserving the illusion of separate objects
From the outset GENESIS was designed for network modeling and parallelism
Powerful scripting language allows custom-scripted simulation GUIs and specification of large network models with only a few lines of code.

Website:

http://genesis-sim.org/GENESIS/ - the stable, "classic" GENESIS 2 simulator

http://genesis-sim.org - GENESIS 3 (G-3), a 21st century neural simulator under development

TREES toolbox

1. Tools to automatically reconstruct neuronal branching from microscopy image stacks and to generate synthetic axonal and dendritic trees.
2. The basic tools to edit, visualize and analyze dendritic and axonal trees.
3. Methods for quantitatively comparing branching structures between neurons.
4. Tools for exploring how dendritic and axonal branching depends on local optimization of total wiring and conduction distance.

This software package is written in Matlab (Mathworks, Natick, MA), the most widely used scientific programming language. We hope that other groups will benefit from this package and that they will add their own code to the TREES toolbox based on their own specific applications. The code is therefore freely distributed. When publishing scientific work using this toolbox please cite the paper:

"Cuntz H, Forstner F, Borst A, Häusser M (2010). One rule to grow them all: A general theory of neuronal branching and its practical application. PLoS Comput Biol 6(8): e1000877."

CMIND_PY

Cmind_Py was written entirely in Python due to its relative ease of use and the availability of a wide number of high quality scientific (http://scipy.org) and neuroimaging software modules (http://nipy.org) . The software relies on calls to a number of widely tested algorithms from the FMRIB software library (FSL) and the advanced normalization tools (ANTS). Linux and Max OSX are supported. Both LONI and Nipype interfaces are supported. A unique feature provided by the processing pipelines is analysis of simultaneously acquired ASL/BOLD fMRI data. The tool and its code are available at bitbucket (https://bitbucket.org/grlee77/cmind-py). Pediatric templates with 2mm resolution were created for the following age ranges: 0-6 months, 0.5 – 2 years, 2-4 years and 4-18 years using ANTS. These can be found at (https://bitbucket.org/grlee77/cmind-py/downloads). The data used and their behavioral measures can be downloaded from a public database (https://cmind.research.cchmc.org/).

C-MIND Database

The CMIND database contains brain imaging data collected on 3T MRI scanners from over 200 normally developing healthy children from birth to 18 years. There is also longitudinal data collected from a sub-group of children ages 0-3 and 7-9 years who returned scans for three consecutive years. The imaging data stored in the C-MIND database are DTI, HARDI, 3DT1W, 3DT2W, concurrent ASL-BOLD scans during two language tasks (Stories & Sentence-Picture Matching), Resting State fMRI and Baseline ASL scans. The database also hosts extensive age-appropriate behavioral measures for all the subjects. The data and their behavioral measures can be downloaded from a public database (https://cmind.research.cchmc.org/). Registration to this website is available and the information can be found in the opening page (https://cmind.research.cchmc.org/auth/consent). A tool using python was developed to process the data and templates of various age ranges are available at bitbucket ((https://bitbucket.org/grlee77/cmind-py).

The Virtual Brain (TVB)

TVB allows the reproduction and evaluation of personalized configurations of the brain by using individual subject data. This personalization facilitates an exploration of the consequences of pathological changes in the system, permitting to investigate potential ways to counteract such unfavourable processes. The architecture supports interaction with MATLAB/octave packages, for example, the well known Brain Connectivity Toolbox. TVB can be used in a client-server configuration, such that it can be remotely accessed through the Internet thanks to its web-based HTML5, JS and WebGL graphical user interface. TVB is also accessible as a standalone cross-platform Python library and application, and users can interact with the scientific core through the scripting interface using IPython Notebook, enabling easy modelling, development and debugging of the scientific kernel. This second interface makes TVB extensible by combining it with other libraries and modules developed by the Python scientific community.

Parallel Stochastic Ion Channel Simulator

PSICS is software for efficient generation and simulation of models containing stochastic ion channels distributed across dendritic and axonal membranes

Brian simulator

Brian is a simulator for spiking neural networks available on almost all platforms. The motivation for this project is that a simulator should not only save the time of processors, but also the time of scientists.

Brian is easy to learn and use, highly flexible and easily extensible. The Brian package itself and simulations using it are all written in the Python programming language, which is an easy, concise and highly developed language with many advanced features and development tools, excellent documentation and a large community of users providing support and extension packages.

CARLsim: a GPU-accelerated SNN Simulator

Installation (see http://www.socsci.uci.edu/~jkrichma/CARLsim/index.html)

Supported platforms: Linux / Windows

Dependencies: NVIDIA CUDA SDK
The code has been predominately developed under Linux and the build instructions reflect that. While the code should function under MS Windows these instructions are not applicable. We will hopefully release instructions in the future on how to setup the code base under Visual Studio on Windows based systems.
Detailed installation instructions can be found in the file INSTALL in the code package.

L-Neuron

The L-Neuron program creates anatomically realistic virtual neurons using the formalism of the Lyndenmayer systems to implement sets of neuroanatomical rules discovered by several research groups (and in particular, Hillman's, Tamori', and Burke's). These rules are local and recursive. The L-Neuron algorithms read in experimental data to generate virtual structures. The experimental data are in the form of statistical distributions (for example, bifurcation angles in Purkinje cells can be represented with a Gaussian distribution, with a certain average and standard deviation). L-Neuron samples the values of the parameters within these statistical distributions in a stochastic (random) fashion during dendritic growth. Therefore, with the same set of parameter distributions, the program can generate an unlimited number of virtual neurons.

NIAGADS - NIA Genetics of Alzheimer's Disease Data Storage Site

NIAGADS (NIA Genetics of Alzheimer's Disease Data Storage Site) is a national genetics data repository that facilitates access of genotypic data to qualified investigators for the study of the genetics of late-onset Alzheimer's disease. It is the policy of the NIA that all genetic data derived from NIA-funded studies for the genetics of late-onset Alzheimer's disease be deposited at NIAGADS, another NIA-approved site, or both. NIAGADS, along with other NIA-approved sites, will make these Genetic Data and Associated Phenotypic Data available to qualified investigators in the scientific community for secondary analysis.

Brain Entropy in space and time (BEst)

Are you interested in estimating the spatial extent of EEG/MEG sources?

Are you interested in localizing oscillatory patterns?

Are you interested in localizing synchronous cortical sources?

We introduce here the toolbox BEst – "Brain Entropy in space and time" that implements several EEG/MEG source localization techniques within the “Maximum Entropy on the Mean (MEM)” framework. These methods are particularly dedicated to estimate accurately the source of EEG/MEG generators together with their spatial extent along the cortical surface. Assessing the spatial extent of the sources might be very important in some application context, and notably when localizing spontaneous epileptic discharges. We also proposed two other extensions of the MEM framework within the time frequency domain dedicated to localize oscillatory patterns in specific frequency bands and synchronous sources.

XNBC

XNBC is a full featured application for computer naive neuroscientists. It simulates biological neural networks using graphic tools to edit neurons and networks, to run the simulation and to analyze results. Written in C, it runs on Unix and Windows.

Neoseg

This tool computes an automatic segmentation of neonatal brain MRI. Automatic segmentation of these images is a challenging task mainly due to the low intensity contrast and the non-uniformity of white matter intensities, where white matter can be divided into early myelination regions and non-myelinated regions. The degree of myelination is a fractional voxel property that represents regional changes of white matter as a function of age. Our method makes use of a registered probabilistic brain atlas to select training samples and to be used as a spatial prior.

freesurfR

This package contains tools for doing group analysis of FreeSurfer (https://surfer.nmr.mgh.harvard.edu/) surface data using the general linear model in R (lm).
Results can be rendered in FreeSurfer's freeview or AFNI's SUMA. Plots for selected vertices can be rendered in R with ggplot2.

Unbiased, Deformable Spatiotemporal Atlas of the Fetal Brain

The CRL has developed a mathematical framework for the generation of an unbiased, deformable spatiotemporal atlas of the fetal brain from magnetic resonance imaging (MRI) of normal fetuses scanned prenatally.

Our atlas serves to capture the inter-subject anatomic variability of the fetal brain over the fetal brain growth period and is currently available between 27 weeks gestational age to 35 weeks. The atlas has been constructed following an unbiased minimum distance template estimation approach which utilizes symmetric diffeomorphic deformation and the cross-correlation (CC) similarity metric integrated with kernel regression in age.

Visit http://crl.med.harvard.edu/research/fetal_brain_atlas/ for more information.

GraphVar: A user-friendly toolbox for comprehensive graph analyses of functional brain connectivity

"Journal of Neuroscience Methods" (2015, 2018).

“GraphVar” is a user-friendly graphical-user-interface based toolbox (MATLAB) for comprehensive graph-theoretical analyses of brain connectivity, including network construction and characterization, statistical analysis (GLM and Machine Learning) on network topological measures, and interactive exploration of results. By combining together features across multiple current toolboxes, such as the Brain Connectivity Toolbox, Network Based Statistic Toolbox, BRAPH, and BrainNetClass (BCT, Rubinov and Sporns 2010; NBS, Zalesky et al., 2010; BRAPH, Mijalkov 2017; BrainNetClass, Zhou et al., 2020), GraphVar represents a comprehensive collection of graph analysis routines for functional neuroimaging researchers. GraphVar offers an interactive viewer that allows intuitive exploration of statistical results. Results can easily be exported and reloaded. The program entails a detailed manual that includes usage instructions and comprehensive video tutorials.

Brainnetome Atlas Viewer

Brainnetome Atlas Viewer(v1.0) shows the anatomical connectivity-based parcellation results, including the maximum probabilistic maps,probabilistic maps and both the anatomical and functional connectivity patterns, which have been developed in Brainnetome Center(http://www.brainnetome.org/), CASIA. The atlas is based on the analysis of connectional architecture with in vivo multi-modal MRI data during the last 6 years. Please see more details in our homepage: http://atlas.brainnetome.org/

Online Brain Atlas Reconciliation Tool

The Online Brain Atlas Reconciliation Tool (OBART) aims to provide a quantitative solution to the so-called neuroanatomical nomenclature problem by comparing overlap relations between regions defined as spatial entities in different MRI-based human brain atlases.

The tool currently allows users to:

1. Explore the regions defined by individual atlases, choose individual regions to view in 2D projection images, and list other similar regions and the degree to which they overlap the chosen region

2. Compare two atlases using a graph based visualization to understand "higher-order" neuroanatomical correspondences

3. Simultaneously their own binary mask image based on ROIs from multiple atlases. This requires the user to upload an image registered to MNI-305 space, and returns a table of overlapping ROIs.

Efficient Longitudinal Upload of Depression in the Elderly (ELUDE)

The ELUDE dataset is a longitudinal study of late-life depression at Duke University. There are 281 depressed subjects and 154 controls included. An MR scan of each subject was obtained every 2 years for up to 8 years (total of 1093 scans). Clinical assessments occurred more frequently and consists of a battery of psychiatric tests including several depression-specific tests.

HDBIG

Recent advances in brain imaging and high throughput genotyping and sequencing techniques enable new approaches to study the influence of genetic variation on brain structure and function. HDBIG is a collection of software tools for high dimensional brain imaging genomics. These tools are designed to perform comprehensive joint analysis of heterogeneous imaging genomics data.

Topographica

Topographica is a software package for computational modeling of neural maps, developed as part of the NIMH Human Brain Project under grant 1R01-MH66991. The goal is to help researchers understand brain function at the level of the topographic maps that make up sensory and motor systems.

Topographica is intended to complement the many good low-level neuron simulators that are available, such as Genesis and Neuron. Those simulators focus on modeling the detailed internal behavior of neurons and small networks of them. Topographica instead focuses on the large-scale structure and function that is visible only when many thousands of such neurons are connected into topographic maps containing millions of connections. Topographica provides a general-purpose framework for building models at this level, at an appropriate level of detail and complexity, as determined by the available computing power, phenomena of interest, and amount of biological data available for validation.

DICOMConvert

Dicom Converteris based on the ITK IO mechanism for reading and writing images. The formats currently supported by the converter are DICOM to:
Analyze (*.hdr)
MetaImage (*.mhd)
Nrrd (*.nhdr, *.nrrd)
In addition the application supports drop directory support. Please check for updates and leave feedback at:

Dr Awais Mansoor (awais.mansoor@gmail.com) and Dr. Ulas Bagci (Ulasbagci@gmail.com).
Center for Infectious Disease Imaging (CIDI)
Department of Radiology and Imaging Sciences
National Institutes of Health (NIH), Bethesda, MD.

NeoSegPipeline - MultisegPipeline

This tool allows you to do multi-modality multi-atlas brain MRI segmentation. Two separate strategies are possible: a) a subject-specific atlas segmentation generated from multiple atlases, as well as b) a multi-atlas segmentation (based on ANTS joint label fusion) of regional label definitions. The tool is quite customizable and users can provide their own multi-atlas libraries. A quick demo of an older version of the tools is here: https://www.youtube.com/watch?v=yTr3NJZaMos .

Publication https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10953/109530K/Multiseg-pipeline--automatic-tissue-segmentation-of-brain-MR-images/10.1117/12.2513237.short?SSO=1

High-quality diffusion-weighted imaging of Parkinson's disease

This project contains data and analysis pipelines for a set of 53 subjects in a cross-sectional Parkinson's disease (PD) study. The dataset contains diffusion-weighted images (DWI) of 27 PD patients and 26 age, sex, and education-matched control subjects. The DWIs were acquired with 120 unique gradient directions, b=1000 and b=2500 s/mm2, and isotropic 2.4 mm3 voxels. The acquisition used a twice-refocused spin echo sequence in order to avoid distortions induced by eddy currents.

Processing scripts for the paper can be found on Github: https://github.com/CyclotronResearchCentre/parktdi_scripts

Image Synthesis

The Image Synthesis Tools are a collection of software tools developed for medical image synthesis of typically magnetic resonance (MR) brain images, however, the approaches have been used to create computed tomography (CT) images from MR input. The goal of image synthesis is to recover MR images with a desired optimal contrast for further processing by either registration or segmentation. Image synthesis can be thought of as a new image restoration technique that recovers images with both the desired tissue contrast and a normalized intensity profile.

studyforrest

We provide extensive functional brain imaging data from natural stimulation, a rich set of auxiliary data, (such as structural brain scans, measurements of physiological, and technical confounds), as well as stimulus annotations. http://studyforrest.org

the Multiscale Object Oriented Simulation Environment

MOOSE is the Multiscale Object-Oriented Simulation Environment. It is the base and numerical core for large, detailed simulations including Computational Neuroscience and Systems Biology.

MOOSE spans the range from single molecules to subcellular networks, from single cells to neuronal networks, and to still larger systems.
MOOSE uses Python for scripting compatibility with a large range of software and analysis tools. It recognizes model definition standards including SBML, NeuroML, and GENESIS model file formats.

Spinal Cord Toolbox

The Spinal Cord Toolbox is a comprehensive and open-source library of analysis tools for multi-parametric MRI of the spinal cord. The toolbox includes a template and several atlases (white and gray matter, spinal tracts, spinal and vertebral levels, etc.), along with state-of-the-art methods to register any data to the template. It also includes useful scripts for data preprocessing: extraction of centerline, automatic segmentation, slice-wise motion correction, etc. Check this out!

Kymata Atlas

The Kymata Atlas is a freely available, online atlas of neural functions, developed by Cambridge University and the MRC-CBSU.

BROCCOLI

Analysis of functional magnetic resonance imaging (fMRI) data is becoming ever more computationally demanding as temporal and spatial resolutions improve, and large, publicly available data sets proliferate. Moreover, methodological improvements in the neuroimaging pipeline, such as non-linear spatial normalization, non-parametric permutation tests and Bayesian Markov Chain Monte Carlo approaches, can dramatically increase the computational burden. Here, we therefore present BROCCOLI, a free software package written in OpenCL (Open Computing Language) that can be used for parallel analysis of fMRI data on a large variety of hardware configurations. BROCCOLI (running on a GPU) can perform non-linear spatial normalization to a 1 mm brain template in 4–6 s, and run a second level permutation test with 10,000 permutations in about a minute. The new software is freely available under GNU GPL3 and can be downloaded from github (https://github.com/wanderine/BROCCOLI/).

Preprocessed Connectomes Project

Initiative to systematically preprocess neuroimaging data from open sharing repositories and to share the derivatives. Initiated in 2011 with the ADHD-200 Preprocessed initative, the PCP has grown to include the Beijing Enhanced DTI dataset and ABIDE. To enable the comparison of different preprocessing choices and to accomodate different opinions about the best preprocessings strategies, most of the data is preprocessed using a variety of tools and parameters.

Data is currently hosted in an Amazon Web Services Public S3 Bucket and at NITRC. More information about downloading the data and about the various preprocessing pipelines and strategies employed can be found on the Datasets page.

OpenWalnut

OpenWalnut is an open source tool for multi-modal medical and brain data visualization. Its universality allows it to be easily extended and used in a large variety of application cases. It is both, a tool for the scientific user and a powerful framework for the visualization researcher. OpenWalnut is licensed under the terms of the GNU Lesser General Public License version 3. Written entirely in Standard C++ and using a number of portable libraries (e.g. Qt, Boost and OpenSceneGraph) it runs on common GNU/Linux operating systems, Mac OSX and Windows. For more information, including system requirements, see OpenWalnut.

Colin 3T/7T High-resolution Atlas

High-field extension of the Colin27 single-subject atlas with additional high-resolution, quantitative, averaged scans at both 3T and 7T.

Forward: Accurate finite element electromagnetic head models

This project aims to simplify the preparation of accurate electromagnetic head models for EEG forward modeling.

It builds off of the seminal SimNIBS tool for electromagnetic field modelling of transcranial magnetic stimulation (TMS) and transcranial direct current stimulation. Human skin, skull, cerebrospinal fluid, and brain meshing pipelines have been rewritten with Nipype to ease access parallel processing and to allow users to start/stop the workflows. Conductivity tensor mapping from diffusion-weighted imaging is also included.

NeuroVault

NeuroVault makes uploading statistical maps easy and straightforward. In addition it provides permanent links to attractive volumetric and 3D (surface based) visualisations of your data.

brainhack2014

brainhack.org seeks to provide a forum for collaborative projects in the field of brain science.

We invite everyone to submit projects, either existing ones that you want to see featured or new ones that you want launch and work on in a collaborative, open way.

brainhack.org is an effort of the Neuro Bureau, neuro-collaboration in action.

YMDTI: Diffusion Tensor Images of Healthy Young Males

This dataset contains diffusion tensor images of 93 healthy, young male subjects.

Subject infomration:
age: 18-30, mean age 23
86 right-handed, 7 left-handed
no psychiatric or neurological diseases

DTI-images were recorded on a 3-T Philips Achieva scanner with the following specifications:
8-channel head coil
32 gradient directions
70 axial slices
slice thickness 2 mm
single-shot EPI sequence
TR = 7852 ms
TE = 60 ms
flip angle 90°
FOV = 224 mm
matrix = 112 x 112

7T Structural MRI scans ATAG

Structural brain data is key for the understanding of brain function and brain networks, i.e., connectomics. Here we present data sets available from the ‘atlasing of the basal ganglia (ATAG)’ consortium, which provides ultra-high resolution 7Tesla (T) magnetic resonance imaging (MRI) scans from young, middle-aged, and elderly participants. The ATAG data sets include whole-brain and reduced field-of-view MP2RAGE and T2* scans with ultra-high resolution at a sub millimeter scale. The data can be used to develop new algorithms that help building new high-resolution atlases both in the basic and clinical neurosciences. Also these atlases can be used to inform the exact positioning of deep-brain electrodes relevant in patients with Parkinson’s disease and neuropsychiatric diseases. Our results indicate that ATAG data sets allow direct visualization of smallest structures in the subcortex as well as of the brain stem.
Please see the forum for download support

Brainnetome fMRI toolkit

We have update this toolkit to Brainnetome fMRI toolkit. Detail of this toolkit can be found @ http://brant.brainnetome.org

APERTURE

APERTURE (Analysis of Patterns in Electrophysiological Recordings Toolbox with Utilities for REsearch) is a MATLAB-based toolbox for analysis of EEG, MEG, and ECoG data. It is designed to organize entire analysis pipelines with large datasets, from preprocessing to hypothesis testing and figure generation. APERTURE allows flexible multivariate analysis of ERPs and oscillatory activity. It also supports mass-univariate analysis with advanced statistical tests. Computations are accelerated using parallel computing supported through the MATLAB distributed computing toolbox. Examination of large, high-dimensional datasets is made simple through data visualization tools, including advanced plotting routines and generation of PDF reports with many figures. APERTURE is designed to be flexible and highly extensible, to allow the user to easily implement new analysis protocols.

BraTumIA (Brain Tumor Image Analysis)

BraTumIA, for Brain Tumor Image Analysis, is a software dedicated to multimodal image analysis of brain tumor studies.

It performs volumetric segmentation of healthy and tumor tissues by employing multispectral MRI sequences (currently T1, T1-contrast, T2-contrast, and FLAIR). Segmented tissues include Gray Matter (GM), White Matter (WM), Cerebrospinal Fluid (CSF), necrotic core, edema, non-enhancing tumor and enhancing tumor.

The current version of BraTumIA is designed to work on pre-operative images.

Prediction and Diagnosis for Depression and Schizophrenia

Researches have shown that Depression and Schizophrenia is a dynamic, nonstatic phenomenon. Accordingly, it appears to be more important to prevent the worsening of these diseases over time, and early assessment and prediction for these diseases can play an important role in achieving early intervention and reducing the incidence of them. In our research, we plan to explore the tool and resource about how to predict Depression and Schizophrenia based on Demographics and physiological information(EEG, ERPs, Genetics, MRI, fMRI, etc.). Ultimately, we hope this research can lay the theoretical foundation for the application of prediction for Depression and Schizophrenia.

gCCA

gCCA is a multivariate method for fMRI data analysis based on generalized canonical correlation analysis (gCCA) to maximize SPM reproducibility without adopting any model for the hemodynamic response or other temporal brain responses. The underlying assumption is that there are multiple subjects that share an unknown spatial response (or spatial map) to the common experimental manipulation but may show different temporal responses to external stimulation. For each subject, our gCCA explores a broad range of temporal responses in fMRI time-series space while maximizing the mean of correlation coefficients between the pair-wise spatial maps of the subjects.

[1] Afshin-Pour, B., et. al (2012). Enhancing reproducibility of fMRI statistical maps using generalized canonical correlation analysis in NPAIRS framework. NeuroImage, 60(4), 1970-1981.

[2] Afshin-Pour, et. al (2014). Evaluation of spatio-temporal decomposition techniques for group analysis of fMRI resting state data sets. NeuroImage, 87, 363-382.

MISST - Microstructure Imaging Sequence Simulation ToolBox

Microstructure Imaging Sequence Simulation Toolbox (MISST), is a practical diffusion MRI simulator for development, testing, and optimisation of novel MR pulse sequences for microstructure imaging. MISST is based on a matrix method approach and simulates the signal for a large variety of pulse sequences and tissue models. Its key purpose is to provide a deep understanding of the restricted diffusion MRI signal for a wide range of realistic, fully flexible, scanner acquisition protocols, in practical computational time.

Ruby NIfTI

Ruby NIfTI is a pure-ruby gem [library] for handling NIfTI data in the Ruby programming language.

Ruby NIfTI currently supports basic read and write access to NIfTI files, including basic & extended header information and image information.

More information on ruby-nifti can be found on the github page; please use the Github Issue Tracker for any questions:

https://github.com/brainmap/nifti

INSTALLATION
------------

gem install nifti

or add this to your Gemfile and `bundle install`:

gem 'nifti', '~>0.0.2'

USAGE
------

Read file:

obj = NIFTI::NObject.new("T1.nii")

Display some key information about the file:

puts obj.header['sform_code_descr']
=> "NIFTI_XFORM_SCANNER_ANAT"

Retrieve the pixel data in a Ruby Array:

image = obj.get_image

Full Documentation is at:

http://rdoc.info/gems/nifti/frames

OpenViBE

OpenViBE is a software platform dedicated to designing, testing and using brain-computer interfaces (BCI). The package includes an Acquisition Server application to stream data from various hardware devices, and a Designer tool to create and run real-time signal-processing chains assembled with a graphical design language. OpenViBE package includes several BCI demo applications (based on e.g. P300, SSVEP, Motor Imagery, etc) which are ready for use.

BiofilmQuant

A semi-automated software tool for dental plaque biofilm quantification in quantitative light-induced fluorescence (QLF) images. Please cite our paper:

Mansoor, A.; Patsekin, V.; Scherl, D.; Robinson, J.; Rajwa, B., "A Statistical Modeling Approach to Computer-Aided Quantification of Dental Biofilm," Biomedical and Health Informatics, IEEE Journal of
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6758338&isnumber=6363502

Multivariate General Linear Models (MGLM) on Riemannian Manifolds

Statistical analysis tool for manifold-valued data. The SPD manifold for diffusion tensor images (DTI) and the Hilbert unit sphere for square root representation of orientation distribution functions (ODF) can be used.

Fast T2 relaxation data analysis with stimulated echo correction and non-local spatial regularisation

This tool is developed to offer a fast algorithm for computing myelin maps from multiecho T2 relaxation data using parallel computation with multicore CPUs and graphics processing units (GPUs). The tool also provides non-local spatial regularization to produce more accurate and reliable myelin maps for noisy T2 relaxation data.

The details of the method are given the following papers:

Yoo, Youngjin et al., "Fast computation of myelin maps from MRI T2 relaxation data using multicore CPU and graphics card parallelization." Journal of Magnetic Resonance Imaging (2014).

Yoo, Youngjin, and Roger Tam. "Non-local spatial regularization of MRI T2 relaxation images for myelin water quantification." In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013, pp. 614-621. Springer Berlin Heidelberg, 2013.

The JMRI paper stated that we intented to contribute to Gadgetron, but we found that NITRC seems to be a better fit so we have decided to share our code through NITRC.

MRI denoising

The Matlab package MRIdenoisingPackage contains five denoising filters and a noise estimation method for 3D MRI. The package includes nonlocal means, Oracle DCT and locally adaptive NLM methods.
Based on image redundancy and/or sparsity, the proposed filters provide efficient denoising while preserving fine structures. The package is compiled for Linux (64 bits), Windows (64 bits) and MAC (64bits). The proposed GUI enables the processing of several .nii files in 2-clicks to make easier the processing of large database.

DTI denoising

The proposed Matlab package DWIdenoisingPackage contains six denoising filters and a noise estimation method for 4D DWI.
The package includes nonlocal means, local PCA and Oracle DCT methods.
Based on image redundancy and/or sparsity, the proposed filters provide efficient denoising while preserving fine structures.
The package is compiled for Linux (64 bits), Windows (64 bits) and MAC (64bits). The proposed GUI enables the processing of several .nii files in 2-clicks to make easier the processing of large database.

Wisconsin Cortical Thickness Analysis (CTA) Toolbox

Wisconsin CTA toolbox is a Matlab tool to perform statistical analysis on cortical thickness signals on brain surfaces obtained from Freesurfer. In particular, this tool is useful for multi-resolutional analysis of such cortical thickness signals and detecting group differences. We find that compared to standard analysis, this method is much more sensitive in detecting such differences. It is based on the Spectral Graph Wavelet Transform (SGWT) toolbox (http://wiki.epfl.ch/sgwt), and provides plug and play methods for deriving Wavelet Multiscale Descriptor (WMD), cortical thickness smoothing using SGWT, Multivariate General Linear Model (MGLM), False Discovery Rate (FDR) and so on.

Won Hwa Kim (http://pages.cs.wisc.edu/~wonhwa)

Graph Theory GLM (GTG) MATLAB Toolbox

This MATLAB toolbox calculates & runs a GLM on graph theory properties derived from brain networks. The GLM accepts continuous & categorical between-participant predictors & categorical within-participant predictors. Significance is determined via non-parametric permutation tests, including correction for multiple comparisons. Both fully connected & thresholded networks are tested.

The toolbox also provides a data processing path for resting state & task fMRI data. Options for partialing nuisance signals include: local & total white matter signal (Jo et al., 2013), PCA of white matter/ventricular signal (Muschelli et al., 2014), Saad et al. (2013)'s GCOR, & Chen et al. (2012)’s GNI. In addition, Power et al. (2014)'s motion scrubbing method & Patel et al. (2014)'s WaveletDespike are available.

Please sign up for the mailing list, as this is where we announce new releases.

The Striatal Subregional VOImap

The Striatal Subregional VOImap is intended to provide accurate data in terms of specific uptake location to make the BP quantitation. Available in .obj format.

The VOIs were manually drawn with software Analyze 9,0 (Mayo Clinic) in 18F-DOPA brain image after spatial normalization with a 18F-DOPA Template developed by our group (available at: https://www.nitrc.org/projects/spmtemplates). Each striatum was divided into 6 sub-regions: ventral caudate, anterior dorsal caudate, posterior dorsal caudate, ventral putamen, anterior dorsal putamen and posterior dorsal putamen, based on Mawlawi et al. (J Cereb Blood Flow Metab.2001;21:1034–57) and Oh et al. (J Nucl Med.2012;53:399–406) segmentation. A nonspecific background volume was drawn in the occipital cortex.

Terms of use:
The Striatal Subregional VOImap is intended for free use by the neuroimaging community. However, we do ask that you please cite.

J.A. Lojo-Ramírez, F.J. García-Gómez & D. García-Solís.
Contact: jalojoram@gmail.com

Open Science Framework

The OSF supports the scientific workflow. Create projects and use them to organize and archive research materials and data. Share those materials with just collaborators, or make them available publicly. Get credit for making materials available when others view, download, use, or extend them. Register projects to create frozen versions to mark the state of a project at a particular point in its history - onset of data collection, at manuscript submission, final version for publication. OSF is part network of research materials, part version control system, and part collaboration software.

Generation R Pediatric MRI Resources

Generation R (GenR) Pediatric MRI Resources: Pediatric MRI experiences unique challenges with respect to spatial normalization/registration. An average, age-appropriate T1-weighted image, constructed from 130 typically developing children ages 6-to-10, is available.

Further, a set of 25 resting-state ICA components are made available. The components were generated from 536 typically developing children, ages 6-to-10 years old. The components are the result of a meta-ICA which combined the output of 500 different ICA analyses where 50 subjects were randomly sub-sampled.

Both of these resources are described in detail the following publication:

http://www.ncbi.nlm.nih.gov/pubmed/27417416

Contents:

GenR130 T1-weighted template. 1mm and 2mm resolution brain images and brain masks.

GenR25_metaICA resting state components. 25 Resting state components in 3mm and 2mm resolution. These are available in the GenR130 space (space in which original ICAs and metaICA was conducted), and also in MNI152 space.

18F-DOPA PET and 123I-FP-CIT (Ioflupane - DaTSCAN) SPECT templates for SPM normalisation.

The 18F-DOPA and 123I-FP-CIT (Ioflupane/DaTSCAN) templates are intended for use as templates for SPM automated normalisation. Created for SPM8 but SPM12 compatible. Available in NifTI file format.

The updated templates are symmetrical and adjusted to MNI-space templates from 12 (18F-DOPA) and 30 controls without evidence of nigrostriatal degeneration (123I-FP-CIT).

Authors: F.J García-Gómez, I Huertas, JA Lojo, D García-Solís.
Contact: javier191185@gmail.com

Terms of use:
These images are intended for free use by the neuroimaging community. However, we do ask that you please cite the following references in any publications which make use of these images:
- García-Gómez FJ, et al. Elaboration of the SPM template for the standardization of SPECT images with 123I-Ioflupane. Rev Esp Med Nucl Imagen Mol. 2013;32:350-6.
- García-Gómez FJ, et al. Elaboración de una plantilla de SPM para la normalización de imágenes de PET con 18F-DOPA. Imagen Diagn. 2018; 9(1):23−25.

MDR Multifactor Dimensionality Reduction

Multifactor Dimensionality Reduction (MDR) is a nonparametric and genetic model-free machine learning alternative to logistic regression for detecting and characterizing nonlinear interactions among discrete genetic and environmental attributes. The MDR method combines attribute selection, attribute construction, and classification with cross-validation and permutation testing to provide a comprehensive and powerful approach to detecting epistasis. MDR has been successfully applied to numerous different diseases and is currently being adapted to genome-wide assoication studies (GWAS).

ExPosition Packages

Exposition is defined as a comprehensive explanation of an idea. In the world of high dimensional data analysis and multivariate analysis one technique stands out because of its versatility and ubiquity: The singular
value decomposition (SVD). With the ExPosition packages for R, a comprehensive explanation of your data will be provided with minimal effort.

UNC-Wisconsin Neurodevelopment Rhesus MRI Database

This is a macaque brain MRI database characterizing the normal postnatal macaque brain development. This longitudinal primate database was acquired from a cohort of healthy macaque monkeys ranging from a few week olds up to 3-year-old adolescents.

Each scan consists of structural (both T1 and T2) and diffusion MRI. T1 and T2 are provided as NRRD format in the original scanner space as well as in a common atlas space where they are rigidly aligned. Diffusion MRI is provided as raw diffusion weighted images. In addition, pre-processing was performed including motion and eddy current distortion correction on the diffusion weighted MR and tensors were calculated with diffusion property maps (FA, MD, Baseline and iDWI).

For further information, see also Young JT et al. The UNC-Wisconsin Rhesus Macaque Neurodevelopment Database: A Structural MRI and DTI Database of Early Postnatal Development. Frontiers in Neuroscience. 2017 Feb 2;11:292–11.

Notion ResearchPACS

Notion PACS
===========

Notion is a stand-alone [PACS](http://en.wikipedia.org/wiki/Picture_archiving_and_communication_system) designed to be used by radiology researchers for storage and anonymization of research images.

### Why use Notion?

If you have a need to:
- store DICOM images, but do not want/have a dedicated research PACS
- anonymize DICOM images
- map Names, IDs and Accession numbers during anonymization
- maintain separation of image data across projects
- scale to 100's of independant research projects

### Why *not* use Notion?

If you:
- already have a research PACS
- do not need to anonymize data
- do not care about isolation between research projects
- are happy using manual anonymization tools

GIMME (Group Iterative Multiple Model Estimation)

**** We have transitioned GIMME completely into R. The R package is maintained. ****
Group Iterative Multiple Model Estimation (GIMME) is a free Matlab toolbox for directed functional connectivity analysis of the fMRI BOLD signal from predefined regions of interest. GIMME reliably recovers the true structure of connections and estimates the weights attributed to each connection (equivalently termed “path” or “adjacency”). Importantly, patterns are obtained at both the group and individual levels. GIMME is one of the few approaches that accommodates heterogeneity in brain connectivity patterns across individuals within a sample. Req: Matlab, Lisrel, preproccesing toolbox/software. Gates, K. M. & Molenaar, P. C. M. (2012). Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples, NeuroImage, Volume 63, Issue 1(15), 310-319, http://dx.doi.org/10.1016/j.neuroimage.2012.06.026

International Imaging Genetics Conference

This international symposium was held initially to assess the new technology and innovation in the various established fields of genetics and imaging, and to facilitate the transdisciplinary fusion needed to optimize the development of the emerging field of Imaging Genetics. Over the past decade, the conference has helped to define and document progress in the field and to create a dialogue for the challenges and opportunities it faces.

European Epilepsy Database EPILEPSIAE

Many technological applications in the field of neurology and neuroscience depend on evaluations based on electroencephalographic data (EEG). So far, public resources for EEG recordings are limited. We present here the European database developed in the EU-founded project “EPILEPSIAE” (Grant 211713).

The EPILEPSIE database is by far the largest and most comprehensive database for human surface and intracranial eeg data. It is suitable for a broad range of applications e.g. of time series analyses of brain activity. The database is accessible for users who apply to the research groups in charge (http://epilepsy-database.eu).

Currently, the EU database contains annotated EEG datasets from more than 200 patients with epilepsy, 50 of them with intracranial recordings with up to 122 channels. Each dataset provides EEG data for a continuous recording time of at least 96 hours (4 days) at a sample rate of up to 2500 Hz. Clinical patient information and MR imaging data supplement the EEG data.

Efficient Permutation Testing

Matlab implementation for efficient permutation testing (http://pages.cs.wisc.edu/~vamsi/pt_fast.html).

Permutation testing is a non parametric procedure to estimate the distribution of max null statistic. However it computationally very expensive, particularly in neuroimaging and biostatistics studies where dimensionality of data is on the order of millions. We proposed an efficient procedure to estimate this max null using matrix completion. A speed up of atleast 50 times is achieved while estimating the max null upto a high degree of accuracy. For complete details please refer to the project website (link above).

Please cite the following paper upon usage of the tool.
C. Hinrichs, V. K. Ithapu, Q. Sun, V. Singh, S. C. Johnson, Speeding up Permutation Testing in Neuroimaging, Neural Information Processing Systems (NIPS), 2013

GLMdenoise: a fast, automated technique for denoising task-based fMRI data

GLMdenoise is a MATLAB toolbox for denoising task-based fMRI data. The basic idea is to derive noise regressors from voxels unrelated to the experimental paradigm and to use these regressors in a general linear model (GLM) analysis of the data. The technique is fast and automated and general: it requires only a design matrix indicating the experimental design and an fMRI dataset. In the published paper (Kay et al., Frontiers in Neuroscience, 2013), it is shown that GLMdenoise outperforms other popular denoising methods, such as using motion parameters as noise regressors, ICA-based denoising, and RETROICOR/RVHRCOR.

MABMIS for Slicer 4: Multi-Atlas Based Multi-Image Segmentation

MABMIS is a module for Slicer 4 that implements a multi-atlas based multi-image method for group-wise segmentation. (H. Jia, P-T Yap, D. Shen, "Iterative multi-atlas-based multi-image segmentation with tree-based registration", NeuroImage, 59:422-430, 2012. )
The method utilizes a novel tree-based groupwise registration method for concurrent alignment of both the atlases and the target images, and an iterative groupwise segmentation method for simultaneous consideration of segmentation information propagated from all available images, including the atlases and other newly segmented target images.

MABMIS contains a training step and a testing step. In the training step, a groupwise registration is performed to generate a tree structure for the atlases. In the testing step, a group of target images are simultaneously and iteratived segmented using the pre-constructed atlas tree.

Prior to applying MABMIS, the atlas images and target images need be skullstripped, and linearly transformed to a common space.

Intrinsic Unscented Kalman Filter (IUKF) Tractography Software v1.0

Intrinsic Unscented Kalman Filter (IUKF) is a recently introduced tractography algorithm for HARDI. IUKF provides a relatively accurate and efficient fiber tracking mechanism, compared to most existing tractography methods. Following are the key features of IUKF:
• Given HARDI data sets, it is capable of tracking in the presence of complex local geometries such as crossing and kissing fibers.
• Unlike many existing techniques, IUKF does not require one to reconstruct the multi-tensor field a priori, because reconstruction is only performed at the voxels along estimated fibers. This significantly increases the tracking speed.
• IUKF reconstructs a bi-tensor model for underlying signal and exploits intrinsic operations on the space of diffusion tensors (symmetric positive definite matrices). This will provide more accurate and smooth estimates of the underlying signal and the fiber orientations compared to its non-intrinsic counterparts.

NTU-DSI-122: A Diffusion Spectrum Imaging Template with High Anatomical Matching to the ICBM-152 Space

NTU-DSI-122 is a diffusion spectrum imaging (DSI) template constructed in the standard ICBM-152 space from 122 healthy adults. This template was built through incorporating the macroscopic anatomical information using high-resolution T1-weighted images and the microscopic structural information obtained from DSI datasets, rendering it to achieve a high anatomical matching to the ICBM-152 space. Therefore, this template can serve as a representative DSI dataset for a healthy adult population, and will be of potential value for brain research and clinical applications. This template is released in the original DWI format, so the users have the most freedom to perform their own advanced processing algorithms on NTU-DSI-122. NTU-DSI-122 is distributed under the terms of CC BY-NC-SA 4.0.

Reference: Hsu Y-C, Lo Y-C, Chen Y-J, Wedeen VJ, Tseng W-YI (2015): NTU-DSI-122: A diffusion spectrum imaging template with high anatomical matching to the ICBM-152 space. Hum Brain Mapp doi: 10.1002/hbm.22860.

Iterative dual-regression with sparse prior

Iterative Dual-Regression with Sparse Prior (IDRwSP) is aimed to better estimate an individual's neuronal activation using the results of an independent component analysis (ICA) method applied to a temporally concatenated group of functional magnetic resonance imaging (fMRI) data (i.e., Tc-GICA method).
An ordinary DR approach estimates the spatial patterns (SPs) of neuronal activation and corresponding time courses (TCs) specific to each individual's fMRI data with two steps involving least-squares (LS) solutions. The proposed approach employs iterative LS solutions to refine both the individual SPs and TCs with an additional a priori assumption of sparseness in the SPs (i.e., minimally overlapping SPs) based on L(1)-norm minimization.

See the reference paper.

Kim YH, Kim J, Lee JH., Iterative approach of dual regression with a sparse prior enhances the performance of independent component analysis for group functional magnetic resonance imaging (fMRI) data., Neuroimage. 2012.

Displacement Field Viewer

Displacement field viewer or DFViewer is a simple tool for visualizing displacement fields estimated in association with image registration. Based on the displacement vector field, a mesh is generated for visualization. The mesh can be color mapped with the jacobian determinant at each point for better localization of regions that undergo compression or expansion.

Key features:
1) Jacobian determinant color mapping.
2) View synchronization.
3) Save/Load view configurations.
4) Conversion from deformation fields.
5) Conversion from HAMMER displacement fields.
6) Drag-and-drop mechanism for opening file.
7) Adjustable mesh resolution.
8) Option to save mesh in VTK format.
9) Option to view mesh in different image planes.
10) Glyph (arrow) view.
11) Accepts multiple image formats, including NIfTI (.nii.gz or .nii), NRRD (.nrrd or .nhdr), and MetaImage (.mha or .mhd).

minc-toolkit-testsuite

Sample data in MINC format and collection of scripts to verify correct installation of minc-toolkit.

BEaST segmentation library

Library of anatomical priors for BEaST http://www.bic.mni.mcgill.ca/ServicesSoftwareAdvancedImageProcessingTools/BEaST

bic-mni-models

bic-mni-models anatomical brain template library, includes models from ICBM 2009 template ( http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009 )

3dsvm

3dsvm is a command-line program and plugin for AFNI (http://www.nitrc.org/projects/afni/), built around SVM-Light (http://svmlight.joachims.org/). It performs support vector machine (SVM) analysis on fMRI data and runs on Unix+X11+Motif systems, including SGI, Solaris, Linux, and Mac OS X.

minc-toolkit

Whole set of MINC-based image processing tools packaged together.
Includes: MINC,N3,BICPL,EBKTS,ANIMAL,INSECT,BEaST,Register,Display,xdisp

NIH-CIDI Lung Segmentation Tool

Open-source software for automatic segmentation of lung in CT images.

The software is an segmentation tool for the segmentation of lung from CT images. The sofware can be run in two modes: (a) fully automatic and (b) semi-automatic with manual seeding by the user. The software also allows the user perform basic filtering operations and manual correction to the segmentation, if needed. The VTK-based rendering implemntation along with option to view in axial, coronal, and saggital provide user with better visualization of the segmented lung. A short video on the working of the software can be viewed here:

http://www.youtube.com/watch?v=VD3GQ0O7weE

The algorithm driving the software has been tested on a publically available database (LOLA Challenge 11, http://www.lola11.com). The results based on the overlap score can viewed on the challenge website.

SPIKECOR: fMRI tool for automated correction of head motion spikes

This algorithm corrects for "spikes" in fMRI data, typically caused by abrupt head motion during scanning. SPIKECOR identifies outliers using Principal Component Analysis (PCA) in a sliding time-window; it is sensitive to global motion artifact, and stable against non-stationary signal changes.
Unlike typical "scrubbing" protocols it is automated, and identifies motion outliers using purely statistical criteria - no need to select arbitrary motion thresholds! It also replaces outlier volumes with interpolated values using cubic splines, which minimizes discontinuities in the data.

NIH-CIDI Segmentation of PET Images based on Affinity Propagation Clustering

Presented here is a MATLAB GUI for segmenting and quantifying PET images with multi-focal and diffuse uptakes. The segmentation algorithm was presented at the 2013 IEEE International Symposium on Biomedical Imaging and IEEE Transactions on Biomedical Engineering (In Press).

The MATLAB GUI imports a PET image and allows the user to draw region of interests (ROIs) in 2D or 3D to roughly separate the object of interest from the background. Then, the areas are segmented using a PET image segmentation method based on Affinity Propagation clustering to cluster the image intensities into meaningful groups.

For quantification, the Standardized Uptake Value measurements of the binary or the user defined ROI are SUVmax, SUVmean, and Volume (mm^3) and can be exported into an excel sheet. We believe that there is meaningful information in the secondary groups, not just the highest uptake group which is the current standard.

Renderings with the functional information overlaid can also be made for visualization.

Corpus Callosum Thickness Profile Analysis Pipeline

An end-to-end pipeline for corpus callosum processing that provides automated midsagittal alignment, CC segmentation with a quality control tool, and thickness profile generation. Groupwise analysis is facilitated by permutation testing with FWER and FDR multiple comparison correction. Results display is facilitated by a display script that shows p-values on a 3D pipe representation of a CC. This pipeline is implemented in Python.

BetA-Series COrrelation

BASCO (BetA-Series COrrelation) is a software tool for investigating inter-regional functional connectivity in event-related fMRI data and allows you to assess the modulation of functional connectivity by an experimental condition. This tool is inspired by an approach introduced by Rissman and colleagues (2004). The method is based on a general linear model (GLM) where the evoked activity in each trial is modeled by a separate covariate. This renders a series of beta-values for each voxel which is related to a given experimental condition. The functional connectivity between brain regions is derived from correlating voxel beta-series. BASCO offers the following analysis approaches: i) Seed-based functional connectivity analysis. ii) ROI-based network analysis. Given a parcellation of the brain the mean beta-series are extracted for each ROI and a network matrix is calculated correlating all ROI beta-series. iii) Voxel-based whole brain network analysis.

Stark Cross-Sectional Aging

NOTE -Some, but not all of the data are are live. More will be added as time and disk space allow.

Behavioral and imaging data from ~120 participants aged 18-89. Data were collected as part of a grant to use high-resolution imaging and advanced behavioral tasks to understand how aging affects the hippocampus and how this is related to age-related cognitive decline. The full dataset includes:
- Traditional neuropsycholgical measures
- Hippocampal-specific behavioral measures
- Whole-brain DTI
- High-resolution DTI of the medial temporal lobes
- Structural MRI including segmentation of grey/white/CSF, of cortical regions (Freesurfer) and of hippocampal subfields

Turbo-BrainVoyager

Turbo-BrainVoyager (TBV) is a highly optimized, easy to use software package for the real-time analysis and dynamic visualization of functional magnetic resonance imaging data sets. Turbo-BrainVoyager allows to observe the working brain "online" by incrementally computing statistical maps as contrasts of a General Linear Model (GLM). The program also performs real-time pre-processing, including 3D motion correction, spatial Gaussian smoothing and temporal filtering (drift removal).

Simulated DW-MRI Brain Data Sets for Quantitative Evaluation of Estimated Fiber Orientations

This resource provides simulated DW-MRI brain images and quantitative tools for evaluating the performance of diffusion analysis methods in terms of fiber orientation estimation and false-positive/-negative fiber rates, which are of fundamental importance to tractography based studies. The data and tools aim to encourage greater understanding of the fiber estimation abilities of existing methods by use of standardized metrics for evaluation and comparison, and help development of improved diffusion analysis methods.

DW data was generated using a multi-tensor model at SNRs of 9, 18 and 36, for sets of 20, 30, 40, 60, 90 and 120 gradient directions. For each combination of SNR and gradient direction set, 10 realizations of data are provided. All data is simulated with a diffusion-weighting of b=1000, as is common for clinical acquisitions.

>> See "Docs" for all documentation and citation information.
>> See "Downloads" for data and quantitative tools.

>> Published in NeuroImage (Vol 109, April 2015).

Papaya

Papaya is a pure JavaScript medical research image viewer, supporting DICOM and NIFTI formats, compatible across a range of web browsers. This orthogonal viewer supports overlays, atlases, GIFTI surface data and DTI data. The Papaya UI is configurable with many display, menu and control options and can be run on a web server or as a local, shareable file.

More info: http://rii-mango.github.io/Papaya/
Demo: http://rii.uthscsa.edu/mango/papaya/index.html

Lightweight Data Pipeline

This is a lightweight framework for setting up dependency-driven processing pipelines. The tool is essentially a configurable shell script (sh/bash), which can be included in other scripts and primarily provides a small number of utility functions for dependency checking and NFS-safe file locking for cluster processing.

While the framework is not very powerful (deliberately so), it is extremely simple and easy to apply. There is no need to provide any command wrappers. An existing processing script (or sequence of shell commands) can be turned into a data-driven, cluster-safe pipeline by adding just a few extra lines of shell code.

Functional Real-time Interactive Endogenous Neuromodulation and Decoding (FRIEND)

FRIEND is a GUI-based user-friendly software for real-time fMRI processing, multivoxel pattern decoding and neurofeedback. The package integrates routines for image preprocessing in real-time, ROI-based feedback (single-ROI percent signal change and functional connectivity) and brain decoding-based feedback using the FSL and libSVM libraries. Users can create or employ pre-specified visual stimuli for neurofeedback experiments.
The FRIEND for Windows standalone version runs embedded FSL and libSVM functions and provides a simple, straightforward solution. The FRIEND Engine is a multiplatform (Mac/Linux) toolbox that incorporates the same processing capabilities, but in which the front-end is separated from the processing module allowing users to develop their own front-end GUIs. Advanced users can develop plugins to extend the original pipeline provided by the engine (e.g., Matlab functions for data preprocessing or stimulus presentation features from standard packages such as E-Prime).

Connectome Workbench

Connectome Workbench is a brain visualization, analysis and discovery tool for fMRI and dMRI brain imaging data, including functional and structural connectivity data generated by the Human Connectome Project. The software package includes wb_view, a GUI-based visualization platform and wb_command, a command-line program for performing a variety of analytical tasks for volume, surface, and CIFTI grayordinates data.

Downloads and other related resources are on this site: http://www.humanconnectome.org/software/get-connectome-workbench.html

Source code is available here: https://github.com/Washington-University/workbench

KWScene: MRML-based Atlas and Scene Builder/Reader/Writer

ITK-based processing and 3D Slicer scene management in ParaView. We believe this will broaden the use of ParaView for high performance computing and visualization in the medical imaging research community. The effort is focused on developing ParaView plug-ins for managing VTK structures from 3D Slicer MRML scenes and encapsulating ITK filters for deployment in ParaView.

Principal Components Analysis of Scalar, Vector, and Mesh Vertex Data

An implementation of standard PCA algorithms for use on
scalar or vector data sets. Kernel PCA is implemented in this class as well, where the data sets are scalar or
vector valued functions assigned at each of the points in a PointSet. A Gaussian Distance Kernel class is
provided with the PCA class.

This contribution is part of a shape analysis software pipeline created at Johns Hopkins. PCA will be used
to develop a set of basis vectors for use with Gaussian Random Field analysis. The output of PCA will be
analyzed for significance with various statistical methods such as t-tests built upon the built-in statistical
capabilities of ITK.

Laplace Beltrami Filter on QuadEdge Meshes

The purpose of this filter is to use the Laplace-Beltrami operator to determine surface harmonics in terms
of PointData at each vertex. In the same way that a sound signal can be approximately characterized by a
combination of its most significant frequency components, so can a surface be expressed as a combination
of its surface harmonics. This filter determines the requested N most significant harmonics.

The Brain Coactivation Map

The Brain Coactivation Map describes all the coactivation networks in the human brain based on the meta-analysis of more than 5,400 neuroimaging articles (from NeuroSynth) containing more than 16,000 individual experiments. It also provides a list of the MeSH tags that more closely match the structure of each network.
You can use our viewer CoactivationMap (https://github.com/r03ert0/CoactivationMap.app) to interactively browse the map, or our command line cmtool (https://github.com/r03ert0/cmtool) to query the map from a shell.

Imeka Tractography Service

Imeka Tractography is a diffusion MRI service that handles the processing of diffusion data from raw data to structural connectivity, including Quality Assurance. We work with neuroscience researchers who want quality diffusion data reconstruction. These researchers do not necessarily have the knowledge to perform diffusion analysis or don't have the manpower.

Our service is not a black box solution: we work with the researchers and make sure they can work with the tractography results and are aware of the pitfalls of the techniques by providing training sessions. We have a system to work at a distance as if we were in your lab. We can do high angular resolution (HARDI) reconstruction from DTI data, with as little as 20 gradient directions acquisitions.

State-of-the-art diffusion MRI is just one click away. Contact us right now to submit your project and have access to structural Connectome research today.

Visit us at www.imeka.ca for more details

ShapePopulationViewer

ShapePopulationViewer is a software that allows you to dynamically interact with multiple surfaces at the same time. It is very useful for visualisation and comparison of 3D surfaces by also displaying their scalars or vectors attributes stored in the points, and allowing the user to simply modify the colormap.
ShapePopulationViewer is available as an extension of 3D Slicer (http://www.slicer.org)

MARS (Multi-Atlas Robust Segmentation)

MARS (Multi-Atlas Robust Segmentation) provides the automatic solutions for efficent segmentation/labeling anatomcial structures from medical images.

Specifically, this software has integrated several state-of-the-art multi-atlas based segmentation methods, such as majority voting, local weighted voting, and non-local patch based segmentation methods.

More importantly, we also included our recently-developed joint sparse patch based segmentation method in this software. Compared with convention methods, our method has the following advantages: (1) add sparsity constraint to suppress the influence of misleading patches; (2) reduce the joint risk of two patches jointly making the same segmentation errors, and (3) use iterative framework to correct the possible mis-segmentations.

This software package was developed in the IDEA group at UNC-Chapel Hill ( http://bric.unc.edu/ideagroup).
Wu et. al., "A generative probability model of joint label fusion for multi-atlas based brain segmentation", MIA, 2013.

CAWorks

Current enhancements to CAWorks have focused on interactive support for landmarking of subcortical structures. Specific plugins are available for landmark placement of the hippocampus, amygdala and entorhinal cortex regions. After landmarking is completed, CAWorks facilitates submission for automated segmentation processing. CAWorks is being used by the Biomarkers for Older Controls At Risk for Dementia (BIOCARD), Resource for Quantitative Functional MRI: TRD4, Validation of Structural/Functional MRI Localization and Biomedical Informatics Research Network (BIRN) projects.

CAWorks has been further enhanced with a browser plugin module for the Extensible Neuroimaging Archive Toolkit (XNAT). The XNAT software facilitates the storage and management of neuroimaging and associated data. The XNAT browser enables the retrieval of medical image data from XNAT for analysis in CAWorks and the storage of CAWorks analysis results in XNAT.

Northwestern University Schizophrenia Data and Software Tool (NUSDAST)

The Northwestern University Schizophrenia Data and Software Tool (NUSDAST) is a repository of schizophrenia neuroimaging data collected from over 450 individuals with schizophrenia, healthy controls and their respective siblings, most with 2-year longitudinal follow-up.
The data include:
Neuroimaging data: Structural MR scans, landmarks and surface maps, and FreeSurfer parcellation and measurement)
Cognitive data: domain scores for crystallized intelligence, working memory, episodic memory, and executive function
Clinical data: demographic, sibling relationship, SAPS and SANS psychopathology
Genetic data: 20 SNPs
The imaging data are stored in the image repository. The non-imaging data are stored as "Resouces" within each imaging session.
CAWorks, a neuroimaging mapping, analysis and visualization software tool, can be found here: https://www.cis.jhu.edu/software/caworks/index.php

Atlases of amygdala and hippocampus for pediatric populations

The long term goal of Computational Anatomy (CA) is to create algorithmic
tools that aid basic and clinical neuroscientists in the analysis of
variability in anatomical structures at different scales.
The aim is to provide capability for integrating 3D Slicer application and ITK software library with the statistical shape analysis
pipeline and thus enable the wider neuroimaging community to efficiently
analyze anatomical variations in disease. We have constructed anatomical atlases needed for analysis of shape vectors. These atlases were generated from
segmented hippocampal and amygdala structures in acquired populations of
children, adolescents and young adults in neuroimaging studies of major
depression disorder (MDD) at Washington University at St Louis. As a major
public health burden, MDD provides the biological testbed for the pipeline
from which probabilistic atlases will be generated. Software and datasets for statistical shape analysis of anatomical structures are thus provided.

3DMeshMetric

3DMeshMetric is a visualization tool based on the VTK library.
Its main feature is to compute and display surface-to-surface distance between two triangle meshes using user-specified uniform sampling (based on the source code of MeshValmet: http://www.nitrc.org/projects/meshvalmet/).
3DMeshMetric also offers all the basic tools to visualize meshes such as color, opacity, smoothing, down sampling or type of representation.
3DMeshMetric also contains an additional tool called ModelToModelDistance that computes distances between two triangle meshes (using the VTK library). This tool can be used either as a command line tool or integrated in 3D Slicer (http://www.slicer.org) as a CLI module. It is available in 3D Slicer as an extension to facilitate the download and installation process.

ANNOVAR: Functional annotation of genetic variants from high-throughput sequencing data

ANNOVAR is an efficient software tool to utilize update-to-date information to functionally annotate genetic variants detected from diverse genomes (including human genome hg18, hg19, as well as mouse, worm, fly, yeast and many others).

GenGen

GenGen is a suite of free software tools to facilitate the analysis of high-throughput genomics data sets. The package is currently a work-in-progress and infrequently updated.

PHYCAA+: adaptive physiological noise correction for BOLD fMRI

The PHYCAA+ algorithm automatically estimates and removes physiological noise in BOLD fMRI data, including the effects of heartbeat and respiration. This algorithm (1) masks out high-variance CSF and vascular tracts that may otherwise confound analyses, and (2) regresses out noise timeseries in grey matter tissue, using an adaptive multivariate component decomposition (Canonical Autocorrelations Analysis). PHYCAA+ is an efficient, automated procedure that does NOT require external measures of physiology, nor does it require the user to manually identify noise components.

Based on the peer-reviewed article:
Churchill & Strother (2013). "PHYCAA+: An Optimized, Adaptive Procedure for
Measuring and Controlling Physiological Noise in BOLD fMRI". NeuroImage 82: 306-325

MINC Example files

This is a reference MINC set of files. It currently includes human head images only of standard modalities.

The goal is to build a well curated collection of files that demonstrate the capabilities of MINC

NIRx2nirs: A NIRx to .nirs data converter

NIRx2nirs is a matlab script which takes near-infrared spectroscopy data recorded by NIRx system(s) and converts it to a .nirs file format for use with the HOMER2 NIRS processing pacakge.

CBFBIRN

The Cerebral Blood Flow Biomedical Informatics Research Network (CBFBIRN) (https://cbfbirn.ucsd.edu) provides a web based central repository for individual and group analysis of Arterial Spin Labeling (ASL) data sets and ASL pulse sequences developed at CMFRI UCSD for MRI researchers.

This resource currently hosts more 1300 ASL data sets from 22 projects and consists of mainly two main tools
1) The Cerebral Blood Flow Database and Analysis Pipeline (CBFDAP) is a web enabled data and workflow management system extended from the HID codebase on NITRC specialized for Arterial Spin Labeling data management and analysis (including group analysis) in a centralized manner.
2) Pulse Sequence Distribution System (PSDS) for managing dissamination of ASL pulse sequences developed at the UCSD CFMRI.

This resource also includes web and video tutorials for end users.

Multi-Echo Independent Component Regression (ME-ICR) Group-Level Connectivity (GroupInCorr) Dataset

The data provided is an AFNI GroupInCorr session for multi-echo independent component regression (ME-ICR) for a cohort of 52 subjects. This dataset provides a high-quality atlas of seed-based functional connectivity with strong statistical conditioning.

cPPI Toolbox for fMRI

This is a Matlab toolbox that allows computation of task-related functional connectivity between multiple pairs of regions.

Task-related functional connectivity is computed using the correlational psychophysiological interaction (cPPI) methodology described in Fornito et al. (2012) PNAS, 109: 12788-12793.

The toolbox assumes that first-level design matrices have been specified and estimated using SPM5 or later.

It takes as input these design matrices as well as user-extracted regional time courses and returns a matrix of pair-wise, task-related functional connectivity for each participant. The method is scalable to large networks comprising hundreds of regions and is well-suited to graph theoretic analyses and functional connectomics.

One modifiable script, cPPI_master.m, can be used to run the analysis for an entire sample of participants.

2008 MICCAI MS Lesion Segmentation Challenge

MS lesion segmentation challenge 2008: The goal of this competition is to compare algorithms to segment the MS lesions.

Please download data from links here (with the password in the README), and submit your segmentation results at http://www.ia.unc.edu/MSseg after registering your team. We require team name, password, and email for future contact. Please submit segmentations in a zip file and refer submission page for data format.

The data has a special license:
- the data sets and associated segmentation data, must not be given nor redistributed to persons not belonging to the registered team.
- you need to upload the results of your algorithm and allow us to make the quantitative evaluation results publicly available on the challenge site.

Note: Cases were added after the MICCAI challenge. We minimally request the submission of test data UNC 1 - 10 (excluding UNC 2) and CHB 1-15 (excluding CHB 14), but the inclusion of the additional data (UNC 11-14 and CHB 16-18) is optional.

EYE-EEG (combined eye-tracking & EEG)

EYE-EEG is a plugin for the open-source MATLAB toolbox EEGLAB developed with the goal to facilitate integrated analyses of electrophysiological and oculomotor data. The plugin parses, imports, and synchronizes simultaneously recorded eye tracking data and adds it as extra channels to the EEG.

Saccades and fixations can be imported from the eye tracking raw data or detected with an adaptive velocity-based algorithm. Eye movements are then added as new time-locking events to EEGLAB's event structure, allowing easy saccade- and fixation-related EEG analysis (e.g., fixation-related potentials, FRPs). Alternatively, EEG data can be aligned to stimulus onsets and analyzed according to oculomotor behavior (e.g. pupil size, microsaccades) in a given trial. Saccade-related ICA components can be objectively identified based on their covariance with the electrically independent eye tracker.

All functions can be accessed via EEGLAB's GUI or called from the command line.

g.BSanalyze

g.BSanalyze - gtec's Biosignal Analysis Software, is an interactive environment for multimodal biosignal data processing and analysis in the fields of clinical research and life sciences. It is the most comprehensive package to analyze non-invasive and invasive brain-, heart- and muscle-functions and dysfunctions. It includes many functions such as support vector machines, event-related ECG, support for P300 and SSVEP/SSSEP BCIs, zero class detection for BCIs, compressed spectral array, minimum energy, and more!

g.BSanalyze consists of a base version for data import, visualization, transformation and pre-processing and has several dedicated toolboxes.

The package comes with many sample biosignal data-sets, including P300, SSVEP, motor imagery, CSP BCIs, Tilt-Table, EPs, multi-unit activity, CFM, and ERD/ERS.

UNC/Utah NAMIC DTI Fiber Analysis Framework

This project can be seens as a master project encompassing several current NITRC projects into a coherent set. The project will host binary packaged distributions, scripts, example datasets, and corresponding results of analysis using our UNC/Utah NAMIC DTI Fiber Analysis Framework.

Our workflow utilizes tools already available on NITRC including:
-DTIPrep (available as a 3D Slicer extension http://www.slicer.org)
-DTIAtlasBuilder (available as a 3D Slicer extension http://www.slicer.org)
-FiberViewerLight (available as a 3D Slicer extension http://www.slicer.org)
-DTIAtlasFiberAnalyzer (available as a 3D Slicer extension http://www.slicer.org)
-FADTTS with the updated GUI FADTTSter

Cerebral Blood Flow Database and Analysis Pipeline (CBFDAP)

The Cerebral Blood Flow Database and Analysis Pipeline (CBFDAP) is a web enabled data and workflow management system extended from the HID codebase on NITRC specialized for Arterial Spin Labeling data management and analysis (including group analysis) in a centralized manner.

Cognitive Paradigm Ontology

The Cognitive Paradigm Ontology (CogPO) is a domain ontology of cognitive paradigms for application and use in the functional neuroimaging community. CogPO has been developed through the integration of the Functional Imaging Biomedical Informatics Research Network (FBIRN) Human Imaging Database (HID) and the BrainMap Database. The design of CogPO concentrates on what can be observed directly: categorization of each paradigm in terms of (1) the stimulus presented to the subjects, (2) the requested instructions, and (3) the returned response.

ADJUST - EEG Automatic Artifact Removal

ADJUST is a completely automatic algorithm for artifact identification and removal in EEG data. ADJUST is based on Independent Component Analysis (ICA), a successful but unsupervised method for isolating artifacts from EEG recordings. ADJUST identifies artifacted ICA components by combining stereotyped artifact-specific spatial and temporal features. Features are optimised to capture blinks, eye movements and generic discontinuities. Once artifacted IC are identified, they can be simply removed from the data while leaving the activity due to neural sources almost unaffected.

ADJUST runs in Matlab as a plugin of the EEGLAB software, the most popular software for analysis of electrophysiological data. No Matlab toolboxes are required.

For more details, please read ADJUST reference paper and ADJUST tutorial ("Docs" tab on the left of the page).

BrainMagix SPM Viewer

BrainMagix SPM Viewer is a free, professional viewer for SPM fMRI results.

SPM (Statistical Parametric Mapping, UCL, London) is a powerful fMRI analysis software but its visualization capabilities are sometimes a limitation for the researchers. That's why Imagilys has decided to offer the neuroimaging community a free version of its commercial "BrainMagix" neuroimaging software, called "BrainMagix SPM viewer".

BrainMagix SPM Viewer's Features

- Professional viewer for your SPM-based fMRI activations
- JAVA-programmed, cross-platform (Windows, MAC, Linux), without Matlab license, making it possible to share your results with colleagues who do not have SPM installed
- Reads SPM.mat files and NIfTI images in an user-friendly way
- Overlay the blobs with an atlas or any anatomical image
- On the fly adjustment of threshold and cluster size
- Localize your activations in an atlas
- BOLD signal curves in ROIs (future feature)
- Export your results as PNG images

EPILAB

A Matlab®-based software package, EPILAB, was developed for supporting researchers in performing studies on the prediction of epileptic seizures. It provides an intuitive and convenient graphical user interface.
Fundamental concepts that are crucial for epileptic seizure prediction studies were implemented.This includes, for example, the development and statistical validation of prediction methodologies in long-term continuous recordings. Seizure prediction is usually based on electroencephalography (EEG) and electrocardiography (ECG) signals. EPILAB is able to process both EEG and ECG data stored in different formats. More than 35 time and frequency domain measures (features) can be extracted based on univariate and multivariate data analysis. These features can be post-processed and used for prediction purposes. The predictions may be conducted based on optimized thresholds or by applying classifications methods such as artificial neural networks, cellular neuronal networks, and support vector machines.

EONS

EONS (Elementary objects of the Nervous System) is the perfect modeling platform to study the dynamic interactions between synaptic elements in a friendly manner. There are no complicated equations to write: all the elementary models are predefined using state of the art models!
EONS consists of a graphical modeling platform containing the major elements that comprise a glutamatergic synapse (both pre- and post-synaptically). These elements parameters as well as the underlying synaptic geometry can be modified.

EONS is a parametric model of a generic glutamatergic synapse that takes into account pre-synaptic mechanisms, such as calcium buffering and diffusion, neurotransmitter release, diffusion and uptake in the cleft, and postsynaptic elements, such as ionotropic AMPA and NMDA receptors, their distribution and synaptic geometry, as well as metabotropic glutamate receptors.

Neuro-Image Management System (NIMS)

NIMS is a scientific data management system specifically designed for neuroimaging data. NIMS automatically reaps data from the measurement instrument (e.g., MR scanner), sorts and organizes the data based on header information, does some basic processing on the data, and makes the data available to authorized users through a web-based interface. The data are also available from the command-line through a FUSE-based filesystem. See a live demo at the CNI public data site: http://cni.stanford.edu/nims/pub/browse.

Multicomponent T2 estimation with stimulated echo correction

This tool is designed to assist users in the estimation of multiple relaxation components from MRI T2 weighted spin-echo data such as that produced by a Carr-Purcell-Meiboom-Gill (CPMG) sequence. This problem is important to study myelin content in white matter diseases such as multiple sclerosis.

Stimulated echoes arising from non-ideal flip angles are accounted for using the Extended Phase Graph (EPG) algorithm. The distribution is modelled as a small number of discrete components and a Bayesian estimation algorithm is provided to determine the weights and locations of the components as well as the actual flip angle. This algorithm outperforms iterative gradient descent based approaches.

The details of the algorithm are given the following paper:
Layton, K. J. et al. (2013) Modelling and Estimation of Multicomponent T2 Distributions, IEEE Transactions on Medical Imaging 32:1423-1434

SOCK

SOCK (Spatially Organized Component Klassifikator) is a software toolbox that can automatically identify many of the artifact components that are often present in independent component analysis (ICA) of functional MRI (fMRI). The method:
• Does not require temporal information about the fMRI paradigm.
• Does not require the user to train the algorithm.
• Requires only the EPI images (additional acquisition of anatomical images is not required).
• Is able to identify a high proportion of artifact-related ICs without removing components that are likely to be of neuronal origin.
• Can be applied to resting-state fMRI.
• Is automated, requiring minimal or no human intervention.

The method is described in the following paper:
Bhaganagarapu K, Jackson GD, Abbott DF.
An automated method for identifying artifact in
Independent Component Analysis of resting-state fMRI.
Frontiers in Human Neuroscience 7(343):1-16 (2013).
http://dx.doi.org/10.3389/fnhum.2013.00343

PROPixx DLP LED projector

The PROPixx is a unique DLP LED projector which has been designed to be the most flexible display solution for vision research and neuroscience research. The PROPixx features a native resolution of 1920 x 1080, and can be driven with refresh rate up to 500Hz with deterministic timing. The PROPixx uses high brightness LEDs as a light source, giving a wide colour gamut and much longer lifetime than halogen light sources. It features high-bit depth, up to 12-bit per color for high-frequency full colour stimulation. For stereo vision applications, our high-speed ferro-electric circular polarizer can project stereoscopic stimuli with the use of passive glasses at up to 400Hz. In addition the PROPixx includes an array of peripherals which often need to be synchronized to video during an experiment, and with perfect microsecond precision.

VIEWPixx /3D

The VIEWPixx /3D is a complete display toolbox which has been developed specifically to replace CRTs in vision science labs. The VIEWPixx /3D incorporates industrial LCD glass, and a controller which has been custom designed to support vision research. Our innovative LED backlight design features superior display uniformity, and a wide color gamut exceeding that of any CRT. In addition, it includes an array of peripherals which often need to be synchronized (with microsecond precision) to video during an experiment, including a stereo audio stimulator, a button box port for precise reaction-time measurement, triggers for electrophysiology equipment, and even a complete analog I/O subsystem. It is perfectly suited for presentation of dynamic stimuli, it features 1920 x 1080 resolution, 10-bit intensity on each colour and true 120Hz refresh rate.

MCIC- schizophrenic and matched control data

Expertly collected, well-curated data sets consisting of comprehensive clinical characterization and raw structural, functional and diffusion-weighted DICOM images in schizophrenia patients and gender and age-matched controls are now accessible to the scientific community through an on-line data repository (coins.mrn.org). This data repository will be useful to 1) educators in the fields of neuroimaging, medical image analysis and medical imaging informatics who need exemplar data sets for courses and workshops; 2) computer scientists and software algorithm developers for testing and validating novel registration, segmentation, and other analysis software; and 3) scientists who can study schizophrenia by further analysis of this cohort and/or by pooling with other data.

Philips Users Community

This communnity project is to help support the efforts of investigators using Philips Healthcare systems. This clearingsite helps users find forums, mailinglists, etc. that support this community. If you have suggestions for inclusion, let the project admin know!

Nilearn

Nilearn: Machine learning for Neuro-Imaging in Python, is a software package to facilitate the use of statistical learning on NeuroImaging data.

Namely Nilearn leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.

ModelGUI

ModelGUI (or simply mgui) is an open source Java-based project intended to provide a graphic user interface (GUI) for interactions between scientists (or enthusiasts) and their data. In its current (beta) form, mgui offers the following functionality:

Established:

Cross-platform functionality (with a Java Runtime installation, runs on Linux, Windows, Mac, or Solaris)
2D rendering of data based upon Java2D, and 3D rendering based upon Java3D
An extensible I/O framework accommodating a variety of standard and non-standard file formats
Database connectivity using JDBC
Graph visualization based upon the JUNG library
An intuitive Swing-based GUI for managing, querying, and visualizing data

Experimental:

Various CAD-type tools for editing and creating geometry
The ability to organize complex datasets into intuitive mgui projects
A processing pipeline interface which allows users to process their datasets with any available Java or native software tools
A computational modelling framework

BraVa

The BraVa database contains digital reconstructions of the human brain arterial arborizations from 61 healthy adult subjects along with extracted morphological measurements.

The arterial arborizations include the six major trees stemming from the circle of Willis, namely: the left and right Anterior Cerebral Arteries (ACAs), Middle Cerebral Arteries (MCAs), and Posterior Cerebral Arteries (PCAs).

4D-PARSeR (Pathological Anatomy Regression via Segmentation and Registration)

4D-PARSeR (Pathological Anatomy Regression via Segmentation and Registration) is a tool for analyzing 4D images with pathology. Originally developed for processing longitudinal images of patients with traumatic brain injury. The tool contains new image analysis algorithms that combine registration and segmentation in a coherent framework, accounting for extreme changes due to extensive tissue damage.

Network Modification (NeMo) Tool Lite

The NeMo Tool is the first that associates localized white matter (WM) lesions with
disruptions in gray matter connectivity as a step toward understanding the lesions’ functional
implications. A Tractogram Reference Set (TRS), i.e. collections of white matter fibers, is
constructed from 73 normal healthy individuals and coregistered to a common space (MNI). The
NeMo Tool uses the TRS to assess structural network disruption due to a particular WM lesion
mask on a region and network-wise level. This tool is an easy way for researchers and clinicians
to investigate changes in the structural brain network without having to perform tractography on
their own normal data or on diseased/injured brains where the results may not represent the
underlying physiology.

HERMES

Here we present HERMES (http://hermes.ctb.upm.es), a toolbox for the Matlab® environment, which is designed to study functional and effective brain connectivity from neurophysiological data such as multivariate EEG and/or MEG records. It includes also visualization tools and statistical methods to address the problem of multiple comparisons. We believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis.

GSA-SNP

A tool for the gene-set (or pathway) analysis of a genome-wide association study result. It accepts a genome-wide list of SNPs and their association P-values. It summarizes the SNP P-values into nearby genes. The gene-by-gene summary results are then further summarized by gene-sets such as Gene Ontology, KEGG pathways, or user-created gene-sets. Various standardization and statistical tests can be performed and the resulting gene-sets that pass a significance level after multiple-testing correction are reported. The tool is written in Java and is available as a standalone version. It has been published as "Nam, D., Kim, J., Kim, S. Y. & Kim, S. GSA-SNP: a general approach for gene set analysis of polymorphisms. Nucleic Acids Res. 38 (Suppl. 2), W749–W754 (2010).

The Brain Catalogue

Our aim is to celebrate the diversity of the vertebrate brain by making high quality data, open and freely available to everyone. Do you have data that you would like to share? Do not hesitate to contact us!

The Brain Catalogue is developed by Florencia Grisanti (Taxidermy Workshop of the Natural History Museum in Paris) and Roberto Toro (Neuroscience Department of the Institut Pasteur). Many of our specimens come from the Vertebrate Brain Collection of the Jardin des Plantes, curated by Marc Herbin, and are scanned at the Institut du Cerveau et de la Moelle (ICM) by Mathieu Santin and Alexandra Petiet, from the CENIR laboratory, with financial and methodological support kindly provided by Olivier Colliot, head of the Cogimage team at the ICM.

Wisconsin White Matter Hyperintensities Segmentation Toolbox

W2MHS is an open source MATLAB toolbox designed for detecting and quantifying White Matter Hyperintensities(WMH) in Alzheimer’s and aging related neurological disorders.Our toolbox provides a self-sufficient set of tools for segmenting these WMHs reliably and further quantifying their burden for down-processing studies. WMHs arise as bright regions on T2-weighted FLAIR images. They reflect comorbid neural injury or cerebral vascular disease burden. Their precise detection is of interest in Alzheimer’s disease (AD) with regard to its prognosis.

Please cite the following paper upon usage of this toolbox.
Ithapu, V., Singh, V., Lindner, C., Austin, B. P., Hinrichs, C., Carlsson, C. M., Bendlin, B. B. and Johnson, S. C. (2014), Extracting and summarizing white matter hyperintensities using supervised segmentation methods in Alzheimer's disease risk and aging studies. Hum. Brain Mapp.. doi: 10.1002/hbm.22472

S-rep Fitting, Statistics, and Segmentation

S-reps are skeletal models for anatomic objects especially suited for computing probability distributions from populations of these objects and for providing object-related coordinates for the interior of these objects. They allow classification and hypothesis testing using their geometric features and physiological features derived from medical images. They also allow the definition of shape spaces, probability-based geometric typicality functions, and appearance models used for segmentation or registration. A variety of successful applications to objects in neuroimages have already been performed.

The resource contains software to fit s-reps to segmented anatomic objects, to compute probability distributions on these s-reps, to train and to apply classifiers between two classes of anatomic objects, and to apply hypothesis testing to determine which geometric or physiological features vary significantly between two classes. Software for object segmentation from medical images may also be included.

NODDI Matlab Toolbox

This MATLAB toolbox implements a data fitting routine for Neurite Orientation Dispersion and Density Imaging (NODDI). NODDI is a new diffusion MRI technique for imaging brain tissue microstructure. Compared to DTI, it has the advantage of providing measures of tissue microstructure that are much more direct and hence more specific. It achieves this by adopting the model-based strategy which relates the signals from diffusion MRI to geometric models of tissue microstructure. In contrast to typical model-based techniques, NODDI is much more clinically feasible and can be acquired on standard MR scanners with an imaging time comparable to DTI.

HBM Hackathon

Aloha! Registration is now open for the 3rd Annual OHBM Hackathon taking place at the Hawaii Convention Center in Hawaii on the beautiful island of Oahu from June 12-14, 2015.

During this three day event, the first two days will focus on collaborative and open neuroscience projects in data analysis and methods development. See brainhack.org for examples of previous projects and to submit your project ideas!

The third and final day of the event will feature a FREE morning course on “Brain Hacking 101” where participants will be introduced to brain imaging as a data science. The afternoon will feature a session freely available to all OHBM attendees on the hackathon project outcomes.

The spirit of the Hackathon will continue into the OHBM Annual Meeting, June 14-18 in Honolulu through an on-site collaboration space available throughout the conference.

Dates: June 12-14, 2015
Location: Hawaii Convention Center, Honolulu, HI
OHBM 2015 website
Project website: www.brainhack.org

MisterI

MisterI (Mr. I) is a powerful and modular medical image viewer/editor. It should be particularly useful to Undergraduates, Postdocs and Researchers in Medical Imaging to visualize data and to easily make attractive figures (for papers or presentations). It will also in a near future offer a number of advanced algorithms for medical image processing. Look at the video to get an idea !

http://www.benoitscherrer.com/MisterI/videos.html

MIAS Registration Toolkit

MIAS toolkit provides the following libraries and functions on linux platform:

1. Multi-resolution registration of MR images include T1, multimodality, and DTI images.

2. The registration model is B-spline, and users can custermize their own image similarity measures by writing a plugin function and recompile the program.

NiftyReg

NiftyReg includes tools for global and local image registration.

The algorithm used for global registration is based on a block matching approach enabling robust registration (outliers rejection). The local registration implementation uses a cubic B-Spline parametrisation (Free-Form Deformation). All registration algorithms are based on symmetric approaches where forward and backward transformations can be optimised concurrently.

NiftyReg has been implemented for both CPU and GPU (through the use of CUDA).

Nifty Sim

Nifty Sim is a high-performance nonlinear finite element solver, developed at University College London. A key feature is the option of GPU-based execution, which allows the solver to significantly out-perform equivalent commercial packages.

caGWAS

Cancer Genome-Wide Association Studies (caGWAS) allows researchers to integrate, query, report, and analyze significant associations between genetic variations and disease, drug response or other clinical outcomes. SNP array technologies make it possible to genotype hundreds of thousands of single nucleotide polymorphisms (SNPs) simultaneously, enabling whole genome association studies. Within the Clinical Genomic Object Model (CGOM), the caIntegrator team created a domain model for Whole Genome Association Study Analysis. CGOM-caGWAS is a semantically annotated domain model that captures associations between Study, Study Participant, Disease, SNP Association Analysis, SNP Population Frequency and SNP annotations.

Autism Tissue Program - ATP

The Autism Tissue Program (ATP) is a research program organized to provide post-mortem brain tissue to qualified neuroscientists all over the world.

Registry Builder Data Harmonization and Aggregation Tool

Remedy Informatics' platform aggregates data from multiple sources, harmonizes the data via Ontology, and provides data visualization and pattern recognition and querying tools. Our software helps harmonize the data that you have in different Excel files, databases, repositories, biospecimen applications, etc. and maps it to one common registry.

Spatial Statistical Parametric Mapping

SSPM represents Spatial Statistical Parametric Mapping.This package includes two tools presently: MAGEE and FADTTS.
MAGEE represents the Multiscale Adaptive Generalized Estimating Equation. It was developed specifically for analyzing multivariate neuroimaging data in 3-dimensional volume (or on 2-dimensional surface) from longitudinal neuroimaging studies.
FADTTS represents Functional Analysis of Diffusion Tensor Tract Statistics. The aim of this tool is to implement a functional analysis pipeline, for delineating the structure of the variability of multiple diffusion properties along major white matter fiber bundles and their association with a set of covariates of interest, in various diffusion tensor imaging studies.

4D Atlas Construction

4DAtlas (4D Atlas Construction Toolbox) provides solutions for constructing longitudinal atlases, which are the necessary steps for many brain-related applications.

This software package was developed in the IDEA group at UNC-Chapel Hill ( http://bric.unc.edu/ideagroup ).

Inter-Group Registration Toolbox

InterGroupReg (Inter-Group Registration Toolbox) provides solutions for registering two groups of images, which are the necessary steps for many brain-related applications.

This software package was developed in the IDEA group at UNC-Chapel Hill ( http://bric.unc.edu/ideagroup ).

fMRI Grocer

This toolbox contains many kinds of kits that you may be interested in during fMRI data analysis.

This toolbox is a homebrew kits built during practical ASL(arterial spin labeling) based CBF data analysis. Meanwhile, this toolbox is also compatible with BOLD data analysis. Everyone would find something useful for their own data analysis!

This toolbox is run and tested on SPM8 with MATLAB 7.6.0(R2008a) under the Linux OS.

Theoretically, most of the functions (except the menu1&2 which are specially designed for the Batch Editor of SPM8) of this toolbox should be compatible with SPM5 and should also work smoothly under the Windows OS.

Feel free to give feedback to authors if you encounter any bugs or problems.

Senhua Zhu
Center for functional Neuroimaging, University of Pennsylvania
3 W.Gates Bldg, 3400, Philadelphia, PA (19104), United States

Email: zshtom@gmail.com ; senhua@mail.med.upenn.edu

QQ group number (QQ群): 60524357

Google group: https://groups.google.com/d/forum/fmri-grocer

PennCNV

PennCNV is a free software tool for Copy Number Variation (CNV) detection from SNP genotyping arrays. Currently it can handle signal intensity data from Illumina and Affymetrix arrays. With appropriate preparation of file format, it can also handle other types of SNP arrays and oligonucleotide arrays.

PennCNV implements a hidden Markov model (HMM) that integrates multiple sources of information to infer CNV calls for individual genotyped samples. It differs form segmentation-based algorithm in that it considered SNP allelic ratio distribution as well as other factors, in addition to signal intensity alone. In addition, PennCNV can optionally utilize family information to generate family-based CNV calls by several different algorithms. Furthermore, PennCNV can generate CNV calls given a specific set of candidate CNV regions, through a validation-calling algorithm.

Learning based Image Segmentation Toolbox

Local Label Learning for Multi-atlas based Image Segmentation Toolbox

Groupwise Image Registration

Groupwise Image Registration Toolbox

1. A method for group-wise image registration by pairwisely registering similar images identified using graph theoretic techniques. Particularly, we use sparse coding to estimate image similarity measures among images to be registered, yielding asymmetric, group-wise image similarity measures for each image to others in the group. Based on the asymmetric group-wise image similarity measures among different images, a directed graph is built for learning a manifold of images and identifying a group center image so that all other images can be registered to the center image following the shortest directed paths, each of them decomposing a large deformation into a series of small and anatomically meaningful deformations.

2. A method for group-wise registration of functional MRI images based on their local functional connectivity patterns.

NiftyRec

The NiftyRec Tomography Toolbox includes reconstruction tools for emission and transmission imaging modalities, including Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), cone-beam X-Ray CT and parallel-beam X-Ray CT.

At the core of NiftyRec are efficient, GPU accelerated, projection, back-projection and iterative reconstruction algorithms.

The easy to use Matlab and Python interfaces of NiftyRec enable fast prototyping and development of reconstruction algorithms. NiftyRec includes standard iterative reconstruction algorithms such as Maximum Likelihood Expectation Maximisation (MLEM), Ordered Subsets Expectation Maximisation (OSEM) and One Step Late Maximum A Posteriori Expectation Maximisation (OSL-MAPEM), for multiple imaging modalities.

ENIGMA: Enhancing Neuro Imaging Genetics through Meta-Analysis

The ENIGMA Network brings together researchers in imaging genomics, to understand brain structure and function, based on MRI, DTI, fMRI and genomewide association scan (GWAS) data. The ENIGMA Network has several goals:

* To create a network of like-minded individuals, interested in pushing forward the field of imaging genetics.
* To ensure promising findings are replicated via member collaborations, in order to satisfy the mandates of most journals.
* To share ideas, algorithms, data, and information on research studies and methods.
* To facilitate training, including workshops and conferences on key methods and emerging directions in imaging genetics.

Pythagorean Displacement and Motion Regressors

This Matlab script uses the Pythagorean Theorem to calculate head motion and position, while preserving degrees of freedom. The motion parameters output by SPM (rp*.txt) estimate head position relative to the first volume in 3D translation and 3D rotation, which are often entered as a nuisance regressor during individual-level statistics. Regressing the total displacement and relative position can potentially explain more variance in voxel-level BOLD signals that is related to head movement during an fMRI experiment.

Rodent Cortical Thickness Analysis

RodentThickness is an automatic cortical thickness measurement tool for rat brains.

The pipeline consists of four steps: preprocessing to create binary mask and label map, thickness measurement which produces laplacian field and thickness map in order, run particle correspondence followed by statistical analysis resulting in mean thickness color map and t-test result.

By running RodentThickness, you will need to fill in informations in a Graphical User Interface, and then compute. You can also run the tool in command line without using the GUI.
Using the GUI, you will be able to save or load a dataset file or a configuration file.

The tool needs these other tools to work, so be sure to have these installed on your computer:

ImageMath
measureThicknessFilter
GenParaMeshCLP
ParaToSPHARMMeshCLP
ShapeWorksRun
ShapeWorksGroom
SegPostProcessCLP
BinaryToDistanceMap
MeshPointsIntensitysampling

Rosetta Bit

The Rosetta Bit project will house public datasets that have been transcoded into multiple formats. This library of valid file format conversions (DICOM->NIFTI, DICOM->PAR/REC, etc.) will provide a reference for tool developers seeking to support multiple sources of data.

Yvernault BC, Theobald CD Jr, Smith JC, Villalta V, Zald DH, Landman BA.
Neuroinformatics. 2014 Oct;12(4):615-7. doi: 10.1007/s12021-014-9230-9.
Validating DICOM Transcoding with an Open Multi-Format Resource.
Pubmed: http://www.ncbi.nlm.nih.gov/pubmed/24777387

RT_Image

RT_Image is an application developed in the Department of Radiation Oncology and MIPS at Stanford University to visualize, segment, and quantify three-dimensional images. Multiple datasets may be loaded, displayed, fused, processed, and quantitatively analyzed simultaneously. Data may be imported from any DICOM-compatible three dimensional imaging modality. Regions-of-interest may be defined using a number of manual, semi-automatic, and automated tools to segment three-dimensional pixel volumes. They may also be imported from and exported to DICOM structure sets. This software has been applied to preclinical and clinical computed tomography (CT), positron emission tomography (PET), single photon emission computed tomography (SPECT), magnetic resonance imaging (MRI), and optical imaging data.

mach2qtl

QTL analysis based on imputed dosages/posterior_probabilities.

mach2dat

mach2dat performs logistic regression, using imputed SNP dosage data and adjusting for covariates.

MaCH-Admix

MaCH-Admix is an extension to MaCH for faster and more flexible imputaiton, especially in admixed populations.

It has incorporated a novel piecewise reference selection method to create reference panels tailored for target individual(s). This reference selection method generates better imputation quality in shorter running time. MaCH-Admix also separates model parameter estimation from imputation. The separation allows users to perform imputation with standard reference panels + pre-calibrated parameters in a data independent fashion. Alternatively, if one works with study-specific reference panels, or isolated target population, one has the option to simultaneously estimate these model parameters while performing imputation. MaCH-Admix has included many other useful options and supports VCF input files. All existing MaCH documentation applies to MaCH-Admix.

MaCH

MaCH is a tool for haplotyping, genotype imputation and disease association analysis developed by Goncalo Abecasis and Yun Li.

BTK : Baby Brain Toolkit

Fbrain is a ERC funded project. The purpose of this project is to develop image processing tools for a better understanding of the fetal brain development.

BTK (baby brain toolkit), which is the toolkit developed for the fbrain project, consists of several image processing tools: image reconstruction, image denoising, image segmentation, tractography etc.

DICCCOL predictor (v0.1)

DICCCOL predictor (v0.1) is a toolbox to predict 358 DICCCOL landmarks on a new brain given b0, brain surface data and DTI derived fiber data (vtk format). DICCCOL is the abbreviation of Dense Individualized and Common Connectivity-based Cortical landmarks (http://dicccol.cs.uga.edu) and developed by CAID (caid.cs.uga.edu). Each DICCCOL landmark is defined by group-wise consistent white-matter fiber connection patterns derived from diffusion tensor imaging (DTI) data. DICCCOL aims to provide large-scale cortical landmarks with finer granularity, better functional homogeneity, more accurate functional localization, and automatically-established cross-subjects correspondence.

MIRIAD (Minimal Interval Resonance Imaging in Alzheimer's Disease)

The MIRIAD dataset is a database of volumetric MRI brain-scans of 46 Alzheimer's sufferers and 23 healthy elderly people. Many scans were collected of each participant at intervals from 2 weeks to 2 years, the study was designed to investigate the feasibility of using MRI as an outcome measure for clinical trials of Alzheimer's treatments. It includes a total of 708 scans and should be of particular interest for work on longitudinal biomarkers and image analysis. The dataset can be found at http://miriad.drc.ion.ucl.ac.uk/

Joint Anisotropic LMMSE Filter for Stationary Rician noise removal in DWI

This module reduces Rician noise on DWI. It filters the image in the mean squared error sense using a Rician noise model. The N closest gradient directions to the direction being processed are filtered together to improve the results: the noise-free signal is seen as an n-dimensional vector which has to be estimated with the LMMSE method from a set of corrupted measurements. The covariance matrix of the noise-free vector and the cross covariance between this signal and the noise have to be estimated, which is done taking into account the image formation process.

All these estimations are performed as sample estimates in a 'shaped neighborhood' defined by the weights extracted from the structural similarity of the voxels following the same idea as in the Non-Local Means filter.

This C++ code can be compiled as either a standalone (ITK required. Versions 3 and 4 are supported) or as a CLI module for 3-D Slicer (versions 3 and 4). Only "nhdr/nrrd" DWIs are supported.

The Cancer Imaging Archive (TCIA)

The Cancer Imaging Archive is a large archive of medical images of cancer accessible for public download. The images are organized as "Collections", typically patients related by a common disease (e.g. lung cancer), image modality (MRI, CT, etc) or research focus. A full listing of the available data sets can be found at https://www.cancerimagingarchive.net/access-data.

There are a number of neuroimaging data sets available. Many include clinical outcomes, pathology, and genomics in addition to the images.

Medical Image Processing and Visualization in Virtual Environments

This resouce will centralize development of tools for interaction with medical imaging data in immersive virtual environments (based on the Vizard platform).

PDBP: Parkinson's Disease Biomarkers Program

The National Institute of Neurological Disorders and Stroke (NINDS) Parkinson's Disease Biomarkers Program (PDBP) was developed to accelerate the discovery of promising new diagnostic and progression biomarkers for Parkinson's Disease. The PDBP is focused on promoting the discovery of biomarker candidates for early detection and measurement of disease progression. The PDBP will coordinate the efforts of multiple stakeholders through a common data management resource and web portal. The PDBP will serve as a multi-faceted platform for:
- Integrating existing biomarker efforts
- Standardizing data collection and management across these efforts
- Accelerating the discovery of new biomarkers
- Fostering and expanding collaborative opportunities for all stakeholders.

FITBIR: Federal Interagency Traumatic Brain Injury Research

The Federal Interagency Traumatic Brain Injury Research (FITBIR) informatics system was developed to share data across the entire TBI research field and to facilitate collaboration between laboratories, as well as interconnectivity with other informatics platforms. Sharing data, methodologies, and associated tools, rather than summaries or interpretations of this information, can accelerate research progress by allowing re-analysis of data, as well as re-aggregation, integration, and rigorous comparison with other data, tools, and methods. This community-wide sharing requires common data definitions and standards, as well as comprehensive and coherent informatics approaches.

SCORE

SCORE is a collection of methods for comparing the performance of different image algorithms. These methods generate quantitative scores that measure divergences to a standard (ground truth).

NCANDA: Data Integration Component

This is the Data Integration, MRI, and Bioinformatics Component of the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA), funded by the NIAAA.

The NCANDA consortium consists of an Administrative Component at UC San Diego, the Data Integration Component at SRI International, and five data collection sites, Duke University, Oregon Health & Sciences University, SRI International, University of Pittsburgh, and UC San Diego. Each collection site will collect data from about 150 adolescents, each of them seen for one baseline and three annual follow-up visits.

Here, we are making available manuals, training materials, and computational tools developed by the NCANDA Data Component.

elastix

Welcome to elastix: a toolbox for rigid and nonrigid registration of images.

elastix is open source software, based on the well-known Insight Segmentation and Registration Toolkit (ITK). The software consists of a collection of algorithms that are commonly used to solve (medical) image registration problems. The modular design of elastix allows the user to quickly configure, test, and compare different registration methods for a specific application. A command-line interface enables automated processing of large numbers of data sets, by means of scripting.

The website is found at http://elastix.isi.uu.nl/.

A paper describing elastix contains more details: S. Klein, M. Staring, K. Murphy, M.A. Viergever, J.P.W. Pluim, "elastix: a toolbox for intensity based medical image registration," IEEE Transactions on Medical Imaging, vol. 29, no. 1, pp. 196 - 205, January 2010.

Program for optimal design of blocked fMRI experiments

The computer program POBE (program for optimal design of blocked experiments, version 1.1) provides a graphical user interface for fMRI researchers to easily and efficiently design their blocked experiments. The computer program POBE calculates the optimal number of subjects and the optimal scanning time for user specified experimental factors and model parameters so that the statistical efficiency is maximised for a given study budget. POBE can also be used to determine the minimum budget for a given power. Furthermore, a maximin design can be determined as efficient design for a possible range of values for the unknown model parameters.

3-Dimensional Diffusion Tensor Imaging (DTI) Atlas of the Rat Brain In Postnatal Day 5, 14, and Adulthood

3D DTI anatomical rat brain atlases have been created by the UNC- Chapel Hill Department of Psychiatry and the CAMID research collaboration. There are three age groups, postnatal day 5, postnatal day 14, and postnatal day 72.
The subjects were Sprague-Dawley rats that were controls in a study on cocaine abuse and development. The P5 and P14 templates were made from scans of twenty rats each (ten female, ten male); the P72, from six females. The individual cases have been resampled to isotropic resolution, manually skull-stripped, and deformably registered via an unbiased atlas building method to create a template for each age group. Each template was then manually segmented using itk-SNAP software.
Each atlas is made up of 3 files, a template image, a segmentation, and a label file. This work was funded by the UNC Neurodevelopment Disorders Research Center HD 03110, the NIH STTR grant R41 NS059095, and the NIH Program Project IP01DA022446-02.

GRETNA

GRETNA is a graph theoretical network analysis toolbox which allows researchers to perform comprehensive analysis on the topology of brain connectome by integrating the most of network measures studied in current neuroscience field.

Mindboggle-101 manually labeled brains

Mindboggle-101 is the largest and most complete set of free, publicly accessible, manually labeled human brain images. See http://mindboggle.info/data.html for data and article: http://www.frontiersin.org/brain_imaging_methods/10.3389/fnins.2012.00171/full

All data are accessible on the Open Science Framework: https://osf.io/nhtur/

Group Information Guided ICA

The toolbox is for group-information guided ICA (GIG-ICA). In GIG-ICA, group information captured by standard Independent Component Analysis (ICA) on the group level is used as guidance to compute individual subject specific Independent Components (ICs) using a multi-objective optimization strategy. For computing subject specific ICs, GIG-ICA is applicable to subjects that are involved or not involved in the computation of the group information. Besides the group ICs, group information captured from other imaging modalities and meta analysis could be used as the guidance in GIG-ICA too.

References:
Y. Du, Y. Fan. Group information guided ICA for fMRI data analysis. Neuroimage. 2013 Apr 1;69:157-97. doi: 10.1016/j.neuroimage.2012.11.008. Epub 2012 Nov 27.
Y. Du, Y. Fan. Group information guided ICA for analysis of multi-subject fMRI data. 2011, 17th Annual Meeting of the Organization for Human Brain Mapping, Quebec City, Canada. Trainee Abstract Travel Awards, Interactive poster.

Hitachi2nirs

SUPPORT: rebecca.dewey@physics.org

A Matlab script to convert the raw .csv Hitachi ETG4000 output file into a .nirs file for use with Homer2. The script also requires a .pos file. This is the output of the polhemus 3D digitiser that we use to record where the optodes are located spatially. I realise that not everyone uses a 3D digitiser so I have included three example .pos files - one for each of the possible optode arrangements of the Hitachi system (either two 3x3 arrays, one 3x5 array or one 4x4 array). If you use a different arrangement or have more probes than us, feel free to get in touch and I may be able to advise on how to create a model .pos file.

There are two versions of the conversion script:
1. single - this will read in ONE .csv file and ONE .pos file and create ONE .nirs file
2. multi - this will read in a user-specified number of .csv files and ONE .pos file. It will then create one .nirs file for each .csv file that was read in and deposit it in the same directory as that .csv file.

NITRC Computational Environment (NITRC-CE)

NITRC Computational Environment (NITRC-CE) is an on-demand, cloud based computational virtual machine pre-installed with popular neuroimaging tools. We have leveraged NeuroDebian as well as manually added in a variety of popular software tools. NITRC-CE is available to use on your own infrastructure or via commercial cloud providers including Amazon and Microsoft.

GMAC: A Matlab toolbox for spectral Granger causality analysis of fMRI data

The open-source software toolbox GMAC (Granger Multivariate Autoregressive Connectivity) implemented multivariate spectral Granger Causality Analysis for studying brain connectivity using fMRI data. Available features are: fMRI data importing, network nodes definition, time series preprocessing, multivariate autoregressive modeling, spectral Granger causality indexes estimation, statistical significance assessment using surrogate data, network analysis and visualization of connectivity results. All functions are integrated into a graphical user interface developed in Matlab environment.
Dependencies: Matlab, BIOSIG, SPM, MarsBar.

GMAC documentation: Tana MG, Sclocco R, Bianchi AM (2012) GMAC: a Matlab toolbox for spectral Granger causality analysis of fMRI data. Computers in Biology and Medicine 42: 943-956 (http://dx.doi.org/10.1016/j.compbiomed.2012.07.003)

Rodent Brain Extraction Tool

A modified version of the Brain Extraction Tool (BET) that can process rodent brains.

ORS Visual SI

ORS Visual SI's advanced visualization techniques and state-of-the-art volume rendering provide unparalleled insight into the details and properties of neurological data acquired by CT, micro-CT, MRI, PET, SPECT, microscopy and other modalities. With data fusion tools, intramodality and multimodality registration of MR/CT or PET/CT is easily accomplished, while semi-automatic VOI delineation on fused datasets can improve analysis.

Standard formats, such as DICOM, RAW, JPEG, NIFTI, Analyze are supported and 3D/4D sequences can be played. Other features include MPR, oblique, CPR, volume clipping, and surface visualization of cortex, skull, and scalp models. Also standard are easy-to-use tools for voxel-based delineation of features and the measurement of properties, including areas, volumes, counts, and intensity profiles. Present your findings by creating annotated animations or high-resolution images for posters.

An SDK is also available to create plug-ins that provide new workflows or functionalities.

EMSE Suite

EMSE Suite is a professionally supported modular platform for space-time-frequency analyses of EEG/MEG/ECoG integrated (optionally) with structural MRI and functional hemodynamic measures (fMRI and NIRS). The Locator module digitizes 3D sensor coordinates. Data Editor provides live and offline pipelines of spatial and temporal filters, and easy-to-use event pipelines for conditional binning of time, frequency, and time-frequency data across participants, with group results. Coherence, phase synchronication, and quasi-causal information assess connectivity. Source Estimator enables modeling of discrete overdetermined and distributed underdetermined sources, and spatial filtering for 3D brain regions of interest. Statistical nonparametric mapping (SnPM) may be performed for all measures. MR Viewer and Image Processor comprise tools for BEM and FEM volume conductor models, using cortical source space models. See http://www.cortechsolutions.com/EMSE/ for details and a supported free trial.

Manually Labeled MRI Brain Scan Database

This is a continuously growing and improving database of high-quality neuroanatomically labeled MRI brain scans, created not by an algorithm, but by neuroanatomical experts. All results are checked and corrected. Regions of interest include the usual sub-cortical structures (thalamus, caudate, putamen, hippocampus, etc), along with ventricles, brain stem, cerebellum, and gray and white matter. We also sub-divide the cortex into "parcellation units" that are defined by gyral and sulcal landmarks. There are 157 ROIs now and more to come.
This data is offered as a subscription and while it is not free, it is a tiny fraction of the cost of creating the database. The idea is to spread the cost of adding new labeled scans to the database so we can continue to increase the number of scans, along with the age range and other demographics of the subjects. In 2012, we will have at least 63 comprehensively labeled scans available.

Multiple Correlation Function Tool

The intention of this tool is to provide a convenient environment to simulate NMR diffusion in closed pores. It builds on the eigenfunction expansion of the magnetization.

BSMac: Bayesian Spatial Model for Brain Activation and Connectivity

We provide a statistical and graphical visualization MATLAB toolbox for the analysis of fMRI data, called the Bayesian Spatial Model for activation and connectivity (BSMac). BSMac simultaneously performs whole-brain activation analyses at the voxel and region of interest levels as well as task-related functional connectivity (FC) analyses using a flexible Bayesian modeling framework (Bowman et al., 2008). BSMac allows for inputting data in either Analyze or Nifti file formats. The user provides information pertaining to subgroup memberships, scanning sessions, and experimental tasks (stimuli), from which the design matrix is constructed. BSMac then performs parameter estimation based on MCMC methods and generates plots for activation and FC, such as interactive 2D maps of voxel and region-level task-related changes in neural activity and animated 3D graphics of the FC results.

The toolbox can be downloaded: http://web1.sph.emory.edu/bios/CBIS/links.html (http://www.ncbi.nlm.nih.gov/pubmed/22101143).

Atlasing of the basal ganglia

This atlas takes advantage of ultra-high resolution 7T MRI to provide unprecedented levels of detail on structures of the basal ganglia in-vivo. The ATAG atlas includes probability maps of the striatum, GPe, GPi, red nucleus, substantia nigra, subthalamic Nucleus(STh) and the PAG. The atlas has been created on 30 young (M:24.2), 14 middle-aged (M: 52.5), and 10 elderly (M: 69.6) participants.

Separately a STh atlas was created based on 13 young (M:24.38), 8 middle-aged (M:50.67), and 9 elderly participants (M:72.33).

You can find more details about the creation of these maps in the following papers:

ATAG young atlas:
www.ncbi.nlm.nih.gov/pubmed/24650599

ATAG middle aged, elderly:
www.ncbi.nlm.nih.gov/pubmed/28168364

STh young:
www.ncbi.nlm.nih.gov/pubmed/22227131

STh Middle-aged & Elderly:
www.ncbi.nlm.nih.gov/pubmed/23486960

Participating institutions are the Max Planck Institute for Human Cognitive and Brain Sciences, Germany, and the University of Amsterdam, the Netherlands.

Diffusional Kurtosis Estimator

Diffusional Kurtosis Estimator (DKE) is a software tool for post-processing diffusional kurtosis imaging (DKI) datasets that includes a suite of command-line programs along with a graphical user interface (GUI). DKE supports 32-, 64-bit Windows and linux. DKE generates a set of kurtosis (axial, mean, radial, KFA, MKT) parametric maps with a given set of diffusion weighted images acquired from a valid DKI protocol. Diffusivity (axial, mean, radial) and fractional anisotropy maps using either DKI or diffusion tensor imaging signal models are also calculated in the processing. DKE features include: DICOM, NIfTI and Bruker format support, interactive (GUI) as well as batch mode (command-line) processing, and rigid-body motion correction. DKE implements the methods described in the following paper: Tabesh A, Jensen JH, Ardekani BA, and Helpern JA. Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging. Mag Reson Med. 2011 Mar;65(3):823-36. http://www.ncbi.nlm.nih.gov/pubmed/21337412

fNIRS Data Analysis Environment

Our resource is a data analysis environment for diffuse optical tomography (DOT) functional neuroimaging data. Developed to process data from steady-state time-series measurements, it allows for maximal flexibility in the number and positions of optodes. The central component is an application called NAVI. Features include:
1. An electronic ledger (records metadata for all data transformations).
2. Data conditioning (e.g., frequency-filtering, selection of data on the basis of signal-to-noise ratio.)
3. 2D or 3D image formation and display.
4. Interpretation: atlas-based mapping; automated anatomical labeling; GLM; data-driven methods (e.g., PCA, ICA); model-based (e.g., dynamic causal modeling) and data-driven (e.g., correlation) connectivity analysis.
Another important component is the Brain Model Generator, which includes FEM meshes for all parts of the head accessible to DOT measurements. The user can input the numbers of optodes, and manually specify their locations or input tracking-system data.

Stereoscopic Atlas of Intrinsic Brain Networks (SAIBN)

This tool is a 3D stereoscopic (anaglyph method) full brain functional connectivity atlas created using a parcellation atlas published by Craddock et al. (2012). Using 3D Slicer 3.6.3 and the two hundred ROI version of the Craddock atlas, 200 grayscale surface models were created using a z-stat threshold > 2.3, and each surface model was processed with a surface decimation algorithm, smoothed with the Taubin algorithm and without surface normals.

For improved visualization of the functional connectivity networks and their relative anatomical position, the surface model of five subcortical anatomical structures (corpus callosum, bilateral caudate, pallidum, putamen, thalamus, amygdala and hippocampus) were included in SAIBN. These surfaces were created with 3D Slicer using the segmentation computed with Freesurfer v. 5.1.

The viewer should use red-cyan glasses to see the 3D stereoscopic effect using 3D Slicer (version 3.6.3, http://www.slicer.org/pages/Special:SlicerDownloads).

PRoNTo – Pattern Recognition for Neuroimaging Toolbox

PRoNTo (Pattern Recognition for Neuroimaging Toolbox) is a software toolbox based on pattern recognition techniques for the analysis of neuroimaging data. Statistical pattern recognition is a field within the area of machine learning which is concerned with automatic discovery of regularities in data through the use of computer algorithms, and with the use of these regularities to take actions such as classifying the data into different categories. In PRoNTo, brain scans are treated as spatial patterns and statistical learning models are used to identify statistical properties of the data that can be used to discriminate between experimental conditions or groups of subjects (classification models) or to predict a continuous measure (regression models).

FASST – fMRI Artefact rejection and Sleep Scoring Toolbox

FASST is an EEG toolbox developed to help users with 3 specific types of data and problems: simulatenous EEG-fMRI recording, continuous EEG scoring (e.g. sleep) and handling (visualisation, cutting, power spectrum, etc.) multi-channel recording of spontaneous EEG.
The toolbox is written in Matlab and is specifically compatible with the BrainAmp family of EEG recorders (from BrainProducts GmbH) Three other data formats are now also supported: the edf 'European Data Format', exported raw-EGI data (from Electrical Geodesics, Inc.) and the BCI2000 format.The results are directly compatible with SPM8 and are saved with SPM8 EEG data format.

DRAMMS Deformable Image Registration Toolbox

DRAMMS is for 2D, 3D, 3.5D, 4D and population-wise deformable image registration. Typical applications include,

-- Cross-subject registration;

-- Single- or Multi-site data;

-- Single- or Multi-modal registration;

-- Longitudinal registration for quantifying changes;

-- Registration under missing correspondences.

-- Group-wise registration for unbiased atlas construction.

DRAMMS runs in command line in UNIX/Mac OS, It accepts Nifti/ANALYZE/MetaImage image formats. It is fully-automatic --- takes two input images, and generates a registered image and (optionally) the deformation field; no user initialization or interaction is needed.

More information (installation, tutorial, manual, demonstration, FAQ, etc) can be found at http://www.rad.upenn.edu/sbia/software/dramms/ .

4d-dramms: https://www.nitrc.org/projects/dramms4d/
group-wise dramms: https://www.nitrc.org/projects/popdramms/

NIDB - Neuroinformatics Database

NIDB is a powerful, easy to install neuroimaging database designed to allow simple importing, searching, and sharing of imaging data. NIDB associates any modality imaging or other binary data (MR, CT, US, EEG, etc) with a subject through any number of imaging studies or projects. Data is kept at your site, controlled by you, to be shared with other sites whenever you want. NIDB also provides automated pipelining with importing of results back into NIDB which can be searched along with imaging meta data.

EEG human categorization data

This ressource is a collection of 32-channel data from 14 subjects (7 males, 7 females) acquired using the Neuroscan software. Subjects are performing a go-nogo categorization task and a go-no recognition task on natural photographs presented very briefly (20 ms). Each subject responded to a total of 2500 trials. Data is CZ referenced and is sampled at 1000 Hz (total data size is 4Gb; more details are given later).

BESA

BESA is the one of the most widely used software for source analysis and dipole localization in EEG and MEG research. BESA Research has been developed on the basis of 20 years experience in human brain research by Michael Scherg, University of Heidelberg, and Patrick Berg, University of Konstanz.

BESA Research is a highly versatile and user-friendly Windows® program with optimized tools and scripts to preprocess raw or averaged data for source analysis. All important aspects of source analysis are displayed in one window for immediate selection of a wide range of tools. BESA Research provides a variety of source analysis algorithms, a standardized realistic head model (FEM), and allows for fast and easy hypothesis testing and integration with MRI and fMRI.

Local Binary Pattern Analysis Tools for MR Brain Images

The packaged tools perform Local Binary Pattern on Three Orthogonal Planes(LBP-TOP) analysis on MR brain images. One can use them to extract LBP texture features for machine learning applications or other advance analysis. LBP-TOP mapping programs written by Java are including in this package. The output is the histogram describing the brain morphology.

aBEAT

aBEAT is a 4D Adult Brain Extraction and Analysis Toolbox with graphical user interfaces to consistently analyze 4D adult brain MR images. Single-time-point images can also be analyzed. Main functions of the software include image preprocessing, 4D brain extraction, 4D tissue segmentation, 4D brain labeling, ROI analysis. The software is developed by the IDEA group at the University of North Carolina at Chapel Hill, directed by Dr. Dinggang Shen (dinggang_shen@med.unc.edu).

Linux operating system (64 bit) is required. A computer with 8G memory (or more) is recommended for processing many images simutaneously. The graphical user interfaces and overall framework of the software are implemented in MATLAB. The image processing functions are implemented with the combination of C/C++, MATLAB, Perl and Shell languages. Parallelization technologies are used in the software to speed up image processing.

C-PAC

The Configurable Pipeline for the Analysis of Connectomes (C-PAC) is a configurable, open-source, Nipype-based, automated processing pipeline for resting state functional MRI (R-fMRI) data, for use by both novice and expert users. C-PAC was designed to bring the power, flexibility and elegance of the Nipype platform to users in a plug and play fashion—without requiring the ability to program. Using an easy to read, text-editable configuration file, C-PAC can rapidly orchestrate automated R-fMRI processing procedures, including:
- quality assurance measurements
- image preprocessing based upon user specified preferences
- generation of functional connectivity maps (e.g., correlation analyses)
- customizable extraction of time-series data
- generation of local R-fMRI metrics (e.g., regional homogeneity, voxel-matched homotopic connectivity, fALFF/ALFF)

C-PAC makes it possible to use a single configuration file to launch a factorial number of pipelines differing with respect to specific processing steps.

ASL_spm8

Quick ASL Wrapper for preprocessing ASL Data and computing blood flow measurements using UPenn ASL toolbox.

DTI Atlas Builder

This tool creates an Atlas image as an average of several DTI images that will be registered. The registration will be done in two steps :
- Affine Registration with BRAINSFit
- Non Linear Registration with GreedyAtlas
A final step will apply the transformations to the original DTI images so that the final average can be computed.

The main function writes a python script that will be executed to compute the Atlas.

By running DTIAtlasBuilder, you will need to fill in informations in a Graphical User Interface, and then compute the Atlas. You can also run the tool in command line (no GUI).
Using the GUI, you will be able to save or load a dataset file or a parameter file.

The tool needs these other tools to work, so be sure to have these installed on your computer:
- ImageMath
- ResampleDTIlogEuclidean
- CropDTI
- dtiprocess
- BRAINSFit
- GreedyAtlas
- dtiaverage
- DTI-Reg
- unu
- MriWatcher (requires glut library)

This package is also available as an extension of 3D Slicer (http://www.slicer.org)

Slice:Drop

http://slicedrop.com

Slice:Drop is a viewer for medical imaging data. It supports a variety of scientific file formats out-of-the-box (see https://github.com/xtk/X/wiki/X:Fileformats for a complete list).

We think that the best way to render your files is without any necessary conversions. Just drop'em on a website and they are ready to render.

Getting started: Just drag'n'drop some medical imaging files on this website or try one of the four examples in the right corner. Then, play with the panels on the left and click, drag and rotate the 3d content.

Slice:Drop uses WebGL and HTML5 Canvas to render the data in 2D and 3D. We use our own open-source toolkit to perform the rendering, called XTK ( http://goxtk.com ).

PICSL Multi-Atlas Segmentation Tool

This package contains a software implementation for joint label fusion and corrective learning, which were applied in MICCAI 2012 Grand Challenge on Multi-Atlas Labeling and finished in the first place.

Joint label fusion is for combining candidate segmentations produced by registering and warping multiple atlases for a target image. Corrective learning can be applied to further reduce systematic errors produced by joint label fusion. In general, corrective learning can be applied to correct systematic errors produced by other segmentation methods as well.

MultiTracer version 2

MultiTracer version 2 is a compiled Java 1.6 application. It supports NIfTI-1.1 format float, double and signed and unsigned byte, short, and integer formats and provides legacy support for Analyze 7.5 8 and 16 bit images. It provides image display, editing, delineation of structure boundaries, export of traced contours and generation of masked volumes. Images are displayed in 3 orthogonal views. Time series can be displayed as averaged or contrast images and time courses can be visualized graphically. Version 2 provides enhancements to the original MultiTracer feature set described in Woods RP. Multitracer: a Java-based tool for anatomic delineation of grayscale volumetric images. Neuroimage 2003 Aug;19(4):1829-34.

Documentation: http://air.bmap.ucla.edu/MultiTracer2/MultiTracer.html
Download: http://www.bmap.ucla.edu/portfolio/software/MultiTracer/

Functional ROI Atlas

The Functional ROI Atlas is an effort to provide a set of quasi-probabilistic atlases for established "functional ROIs" in the human neuroimaging literature. Many atlases exist for various anatomical parcellation schemes, such as the Brodmann areas, the structural atlases, tissue segmentation atlases, etc. To date, however, there is no atlas for so-called "functional ROIs". Such fROIs are typically associated with an anatomical label of some kind (e.g. the _fusiform_ face area), but these labels are only approximate and can be misleading inasmuch as fROIs are not constrained by anatomical landmarks, whether cytoarchitectonic or based on sulcal and gyral landmarks.

The goal of this project is to provide quasi-probabilistic atlases for fROIs that are based on published coordinates in the neuroimaging literature. This is an open-ended enterprise and the atlas can grow as needed. Members of the neuroscience and neuroimaging community interested in contributing to the project are encouraged to do so.

vIST/e

vIST/e is a C++ application that allows interactive visualization of various kinds of imaging datasets, both simple scalar data as well as complex high-dimensional data such as diffusion tensor imaging (DTI) and high angular resolution imaging data (HARDI). vIST/e is based on VTK, Qt and is designed as a plugin system which allows it to be extended in a flexible way. vIST/e is the main research tool of Imaging Science and Technology Eindhoven (IST/e), a major imaging group Eindhoven University of Technology, the Netherlands which brings together all university faculties involved in medical or industrial imaging.

CalaTK - Atlas Building TK

CalaTK is an open-source toolkit for cross-sectional and longitudinal atlas building.

The CalaTK project develops innovative methods and tools for longitudinal atlases with a focus on neurodevelopment. The computational toolbox is developed with the objective to analyze the neural developmental patterns observed in human and non-human primate structural and diffusion tensor magnetic resonance (MR) images.

Network-Based Statistic (NBS)

Matlab toolbox for testing hypotheses about the human connectome. NBS has been widely used to identify connections and networks comprising the connectome that are associated with an experimental effect or a between-group difference. User provides a series of connectivity matrices from different cohorts, or from the same subject during different conditions. Connectivity matrices are inferred from neuroimaging data using packages that, for example, count the number of tractography streamlines that interconnect each pair of regions (diffusion-MRI), or measure the extent of correlation in BOLD response (fMRI). User specifies hypothesis with the GLM. Features include: graphical user interface; NBSview, a basic network viewer modeled on SPMresults; exchange blocks for repeated measures. Developed by Zalesky, Fornito, Cocchi & Bullmore.

- NBS extension for directed networks by Max von Gellhorn (NBSDirected.zip).

- R package for NBS by Zeus Gracia-Tabuenca: https://cran.r-project.org/web/packages/NBR/index.html

Local Label Learning (LLL) Segmentation

Automatic and reliable segmentation of subcortical structures is an important but difficult task in quantitative brain image analysis. Recently, multi-atlas based segmentation methods have attracted great interest due to their competitive performance. While many label fusion strategies have been developed, most of these methods adopt predefined weighting models which were not necessarily optimal. In this paper, we proposed a novel local label learning strategy to estimate the target image’s segmentation label using statistical machine learning techniques. We used a support vector machine (SVM) with a K nearest neighbor (KNN) based training sample selection strategy to learn a classifier for each of the target image voxel based on a training dataset consisting of its neighboring voxels in the atlases. Validation experiments on hippocampus segmentation of 117 MR images demonstrated that our method can produce segmentation results consistently better than state-of-the-art label fusion methods.

Diffusion MRI - In-vivo and Phantom Data

This projects is an open-data initiative for the distributation of common datasets for the evaluation and validation of diffusion MRI processing methods.

Diffusion MRI @ DKFZ Heidelberg

This central project points to all open-source and open-data initiatives provide by the German Cancer Research Center in the field of diffusion MRI.

DentalTools

This package provides 3Dimaging resources such as multimodal imaging, volume and mesh processing or segmentation for Dental Research. DentalTools package intends to contribute to the free exchange of information and methods in the dentistry research community.

NIRFAST

Nirfast is a software package for modeling Near-Infrared light transport in tissue and image reconstruction. This includes: Standard single wavelength absorption and reduced scatter, Multi-wavelength spectrally constrained models and Fluorescence models.

Robust Brain Extraction (ROBEX)

ROBEX is an automatic whole-brain extraction tool for T1-weighted MRI data (commonly known as skull stripping). Whole-brain segmentation is often the first component in neuroimage pipelines and therefore, its robustness is critical for the overall performance of the system. Many methods have been proposed in the literature, but they often:
- work well on certain datasets but fail on others.
- require case-specific parameter tuning

ROBEX aims for robust skull-stripping across datasets with no parameter settings. It fits a triangular mesh, constrained by a shape model, to the probabilistic output of a supervised brain boundary classifier. Because the shape model cannot perfectly accommodate unseen cases, a small free deformation is subsequently allowed. The deformation is optimized using graph cuts.

The method ROBEX is based on was published in IEEE Transactions on Medical Imaging; please visit my website http://www.jeiglesias.com to download the paper.

NIRS-SPM

NIRS-SPM is a SPM and MATLAB-based software package for statistical analysis of near-infrared spectroscopy (NIRS) signals, developed at the Bio Imaging Signal Processing (BISP) lab. at KAIST in Korea. Based on the general linear model (GLM), and Sun's tube formula / Lipschitz-Killing curvature (LKC) based expected Euler characteristics, NIRS-SPM not only provides activation maps of oxy-, deoxy-, and total-hemoglobin, but also allows for super-resolution activation localization. Additional features, including a wavelet-minimum description length detrending algorithm and cerebral metabolic rate of oxygen (CMRO2) estimation without hypercapnia, were implemented in the NIRS-SPM software package.

MCML & CONV

MCML is a Monte Carlo simulation program for Multi-layered Turbid
Media with an infinitely narrow photon beam as the light source. The
simulation is specified by an input text file called, for example,
"sample.mci", which can be modified by any simple text editor. The
output is another text file called, for example, "sample.mco". (The
names are arbitrary.)

CONV is a convolution program which uses the MCML output file to
convolve for photon beams of any size in a Gaussian or flat
field shape. CONV can provide a variety of output formats (reflectance,
transmission, iso-fluence contours, etc.), which are compatible with
standard graphics applications.

Homer2

HOMER2 is a set of matlab scripts used for analyzing fNIRS data to obtain estimates and maps of brain activation. This set of scripts has evolved since the early 1990s, first as the Photon Migration Imaging toolbox, then HOMER, and now HOMER2.

HOMER2 has a familiar GUI interface, but now more easily supports group analyses and re-configuration of the processing stream. Further, it enables users to integrate their own algorithms into the processing stream. Underneath the GUI, all of the processing functions can be accessed at the script level, adding additional flexibility.

HOMER2 also includes the head and probe registration and brain imaging tool, AtlasViewer.

Users are encouraged to contribute their own scripts for public dissemination within HOMER2.

For Documentation and Installation Instructions visit https://www.nitrc.org/plugins/mwiki/index.php/homer2:MainPage .

ShapeComplexAtlas

This is a Matlab demo for constructing a neuro-anatomical shape complex atlas from 3D MRI brain structures, which is based on the paper "Ting Chen, Anand Rangarajan, Stephan J. Eisenschenk and Baba C. Vemuri, Construction of a Neuroanatomical Shape Complex Atlas from 3D MRI Brain Structures. In NeuroImage, Volume 60, Page 1778-1787, 2012".

CDF-HC PointSetReg

This is a Matlab demo for group wise point set registration using a novel CDF-based Havrda-Charvat Divergence, which is based on the paper:
Ting Chen, Baba C. Vemuri, Anand Rangarajan and Stephan J. Eisenschenk, Group-wise Point-set registration using a novel CDF-based Havrda-Charvat Divergence. In IJCV : International Journal of Computer Vision, 86(1):111-124, January, 2010.

ERP PCA Toolkit

This Matlab toolkit is a general purpose tool for editing, visualizing, and analyzing EEG data (both ERP and spectral) whose most recent version has been downloaded over 1000 times. Its three chief highlights are: 1) an optimized automatic artifact correction function that includes ICA correction for eye blinks and saccades. 2) Extensive support for easily conducting PCA and ICA through all stages of the procedure, including inspection of reconstituted waveforms and batch ANOVAs. 3) Implementation of robust ANOVAs, including McCarthy-Wood vector test. It has a graphical user interface for point and click usage and comes with an extensive illustrated tutorial. A description of the toolkit was published in Dien (2010) in Journal of Neuroscience Methods. It relies on both internal functions as well as borrowed functions from both EEGlab and FieldTrip.

MITK Diffusion

The MITK Diffusion application offers a selection of image analysis algorithms for the processing of diffusion-weighted MR images. It encompasses the research of the Division [https://www.dkfz.de/en/mic/index.php Medical Image Computing] at the German Cancer Research Center (dkfz).

Features & Highlights
- Support for most established image formats, such as DICOM, NIFTI, and NRRD
- Tensor and Q-ball reconstruction
- Intravoxel Incoherent Motion (IVIM) analysis
- Fiber tractography and fiber processing
-- Global tractography
-- Interactive peak, ODF and tensor streamline tractography
-- Machine learning based tractography
- Brain network statistics and visualization (connectomics)
- Fiberfox: simulation of diffusion-weighted MR images
- Command line tools for most functionalities

DbGaP_Cleaner

This tool will help assist site staff with curation of data dictionary, data item, and subject item files for preparation to uploading and sharing data with DbGaP resource. The intent is to help the user save time and effort in finding and fixing data artifacts prior to submission, since processing times can take some time and effort.

Netstation

Net Station also offers specialized tools and workflow options for both clinical and research applications, allows you to save different combinations of view settings (called workspaces) and helps with your reporting requirements by letting you set up and print custom cover pages. For more specialized work, Net Station also provides an optional electrical source estimation module (GeoSource) and an optional sensor location digitizer (Geodesic Photogrammetry System).

The Neurophysiological Biomarker Toolbox

The Neurophysiological Biomarker Toolbox (NBT) is an open source Matlab toolbox for the computation and integration of neurophysiological biomarkers. NBT offers a pipeline from data storage to statistics including artefact rejection, signal visualization, biomarker computation, and statistical testing. NBT allows for easy implementation of new biomarkers, and incorporates an online wiki (http://www.nbtwiki.net/) that facilitates collaboration among NBT users including extensive help and tutorials.
NBT is specialized in analyzing EEG data, however it allows the processing of any kind of signal.
NBT can, e.g., be used to analyze ongoing oscillation between:

- Eyes-closed rest of subject populations (e.g., healthy subjects and patients, males vs. females, young vs. old, etc.).
- Two experimental condition (e.g., classical eyes-closed rest vs. meditation, or before vs. after consumption of a CNS-active substance (a drug, coffee, nicotine, alcohol, etc.).

More details can be found on http://www.nbtwiki.net/

MGDM: Multi Geometric Deformable Model

MGDM is an efficient level set framework for multi-object segmentation. Its representation inherently prevents overlaps and gaps and it readily preserves object topology and object relationships. MGDM is efficient, storing only a fixed number of functions for any number of objects, and therefore scales well to segmentation problems with many classes and large images. It's representation also avoids some instabilities in other multi-class level set methods. MGDM is cross-platform; MATLAB wrappers, Java source and API are provided, with MIPAV plugins forthcoming.

NIMH Data Archive / National Database for Autism Research

The NIMH Data Archive (NDA) and National Database for Autism Research (NDAR) is a secure research data repository promoting scientific data sharing and collaboration among autism spectrum disorder (ASD) investigators. The goal of the project is to accelerate scientific discovery through data sharing, data harmonization and the reporting of research results. NDAR currently has data from over 25,000 research participants available to qualified investigators.

This resource also host the data releases for: Adolescent Brain Cognitive Development Study (ABCD), Research Domain Criteria Database (RDoCDb), and National Database for Clinical Trials Related to Mental Illness (NDCT).

NeuroScope

NeuroScope is an advanced viewer for electrophysiological and behavioral data: it can display local field potentials (EEG), neuronal spikes, behavioral events, as well as the position of the animal in the environment. It also features limited editing capabilities.

Seed-based d Mapping (SDM, formerly Signed Differential Mapping)

Seed-based d Mapping (formerly "Signed Differential Mapping") is a statistical technique for meta-analyzing studies on differences in brain activity or structure that used neuroimaging techniques such as fMRI, VBM, DTI or PET. The methods have been fully validated in several studies, and meta-analyses using this method have been already published at the highest quality journals, such as Molecular Psychiatry, JAMA Psychiatry or the American Journal of Psychiatry.

CBS High-Res Brain Processing Tools

With 7 Tesla MRI, neuroscientists can investigate the structure of the human brain with enhanced resolution, currently up to 350µm isotropic in vivo. Quantitative MR contrasts and increased level of detail within tissues reveal many fine anatomical structures, but require efficient computational methods that handle various contrasts and scale well with data complexity.

The CBS High-Res Brain Processing Tools provide a fully automated processing pipeline for cortical analysis of structural MR images at a resolution of up to 400µm, including skull stripping, whole brain segmentation, cortical extraction, surface inflation and mapping, as well as dedicated tools for profile estimation across the cortical thickness.

The tools are released as a set of plug-ins for the MIPAV software package and the JIST pipeline environment. They are therefore cross-platform and compatible with a wide variety of file formats.

OpenElectrophy

The OpenElectrophy project aims to simplify data- and analysis-sharing for intra- and extra-cellular recordings. With OpenElectrophy you will be able to play with neural signals, spikes and oscillations.

OpenElectrophy is a Python module for electrophysiology data analysis (intra- and extra-cellular). OpenElectrophy is built on top of Neo :

- It includes the powerful Neo IO that can read a large number of data formats (Plexon, NeuroExplorer, Spike2, TDT, Axon, BlackRock, ...)
- Neo objects ready for analyses (AnalogSIgnal, SpikeTrain, RecordingChannel, Segment, Block ...)

But OpenElectrophy also provides:

- A GUI for exploring datasets
- A complete offline spikesorting tool chain = GUI and/or command line.
- A timefrequency toolbox = fast wavelet scalogram plotting + transient oscillation in LFP detection.
- Viewers for Neo objects.
- A database for storage.

False Discovery Rate Weighted

Simple and efficient, this application performs the Weighted False Discovery Rate procedure of Benjamini and Hochberg (1997) to correct for multiple testing. The good think is that you can test virtually any number of p-values (even millions) obtained with any test-statistics for any data set. The bonus is that you can assign a-priori weights to give a better chance to those variables that you deem important. In practice, this procedure is powerful only with a relatively small number of p-values.

Working Memory Trainer

This is a stand-alone axecutable under all main Windows OS. It is a program for training the working memory. The WM Trainer looks and behaves a little bit like a video-game and has been specifically conceived for children attending the primary school. However, it can be used purposefully by people of any age, including adult and elderly.

This application features highest graphic quality, a powerful adaptive engine for the difficulty level, a database of users and statistical tools to evaluate the progress.

Currently English, French and Italian are supported, but any language can be easily supported.

Normative Independent Component Analysis

This program, executable under any Windows32 OS, performs Group BSS (Blind Source Separation) analysis comparing two groups of individuals and it performs NICA (Normative ICA) analysis where individuals are compared individually to a (normative) group. All analysis is performed in the frequency domain, that is, for all frequencies. The program also performs all these analysis for qEEG, that is, at the electrode level, without any BSS. The program does all computations, saves and displays results.
The rationale and methods used in this program are explained in all details in the following paper:

Congedo M, John ER, De Ridder D, Prichep L (2010)
Group Independent Component Analysis of Resting-State EEG in Large Normative Samples International Journal of Psychophysiology 78, 89-99.

TARQUIN MRS analysis package

TARQUIN is an analysis tool for automatically determining the quantities of molecules present in NMR spectroscopic data. The intended purpose of TARQUIN is to aid the characterisation of pathologies, in particular brain tumours, both non-invasively with in-vivo 1H MRS and ex-vivo with 1H HR-MAS. TARQUIN has the following features:

Free to use and modify under the GPL licence.
Based on a flexible time-domain fitting routine designed to give accurate rapid and automated quantitation for routine analysis.
Cross platform, works on Windows, Linux and OSX.
Comes packaged with a quantum mechanically based metabolite simulator to allow basis set construction optimised for the investigation of particular pathologies sequence parameters.
Includes both GUI and command line interface for one-off and batch analyses.

EEG Toolbox

This toolbox has been developed to facilitate quick and easy import, visualisation and measurement for ERP data. The toolbox can open and visualise ERP averaged data (Neuroscan, ascii formats), 2D/3D electrode coordinates and 3D cerebral tissue tesselations (meshes). All the features can be explored quickly and easily using the example data provided in the toolbox. The GUI interface is simple and intuitive.

For more details, see: http://eeg.sourceforge.net/

MRI Neuroanatomy Labeling Services

Neuromorphometrics provides brain labeling and measurement services. Given raw MRI brain scans, we make precise quantitative measurements of the volume, shape, and location of specific neuroanatomical structures. The final measurements result from automated analyses that are manually guided, inspected and certified by a neuroanatomical expert. More than two months of training and a great deal of practice are required for this person to be able to identify and delineate the borders of the structures on each slice where they appear. All data from raw images through final results are checked for completeness and integrity.

cortex

This software package contains functions that will help researchers plan how many subjects per group need to be included in an MRI-based cortical thickness study to ensure a thickness difference is detected. The package requires cortical thickness mapping and co-registration to be carried out using Freesurfer. The power analyses are implemented in the R software package.

COINS

As neuroimaging research continues to grow, dynamic neuroinformatics systems are necessary to store, retrieve, mine and share the massive amounts of data. The Collaborative Informatics and Neuroimaging Suite (COINS) has been created to facilitate communication and cultivate a data community. This web-based neuroimaging and neuropsychology software suite offers versatile, automatable data upload/import/entry options, rapid and secure sharing of data among PIs, querying and export all data, real-time reporting, and HIPAA and IRB compliant study-management tools suitable to large institutions as well as smaller scale neuroscience and neuropsychology researchers.

COINS manages over 180 studies and their data at the Mind Research Network, the Nathan Kline Institute, University of Colorado – Boulder, the Olin Neuropsychiatry Research Center @ Hartford Hospital, and other sites. Please click on COINS website on the left menu to visit our promotional site with a Live Demo!

BrainLiner.jp

http://brainliner.jp/

A web portal for sharing neurophysiological and behavioral data. Users can search for existing data or login with their Google, Facebook, or Twitter account and upload new data.

Our main focus is on supporting brain-machine interface research, so we encourage users to not just provide recordings of brain activity data, but also information about stimuli, etc., so that statistical relationships can be found between stimuli and/or subject behavior and brain activity.

Data on the site is freely available using mostly either CC-BY or CC-0 Creative Commons licenses, though some custom licenses also exist.

We also have a page at https://www.facebook.com/brainliner where you can contact us

VIEWPixx

The VIEWPixx is a complete display toolbox which has been conceived specifically to replace CRTs in vision science labs. The VIEWPixx features high-performance industrial LCD glass, and a panel controller which has been custom designed to support vision research.Our innovative LED backlight design features superior display uniformity, and a wide color gamut exceeding that of any CRT. In addition, it includes an array of peripherals which often need to be synchronized to video during an experiment, including a stereo audio stimulator, a button box port for precise reaction-time measurement, triggers for electrophysiology equipment, and even a complete analog I/O subsystem. Because we implemented the video controller and peripheral control on the same circuit board, you can now successfully synchronize all of your subject I/O to video refresh with microsecond precision.

Triangle BioSystems

TBSI is a biomedical device company focused on developing and manufacturing neural hardware solutions for application in medical research using animals when bio-monitoring, recording and stimulation functions are needed.

- Neural Recording Equipment
- Neural Stimulation Equipment
- Data Acquisition Hardware and Software

COGNISION (TM) System

COGNISION TM with auditory or visual event-related potential (ERP) technology, provides a direct physiologic measure of patients' cognitive processing (i.e., a "cognitive biomarker"). COGNISION is a portable, highly integrated, internet-enabled, hardware/software platform and patient management system, which includes an online Patient Manager module which complies with the HIPAA Final Security Rule, an ERP Viewer module to view and analyze raw and average ERP waves, and a Protocol Editor module to simplify the choice and administration of selected ERP protocols. It is also easy to train and administer with non-specialized personnel, and is designed to be used in an out-patient setting.

DONE: Detection of Outlier NEurons

This tool was used by Zawadzki et al. (2012), who reported on a morphology-based approach for the automatic identification of outlier neurons and its application to the NeuroMorpho database. For the analysis, each neuron is represented by a feature vector composed of 20 measurements, which are projected into lower dimensional space with PCA. Bivariate kernel density estimation is then used to obtain a probability distribution for cells. Cells with high probabilities are understood as archetypes, while those with the small probabilities are classified as outliers. Further details about the method and its application in other domains can be found in Costa et al. (2009) and Echtermeyer et al. (2011).

References:
* Costa, Rodrigues, Hilgetag, and Kaiser. Europhysics Letters, 87, 1 (2009)
* Echtermeyer, Costa, Rodrigues, Kaiser. PLoS ONE 6, 9 (2011)
* Zawadzki, Feenders, Viana, Kaiser, and Costa. Neuroinformatics (2012)

Graphtools

Graphtools is a set of MATLAB scripts for analysis of networks derived from neuroimaging data. Some of these scripts are entirely original, while some are adapted (or just copied) from the Brain Connectivity Toolbox (https://sites.google.com/a/brain-connectivity-toolbox.net/bct)

The source code is available via git:

git clone ssh://{user}@www.nitrc.org/home/groups/graphtools/git

Epsilon Radial Networks

Currently there is no agreed-upon method for constructing the brain anatomical connectivity graphs out of large number of white matter tracts. In this paper, we present an efficient framework for building and analyzing graphs called epsilon radial networks (ERNs) using tractography data in a normalized space.

The key challenge in defining brain networks is node delineation and our method defines nodes in the graph using tract-end points clustered in a sphere of a given radius (epsilon). Using a kd-tree based search algorithm we can identify the nodes computationally efficiently and in a fully automatic way.

These networks can be used not only to analyze topo-physical properties of the structural brain networks but also to perform classical region-of-interest (ROI) analyses in a very efficient way. Thus ERNs can be used as a novel image processing lens for statistical and machine learning based analyses.

Group Level Imputation of Statistic Maps

Group Level Imputation of Statistic Maps (version 1.0) is a toolkit that performs multiple imputation for group level, single sample t-tests. Whole brain group level statistic maps from fMRI rarely cover the entire brain as a result of missing data. Missingness between subjects in fMRI datasets can result from susceptibility artifacts, bounding box (acquisition parameters), and small differences in post-normalized morphology. The toolkit consists of several interactive command line scripts that guide the user to map the spatial distribution of missing data across contrast images, calculate spatial neighborhood averages that help impute values, perform conventional and multiple imputed t-statistics, save the results to brain maps, and create result tables. The toolkit contains an instruction manual (pdf), two Matlab scripts and one R-Statistics script, which depend on functions defined in the popular SPM toolbox and functions defined in the MICE package for [R].

EEGVIS

EEGVIS is a MATLAB toolbox for exploration of multi-channel EEG and other large array-based data sets using multi-scale drill-down techniques. The toolbox can be used directly in MATLAB at any stage in a user's processing pipeline, as a plug in for EEGLAB, or as a standalone precompiled application without MATLAB running. EEGVIS and its supporting packages are freely available under the GNU general public license. The toolbox also supplies a number of extensible base classes for users who wish to develop their own visualizations. Additional information is provided at http://visual.cs.utsa.edu/eegvis

PANDA: a pipeline tool for diffusion MRI

PANDA (Pipeline for Analyzing braiN Diffusion imAges) is a matlab toolbox for pipeline processing of diffusion MRI images. For each subject, PANDA can provide outputs in 2 types: i) diffusion parameter data that is ready for statistical analysis; ii) brain anatomical networks constructed by using diffusion tractography. Particularly, there are 3 types of resultant diffusion parameter data: WM atlas-level, voxel-level and TBSS-level. The brain network generated by PANDA has various edge definitions, e.g. fiber number, length, or FA-weighted.

The key advantages of PANDA are as follows:

1.) fully-automatic processing from raw DICOM/NIFTI to final outputs;

2.) Supporting both sequential and parallel computation. The parallel environment can be a single desktop with multiple-cores or a computing cluster with a SGE system;

3.) A very friendly GUI (graphical user interface).

DW-MRI registration in FSL

This code registers linearly and non-linearly Diffusion Weighted Magnetic Resonance Images (DW-MRIs) by extending FLIRT (linear registration of 3D scalar volumes) and FNIRT (non-linear registration of 3D scalar volumes) in the FMRIB Software Library (FSL) to work with 4D volumes. The basis for registering DW-MRIs is the concept of Angular Interpolation (Tao, X., Miller, J. V., 2006. A method forregistering diffusion weighted magnetic resonance images. In: MICCAI. Vol. 9. pp. 594–602), which is implemented and extended to non-linear registration, based on the FLIRT and FNIRT models in FSL. The paper corresponding to this DW-MRIs tool can be found in https://www.frontiersin.org/articles/10.3389/fnins.2013.00041/full

The code does not overwrite FLIRT, FNIRT or any of the FSL C++ code. It is added as FLIRT4D, FNIRT4D and supporting cost functions. The makefiles will however be overwritten to compile the new code, without affecting any version of FSL.

BrainNetworkConstructionAnalysisPlatform

Construct and analyse brain network is a brain network visualization tool, which can help researchers to visualize construct and analyse resting state functional brain networks from different levels in a quick, easy and flexible way. Entrance parameter of construct and analyse brain network is export parameters of dparsf software.It would be greatly appreciated if you have any suggestions about the package or manual.

Curry 7

Throughout the world, Curry has the well deserved reputation as the most advanced and comprehensive tool for Multimodal Neuroimaging. Curry’s strength has always been centered on combining functional data such as EEG and MEG with imaging data from MRI and CT to optimize source reconstruction. Now we are combining Curry’s strength with the acquisition and signal processing features of the SCAN software for a comprehensive EEG acquisition, data analysis, source localization and source imaging package.

Rockland Download Link Script

Script which points browser to NKI Rockland Sample

Age Related Atrophy Dataset

The Age Related Atrophy dataset contains structural MR images of 70 subjects collected during 2008-2010 across a wide range of ages. The dataset also contains resting state fMRI for most subjects. The structural images are T1 weighted, T2 weighted-FLAIR, 25 direction DTI, and the T1 mapping DESPOT [1] sequence. Reconstructed T1 maps for each subject are also available.

The aquisition protocol was designed to study structural differences between young and older adults including both shape and intensity changes. Anonymized DICOM image sessions and processed images for each subject are available. The data is licensed under the Creative Commons Attribution License. It may be used freely for commercial, academic, or other use, as long as the original source is properly cited.

http://www.bsl.ece.vt.edu/index.php?page=ara-dataset

CUDA accelerated spherical model M/EEG

CUDA-SPHERE-FWD-MEEG is a CUDA C based toolkit which provides a GPU based implementation of the spherical model forward solution for the 306 channel Elekta Neuromag MEG system and the EEG. The 1-Sphere forward solution for the MEG and the 4-Sphere forward solution for the EEG is implemented in CUDA C and an accelerated solution is obtained using the NVIDIA GPU when the solution is calculated for a large number of dipoles (on the order of 15000 and above) and sensor location. Speedup by a factor of 22 and 32 is obtained for the EEG and MEG solution respectively when compared to the fastest CPU implementation available in the public domain. The complete source code and pre-compiled binaries are also made available via an open source license (GPL Version 3). A CUDA enabled NVIDIA graphics card is required to use the software.

iBEAT

iBEAT: Infant Brain Extraction and Analysis Toolbox. Since 2008, PIs in UNC-Chapel Hill have been working on developing infant-dedicated computational tools. In 2012, the iBEAT toolbox was initially released. In 2020, iBEAT V2.0 Cloud was re-developed with more advanced techniques, available online (http://www.ibeat.cloud/). Users can process any age of pediatric images via uploading images into iBEAT Cloud. Up to date, iBEAT V2.0 Cloud has successfully processed 13,000+ infant brain images from 100+ institutions. Please check user feedback: https://ibeat.wildapricot.org/Feedbacks
and demos: https://ibeat.wildapricot.org/Demos

About us:
The iBEAT V2.0 software is developed by the UNC at Chapel Hill:

Volume-based analysis was designed in the Developing Brain Computing Lab, led by Dr. Li Wang (li_wang@med.unc.edu);

Surface-based analysis was designed in the Baby Brain Mapping Lab, led by Dr. Gang Li (gang_li@med.unc.edu).

CANDI Share: Schizophrenia Bulletin 2008

This project hosts data for CANDI Share Schizophrenia Bulletin 2008 (reference below) as part of the CANDI Neuroimaging Access Point. This set includes preprocessed MRI images and segmentation results of all 4 diagnostic groups (Healthy Controls, N=29; Schizophrenia Spectrum, N=20; Bipolar Disorder with Psychosis, N=19; and Bipolar Disorder without Psychosis, N=35).

Frazier JA, Hodge SM, Breeze JL, Giuliano AJ, Terry JE, Moore CM, Kennedy DN, Lopez-Larson MP, Caviness VS, Seidman LJ, Zablotsky B, Makris N. Diagnostic and sex effects on limbic volumes in early-onset bipolar disorder and schizophrenia. Schizophr Bull. 2008 Jan;34(1):37-46.

CleanLine

Sinusoidal noise can be a prominent artifact in recorded electrophysiological data. This can stem from AC power line fluctuations (e.g. 50/60 Hz line noise + harmonics), power suppliers (e.g. in medical equipment), fluorescent lights, etc. Notch filtering is generally undesirable due to creation of band-holes, and significant distortion of frequencies around the notch frequency (as well as phase distortion at other frequencies and Gibbs rippling in the time-domain). CleanLine is an EEGLAB plugin which adaptively estimates and removes sinusoidal artifacts from ICA components or scalp channels using a frequency-domain (multi-taper) regression technique with a Thompson F-statistic for identifying significant sinusoidal artifacts. This approach has been advocated by Partha Mitra and Hemant Bokil (Observed Brain Dynamics, Chapter 7.3.4., 2007) and CleanLine utilizes modified routines from the Mitra Lab's Chronux Toolbox (www.chronux.org).

PING

Pediatric Imaging, Neurocognition, and Genetics - PING. PING is a multi-site project involving a Coordinating Center, and 4 Scientific Cores. Leading pediatric researchers across the country are participating at nine universities nation-wide: UC San Diego, the University of Hawaii, UCLA, UC Davis, Kennedy Krieger Institute at Johns Hopkins, Sackler Institute at Cornell University, the University of Massachusetts, Massachusetts General Hospital at Harvard University, and Yale.

The goal is to create a large MRI and genetics data resource to be shared openly with the scientific community. The data resource includes information about the developing mental and emotional functions of the children. The study includes 1400 children between the ages of 3 and 20 years so that links between genetic variation and developing patterns of brain connectivity can be examined.

New data access is managed thorough the NIMH Data Archive (NDA), instructions available in the " NDA Data Access Instructions" on the left.

Analyzer 2

Since 1997, BrainVision Analyzer has made its way into thousands of research labs, helping scientists to manage their daily work of analyzing various neurophysiological data.

Thanks to its ease of use, the handling of an amazing number of powerful features is just a snap. The editable History Tree® lets you organize, explore and trace evaluation steps.

Automated analysis with drag & drop functions speeds up recurring tasks.

Various data format readers recognize files from different EEG manufacturers automatically and guarantee compatibility & exchangeability with other research labs.

Analyzer includes all necessary pre-processing functions, enhanced time-frequency analysis options, ICA, LORETA, MRI correction and a direct interface to MATLAB®.

Various views create unlimited output possibilities to publish your results.

PLEASE NOTE: Running Analyzer requires a dongle that has to be purchased through Brain Products or one of its distributors (http://www.brainproducts.com/distributors.php).

Micro-Manager

Micro-Manager (http://micro-manager.org) operates motorized light microscopes and associated equipment. It runs as a plugin in ImageJ, works with virtually all scientific grade microscope equipment, and has a simple interface towards routine image acquisition strategies such as time-lapse, z- stacks, multi-channel, and multi-position acquisition. In addition, it uses a device abstraction layer available from various programming environments (such a C, Java, Python, Matlab and LabView), facilitating development of novel approaches to image acquisition.

imcalc: Batch image calulations and transformations for SPM

Image maths and transformations with batch functionality for SPM5, SPM8, or SPM12; features include:

(1) Binarize: non-zero voxels, or to specific thresholds
(2) Pairwise operations: add, subtract, mean, min, max ...
(3) Create or split cluster images
(4) Create inter-subject agreement maps
(5) Statistical transformations: T-to-Z, Z-score calculation
(6) Create masks based on an image
(7) Replace zeros / NaNs
(8) Pad a bounding box with extra voxels

For download and documentation, please visit: https://robjellis.net/tools.html

INVIZIAN

Fly through and interact with hundreds of human brains to compare structural differences or carefully inspect individual specimens. Introducing the “Informatics Visualization in Neuroimaging (INVIZIAN©)” Project (http://invizian.loni.usc.edu), a 21st century visualization environment that enables you, via your computer, to display and interact with hundreds of neuroimaging data sets at once —bringing together brain image data from some of the world’s best neuroscience research teams. INVIZIAN© empowers both researchers and students of neuroscience to explore and understand the human brain using a simple yet powerful user interface for neuroimaging data exploration and discovery. See a beautiful example of a cloud of individual brains tumbling around in the INVIZIAN© interface in Vimeo (http://vimeo.com/67984681). Visit often to see how we are making continuing progress to make Invizian© even more amazing.

SINOMO (SIngular NOde MOtifs)

Network nodes can be described by node-motifs--a combination of local network features. Certain node-motifs, such as highly connected nodes or hubs, have been shown to be important components of networks. Costa et al. (2009) have presented a technique to detect and specify more complex compound motifs, which are characterised by multiple features in combination. We described improvements to that method and showed how its parameters can be determined automatically [Echtermeyer et al. 2011]. SIMONO is our implementation of the enhanced workflow, which can be controlled via a graphical user interface or through the command-line for batch processing.

Documentation is available at:
http://www.biological-networks.org/p/sinomo/
You can also directly download the Matlab/Octave code following the link below.

References:
* L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett., 87, 1, 2009
* C. Echtermeyer, L. Da Fontoura Costa, F. A. Rodrigues, M. Kaiser, PLoS ONE 6, 9, 2011

NFT: Neuroelectromag Forward Modeling

Neuroelectromagnetic Forward Head Modeling Toolbox (NFT) (http://sccn.ucsd.edu/nft) is a MATLAB Toolbox for generating realistic head models from available data (MRI and/or electrode locations), for computing numerical solutions for the forward problem of electromagnetic source imaging and for single dipole source localization. The NFT includes tools for segmenting scalp, skull, cerebrospinal fluid (CSF) and brain tissues from T1-weighted magnetic resonance (MR) images. The Boundary Element Method (BEM) and Finite Element Method (FEM) are used for the numerical solution of the forward problem. When a subject MR image is not available a template head model can be warped to measured electrode locations to obtain an individualized head model. Toolbox functions may be called either from a graphic user interface compatible with EEGLAB or from the MATLAB command line. The toolbox is freely available under the GNU Public License for noncommercial use and open source development.

peak_nii

peak_nii: Statistical image clustering, peak detection and data extraction.
The Peak_nii toolbox was developed to allow the user to have flexibility of clustering their data. Based on your threshold, it will cluster your data and find the peaks within each cluster. Additionally, it has been combined with a data extraction tool that allows one to extract the data from all the scans of the analysis from all the clusters, along with several other extraction options, with a single command.

vis: Visualized statistics toolbox for SPM

A simple, menu-driven toolbox for exploratory data analysis of brain images in SPM5, SPM8, or SPM12; features include:

(1) Histograms or kernel density estimates of voxel values;
(2) Scatter, Bland-Altman, or quantile-quantile plots comparing two images;
(3) Surface plot of all voxel values at a particular axial slice;
(4) ROI-based extraction of voxel value point estimates (mean, std, etc.)

For download and documentation, please visit: https://robjellis.net/tools.html

INIA19 Primate Brain Atlas

The INIA19 primate brain atlas was created from over 100 structural MR scans of 19 rhesus macaque animals. The atlas currently comprises high-resolution T1-weighted average MR images with and without skull stripping, tissue probability maps, and a detailed parcellation map based on the NeuroMaps atlas.

INIA19 was created by researchers of the NIH-funded Integrative Neuroscience Initiative on Alcoholism (INIA).

Measure Projection Toolbox (MPT)

This toolbox is an EEGLAB plugin for performing Measure Projection Analysis.

Measure Projection Analysis (MPA) is a novel probabilistic multi-subject inference method that overcomes EEG Independent Component (IC) clustering issues by abandoning the notion of distinct IC clusters. Instead, it searches voxel by voxel for brain regions having event-related IC process dynamics that exhibit statistically significant consistency across subjects and/or sessions as quantified by the values of various EEG measures.

Local-mean EEG measure values are then assigned to all such locations based on a probabilistic model of IC localization error and inter-subject anatomical and functional differences.

BCILAB

BCILAB is a MATLAB toolbox for Brain-Computer Interface (BCI) research. It facilitates the design and development of new methods for cognitive state estimation and their use in both offline data analysis and real-time applications. BCILAB includes an easily extensible collection of currently over 100 methods from the literature (covering signal processing, machine learning and BCI-specific methods). Aside from supporting advanced BCI research, a special aim of BCILAB is to facilitate the adoption of machine learning and advanced statistical modeling for functional neuroimaging purposes in tandem with the EEGLAB platform.

Solar Eclipse Imaging Genetics tools

We are developing software tools optimized for performing univariate and multivariate imaging genetics analyses while providing practical correction strategies for multiple testing.

Official beta SOLAR Eclipse Version 8.3.X, adds multiple developments. This includes: Empirical pedigree from plink files, fast heritability (FPHI) and GWAS (NINGA) updates for CPU/GPU computing, more accurate p-value approximation for fast inference, polyclass_normalize function for multi-site homogenization and mega-analysis.

Upcoming implements: CPU/GPU-based voxel-wise GWA using new HDF5 file format and cluster based statistical inference based on permutations.

Source codes are distributed as a tarball or at github.com/kochunov/solar-eclipse

The changes are described at http://solar-eclipse-genetics.org/

BioSig

Biosig provides tools for processing of electroencephalogram (EEG) and other biomedical signals like ECG, EOG, EMG, etc. Biosig contains tools for quality control, artifact processing, time series analysis, feature extraction, classification and machine learning, and tools for statistical analysis.

Many tools are able to handle data with missing values (statistics, time series analysis, machine learning). Another feature is that more then 40 different data formats are supported, and a number of converters for EEG,, ECG and polysomnography are provided.

Biosig has been widely used for scientific research on EEG-based BraiN-Computer Interfaces (BCI), sleep research, and ECG and HRV analysis.

It provides software interfaces several programming languages (C, C++, Matlab/Octave, Python), and it provides also an interactive viewing and scoring software for adding, and editing of annotations, markers and events.

Neuroimaging Made Easy Blog

The main aim of this blog is presenting some scripts that can be used to facilitate and automate processing and analysis of brain data. In addition, it could be helpful explaining non clear stages and steps of brain data processing using some software such as; Freesurfer, FSL, Brainvoyager QX... At the moment, there are more than 10 applescripts in the main website http://www.easyneuroimaging.com that control different tools and commands (aparcstats2table, asegstats2table, BET, dcm2nii, FIRST, fslsplit, fslswapdim, fslview, mri_convert, Qdec, Recon-all, SIENAX, tkmedit, tksurfer)

EEProbe

EEProbe is a complete software package for the study of event-related brain activity with high-resolution EEG/MEG. This package has been designed to suit the high standards of neuroscience research. The software has been developed originally at the Max Planck Institute for Cognitive Neuroscience in Leipzig, Germany, and is available for other institutions through ANT Neuro B.V., The Netherlands, enhanced with the EEProbe Databrowser.

ERP investigations, both in psychophysiology research and clinical applications require a multitude of processing steps. Analysis of large data sets is made efficient through advanced scripting possibilities.

All different aspects of data handling are efficiently available in the EEProbe Databrowser. Alternatively, external data can be imported from a multitude of formats.

Processing in EEProbe makes use of open file formats (see LIBEEP) and is designed to integrate with ASA for advanced source analysis.

EEProbe is available for Linux and Mac OS X.
www.ant-neuro.com

ASA - Advanced Source Analysis

ASA is a highly flexible EEG/ERP and MEG analysis package with a variety of source reconstruction, signal analysis and MRI processing features. ASA combines functional brain imaging with the visualization and incorporation of morphological information obtained from MRI or CT. ASA is a highly interactive and flexible software tool that can be applied to neuro-physiological and clinical brain research.

ASA gives a realistic impression of your experimental configuration together with topographical mapping of EEG and MEG and the results of your analysis. ASA is developed for and by people dedicated to brain research. The concept of flexibility and openness covers even most complex analysis demands. The ASA environment is particularly attractive for those that wish to develop their own methods in third party packages like Matlab and use ASA for pre-processing and visualization purposes.

LIBEEP

The LIBEEP library deals with reading and writing RIFF-format CNT/AVR-files.

This file format is also called "EEProbe data format", and is used in the software packages EEProbe, ASA, ASA-Lab, Cognitrace, eemagine EEG, Visor, by ANT Neuro B.V., The Netherlands.

The file format provides for storage of EEG/ERP/MEG data as 32-bit values, and includes a very efficient compression algorithm. Encoding/decoding from the compressed data is performed automatically through the LIBEEP interface functions.

ParaView

ParaView is an open-source, multi-platform data analysis and visualization application. ParaView users can quickly build visualizations to analyze their data using qualitative and quantitative techniques. The data exploration can be done interactively in 3D or programmatically using ParaView's batch processing capabilities.

ParaView was developed to analyze extremely large datasets using distributed memory computing resources. It can be run on supercomputers to analyze datasets of terascale as well as on laptops for smaller data.

Net Station

APIs for Net Station data files. APIs are available for C++, C#, and Java.

Source Information Flow Toolbox

The Source Information Flow Toolbox (SIFT) is an GUI-enabled EEGLAB plugin for modeling and visualizing dynamical interactions between electrophysiological signals (EEG, ECoG, MEG, etc), preferably after transforming signals into the source domain. The toolbox consists of four modules: (1) Data Preprocessing, (2) Model Fitting and Connectivity Estimation, (3) Statistical Analysis, (4) Visualization, with a fifth Group Analysis module in development. Module 2 currently includes several adaptive multivariate autoregressive modeling (AMVAR) algorithms, including segmentation AMVAR and Kalman filtering. This subsequently allows the user to validate the model and estimate (in the time-frequency domain) a wide range of multivariate Granger-causal and coherence measures published to date. Module 3 includes routines for parametric and non-parametric significance testing. Module 4 contains routines for interactive visualization of dynamical interactions across time, frequency and anatomical source location.

Brain Segmentation Testing Protocol

The Brain Segmentation Testing Protocol (Neuroimage 2011, http://dx.doi.org/10.1016/j.neuroimage.2011.06.080) is a freely available collection of MRI images for testing segmentation algorithms. The 312 MRI datasets may be downloaded to assess the accuracy, reproducibility and sensitivity of MRI segmentation software. The accuracy/validation dataset includes images from infants, adults and patients with Alzheimer’s disease and the reproducibility dataset includes 9 subjects who have been scanned with 8 different sequences at 1.0T and 3.0T.

ALVIN - Lateral Ventricle Segmentation

ALVIN - Automatic Lateral Ventricle delIneatioN is a fully automated algorithm which works within SPM8 to segment the lateral ventricles from structural MRI images. The algorithm has been validated in infants, adults and patients with Alzheimer's disease (ICC>0.95). ALVIN is insensitive to different scanner sequences (ICC>0.99, 8 different sequences 1.5T and 3T) and sensitive to changes in ventricular volume. Processing time is approx 10mins per subject. Download from here or visit our main website http://sites.google.com/site/mrilateralventricle/

TumorSim

TumorSim is a cross-platform simulation software that generates pathological ground truth from a healthy ground truth (e.g., the BrainWeb data). The software requires an input directory that describes a healthy anatomy (anatomical probabilities, mesh, diffusion tensor image, etc). Example output simulation images are available for download for validating various image analysis algorithms such as registration and segmentation algorithms.

The simulation method is described in the following paper:
Marcel Prastawa, Elizabeth Bullitt, and Guido Gerig. Simulation of Brain Tumors in MR Images for Evaluation of Segmentation Efficacy. Medical Image Analysis (MedIA), Vol 13, No 2, April 2009, Pages 297-311.

Research efforts on the simulator was supported by NIH grant NIBIB R01 EB000219 (PI: Elizabeth Bullitt).

MazeSuite

MazeSuite is a complete set of tools that enables researchers to perform spatial and navigational behavior experiments within interactive, easy to create, and extendable (e.g., multiple rooms) 3D virtual environments. MazeSuite can be used to design/edit adapted 3D environments where subjects’ behavioral performance can be tracked. MazeSuite consists of three main applications; an editing program to create and alter maps (MazeMaker), a visualization/rendering module (MazeWalker), and finally an analysis/mapping tool (MazeAnalyzer). Additionally, MazeSuite has the capabilities of sending signal pulses to physiological recording devices using standard computer ports. MazeSuite, with all 3 applications, is a unique and complete toolset for researchers who want to easily and rapidly deploy interactive 3D environments. For more information and related publications please see www.mazesuite.com

AMILab

AMILab is an opensource software for image analysis, processing and visualization. It provides convenient visualization tools for 2D and 3D images and it is highly extensible through its own scripting language. At visualization level, AMILab includes a 2D/3D image viewer, a 3D polygon viewer based on OpenGL, a 2D Curve viewer to visualize 2D curves, histograms and color/opacity transfer functions, and a GPU-enabled raycasting script for Volume Rendering based on VTK. The software includes an automatic C++ wrapping system which permits fast development of new visualization tools and image processing algorithms. This wrapping system currently wraps about 200 classes from wxwidgets library and about 100 classes from VTK.

LORIS

LORIS (Longitudinal Online Research and Imaging System) is an open-source database solution platform for neuroimaging and neuroscience research.

LORIS is a platform designed for seamless data acquisition, curation, processing, and dissemination across large neuroimaging, clinical and genomic datasets. Its secure web-accessible database infrastructure automates the flow of data throughout the lifecycle of multi-site longitudinal studies, and includes extensive Quality Control and Data Validation processes for both scalar and multidimensional data.

LORIS’ extensible design enables researchers to aggregate, query, and distribute subject data in a powerfully customizable and flexible manner to image processing pipelines and analysis suites frequently used in neuroimaging research studies.

More info? visit www.LORIS.CA

Try our demonstration database at https://demo.loris.ca

Find our latest Release and Documentation Wiki on GitHub --
https://github.com/aces/Loris

ERPLAB

ERPLAB Toolbox is a set of open source, freely available Matlab routines for analyzing ERP data. It is tightly integrated with the EEGLAB Toolbox. ERPLAB routines can be accessed from the Matlab command window and from Matlab scripts in addition to being accessed from the EEGLAB GUI. Consequently, ERPLAB provides the ease of learning of a GUI-based system but also provides the power and flexibility of a scripted system.The development of ERPLAB Toolbox is being coordinated by Steve Luck and Javier Lopez-Calderon at the UC-Davis Center for Mind & Brain, with financial support from NIMH.

MEGSIM

MEGSIM, http://cobre.mrn.org/megsim/, contains realistic simulated MEG datasets ranging from basic sensory to oscillatory sets that mimic functional connectivity; as well as basic visual, auditory, and somatosensory empirical sets. The simulated sets were created for the purpose of testing analysis algorithms across the different MEG systems when the truth is known. MEG baseline recordings were obtained from 5 healthy participants, using three MEG systems: VSM/CTF Omega, Elekta Neuromag Vectorview, 4-D Magnes 3600. Simulated signals were embedded within the CTF and Neuromag 306 baseline recordings (4-D to be added). Participant MRIs are available. Averaged simulation files are available as netcdf files. Neuromag 306 averaged simulations are also available in fif format. Also available: single trials of data where the simulated signal is jittered about a mean value, continuous fif files where the simulated signal is marked by a trigger, and simulations with oscillations added to mimic functional connectivity.

Microstructural correlation toolbox

Microstructural correlation toolbox (MSC) is a Matlab-based software library to perform independent component analysis on group white matter skeleton generated by FSL TBSS.
The script produces stable estimates of the white matter tract or tract segments that resemble highly correlated variation profiles across a group of subjects. Please refer to the following publication for detailed information:
Li YO, et al., "Independent component analysis of DTI reveals multivariate microstructural correlations of white matter in the human brain", Hum Brain Mapp.2011.

EEGLAB

EEGLAB is to date the most popular EEG/MEG/ECoG software with about 100,000 download worldwide since 2003. EEGLAB provides an interactive graphic user interface (GUI) allowing users to flexibly and interactively process their high-density EEG and other dynamic brain data using independent component analysis (ICA) and/or spectral time/frequency and coherence analysis, as well as standard methods including event-related potentials (ERP). EEGLAB also incorporates extensive tutorial and help windows, plus a command history function that eases users' transition from GUI-based data exploration to building and running batch or custom data analysis scripts. EEGLAB offers a wealth of methods for visualizing and modeling event-related brain dynamics, both at the level of individual EEGLAB 'datasets' and/or across a collection of datasets. For experienced Matlab users, EEGLAB offers a structured programming environment for storing, accessing, measuring, manipulating and visualizing event-related EEG data.

DTI-Reg

DTI-Reg is an open-source C++ application that performs pair-wise DTI registration, using scalar FA map to drive the registration.

Individual steps of the pair-wise registration pipeline are performed via external applications - some of them being 3D Slicer modules. Starting with two input DTI images, scalar FA maps are generated via dtiprocess. Registration is then performed between these FA maps, via BRAINSFit/BRAINSDemonWarp or ANTS -Advanced Normalization Tools-, which provide different registration schemes: rigid, affine, BSpline, diffeomorphic, logDemons. The final deformation is then applied to the source DTI image via ResampleDTIlogEuclidean.

DTI-Reg is available in a 3D Slicer (http://www.slicer.org) extension called DTIAtlasBuilder

FiberViewerLight

Fiber ViewerLight is an open-source C++ application to analyze fiber bundles. It provides:
- Several methods for clustering: Length, Gravity, Hausdorff, and Mean methods but also a Normalized Cut algorithm.
- 3D fibers visualization
- 3D plane selection

FiberViewerLight is now available as a 3D Slicer extension (http://www.slicer.org)

DPARSF

Data Processing Assistant for Resting-State fMRI (DPARSF) is a convenient plug-in software based on SPM and REST. You just need to arrange your DICOM files, and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data, FC, ReHo, ALFF and fALFF results. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. You can use DPARSF to extract AAL or ROI time courses (or extract Gray Matter Volume of AAL regions, command line only) efficiently if you want to perform small-world analysis.

Please download it and find more information from http://rfmri.org/DPARSF

Create DWI Atlas

This package is a set of three slicer modules which together are able to compute a DWI Atlas given a set of DWI's. The modules included are:

1) A Groupwise Registration module -> compute's a deformation field for each DWI, using, for example, FA maps as input
2) Warp DWI module -> used to warp each DWI using the deformation from (1)
3) DWI Averager -> used to average the set of warped DWI's producing the final DWI Atlas

Fiber Tracking Tool

Used to analyze the fibers produced by ukf tractography

3d Brain Atlas Reconstructor

3d Brain Atlas Reconstructor (3dBAR, http://www.3dbar.org) is a software package for reconstructing three-dimensional models of brain structures from 2-D delineations using a customizable and reproducible workflow.

3dBAR also works as an on-line service (http://service.3dbar.org) offering a variety of functions for the hosted datasets:

- downloading reconstructions of desired brain structures in predefined quality levels in various supported formats as well as created using customizable settings,
- previewing models as bitmap thumbnails and (for webGL enabled browsers) interactive manipulation (zooming, rotating, etc.) of the structures,
- downloading slides from available datasets as SVG drawings.

3dBAR service can also be used by other websites or applications to enhance their functionality.

Check http://www.3dbar.org for detailed description of the software and the latest releases.

CMFreg

We have developed a sequence of fully automated voxel-wise rigid registration that utilizes stable structures of reference for assessment of craniofacial changes overtime.The major strengths of this method are that registration does not depend on the precision of the 3D surface models and that a “stable structure of reference” can be used without the simple “best fit” of all surfaces.

M3

The M3 (multi-modal imaging and multi-level characteristic with multi-classifier) is a brain imaging classification tool, which can help researchers to discriminate patients from normal controls. The M3 includes three steps: feature selection, maximum uncertainty linear discriminant analysis (MLDA)-based classification and multi-classifier. A leave-one-out cross-validation (LOOCV) is further used to estimate the performance of the M3. Finally, the most discriminative features are identified.

Laboratory of Neuro Imaging (LONI)

The Laboratory of Neuro Imaging strives to improve our understanding of the brain in health and disease. LONI develops advanced computational algorithms and scientific approaches for the comprehensive and quantitative mapping of brain structure and function. LONI aims to encourage communication between users and LONI software engineers in order to improve the effectiveness of computational brain mapping software and to promote its use by researchers worldwide. LONI software website (http://www.loni.usc.edu/Software/) includes downloadable and web-accessible tools, training and support. Be sure to visit LONI Forums and discuss our tools with other end-users and LONI investigators.

http://www.loni.usc.edu

XNAT Extras

User contributions for XNAT.

BrainDecoderToolbox

Brain Decoder Toolbox performs “decoding” of brain activity, by learning the difference between brain activity patterns among conditions and then classifying the brain activity based on the learning results.

BDTB is a set of Matlab functions.
BDTB is OS-independent.

UCLA Multimodal Connectivity Database

The UCLA Multimodal Connectivity Database - http://umcd.humanconnectomeproject.org - is a web-based analysis site and data repository for connectivity matrices that have been derived from neuroimaging data. The site is powered by the MGH/UCLA Human Connectome Project. Anyone can browse and analyze connectivity matrices that researchers have shared on this site. The data comes from different imaging modalities (fMRI, DTI, structural MRI, EEG), subject groups, and studies. The website allows users to choose any network shared by another user, compute graph theoretical metrics on the fly, and visualize the results.

Fast Nonlocal Means for MRI denoising

This is a fast and robust implementation of the popular Nonlocal Means for MRI-Rician denoising. It works by computing the non-local weights based on distances in a features space comprising the local mean value and gradients of the image.

It can reach an acceleration factor of 20x over the original implementation, with an improved performance for medium-low SNR images.

We use a bias correction step for Rician noise based on the well-known Conventional Approach.

This software can be compiled either as a Slicer module or a stand-alone:

http://www.nitrc.org/snapshots.php?group_id=518

For diffusion MRI volumes, see also: http://www.nitrc.org/projects/jalmmse_dwi

Key words: nonlocal (non-local) means, NLM, C++, ITK

PySurfer

PySurfer is a Python based program for visualization and interaction with cortical surface representations of neuroimaging data from Freesurfer. It extends Mayavi’s powerful visualization engine with a high-level interface for working with MRI and MEG data. PySurfer offers both a command-line interface designed to broadly replicate Freesurfer’s Tksurfer program as well as a Python library for writing scripts to efficiently explore complex datasets.

scikit-learn

scikit-learn: machine learning in Python

OpenMEEG

A C++ package for low-frequency bio-electromagnetism
solving forward problems in the field of EEG and MEG with very high accuracy.

Wavelet-based Image Fusion

This is a Matlab toolbox that implements the wavelet-based image fusion technique for orthogonal images, introduced in (Aganj et al, MRM 2012).

OpenFMRI

OpenFMRI is a data sharing resource for task-based fMRI data. It aims to provide a resource for any fMRI researcher who wishes to share their data openly. Currently it provides access to raw fMRI datasets along with associated metadata. In the future, it will also provide access to processed datasets.

COST

Connectivity Orientation Spherical distribution Tool (COST) - This tool implements the method from the paper "Efficient, Graph-based White Matter Connectivity from Orientation Distribution Functions via Multi-directional Graph Propagation," Proc. of SPIE 2011; 7962(79620S):1-8. However, with it we also seek to modify the method into a minimum cost-based approach and eventually test it on applications.

NeuroSynth

The NeuroSynth framework includes software tools for automated extraction and synthesis of data from published functional neuroimaging studies, a series of datasets generated using these tools, and a website ( http://neurosynth.org ) for visualizing and accessing many of the tools and results.

LIMO MEEG

The LInear MOdelling of MEEG data (LIMO MEEG) toolbox is a Matlab toolbox dedicated to the statistical analysis of MEEG data. Once data are imported, all computations are performed within the toolbox, and can thus work for any data sets from any software (e.g. EEGLAB, FieldTrip, BrainStorm). It is interfaced with EEGLAB (via STUDY) acting as a plug in ; it also uses topolot from EEGLAB for result visualization.

Almost all statistical designs can be analyzed with our tool. Within subject analyses rely on a dedicated weighted least square approach to downweight trials with different dynamics, between subjects analyses are performed using robust tests and inference rely on boostrap for multiple comparison corrections (max, cluster, tfce).

Licensing

Resource describing licensing issues in software and data. Information includes the basics of licensing, choosing an open-source license, and more.

Most information is included in the MediaWiki. All are invited to participate or join the project and contribute.

BrainNet Viewer

BrainNet Viewer is a brain network visualization tool, which can help researchers to visualize structural and functional connectivity patterns from different levels in a quick, easy, and flexible way.

connectir

Connectir is an R-based package to conduct brain connectivity analyses with a focus on a novel approach to conducting Connectome-Wide Association Studies (CWAS) using functional connectivity.

Please see our web page for more details: http://czarrar.github.io/connectir or click the Home Page link on the right below.

MNI Macaque Atlases

We present unbiased standard macaque monkey magnetic resonance imaging template brain volumes that offers a common stereotaxic reference frame to localize anatomical and functional information in an organized and reliable way for comparison across individual nonhuman primates and studies.

Brainsight

Neuronavigation system for use in human cognitive neuroscience (TMS, EEG, NIRS) and for non-human neurosurgical applications.

NordicNeuroLab

With over a decade of experience, NordicNeuroLab (NNL) provides products and solutions that define the field of functional MR imaging. We understand the growing need for reliable and innovative tools in this growing field. As a result, we closely collaborate with research and clinical teams from both academic and medical centers, MR system manufacturers, and third party vendors to develop and manufacture hardware and software solutions that meet the needs of very experienced centers while developing training programs to make fMRI easy to adopt for more novice users.

From state of the art post-processing and visualization software for BOLD, Diffusion/DTI, and Perfusion/DCE imaging to fMRI hardware for audio and visual stimulation, eye tracking, and patient response collection, NNL's products are used around the world by researchers and clinicians alike. Ultimately, we are dedicated to bringing the most advanced neuro-imaging tools to market while making functional MRI programs easy to implement.

NIRx NIRS Neuroimaging

NIRx Medical Technologies, LLC* is a leader in providing integrated solutions for NIRS neuroimaging.

*Click on the company logo to see more images.

We provide custom technology solutions to the investigative community for a wide range of near-infrared spectroscopy imaging applications. While many of our systems are in the field of neuroscience (infants to adults), they are also used for investigation of breast cancer, peripheral vascular disease and the study of small animals.

Email: info@nirx.net
Web: www.nirx.net

NIRx is closely associated with The Optical Tomography Group (OTG) of SUNY: Downstate Medical Center. For more information on The OTG, visit:

NITRC page: http://www.nitrc.org/projects/fnirs_downstate
https://www.downstate.edu/pathology/otg/otg_publications.html

DATAPixx

The DATAPixx is a complete multi-function data and video processing USB peripheral for vision research. In addition to a dual-display video processor, the DATAPixx includes an array of peripherals which often need to be synchronized to video during an experiment, including a stereo audio stimulator, a button box port for precise reaction-time measurement, triggers for electrophysiology equipment, and even a complete analog I/O subsystem. Because we implemented the video controller and peripheral control on the same circuit board, you can now successfully synchronize all of your subject I/O to video refresh with microsecond precision.

Hitachi Optical Topography System

Hitachi Medical Corporation is a leader in fNIRS neuroimaging with more than 10 years of experience. We provide researchers and clinicians with sophisticated All-in-One solutions in the field of neuroscience.

Data Format Tools

DFT is a loose collection of programs and configuration options that intend to make working with data more transparent to formats.

Currently available is a basic specification for NIfTI-1 for the UNIX file command and proof of concept code for the concept of treating data as an abstract concept and instantiating physical instances on demand.

Mag Design and Engineering

Mag Design and Engineering sells a variety of MEG- and fMRI-compatible hardware for research use. These items include typical response collection devices such as joysticks, response pads, mice, as well as stimulation devices such as vibrotactile stimulators, olfactometers, and pressure/force generators. The company also offers custom design and production services for many different applications.

fNIR Devices

fNIR Imager 1100 is a new generation portable functional near-infrared (fNIR) imaging research tool capable of monitoring brain’s hemodynamics and thereby the cognitive state of the subject in natural environments.

Neuroimaging Solution for Natural Environments:
• fNIR is the only stand-alone and field-deployable technology able to determine localized brain activity.
• fNIR can be readily integrated with other physiological and neurobehavioral measures that assess human brain activity, including eye tracking, pupil reflex, respiration and electrodermal activity. fNIR can also complement other techniques.
• Studies have shown a positive correlation between a participant's performance and fNIR responses as a function of task load.
• It has also been shown that fNIR can effectively monitor attention and working memory in real-life situations.

SR Research EyeLink Eye Trackers

SR Research, makers of the world leading EyeLink High-Speed eye tracker line, have been developing advanced eye tracking technologies and serving world class support to our research user base since 1992. The EyeLink line provides eye tracking capabilities for behavioral labs as well as for MRI, MEG, and EEG environments.

OEI fMRI compatible olfactometer

The OEI OLFACT-fMRI olfactometer is a computerized, odor delivery device that can be used for basic research applications including mapping olfactory centers, cognitive/learning research, neuro-marketing among other uses. Additional products including tests for odor threshold, odor identification, odor discrimination and odor memory.

Fiber-tracking based on Finsler distance

This software is currently provided as a sub-project in the Finsler-tractography module: http://www.nitrc.org/projects/finslertract

NeuroshareLibrary

This is MATLAB library to create Neuroshare data format.
You can convert your own data into Neuroshare format file.

Faceted Search Based Ontology Visualizer

Allows user to do faceted search on an ontology and enables visualization of the search results on the 3D digital atlas. Currently supports faceted search of functional neuroanatomy.

CBRAIN

CBRAIN is a flexible software platform with a small footprint and minimal requirements. CBRAIN was created to allow the integration the often extremely heterogeneous research HPC facilities across Canada and the World. Moreover, the platform's goal was not limited to deploying national and international distributed data and compute grids for neuroimaging sites, CBRAIN is an online collaborative web platform from which users transparently control their data, compute and results, including various forms of 2D and 3D data visualisation, regardless of where these resources and data are.

CBRAIN is currently deployed on 6 Compute Canada HPC clusters, one German HPC cluster and 3 clusters local to McGill University Campus, totaling more than 80,000 potential CPU cores.

Visit us at: http://cbrain.mcgill.ca
CBRAIN code is now available on GuitHub: https://github.com/aces/cbrain

C8: Corpus Callosum Computations

C8 is a small, stand-alone MatLab toolbox that measures sagittal cross-section thickness and area of the human corpus callosum from high-resolution T1 in vivo MR images. C8 takes as input affine normalized white matter segmentations derived from high-resolution (in-plane) T1 images and outputs both regional callosal thicknesses in three different formats and geometrically-defined regional areas in three different configurations. It is a small package that is easily configurable and modifiable and it measures callosa at the rate of several per minute.

MABMIS: Multi-Atlas Based Segmentation

This software package implements MABMIS: Multi-Atlas-Based Multi-Image Segmentation – an algorithm for accurate and consistent segmentation/labeling on a group of images.

MR Connectome Automated Pipeline (MRCAP)

This is the project page for the MR Connectome Automated Pipeline based on JIST and MIPAV. The pipeline combines structural magnetic resonance data with diffusion tensor imaging to estimate a connectome, which is a comprehensive description of the wiring diagram of the brain.

Pydicom

pydicom is a pure python package for working with DICOM files. It was made for inspecting and modifying DICOM data in an easy "pythonic" way. The modifications can be written again to a new file. As a pure python package, it should run anywhere python runs without any other requirements.

Lipsia

Lipsia is a software tool for processing functional magnetic resonance imaging (fMRI) data. It was developed over the course of several years at the Max-Planck-Institute for Human Cognitive and Brain Sciences in Leipzig, Germany.
Lipsia contains software tools for all aspects of fMRI data processing:

* registration and normalization
* preprocessing
* exploratory processing
* statistical evaluation
* region of interest analysis
* timecourse analysis
* visualization, rendering
* converters to various data formats

It was developed in C/C++ on Linux PCs. Lipsia provides extremely fast implementations. A standard analysis sequence from the raw data to a statistical parametric map generally takes less than 10 minutes per test subject.

MagPro Magnetic Stimulator (TMS)

MagPro is a complete line of non-invasive magnetic stimulation systems designed for clinical examinations and for research in the areas of neurophysiology, neurology, cognitive neuroscience, rehabilitation and psychiatry.

Convert MNI coordinates to or from XYZ

Input either normalized "MNI" coordinates from a 3D image, or input "real world" XYZ matrix coordinates, and this code will convert coordinates of one type to the other.

MriWatcher

This simple visualization tool allows to load several images at the same time. The cursor across all windows are coupled and you can move/zoom on all the images at the same time.
Very useful for quality control, image comparison.

Low Resolution Brain Electromagnetic Tomography

Non-invasive scalp measurements of electric potentials (EEG) [as well as magnetic fields (MEG)] can be used for estimating the electric neuronal activity distribution (current density vector field) on the cortex. The methods used here are LORETA, standardized LORETA (sLORETA), and exact LORETA (eLORETA).
Time series of cortical electric neuronal activity computed with LORETA can be used for estimating intracortical functional and effective connectivity. A collection of new methods are included here, especially well-suited for signals from these tomographies:
1. Lagged coherence and lagged phase synchronization.
2. Dynamic intracortical connectivity in terms of senders, hubs, and receivers.
3. Partial coherence fields.
4. Isolated effective coherence.
5. Functional ICA (in the sense of functional data analysis) for discovering generalized functional connectivity across space, frequency, and time.
All these methods and much more are programmed and available in the alpha LORETA-KEY software package.

GLIRT

GLIRT (Groupwise and Longitudinal Image Registration Toolbox) provides solutions for both groupwise registration and longitudinal registration, which are the necessary steps for many brain-related applications.

Specifically, groupwise registration is important for unbiased analysis of a large set of MR brain images. Therefore, in this software package, we have included two of our recently-developed groupwise registration algorithms: 1) Improved unbiased groupwise registration guided with the sharp group-mean image, and 2) Hierarchical feature-based groupwise registration with implicit template (Groupwise-HAMMER for short).

On the other hand, we also included our recently-developed groupwise longitudinal registration algorithm that aligns not only the longitudinal image sequence for each subject, but also align all longitudinal image sequences of all subjects to the common space simultaneously.

This software package was developed in the IDEA group at UNC-Chapel Hill ( http://bric.unc.edu/ideagroup ).

Open Connectome Project

The Open Connectome Project (OCP: openconnectomeproject.org) provides access to high resolution neuroanatomical images that can be used to explore connectomes. We also provide programmatic access to this data for human and machine annotation, with a long-term goal of reconstructing the neural circuits comprising an entire brain. This project aims to bring the most state-of-the-art scientific data in the world to the hands of anybody with internet access, so collectively, we can begin to unravel connectomes.

Mouse Connectome Project (MCP)

The Mouse Connectome Project (MCP,http://www.mouseconnectome.org) aims to create a three-dimensional digital connectome atlas of the C57Black/6J mouse brain. MCP offers a catalog of neural tracer injection cases, which is updated continuously to eventually cover the entire brain. Serial sections of each case are available to view at 10x magnification in the interactive iConnectome browser. Sample data set is available to explore in iConnectome. The data set features 5 layers which may be turned on, off, or adjusted for transparency, and split window features are available for comparing data sets.

The MCP project is housed at the Laboratory of Neuro Imaging at USC.

PPMI

PPMI includes a large and comprehensive set of correlated clinical data and biospecimens that is made available to the entire scientific community to help accelerate biomarker verification research. PPMI data and specimens have been collected in a standardized manner under strict protocols developed by the steering committee. The PPMI study dataset includes clinical, biological and imaging data collected at PPMI clinical sites. The data have been aggregated into the PPMI study database, which is managed by the PPMI Bioinformatics Core, the Laboratory of NeuroImaging (LONI) at the University of Southern California.

http://www.ppmi-info.org/access-data-specimens/

ADNI

Data collected as part of the ADNI study are freely available to authorized investigators, through the Image Data Archive (IDA). Information about obtaining access to ADNI data may be found in the How to Apply for Data section (http://adni.loni.usc.edu/data-samples/).

For more detailed information about accessing data from the IDA, please review the IDA User Manual.

The following ADNI data types are available: Clinical: Demographics, Clinical Assessments, Cognitive Assessments); Imaging: MRI: Raw, pre- and post- processed image files; PET: Raw, pre- and post- processed image files; fMRI: (ADNI GO); DTI: (ADNI GO); Chemical Biomarker: Laboratory Results; Genetic: Illumina SNP genotyping; Image Analysis Results: Numeric results derived from image analyses.

The ADNI database is managed and housed by the Laboratory of Neuro Imaging (http://www.LONI.usc.edu/) at USC.

Draw3D and Meshinator

Draw3D is a 3D rendering tool written entirely in VTK-TCL script. It is intended for fast command line rendering and visual inspection of datasets commonly found in medical imaging. It also allows the generation of images for reports or videos. As it is based on pure VTK, it can render whatever VTK can render, and runs wherever VTK can run.

Meshinator is a simpler tool that uses VTKs isosurface functions to generate meshes from volumetric data.

Please see the wiki for documentation

INCF Neuroimaging Data Sharing

Neuroscience data, particularly those in neuroinformatics related areas such as neuroimaging and electrophysiology, are associated with a rich set of descriptive information often called metadata. For data archive, storage, sharing and re-use, metadata are of equal importance to primary data, as they define the methods and conditions of data acquisition (such as device characteristics, study/experiment protocol and parameters, behavioral paradigms, and subject/patient information), and statistical procedures. A further challenge for datasharing is the rapidly evolving nature of investigative methods and scientific applications.

The overall scope of this program is to develop generic standards and tools to facilitate the recording, sharing, and reporting of metadata. It is expected that these efforts will greatly improve upon current practices for archiving and sharing neuroscience data. This group is often refered to as NI-DASH or NIDASH. It has generated the neuroimaging data model called NI-DM.

BrainBrowser

BrainBrowser ( https://brainbrowser.cbrain.mcgill.ca ) is a web-enabled brain surface viewer that allows the user to explore in real time a 3D brain map expressed on a base surface.

BrainBrowser has two modes of operation, exploring either a pre-calculated database of structural correlation maps or working with user-defined data. In this mode, the user may choose to explore the correlation structure for cortical thickness, cortical area or cortical volume, or any other pre-calculated metric.

In the second mode, the user is prompted for the local filenames of the statistical map and the base surface. BrainBrowser can also be used to manipulate 3D fibre pathways derived from DTI, using the same simple file format (.obj) as for surface data.

BrainBrowser on Youtube: http://www.youtube.com/watch?v=HlRTUYUf1Ew

NOTE: BrainBrowser requires a WebGL-enabled browser such as Google Chrome to support its 3D graphics capability.

CCSeg - Corpus Callosum Segmentation

Corpus Callosum Segmentation Tool

CCSeg is an open-source C++-based application developed at UNC-Chapel Hill that allows automatic as well as user-interactive segmentation of the Corpus Callosum. Via a Qt-based graphical user interface, CCSeg also performs semi-automatic segmentation.

Descriptions of methods:

Media 1997: G. Szekely, A. Kelemen, C. Brechbühler, and G. Gerig, “Segmentation of 2-D and 3-D objects from MRI volume data using constrained elastic deformations of flexible Fourier contour and surface models.,” Med Image Anal, vol. 1, no. 1, pp. 19–34, Mar. 1996.

recent SPIE article: http://www.nitrc.org/docman/view.php/466/1131/SPIE_2012_Vachet_CCSeg.pdf

NIRAL Utilities

NIRAL Utilities are open-source applications developped at UNC-Chapel Hill in the Neuro Image Research and Analysis Lab (NIRAL). These utilities are C++ based command line applications that allow image analysis and processing using ITK or VTK libraries.

Specifically the following utilities are contained thus far:
ImageMath - the swiss army knife image modification
ImageStat - compute stats on images
IntensityRescaler - rescale/normalize intensities using a prior brain tissue segmentation
convertITKformats - convert 3D images in all ITK formats (NRRD, NIFTI, GIPL, Meta etc)
DWI_NiftiNrrdConversion - convert DWI and DTI from/to NRRD and NIFTI, works with UNC DTI tools and FSL
CropTools - crops 3D and 4D images
PolydataMerge - Merges VTK polydata files
PolydataTransform - Transforms polydata files
TransformDeformationField - concatenates or average deformation fields (H-fields or displacement fields)
DTIAtlasBuilder - Creates a DTI average from multiple DTI images

Finsler tractography module for Slicer

This module implements the Finsler tractography method with HARDI data described by J. Melonakos et al. From a set of seeding and target points, the paths are estimated as the shortest path taking into account a local, directional dependent cost.

The output provided is the connectivity map from each voxel in the volume to the seeding points, plus a vector volume with the directions tangent to the fiber bundles at each point. If the Backtracing module within is built, these directions can be traced back to actually compute the fiber bundles (VTK required).

The software can be built as either a stand-alone or a CLI plugin for 3D Slicer.

Bisque

Bisque (Bio-Image Semantic Query User Environment) is a scalable web-based system for biological image analysis, management and exploration. The Bisque system incorporates many features useful to imaging researchers from image capture to extensible image analysis and querying. At the core, bisque maintains a flexible database of images and experimental metadata. Image analyses can be incorporated into the system and deployed on clusters and desktops.

MASI Label Fusion

This collection of tools is maintained by the Medical-image Analysis and Statistical Interpretation (MASI) group at Vanderbilt University.

The purpose of this tool is to provide a unified framework for testing and applying statistical and voting label fusion techniques. The project will include implementations of several different voting techniques including majority vote, weighted voting, and regionally weighted voting. Additionally, multiple statistical fusion methods will be included, notably, STAPLE, Spatial STAPLE, STAPLER and COLLATE.

In addition to the fusion algorithms, code for running specialized simulations and various tools and utilities to test the efficacy of the algorithms will be provided.

fMRI Classification in R

We demonstrate and provide R code that can classify between groups of fMRI scans based on functional network connectivity differences, requiring only 4 lines of code to be altered. In addition, we include a detailed article explaining the methods behind and motivations of this tool. This code can also be altered to perform connectivity analysis and classification using ROI based methods by reading in distance arrays previously created. We run ICA on fMRI data to establish functional networks, measure the functional connectivity between these networks using the temporal cross-correlations between independent component to create a distance matrix and indicating the networking. Connectivity properties are used as a feature matrix for an SVM classifier. Collectively, this project provides and explains both methods and code to perform functional network connectivity and fMRI SVM classiﬁcation.

Paradigm

Flexible and millisecond accurate experimental control for cognitive neuroscience, psychology and linguistics research. Build your experiments using Paradigm's simple drag and drop interface. Presents text, images, sounds, movies, self-paced reading trials and rating scales. An integrated Python scripting API is available to ensure total flexibility and control. Joystick and microphone response are available. Supports button boxes from PST, Cedrus,
fORP and custom built response boxes. Paradigm can detect fMRI triggers through serial and parallel ports. Includes sample experiments that implement many of the most popular experiment designs.

Cluster reporter

This matlab script and associated files will take resultant statistical images and essentially output everything you could ever want to know. It can work off of images that were previously corrected for multiple comparisons, but it can actually do the correction itself. This is because the cluster_correct script is incorporated within. It will iterate through atlases (borrowed from other software) to tell you the location of significant results. It outputs an extremely detailed report as well as a summary table for quick investigation. In addition, it will output statistics for each surviving cluster, and the image as a whole.Feedback would be much appreciated.

Cluster Extent Correction

This script will take any .img file and correct it based on a cluster extent, cluster definition and voxelwise threshold. The threshold entered will be applied to positive and negative values separately, and separate pos and neg corrected images will be output. This script requires a license for the matlab image processing toolbox.

Generalized PPI Toolbox

An automated toolbox for a generalized form of psychophysiological interactions for SPM and FSFAST.

The automated toolbox can do the following:
(a1) produce identical results to the current implementation in SPM
(a2) use the current implementation of PPI in SPM but using the regional mean instead of the eigenvariate
(a3) uses a generalized form that allows a PPI for each task to be in the same model using either the regional mean of eigenvariate
(b) creates the model using the output of one of the (a) options and the first level design
(c) estimates the model (/results directory)
(d) computes the contrasts specified

When using the toolbox, please cite the following paper:
McLaren, DG, Ries, ML, Xu, G, Johnson, SC. A Generalized Form of Context-Dependent Psychophysiological Interactions (gPPI): A Comparison to Standard Approaches. NeuroImage (in press).

HCP WU-Minn Consortium

*THE HUMAN CONNECTOME PROJECT WU-Minn Consortium* is acquiring data and developing analysis pipelines for several modalities of neuroimaging data (rfMRI, tfMRI, dMRI, MEG) plus behavioral and genetic data from 1200 healthy adults (300 twin pairs and siblings). The project will yield invaluable information about brain connectivity, its relationship to behavior, and contributions of genetic and environmental factors to individual differences in brain circuitry. Data is being shared with the scientific community via user-friendly platforms for data mining, analysis, and visualization. Extensive refinements were made for data acquisition and analysis methods in the first phase of the project (2010-2012). Now in production phase (2012-2015), HCP is collecting and releasing data from 400 subjects/year. www.humanconnectome.org

Connectome File Format (CFF)

The Connectome File Format (CFF) is a container format for multi-modal neuroimaging data. It comprises connectome objects of type: CMetadata, CNetwork, CVolume, CSurface, CTrack, CScript, CData, CTimeseries, CImagestack. The Python library cfflib provides read/write functionality.

CIFTI Connectivity File Format

CIFTI (Connectivity Informatics Technology Initiative) standardizes file formats for the storage of connectivity data. These formats are developed by the Human Connectome Project and other interested parties.

The CIFTI-2 specification is in pdf files attached to forum posts, see:

http://www.nitrc.org/forum/forum.php?thread_id=4380&forum_id=1955

http://www.nitrc.org/forum/forum.php?thread_id=4381&forum_id=1955

The specification of CIFTI-1 is on the MediaWiki link to the left.

Access the CIFTI discussion forum using the Forums entry in the menu on the left. Subscribe to the discussion forum and you will be informed about issues involving the CIFTI file formats via email.

pyxnat

pyxnat is a simple python library that relies on the REST API provided by the XNAT platform since its 1.4 version. XNAT is an extensible database for neuroimaging data. The main objective is to ease communications with an XNAT server to plug-in external tools or python scripts to process the data.

CBICA: COMPARE

COMPARE :Generic classification tool for 3D images

MRI Dataset for Hippocampus Segmentation

This dataset contains T1-weighted MR images of 50 subjects, 40 of whom are patients with temporal lobe epilepsy and 10 are nonepileptic subjects. Hippocampus labels are provided for 25 subjects for training. The users may submit their segmentation outcomes for the remaining 25 testing images to get a table of segmentation metrics. More information about the dataset may be found from the following reference:

K. Jafari-Khouzani, K. Elisevich, S. Patel, and H. Soltanian-Zadeh, “Dataset of magnetic resonance images of nonepileptic subjects and temporal lobe epilepsy patients for validation of hippocampal segmentation techniques,” Neuroinformatics, 2011.

MeshValmet: Validation Metric for Meshes

MeshValmet is a tool that measures surface to surface distance between two triangle meshes using user-specified uniform sampling. Thus, users can choose finer sampling level to calculate errors to gain more accuracy in the "error space", or sparser sampling to gain speed and get an approximate feeling of error distribution between boundaries.

Besides its pleasant visualization using the VTK library, MeshValmet also provides useful histogram and statistical information based on the sample errors, such as mean and median distance, root mean square distance, mean square distance, mean absolute distance, Hausdorff distance, 95 percentile, 68 percentile, etc.

MeshValmet is based on the work of Nicolas Aspert, etc.: MESH: Measuring Errors between Surfaces using the Hausdorff distance in the proceedings of the IEEE Int. Conf. on Multimedia and Expo 2002 (ICME), vol. I, pp. 705-708.

The calculation of the Dice's Coefficient is calculated by Joshua Stough using the concept of a Riemannian sum.

TurtleSeg

TurtleSeg is an interactive segmentation tool originally designed for 3D medical images. Accurate and automatic 3D medical image segmentation remains an elusive goal and manual intervention is often unavoidable. TurtleSeg implements techniques that allow the user to provide intuitive yet minimal interaction for guiding the 3D segmentation process.

fanDTasia Java Applet: DT-MRI Processing

FanDTasia is a Java applet tool for DT-MRI processing. It opens dw-mri datasets from user's computer and performs very efficient tensor field estimation using parallel threaded processing on user's browser. No installation is required. It runs on any operating system that supports Java (Windows, Mac, Linux,...). The estimated tensor field is guaranteed to be positive definite second order or higher order and is saved in user's local disc. MATLAB functions are also provided to open the tensor fields for your convenience in case you need to perform further processing. The fanDTasia Java applet provides also vector field visualization for 2nd and 4th-order tensors, as well as calculation of various anisotropic maps. Another useful feature is 3D fiber tracking (DTI-based) which is also shown using 3d graphics on the user's browser.

HAMMER Suite

The tool is a GUI for a complete processing pipeline of brain MR images. It provides functions on skull-stripping, cerebellum removal, tissue segmentation, and HAMMER registration.

AHEAD

Automatic Hippocampal Estimator using Atlas-based Delineation (AHEAD) is an open-source turnkey software for automatic hippocampus segmentation. Its primary use is for delineating hippocampus in T1-weighted MRI images. AHEAD is developed by Jung W. Suh, Hongzhi Wang, Sandhitsu Das, Brian Avants, Philip Cook, John Pluta and Paul Yushkevich, and colleagues at the Penn Image Computing and Science Laboratory (PICSL) at the University of Pennsylvania.

Functional Regression Analysis of DTI

Functional Regression Analysis of DTI Tract Statistics (FRATS), for the analysis of multiple diffusion properties along fiber bundle as functions in an infinite dimensional space and their association with a set of covariates of interest, such as age, diagnostic status and gender, in real applications. The resulting analysis pipeline can be used for understanding normal brain development, the neural bases of neuropsychiatric disorders, and the joint effects of environmental and genetic factors on white matter fiber bundles.

Functional Analysis of Diffusion Tensor

A functional analysis of diffusion tensor tract statistics (FADTTS) pipeline was developed for delineating the association between multiple diffusion properties along major white matter fiber bundles with a set of covariates of interest, such as age, diagnostic status, genetic marker (e.g., SNP), and gender, and the structure of the variability of these white matter tract properties in various diffusion tensor imaging studies. FADTTS can be used to facilitate understanding of normal brain development, the neural bases of neuropsychiatric disorders, and the joint effects of environmental and genetic factors on white matter fiber bundles. The advantages of FADTTS compared with the other existing approaches are that they are capable of modelling the structured inter-subject variability, testing the joint effects, and constructing their simultaneous confidence bands.

ShapeWorks

The ShapeWorks software is an open-source distribution of a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wide range of shape analysis problems, including nonmanifold surfaces and objects of arbitrary topology. The proposed correspondence point optimization uses an entropy-based minimization that balances the simplicity of the model (compactness) with the accuracy of the surface representations. The ShapeWorks software includes tools for preprocessing data, computing point-based shape models, and visualizing the results.

DOTS WM tract segmentation

Diffusion-Oriented Tract Segmentation (DOTS) is a fast, scalable tool developed at the Johns Hopkins University to automatically segment the major anatomical fiber tracts within the human brain from clinical quality diffusion tensor MR imaging. With an atlas-based Markov Random Field representation, DOTS directly estimates the tract probabilities, bypassing tractography and associated issues. Overlapping and crossing fibers are modeled and DOTS can also handle white matter lesions.

DOTS is released as a plug-in for the MIPAV software package and as a module for the JIST pipeline environment. They are therefore cross-platform and compatible with a wide variety of file formats.

RFT_FDR

So far there is a lack for Random Field Theory(RFT)-based multiple comparison correction for surfaces generated in Freesurfer software package. This set of Matlab-based functions can be used for that purpose. They are based on Worsley’s SurfStat toolbox. You also need to have installed Freesurfer software package and included the Freesurfer’s matlab subdirectory in the Matlab’s search path.

In addition, this tool implements the RFT-FDR hierarchical correction that can be used for optimizing the amount of smoothing in cortical thickness analyses (Neuroimage 52, 158-171).

WhiteText - annotated neuroscience text

WhiteText is a corpus of manually annotated brain region mentions. It was created to facilitate text mining of neuroscience literature. The corpus contains 1,377 abstracts with 17,585 brain region annotations. Interannotator agreement was evaluated for a subset of the documents, and was 90.7% and 96.7% for strict and lenient matching respectively. We observed a large vocabulary of over 6,000 unique brain region terms and 17,000 words.

The corpus can be found at http://www.chibi.ubc.ca/WhiteText/

Previous evaluation of automated recognition of the mentions is described in:
"Automated Recognition of Brain Region Mentions in Neuroscience Literature"
by French, Lane, Xu and Pavlidis.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2741206/

NIREP

We have started the Non-rigid Image Registration Evaluation Project (NIREP) to develop software tools and provide shared image validation databases for rigorous testing of non-rigid image registration algorithms. NIREP will extend the scope of prior validation projects by developing evaluation criteria and metrics using large image populations, using richly annotated image databases, using computer simulated data, and increasing the number and types of evaluation criteria.

The goal of this project is to establish, maintain, and endorse a standardized set of relevant benchmarks and metrics for performance evaluation of nonrigid image registration algorithms. Furthermore, these standards will be incorporated into an exportable computer program to automatically evaluate the registration accuracy of nonrigid image registration algorithms.

Scalable Brain Atlas

The INCF Scalable Brain Atlas (scalablebrainatlas.incf.org) is a web-based, interactive brain atlas viewer, containing a growing number of atlas templates for various species, including mouse, rat, macaque, marmoset, ferret and human. Standard features include fast brain region lookup, point and click to select a region and view its full 3D extent, mark a stereotaxic coordinate and view all regions in a hierarchy. Built-in extensions are the CoCoMac plugin to display Macaque connectivity, and a service to search for similar brain regions in other atlases or species.

The SBA is designed to be customizable. External users can create plugins, hosted on their own servers, to interactively attach images or data to spatial atlas locations.

In 2018, a new web-based application 'Scalable Brain Composer' is added, to display volumetric data and transparent meshes of brain regions together in 3D. Users can import their own data into this service to see how it fits within the atlas.

Robust Biological Parametric Mapping

Biological parametric mapping (BPM) has extended the widely popular statistical parametric approach to enable application of the general linear model to multiple image modalities (both for regressors and regressands) along with scalar valued observations. This approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple imaging modalities. However, as presented, the biological parametric mapping approach is not robust to outliers and may lead to invalid inferences (e.g., artifactual low p-values) due to slight mis-registration or variation in anatomy between subjects. To enable widespread application of this approach, we introduce robust regression and non-parametric regression in the neuroimaging context of application of the general linear model.

IIT Human Brain Atlas (v.5.0)

The IIT Human Brain Atlas (v.5.0) contains anatomical, DTI, HARDI templates, probabilistic gray matter labels, probabilistic connectivity-based white matter labels, and major fiber-bundles of the adult human brain in ICBM-152 space. Artifact-free MRI data from 72 subjects were used in the development of the atlas. All diffusion MRI data collection was conducted using Turboprop, and spatial normalization was accomplished in a population-based fashion.

If you use DTI-related resources of the atlas, please reference https://pubmed.ncbi.nlm.nih.gov/29414497/

If you use the gray matter or white matter labels, white matter bundles, structural connectome, or the regionconnect software, please reference https://pubmed.ncbi.nlm.nih.gov/33075560/

- The manual of the atlas (found in "Downloads") contains a description of the available resources of the atlas as well as a number of HOW TOs.
- Post questions or experiences with the atlas on the "Forums".

Version 5.0 was uploaded on 5/12/19

resting-state pediatric imaging template

Group ICA was used to generate spatial templates for 12 common resting-state networks in 62 typically-developing children, ages 9-15. We have made these available for those that will find them useful for masking and spatial template matching procedures. Basic demographic data on the sample is provided along with the protocol used to generate the templates.

MouseTracker

MouseTracker is a free, user-friendly software package that allows researchers to record and analyze real-time hand movements en route to responses on the screen (via the coordinates of an MR-safe mouse, trackball, or sensor). By looking at how the hand settles into a response alternative--and how it may be partially pulled toward other alternatives--researchers glean information about real-time mental processing. It's like opening up a single reaction time into a continuous stream of rich cognitive output. Experiments can incorporate images, letter strings, and sounds. Once recorded, participants' mouse trajectories can be visualized, averaged, and explored, and measures of curvature, complexity, velocity, acceleration, and angle can be computed. Precise characterizations of mouse trajectories' temporal and spatial dynamics are available, and these can shed light on a variety of important empirical questions across psychology, neuroscience, and beyond.

DTI-TEMPLATE-RHESUS-MACAQUES

Diffusion Tensor Imaging (DTI) studies of non-human primates (NHP) are becoming increasingly common. Recently, many DTI analysis methodologies have been developed for human brain studies; however, few are directly applicable to NHP. A prerequisite for most statistical DTI analyses to localize WM differences across populations is to spatially normalize the individual scans to a representative template. Here, we report the development of a population-specific DTI template for young adolescent Rhesus Macaque (Macaca mulatta) monkeys using 271 high-quality scans. Using such a large number of animals in generating a template allows it to account for variability in the species. Our DTI template is based on the largest number of animals ever used in generating a computational brain template. It is anticipated that our DTI template will help facilitate voxel-based and tract specific WM analyses in non-human primate species, which in turn may increase our understanding of brain function, development, and evolution.

NeuroPub Visualizer

NeuroPub is a NIfTI visualisation tool for the iPad. You can use it to store all your statistical images from your fMRI/VBM/TBSS studies and visualise them in 2D and 3D. Use NeuroPub as a library for your statistical images. You can download the app for free from the App Store!

Spatially Constrained Parcellation

Source code and parcellations now available! Go to http://ccraddock.github.io/cluster_roi/ for more information.

Spatially constrained parcellation is a set of tools for deriving ROI atlases by whole brain clustering of task or resting state data. In addition to the tools, this resource also contains several atlases derived by parcellating publicly available resting state fMRI datasets. The initial release will include python scripts and ROI atlases developed to perform the analyses described in Craddock et. al., A whole brain fMRI atlas generated via spatially constrained spectral clustering, which is currently in revision in Human Brain Mapping.

The scripts provide all of the tools necessary to derive an ROI atlases using spatially constrained Ncut spectral clustering. The scripts require python, numpy and scipy to run.

IMOD - 3D Reconstruction and Analysis

IMOD is a free, cross - platform set of image processing, modeling and display programs used for tomographic reconstruction, reconstruction and segmentation of 3D volumes. IMOD has tools for assembling and aligning data within multiple types and sizes of image stacks, viewing 3D data from any orientation, and modeling and displaying the image files.

Diffusion Tractography with Kalman Filter

We present a framework which uses an unscented Kalman filter for performing tractography. At each point on the fiber the most consistent direction is found as a mixture of previous estimates and of the local model.

It is very easy to expand the framework and to implement new fiber representations for it. Currently it is possible to tract fibers using two different 1-, 2-, or 3-tensor methods. Both methods use a mixture of Gaussian tensors. One limits the diffusion ellipsoids to a cylindrical shape (the second and third eigenvalue are assumed to be identical) and the other one uses a full tensor representation.

The project is written in C++. It could be used both as a Slicer3 module and as a standalone commandline application.

Web Interfaces for Multiscale Images

This is a venue for discussing and defining standard web interfaces for sharing images, annotations, and analyses of multiscale biological images. The goal is to increase interoperability of code to share the burden of infrastructure, increase code reuse, and allow us to spend more time focused on scientific questions.

Please visit our Wiki to start participating.

Together we can develop a small group of interfaces which are easy to implement, extensible, and cover the major tasks of developing tools for multiscale data on the web.

AutoSeg

AutoSeg is a novel C++ based application developped at UNC-Chapel Hill that performs automatic brain tissue classification and structural segmentation.

AutoSeg is designed for use with human and non-human primate pediatric, adolescent and adult data.

AutoSeg uses a BatchMake pipeline script that includes the main steps of the framework entailing N4 bias field correction, rigid registration to a common coordinate image, tissue segmentation, skull-stripping, intensity rescaling, atlas-based registration, subcortical segmentation and lobar parcellation, regional cortical thickness and intensity statistics. AutoSeg allows efficient batch processing and grid computing to process large datasets and provides quality control visualizations via Slicer3 MRML scenes.

BRAINSSurfaceStats

This program provides a tool for performing a per vertex statistical analysis across a population. The underlying statistical framework uses the R language.

Viking family of Viewing/Annotation/Analysis tools for Connectomics

Viking is a multi-user modular viewing and annotation system which functions with any type of stacked 2D imagery registered into a 3D volume. Originally designed for transmission electron microscopy connectome data Viking has been used for confocal, bright field, and OCT images.

Viking provides multi-user concurrent annotation of data sets using a variety of 1D or 2D shapes, including arbitrary polygons. Annotations are stored in a spatial database. The largest current database has well over a million annotations.

A paper describing Viking is here:
http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2818.2010.03402.x/abstract

Collaborating labs host images, typically registered with the Nornir tools, locally. Annotations are either stored on the Viking servers or optionally labs deploy local back-end servers with assistance from the developer.

HCP Harvard/MGH-UCLA

***THE HUMAN CONNECTOME PROJECT Harvard/MGH-UCLA***

The HCP is a multi-center project comprising two distinct consortia (Mass. Gen. Hosp. and UCLA; and Wash. U. and the U. of Minn.) seeking to map white matter fiber pathways in the human brain using leading edge neuroimaging methods, genomics, architectonics, mathematical approaches, informatics, and interactive visualization.

This NITRC project site is dedicated to linking all listed connectivity analysis tools under one common heading: HCP-related as well as independently created software packages. Please use this site to explore the variety of connectomics software tools available via NITRC as well well as valuable data resources, forthcoming scientific meetings, etc. Scroll down to view the list of useful tools. If you have such tools yourself, please list them with NITRC, and request to link with us!

See http://www.humanconnectomeproject.org or http://www.humanconnectome.org for details.

Disease State Prediction

These are the scripts used for the analyses reported in: Craddock RC, Holtzheimer PE, 3rd, Hu XP, Mayberg HS. (2009): Disease state prediction from resting state functional connectivity. Magn Reson Med 62(6):1619-28.

Specifically included are scripts for performing t-test filter, reliability filter, recursive feature elimination, and reliability recursive feature elimination feature selection methods. These make use of wrappers that perform .632 bootstrap and k-fold cross validation strategies. The scripts are written in matlab and require the Bioinformatics toolbox. If you do not have the bioinformatics toolbox, the scripts can be easily modified to run with other matlab SVM toolboxes (i.e., libsvm, svmlight, shogun, etc.).

SPM_SS - fMRI functional localizers

This spm-toolbox performs ROI-level and voxel-level between-subjects analyses of functional MRI data, restricting the analyses to those areas identified using subject-specific functional localizers.

Methods: The toolbox implements ROI-level and voxel-level analyses, and it implements an automatic cross-validation procedure when the localizers are not orthogonal to the effects-of-interest. ROI-level analyses allow manually defined parcels of interest, as well as automatically-defined ones (GcSS procedure, Fedorenko et al. 2010). General linear model second-level analyses are implemented, including ReML and OLS estimation of population level effects. Hypothesis testing includes standard univariate tests as well as multivariate tests for mixed within- and between-subject designs (T, F, and Wilks' lambda statistics)

This toolbox requires Matlab and SPM5/SPM8.

http://web.mit.edu/evelina9/www/funcloc.html

vuTools

This site provides access to the VUIIS Image and Data Analysis Core's data processing tools written for MATLAB. These tools are written for ease of use from within MATLAB. Unless stated otherwise, each of the tools is capable of processing 2D/3D images (matrices).

ANTsR

ANTsR is an R extension to ANTs that performs multivariate statistical parametric mapping of DTI, T1 and other datatypes for the purpose of both performing clinical studies and for tracking the performance of ANTs (and other) image processing methodologies. ANTsR depends upon the R statistical language, bash scripts and the ANTs toolkit. Some branches of ANTsR will also depend upon pipedream and specific datasets. Some of these datasets will be open access and, in that case, ANTsR will provide a 100% reproducible neuroimaging study on that data.

NeuroImaging Analysis Kit (NIAK)

NIAK is a library of modules and pipelines for fMRI processing with Octave that can run in parallel either locally or in a supercomputing environment. NIAK has reached its end of life at the end of 2020 and is not actively maintained at the moment.

GIMIAS

GIMIAS is a workflow-oriented environment focused on biomedical image computing and simulation. The open source framework is extensible through plug-ins and is focused on building research and clinical software prototypes. Gimias has been used to develop clinical prototypes in the fields of cardiac imaging and simulation, angiography imaging and simulation, and neurology.

Distributome

The Distributome Project is an open-source, open content-development project for exploring, discovering, navigating, learning, and computational utilization of diverse probability distributions.

TetraMetrix

TetraMetrix is an open-source software project for distributed Tetrahedral Mesh Modeling and Analysis (TMMA) of multidimensional data. Tetrahedra are 3D, space filling, geometric objects that can be used to form representations and partitions of objects like volumes and shapes obtained from biomedical images (e.g., 3D MRI brain images). Tetrahedra are a natural extension of lines (1D) and surface triangulations (2D) which enable finite element analysis of irregular shapes, and permit a greater range of morphometric characterization of multidimensional objects. The project is being developed by researchers at USC Laboratory of Neuro Imaging (http://www.LONI.usc.edu) and Department of Mathematics (http://www.math.ucla.edu).

TetraMetrix is distributed by the Laboratory of Neuro Imaging at USC.

Update: A new version tetrahedral meshing software along with new related image registration software was made available in the fall of 2015.

DFBIdb

DFBIdb is a suite of tools for efficient management of neuroimaging
project data. Specifically, DFBIdb was designed to allow users to quickly
perform routine management tasks of sorting, archiving, exploring, exporting
and organising raw data. DFBIdb was implemented as a collection of Python
scripts that maintain a project-based, centralised database that is based on the
XCEDE 2 data model. Project data is imported from a filesystem hierarchy of
raw files, which is an often-used convention of imaging devices, using a single
script that catalogues meta-data into a modified XCEDE 2 data model. During
the import process data are reversibly anonymised, archived and compressed.
The import script was designed to support multiple file formats and features an
extensible framework that can be adapted to novel file formats. Graphical user interfaces are provided for data exploration. DFBIdb includes facilities to export, convert and organise customisable subsets of project data according to user-specified criteria.

dtiBrainScope

This is a software package for processing diffusion tensor imaging data. The following functions are included:
1. Converting imaging data in DICOME format to ANALYZE format
2. Extracting binary brain mask for quick scalp-removing
3. Correcting eddy-current induced distortion
4. Optimized tensor estimation based on noisy diffusion-weighted imaging (DWI) data
5. Scalp removal using a brain mask image
6. Corregistering imaging data and generating deformation field for mapping images from individual spaces to a template or target space
7. Spatial Normalization and Warping DTI
8. Fiber tracking
9. Clustering fiber tracts
10. Identifying brain ventricles and generating binary masks for the baseline and DW imaging data
11. Deriving diffusion anisotropy indices (DAIs) and principal directions (PD) and the corresponding color-coded PD-map.

CLEAVE: Large Data Set ANOVA

CLEAVE is a UNIX-style command-line program which quickly computes multifactorial ANOVAs for very large data sets with minimal memory use (without loading all of the data into memory). It has been used for fMRI analysis, e.g.

CLEAVE adds the following to the standard ANOVA analyses:

0) Unlimited numbers of factors can be analyzed.
1) Factor Correlation and Unequal Variance Corrections
2) Treatment Magnitudes: omega^2, partial eta^2, and R^2
3) A convenient Ranking of Factors based upon treatment magnitudes and significance levels.
4) Post-Hoc Significance Tests
5) Post-Hoc Power Table to gauge how many subjects will be needed to achieve significance.
6) Allows the use of Random Factors.
7) A Configuration File to make the program more tunable
8) A Histogram and Cell Line Diagrams: which help the user to detect outliers.
9) Associated MATLAB functions: port CLEAVE-style data sets in or out of MATLAB.

Software is at http://www.ebire.org/hcnlab/software/cleave.html

FCP/INDI PRIVATE SITE

You are entering the INDI download portal...

DTI Fiber Tract Statistics

This is a software which allows the user to study the behavior of water diffusion (using DTI data) along the length of the white matter fiber-tracts.
Various tract-oriented scalar diffusion measures obtained from DTI brain images, are treated as a continuous function of white matter fibers' arc-length. To analyze the trend along a given fiber tract, a command line tool performs kernel regression on this data. The idea is to try out different noise models and maximum likelihood estimates within kernel windows (along the tract), such that they best represent the data and are robust to noise and Partial Volume effect.

The package contains several command line based modules and an GUI based tool called DTIAtlasFiberAnalyzer to access most functions.

This package is also available as a 3D Slicer extension (http://www.slicer.org) called DTIAtlasFiberAnalyzer.

Some documentation about the features available in this package are available here: http://www.na-mic.org/Wiki/index.php/Projects:dtistatisticsfibers

GesTr

GesTr is a cross platform, open source gesture tracking program. You launch it from the web, and use it to streamline the way you communicate with the computer. It allows for a more natural method of issuing commands than with keyboard shortcuts or GUI buttons.

GesTr supports simple XML files to customize recognized gestures and their corresponding actions.

GesTr also has experimental support for the Wii Remote used with an infrared pen as an alternative input device.

REST: a toolkit for resting-state fMRI

REsting State fMRI data analysis Toolkit (REST) is a user-friendly convenient toolkit to calculate Functional Connectivity (FC), Regional Homogeneity (ReHo), Amplitude of Low-Frequency Fluctuation (ALFF), Fractional ALFF (fALFF), Gragner causality and perform statistical analysis. You also can use REST to view your data, perform Monte Carlo simulation similar to AlphaSim in AFNI, calculate your images, regress out covariates, extract ROI time courses, reslice images, and sort DICOM files.

Most recent version and any questions could also be checked out in: http://www.restfmri.net.

Temporal-lobe.com

Existing knowledge of hippocampal - parahippocampal connections was integrated into an interactive diagram in which any connection can be turned on or off at the level of cortical layers.

Project Goals:
* To provide an overview of all known anatomical projections of the rat hippocampal - parahippocampal network.
* To provide a graphical interface with which users can turn on or off any connections at regional level down to the level of cortical layers.
* To make it easy to find references for a particular projection.

Download the diagram for free at www.temporal-lobe.com.

Neuroimaging Data Access Group

The NeuroImaging Data Access Group (NIDAG) is an informal working group, dedicated to improving access to neuroimaging results in a free and open-access manner. Our current project involves the creation of a comprehensive database of neuroimaging results searchable based on standardized coordinates. Once complete, this will allow anyone to find all of the articles that report a coordinate, or set of coordinates, easily and without cost. Eventually, we hope to expand this database to include not only coordinates, but statistical parametric maps as well. Formation of such a database will increase the likelihood of relevant papers being found and cited, and also be a very useful tool for those interested in meta-analysis, and hopefully clarify structure-function relationships.

Please visit our website (nidag.org) and sign up to join our mailing list. We are interested in hearing from people who might be willing to contribute to our projects, particularly those with programming experience.

ABSORB for groupwise registration

This software package implements ABSORB: Atlas Building by Self-Organized Registration and Bundling – an algorithm for effective groupwise registration, which has been published as:

Hongjun Jia, Guorong Wu, Qian Wang, Dinggang Shen, "ABSORB: Atlas Building by Self-Organized Registration and Bundling", NeuroImage, Vol. 51, No. 3, 1 July 2010, pp. 1057-1070.

The package is available free to the public for the academic research purpose at http://bric.unc.edu/ideagroup/free-softwares/ .

The required input is a set of 3D MR intensity images (in Analyze format with paired .hdr and .img files) with a text file (.txt) listing all header file (.hdr) names. The output is the set of registered images together with the corresponding dense deformation fields. This software has been tested on Windows XP (32-bit) and Linux (64-bit, kernel version 2.6.18-194.el5).

This software was developed in IDEA group in UNC-Chapel Hill. For more information, please visit our lab at http://bric.unc.edu/ideagroup .

The Cognitive Atlas

The Cognitive Atlas, http://www.cognitiveatlas.org, is a collaborative knowledge building project that aims to develop a knowledge base (or ontology) that characterizes the state of current thought in cognitive neuroscience. The goal is to develop a knowledge base that will support annotation of data in databases, as well as supporting improved discourse in the community. It is open to all interested researchers.

Monte Carlo Simulation Software: tMCimg

tMCimg uses a Monte Carlo algorithm to model the transport of photons through 3D volumes with spatially varying optical properties. Both highly-scattering tissues (e.g. white matter) and weakly scattering tissues (e.g. cerebral spinal fluid) are supported. Using the anatomical information provided by MRI, X-ray CT, or ultrasound, accurate solutions to the photon migration forward problems are computed in times ranging from minutes to hours, depending on the optical properties and the computing resources available.

Mesh-based Monte Carlo (MMC)

Mesh-based Monte Carlo, or MMC, is a Monte Carlo (MC) solver for photon migration in 3D turbid media. Different from existing MC software designed for layered (such as MCML) or voxel-based media (such as MMC or tMCimg), MMC can represent a complex domain using a tetrahedral mesh. This not only greatly improves the accuracy of the solutions when modeling objects with smooth/complex boundaries, but also gives an efficient way to sample the problem domain to use less memory. The current version of MMC support multi-threaded programming and can give a almost proportional speed-up when using multiple CPU cores.

Monte Carlo eXtreme (MCX)

Monte Carlo eXtreme, or MCX, is a Monte Carlo simulation software for photon migration in 3D turbid media. It uses Graphics Processing Units (GPU) based massively parallel computing techniques and is extremely fast compared to the traditional single-threaded CPU-based simulations. Using an nVidia 8800GT graphics card (14MP/114Cores), the acceleration is about 300x~400x compared to a single core of Xeon 5120 CPU; this ratio can be as high as 700x with a GTX 280 GPU and 1400x with a GTX 470.

iso2mesh

"Iso2mesh" is a Matlab/Octave-based mesh generation toolbox designed for easy creation of high quality surface and tetrahedral meshes from 3D volumetric images. It contains a rich set of mesh processing scripts/programs, functioning independently or interfacing with external free meshing utilities. Iso2mesh toolbox can operate directly on 3D binary, segmented or gray-scale images, such as those from MRI or CT scans, making it particularly suitable for multi-modality medical imaging data analysis or multi-physics modeling.

URL: http://iso2mesh.sf.net

Automatic Segmentation Tool Adapter

More and more automatic segmentation tools are publicly available to today's researchers. However, when applied by their end-users, these segmentation tools usually can not achieve the performance that the tool developer reported. Discrepancies between the tool developer and its users in manual segmentation protocols and imaging modalities are the main reasons for such inconsistency.

In this project, we will provide an open source learning-based software that automatically learns how to transfer the output of a host segmentation tool closer to the user's manual segmentation using the image data and manual segmentation provided by the user. The motivation of this project is to bridge the gap between the segmentation tool developer and the tool users such that the existing segmentation tools can more effectively serve the community.

NITRC GForge Extensions

This project contains custom extensions to the GForge collaborative environment used to create NITRC. To get the most updated version, please contact moderator@nitrc.org.

These features include:
MediaWiki integration
Ratings and reviews
Nutch Web crawling integration
Improved project searching
BBCode extensions
Front and project summary page re-design

See our Release Notes page for specifics: http://www.nitrc.org/plugins/mwiki/index.php/nitrc:NITRC_Release_Notes

Vervet Probabilistic Atlas

The vervet (Chlorocebus aethiops sabaeus) probabilistic atlas defines an anatomical space (template) with associated tissue and regional prior probability maps. The atlas was produced from whole head MRI of 10 normal adult animal subjects. The package consists of two atlases. The "Biased" directory contains the average template and probabilistic atlases for selected tissue classes constructed by registering the training population to one subject. The "Unbiased" directory contains the atlas constructed using unbiased estimation. The atlas is suitable for use in any segmentation tool using a probabilistic atlas, for example those in Slicer.

Spanish Resting State Network

Spanish Resting State Network (SRSN); Foro para compartir datos y conocimiento sobre esta red. Se constituye el Spanish Resting State Network como una colaboracion entre distintos grupos de investigacion de España y centros nacionales e internacionales.

SPHARM-MAT

SPHARM-MAT is implemented based on a powerful 3D Fourier surface representation method called SPHARM, which creates parametric surface models using spherical harmonics. It is a matlab-based 3D shape modeling and analysis toolkit, and is designed to aid statistical shape analysis for identifying morphometric changes in 3D structures of interest related to different conditions.

MASIMatlab

The MASI research laboratory concentrates on analyzing large-scale cross-sectional and longitudinal neuroimaging data. Specifically, we are interested in population characterization with magnetic resonance imaging (MRI), multi-parametric studies (DTI, sMRI, qMRI), and shape modeling.

This repository stores and provides opportunities for collaboration through Matlab code, libraries, and configuration information for projects in early stage development.

GAMBIT

GAMBIT is an end-to-end application developped at UNC-Chapel Hill allowing Group-wise Automatic Mesh-Based analysis of cortIcal Thickness as well as other surface area measurements. This cross-platform tool can be run within 3D Slicer as an external module, or directly as a command line.

The Neuro Bureau

The Neuro Bureau is neuro-collaboration in action.

More specifically the Neuro Bureau is a neuroscience collaboratory that supports open neuroscience. To join the Neuro Bureau use the "join the team" link in the yellow box to the right. For more information, please contact: theneurobureau@gmail.com

Preprocessed imaging data can be downloaded using the Downloads link on the left, for more information on the preprocessed data use the MediaWiki link on the left.

Brain-Art Competition 2011 is now over. All of the submissions and the winners are posted at the Brain-Art Gallery.
neurobureau.projects.nitrc.org/BrainArt/Gallery

GPU based affine registration

This tool can be used as a command line module with 3D Slicer (version 3 and above) for the affine registration of image volumes. The registration toolbox has 2 options: 1) a Mutual Information based registration, 2) a Sum-of-Square differences registration method. The final output is in the same space as the fixed image. You do require to have CUDA v2.2 or greater installed on your system with atleast 256MB Nvidia GPU memmory card. All operating systems are supported, but take a look at the CMakeLists.txt file for how to compile for you system.

Vaa3D and Vaa3D-Neuron

Vaa3D (3D Visualization-Assisted Analysis) is a handy, fast, and versatile 3D/4D/5D Image Visualization & Analysis System for Bioimages & Surface Objects. Vaa3D is a cross-platform (Mac, Linux, and Windows) tool for visualizing large-scale (gigabytes, and 64-bit data) 3D/4D/5D image stacks and various surface data. It is also a container of powerful modules for 3D image analysis (cell segmentation, neuron tracing, brain registration, annotation, quantitative measurement and statistics, etc) and data management.

Vaa3D is very easy to be extended via a powerful plugin interface. For example, many ITK tools are being converted to Vaa3D Plugins.

Vaa3D-Neuron is built upon Vaa3D to make 3D neuron reconstruction much easier. In a recent Nature Biotechnology paper (2010, 28(4), pp.348-353) about Vaa3D and Vaa3D-Neuron, an order of magnitude of performance improvement (both reconstruction accuracy and speed) was achieved compared to other tools.

The fMRI Data Center

A free database of fMRI data used in peer reviewed studies (fMRIDC). The database of studies can be browsed and searched, and data is provided via download links that are emailed to users in response to specific requests. All of the data has been rendered anonymous, is free, and can be used in further studies. Any published findings based on these datasets should credit the authors of the original study as well as the fMRI Data Center including the accession number.

CANDI Neuroimaging Access Point

The Child and Adolescent NeuroDevelopment Initiative (CANDI) at UMass Medical School is making available a series of structural brain images, as well as their anatomic segmentations, demographic and behavioral data and a set of related morphometric resources (static and dynamic atlases).

Schiz Bull 2008 data is now available on NITRC-IR. Please register for access: http://www.nitrc.org/project/request.php?group_id=377

DIPY Diffusion Imaging Analysis

DIPY is a free and open source software project for computational neuroanatomy. It focuses on diffusion magnetic resonance imaging (dMRI) analysis and tractography but also contains implementations of other computational imaging methods such as denoising and registration that are applicable to the greater medical imaging and image processing communities. Additionally, DIPY is an international project which brings together scientists across labs and countries to share their state-of-the-art code and expertise in the same codebase, accelerating scientific research in medical imaging.

Twitter https://twitter.com/dipymri
G+ http://bit.ly/dipymri

NiBabel

Read and write access to common neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2 and later), GIFTI, NIfTI1, NIfTI2, CIFTI-2, MINC1, MINC2, AFNI BRIK/HEAD, ECAT and Philips PAR/REC. In addition, NiBabel also supports FreeSurfer’s MGH, geometry, annotation and morphometry files, and provides some limited support for DICOM.

NiBabel’s API gives full or selective access to header information (metadata), and image data is made available via NumPy arrays.

nitime

Nitime is a library for time-series analysis of data from neuroscience experiments.

It contains a core of numerical algorithms for time-series analysis both in the time and spectral domains, a set of container objects to represent time-series, and auxiliary objects that expose a high level interface to the numerical machinery and make common analysis tasks easy to express with compact and semantically clear code.

MATLAB Tutorial on Diffusion Tensor MRI

This is an on-line tutorial on how to use MATLAB for Diffusion-Weighted MRI processing. The following subjects are covered in this tutorial: Generation of Synthetic Diffusion-Weighted MRI datasets, Diffusion Tensor (DTI) Estimation from DW-MRI, DTI Visualization as a field of ellipsoids, Higher-order Diffusion Tensor Estimation from DW-MRI, Computing of Tensor Orientation Distribution Functions (Tensor ODF), Computing of Fiber Orientations, Higher-order Diffusion Tensor Image Visualization as fields of spherical functions, Multi-fiber reconstruction etc.

The tutorial contains numerous illustrations, figures and Matlab scripts embedded in the text. The reader/user can automatically generate Matlab script for a self-designed DW-MRI experiment by selecting which steps needs to be followed. The code that corresponds to the selected steps is then appropriately merged in the "Matlab Script Generator", and the user can easily copy and paste the produced code directly to the Matlab command prompt.

PyNIfTI

The PyNIfTI module is a Python interface to the NIfTI I/O libraries. Using PyNIfTI, one can easily read and write NIfTI and ANALYZE images from within Python. The NiftiImage class provides pythonic access to the full header information and for a maximum of interoperability the image data is made available via NumPy arrays.

NIPY Community

The purpose of NIPY is to make it easier to do better brain imaging research. We believe that neuroscience ideas and analysis ideas develop together. Good ideas come from understanding; understanding comes from clarity, and clarity must come from well-designed teaching materials and well-designed software. The software must be designed as a natural extension of the underlying ideas.

We aim to build software that is:

* clearly written
* clearly explained
* a good fit for the underlying ideas
* a natural home for collaboration

ASHS: Automatic Segmentation of Hippocampal Subfields

ASHS is a software package for automatic segmentation of hippocampal subfields in MRI scans. ASHS requires a T1-weighted scan and an oblique coronal T2-weighted scan with high in-plane resolution. ASHS will automatically label main hippocampal subfields and medial temporal lobe subregions. ASHS is easy to train for other brain segmentation applications as well.

For details, see the ASHS documentation: https://sites.google.com/site/hipposubfields

PsychoPy

PsychoPy - Psychology software in Python. PsychoPy is an open-source application to allow the presentation of stimuli and collection of data for a wide range of neuroscience, psychology and psychophysics experiments. It is intended as a free, powerful alternative to Presentation or e-Prime.

NIPY Structural and Functional Analysis

Nipy aims to provide a complete Python environment for the analysis of structural and functional neuroimaging data. It currently has a full system for general linear modeling of functional magnetic resonance imaging (fMRI).

GAMMA

GAMMA suite is an open-source cross-platform data mining software package designed to analyze neuroimaging data. A neuroimaging study often focuses on biomarker detection and classification. We designed and implemented a Bayesian, multivariate, nonparametric suite of algorithms for analyzing neuroimaging data. The GAMMA suite can be used for brain morphometric analysis, lesion-deficit analysis, and functional MR data analysis.

Pipeline Neuroimaging VirtualEnvironment

The LONI Pipeline Neuroimaging Virtual Environment (PNVE), is a self-contained virtual machine that can be executed on a common laptop or desktop, enabling the Pipeline to run virtually anywhere. Neophytes to the Pipeline can have their own private server running in minutes, software engineers and workflow designers can use the PNVE as a sandbox, and those without access to grid computing facilities can now take full advantage of the Pipeline processing environment.

PNVE is distributed by the Laboratory of Neuro Imaging (http://pipeline.loni.usc.edu/PNVE) at USC.

NeuroDebian

This is a collaborative effort to package neuroscience-related software for the Debian operating system. The main goal of the project is to provide a versatile and convenient environment for neuroscientific research that is based on open-source software. To this end, the project offers a package repository that complements the main Debian (and Ubuntu) archive. NeuroDebian is not yet another Linux distribution, but rather an effort inside the Debian project itself. Software packages are fully integrated into the Debian system and from there will eventually migrate into Ubuntu as well.

With NeuroDebian, installing and updating neuroscience software is no different from any other part of the operating system. Maintaining a research software environment becomes as easy as installing an editor. For example, installing FSL looks like: "apt-get install fsl"

There is also virtual machine to test NeuroDebian on Windows or Mac OS.

If you want to see your software packaged for Debian, please drop us a note.

SFMProject

Structure from motion algorithms repository. Common interface for various sfm algorithms.

Parkinson's Disease Discovery Database

Tools will be available for biomedical data mining and visualization as well as linkages to Google Maps and other online resources.

PESTICA & SLOMOCO: physio and motion correction tools

Physiologic and motion noise are major problems in BOLD imaging. Physiologic EStimation by Temporal ICA or PESTICA is an MRI-based pulse and respiration monitor. SLice-Oriented MOtion COrrection, or SLOMOCO, is a slice-oriented motion correction for 6DOF motion of every slice. These tools are retrospective and work for ASL, multiband and 7T data.

PESTICA detects the pulse and the breathing cycles _from the data itself_, equivalent to a parallel monitored pulse signal and a respiratory chest-bellows signal. One can use parallel monitored or PESTICA-monitored pulse and respiration for correction using the model-based corrections RETROICOR or IRF-RETROICOR.

The other half of PESTICA is an adaptive physiologic noise removal tool (Impulse Response Function or IRF-RETROICOR) that zooms in on noise with only 6 regressors, getting all the noise that 5th order RETROICOR gets (using 20 regressors). These tools will allow you to correct your data for physiologic noise with what you currently have.

Best Practices for Research Science

Best practices in research science are crucial not only for research integrity but also for good science. This tool/resource provides information about best practices in research science to help researchers to carry out good science, promote responsible research practices, and help identify particular practices that should be better understood and adhered to.

About NITRC's Best Practices: https://www.nitrc.org/plugins/mwiki/index.php?title=nitrc:Best_Practices

BioMesh3D

BioMesh3D is a free, easy to use program for generating quality meshes for use in biological simulations. It is included when downloading SCIRun v4.6.

SCIRun

SCIRun is a Problem Solving Environment (PSE), for modeling, simulation and visualization of scientific problems. SCIRun now includes the biomedical components formally released as BioPSE, as well as BioMesh3D. BioMesh3D is a free, easy to use program for generating quality meshes for the use in biological simulations. The most recent stable release is version 4.6.

ImageVis3D

Note ImageVis3D development is hosted elsewhere -- see http://www.imagevis3d.org/

ImageVis3D is a new volume rendering program developed by the NIH/NCRR Center for Integrative Biomedical Computing (CIBC). The main design goals of ImageVis3D are: simplicity, scalability, and interactivity. Simplicity is achieved with a new user interface that gives an unprecedented level of flexibility (as shown in the images). Scalability and interactivity for ImageVis3D mean that both on a notebook computer as well as on a high end graphics workstation, the user can interactively explore terabyte sized data sets. Finally, the open source nature as well as the strict component-by-component design allow developers not only to extend ImageVis3D itself but also reuse parts of it, such as the rendering core. This rendering core, for instance, is planned to replace the volume rendering subsystems in many applications at the SCI Institute and with our collaborators.

Connectome Viewer

Connectome Viewer is free, open source, cross-platform Python-based software application for visualization and analysis in connectome research.

Features of the software include:
* Connectome File Format including metadata, networks, surfaces, volumes, track files
* Complex network analysis toolboxes
* Modular plugin architecture for extensibility
* Mayavi2 for 3D Scientific Visualization and Plotting
* Interactive data manipulation and scripting capabilities
* Neuroimaging and Diffusion in Python libraries

eConnectome

eConnectome (Electrophysiological Connectome) is an open-source MATLAB software package for imaging brain functional connectivity from electrophysiological signals. It provides interactive graphical interfaces for EEG/ECoG/MEG preprocessing, source estimation, connectivity analysis and visualization. Connectivity from EEG/ECoG/MEG can be mapped over sensor and source domains.

This package is designed for use by researchers in neuroscience, psychology, cognitive science, clinical neurophysiology, neurology and other disciplines. The graphical interface-based platform requires little programming knowledge or experience with MATLAB.

eConnectome is developed by the Biomedical Functional Imaging and Neuroengineering Laboratory at the University of Minnesota, directed by Dr. Bin He. The visualization module is jointly developed with Drs. Fabio Babiloni and Laura Astolfi at the University of Rome "La Sapienza".

Generalized Covariance Analysis (gCVA)

Generalized Covariance Analysis: a platform for any PCA-based analysis on functional neuroimaging data (PET and fMRI).

Includes:

- Ordinal Trend Canonical Variance Analysis for parametric designs
(C. Habeck et al. A New Approach to Spatial Covariance Modeling of Functional Brain Imaging Data: Ordinal Trend Analysis. Neural Computation 2005; 17: 1602-1645)

- Partial Least Squares for any design matrix

- Subprofile Scaling Model for cross-sectional designs
(JR. Moeller, Strother SC. A regional covariance approach to the analysis of functional patterns in positron emission tomographic data.J Cereb Blood Flow Metab. 1991 Mar;11(2):A121-35.)

Nipype: NIPY Pipeline and Interfaces

Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. However, this has resulted in a heterogeneous collection of specialized applications without transparent interoperability or a uniform operating interface. Nipype, an open-source, community-developed initiative under the umbrella of Nipy, is a Python project that solves these issues by providing a uniform interface to existing neuroimaging software and by facilitating interaction between these packages within a single workflow.

Nipype provides an environment that encourages interactive exploration of algorithms from different packages (e.g., SPM, FSL), eases the design of workflows within and between packages, and reduces the learning curve necessary to use different packages. Nipype is creating a collaborative platform for neuroimaging software development in a high-level language and addressing limitations of existing pipeline systems.

Non-rigid groupwise registration method

The goal of the project is to provide an open source implementation of a non-rigid groupwise registration method.

This project is implemented by
+ Serdar K Balci (serdar at csail.mit.edu)
and supervised by
+ Polina Golland
+ William M. Wells

All metrics are implementing in a multi-threaded fashion. The algorithm will run faster on
computers with multiple CPU's.

CAMINO-TRACKVIS

With increasing efforts on brain connectivity analyses it becomes important to have tools that can allow increased interoperability among different tractography tools. This package allows interoperability between CAMINO and TRACKVIS. CAMINO is a leading software package in DTI processing. The package is from University of College London. TRACKVIS is a tract visualizing utility with capability of visualizing up to and over a million white matter tracts seamlessly. The package is from Massachusetts General Hospital.

The tools in this package allow conversion of tracts from one format to another in a very effective way with ability to handle over a million tracts.

Please use the sourceforge link below to download source code, instructions example scripts and other miscellaneous tools.
http://sourceforge.net/projects/camino-trackvis/

Pipeline system for Octave and Matlab

The pipeline system for Octave and Matlab (PSOM) is a lightweight library to manage complex multi-stage data processing. A pipeline is a collection of jobs, i.e. Matlab or Octave codes that are using files for inputs and outputs, described in the form of a simple Matlab/Octave structure.

See http://psom.simexp-lab.org for more info.

DIAMOND

UC Davis IDeA Lab Applications for Management Of Neuroimaging Data -
View dicom files and assemble them into 3D volumes.
View and convert between Analyze, Nifti, and Interfile.
Classify and organize dicoms and 3D volumes using metadata.
Search and report on a collection of scans.

Multi-Modal MRI Reproducibility Resource

We have acquired scan-rescan imaging sessions on 21 healthy volunteers (no history of neurological disease). Imaging modalities include MPRAGE, FLAIR, DTI, resting state fMRI, B0 and B1 field maps, ASL, VASO, quantitative T1 mapping, quantitative T2 mapping, and magnetization transfer imaging. All data have been converted to NIFTI format.

This is intended to be a resource for statisticians and imaging scientists to be able to quantify the reproducibility of their imaging methods using data available from a generic "1 hour" session at 3T.

Please cite: Bennett. A. Landman, Alan J. Huang, Aliya Gifford, Deepti S. Vikram, Issel Anne L. Lim, Jonathan A.D. Farrell, John A. Bogovic, Jun Hua, Min Chen, Samson Jarso, Seth A. Smith, Suresh Joel, Susumu Mori, James J. Pekar, Peter B. Barker, Jerry L. Prince, and Peter C.M. van Zijl. “Multi-Parametric Neuroimaging Reproducibility: A 3T Resource Study”, NeuroImage. (2010) NIHMS/PMC:252138 doi:10.1016/j.neuroimage.2010.11.047

DTIProcess ToolKit

DTIProcess is a DTI processing and analysis toolkit developed in UNC and University of Utah. Tools in this toolkit include dtiestim, dtiprocess, dtiaverage, fibertrack, fiberprocess, et al..
Most of the tools included in this package are available in 3D Slicer (http://www.slicer.org) in the DTIProcess extension.

JIP fMRI Analysis Toolkit

Refer to http://www.nmr.mgh.harvard.edu/~jbm/jip for web documentation (in progress). The JIP Toolkit was developed primarily for analysis of rodent and non-human primate fMRI data. The toolkit consists of binary executables, highly portable open-source c code, and image resources that enable 1) Automated registration based upon mutual information (affine, non-linear warps), with flexible control and visualization of each step; 2) visualization of 4-dimensional data using either mosaic or tri-planar display of the z/slice dimension, and integration of a general linear model for graphical display of time series analysis; 3) A simple and flexible 1st-order GLM for fMRI time series analysis, a 1st-order GLM analysis for PET data within the SRTM framework, plus a 2nd-order GLM analysis following the Worsley 2002 scheme, and 4) MRI templates to place your rodent and non-human primate data into standardized spaces.

BrainVoyager

BrainVoyager is a powerful neuroimaging software package. It started as a tool for the analysis of anatomical and functional MRI data sets, but has evolved over the years into a multi-modal analysis software suite for fMRI, DTI, TMS, EEG and MEG data. The software is highly optimized and user friendly running on all major computer platforms including Microsoft Windows (7, 8, 8.1 and 10), Linux (e.g. Ubuntu 14.04 (64 bit), recent versions of Red Hat Enterprise Linux (RHEL), Fedora and most other distributions with a 3.x Linux kernel) and Mac OS X (10.10 or 10.11; it should also run fine on 10.9 and 10.8).

SPHARM-PDM Toolbox

Shape analysis has become of increasing interest to the medical community due to its potential to precisely locate morphological changes between healthy and pathological structures. SPHARM-PDM is a tool that computes point-based models using a parametric boundary description for the computing of Shape analysis.

Since July 2017, SPHARM-PDM is now available as a 3D Slicer extension (http://www.slicer.org) and as part of SlicerSALT (salt.slicer.org). Dissemination of SPHARM-PDM through SlicerSALT is now the major mode of dissemination for downloading new versions of SPHARM-PDM.

For questions, please refer to the new SlicerSALT forum at https://discourse.slicer.org/c/community/slicer-salt

Note: the modules available in the downloads section have not been updated since 2015

Hyperion: MRI Digital Projection System

Visual presentation is central to almost all fMRI experiments. Previous fMRI display technologies involve compromises on image quality and presentation speed. LCD technology is slow, with low contrast images and imprecise analog VGA. These may be acceptable for tenth of a second resolution displays where image quality and repeatability can be overlooked, but we are pleased to announce these compromises are no longer necessary. The Hyperion MRI Digital Projection System features a native resolution of 1920 x 1080 (1080p) and synchronized image frame rate with continuous monitoring for optimum presentation and timing of fMRI experiments. The system uses Digital Light Processing® (DLP) technology that provides microsecond pixel rise times, outstanding contrast with all-digital fiber optic control that allows you to project crystal clear, sharp images. The Hyperion MRI Digital Projection System has been validated for use in 1.5T, 3T, and 7T MRI environments.

ShapeViewer

The ShapeViewer (http://www.loni.usc.edu/Software/ShapeViewer) is a simple, portable geometry viewer that supports the file formats used by CCB researchers and provides their most commonly needed viewing functions. Since it is written in Java, it can run on a wide variety of computers.

ShapeViewer is distributed by the Laboratory of Neuro Imaging (http://www.LONI.usc.edu/Software/ShapeViewer) at USC.

LONI Provenance Editor

The LONI Provenance Editor (http://www.loni.usc.edu/Software/ProvenanceEditor) is a self-contained, platform-independent application that automatically extracts the provenance information from an image header (such as a DICOM image) and generates a data provenance XML file with that information.

Provenance Editor is distributed by the Laboratory of Neuro Imaging (http://www.LONI.usc.edu/Software/ProvenanceEditor) at USC.

Fast Fourier Transform (FFT) JavaLibrary

The FFT Java library (http://www.loni.usc.edu/Software/FFT) is used for the execution of discrete Fourier transforms in 1-D, 2-D and 3-D through the implementation of Fast Fourier Transform (FFT) algorithms.

FFT toolbox is distributed by the Laboratory of Neuro Imaging (http://www.loni.usc.edu/Software/FFT) at USC.

Synchronized Histological Image View Arc

The Synchronized Histological Image Viewing Architecture (http://www.loni.usc.edu/Software/SHIVA) is a Java-based visualization and analysis application. SHIVA can process 2D and 3D image files and provides convenient methods for users to overlay multiple datasets.

SHIVA is distributed by the Laboratory of Neuro Imaging (http://www.loni.usc.edu/Software/SHIVA) at USC.

Automated Image Registration (AIR)

The Automated Image Registration (AIR) tool is used for alignment of 3D and 2D images within and across subjects and across imaging modalities. The AIR library can easily incorporate automated image registration into site specific programs adapted to your particular needs.

AIR is distributed by the Laboratory of Neuro Imaging (http://loni.usc.edu/Software/AIR) at USC.

BrainParser

Brain Parser software uses a novel statistical-learning technique to segment brain regions of interest (ROIs) based on a training set of data and generates 3D MRI volumes. The software comes pre-trained on a provided data set but can be retrained to work with your desired regions of interest.

ShapeTools

The ShapeTools library is a collection of Java classes that enable Java programmers to model, manipulate and visualize geometric shapes and associated data values. It simplifies the creation of application programs by providing a ready-made set of support routines.

ShapeTools are distributed by the Laboratory of Neuro Imaging (http://www.LONI.usc.edu/Software/ShapeTools) at USC.

Center for Computational Biology (CCB)

The Center for Computational Biology (http://ccb.loni.usc.edu) provides 3 types of resources: (1) Stand-alone computational software tools (image and volume processing, analysis, visualization, graphical workflow environments). (2) Infrastructure Resources (Databases, computational Grid, services). (3) Web-services (web-accessible resources for processing, validation and exploration of multimodal/multichannel data including clinical data, imaging data, genetics data and phenotypic data).

CCB is headquartered at the Laboratory of Neuro Imaging (http://CCB.LONI.usc.edu/) at USC.

ABC (Atlas Based Classification)

ABC (Atlas Based Classification) is a comprehensive processing pipeline developed and used at University of North Carolina and University of Utah for brain MRIs. The processing pipeline includes image registration, filtering, segmentation and inhomogeneity correction. The tool is cross-platform and can be run within 3D Slicer or as a stand-alone program.

The image segmentation algorithm is based on the EMS software developed by Koen van Leemput.

1000 Functional Connectomes Project

ATTENTION: The 1000 Functional Connectomes Project has a new home page at NITRC. Please visit us at:
http://fcon_1000.projects.nitrc.org

This is the parent project for ABIDE, ADHD-200 (ADHD200), INDI, CORR, NKI-Rockland (NKI), Healthy Brain Network (HBN), Center for Biomedical Research Excellence (COBRE), Anatomical Tracings of Lesions After Stroke (ATLAS) and other projects.

BrainSolution

BrainSolution is a collection of tools for MRI T1 brain image segmentation in the Windows® environment. It helps construct a complete pipeline with necessary preprocessing and postprocessing procedures besides brainparser, the core program of our fast brain segmentation. The execution of the whole pipeline can be completed in 2 hours with good segmentation results.

shapeAnalysisMANCOVA - SPHARM tools

shapeAnalysisMANCOVA offers statistical shape analysis based on a parametric boundary description (SPHARM) as the point-based model computing method. The point-based models will be analyzed with the methods here proposed using multivariate analysis of covariance (MANCOVA). Here, the number of variates being tested is the dimensionality of our observations. Each point of these observations is a three dimensional displacement vector from the mean. The number of contrasts is the number of equations involved in the null-hypothesis. In order to encompass varying numbers of variates and contrasts, and to account for independent variables, a matrix computation is performed. This matrix represents the multidimensional aspects of the correlation significance and it can be transformed into a scalar measure by manipulation of its eigenvalues.

Details of the methods can be found in its Insight Journal publication: http://hdl.handle.net/10380/3124

MRI_CVPR

Tools processing MRI data with a number of techniques from cvpr conference, including segmentation, matching, features, and classification.

ValMap: simple statistical mapping tool

valmap is a command line voxel-wise statistical analysis program for images. Images can be gray matter density, jacobian images, etc.

The linear model is implemented, i.e. designs that can be modeled as Y=AB, where Y is a vector or matrix of dependent variables, B is a vector or matrix of parameters to be estimated, and A is a design matrix.

Why use valmap?

1. Do not need a Matlab license to run.
2. Can incorporate a spatially varying independent variable (e.g., you have a perfusion map as your dependent variable, and you want to co-vary for gray matter at each voxel, so use a gray matter map as an independent variable).
3. Can use spatially invariant independent variables (e.g., you can have a cognitive test score as the dependent variable, and use jacobian maps as the independent variable).
4. Can have multiple dependent variables and do multivariate analyses (e.g., want to know the overall effect of disease on perfusion and structure, so use perfusion maps and jacobian maps as dependent variables).

Analyze

Analyze provides an integrated, comprehensive set of tools for the visualization, segmentation, registration and analysis of multidimensional biomedical imaging data. With Analyze, both anatomic structure and associated function can be studied and fused together for synergistic display and measurement of important structure-to-function relationships.

Subject Order-Independent Group ICA

While the traditional temporally concatenated Group ICA (TC-GICA) adopting three steps of PCA reduction, it could result in inconsistent and variable components when different subject orders were used, both for the group- and individual-level results. Such instability can further cause instable and thus unreliable statistical results. Subject Order-Independent Group ICA (SOI-GICA) aims to fix this problem by producing stable and reliable GICA results. For details please see the paper "Subject Order-Independent Group ICA (SOI-GICA) for Functional MRI Data Analysis" (Zhang et al., 2010, NeuroImage)(http://dx.doi.org/10.1016/j.neuroimage.2010.03.039). MICA is the toolbox inplemented SOI-GICA for convenience of usage.

TORTOISE

The TORTOISE software package is for processing diffusion MRI data. It contains two main modules, DIFF_PREP and DIFF_CALC.

DIFF_PREP - software for image resampling, motion, eddy current distortion and susceptibility induced EPI distortion corrections, and for re-orientation of data to a common space

DIFF_CALC - software for tensor fitting, error analysis, color map visualization and ROI analysis

Software and sample data downloads, as well as documentation for the software, are available at http://www.tortoisedti.org.

UNC Human Brain Atlas

Human brain atlases and databases generated at UNC-Chapel Hill.

Current available atlases:
- UNC/UCI Neonate hippocampus amygdala multi-atlas database + updated version with Baby Connectome Project subjects
- UNC/UCI Neonate scan rescan database : 6 subjects with each two scans of T1- and T2-weighted MR images
- UNC Neonate multi/population atlas: Multi-atlas population for tissue segmentation in neonates, includes left-right mirrored images for symmetric analysis
- UNC-MNI Pediatric 1-year-old atlas: Symmetric atlas generated from 104 1-year-old subjects, combining children at high familial risk of autism and controls.
- Pediatric 4-year-old atlas: Symmetric atlas generated from 10 4-year-old healthy subjects.
- Adult atlas: Symmetric atlas generated from 50+ healthy adult subjects (20-59 year old).
- Elderly atlas: Atlas generated from 27 healthy elderly subjects (60+ years old).

Additional information and acknowledgment for their usage can be found by clicking on the release notes.

DWI/DTI Quality Control Tool: DTIPrep

DTIPrep performs a "Study-specific Protocol" based automatic pipeline for DWI/DTI quality control and preparation. This is both a GUI and command line tool. The configurable pipeline includes image/diffusion information check, padding/Cropping of data, slice-wise, interlace-wise and gradient-wise intensity and motion check, head motion and Eddy current artifact correction, and DTI computing.

Development of DTIPrep has been stopped, and DMRIPrep (within the DTI playground framework) is its successor (see https://github.com/NIRALUser/DTIPlayground)

Brainstorm

Brainstorm is a free, open-source application dedicated to the analysis of brain data:
MEG, EEG, fNIRS, ECoG, depth and multiunit electrophysiology, all integrated with MRI/CT volumes.

Brainstorm features comprehensive user-friendly tools with an intuitive graphical interface, which does not require any programming knowledge. We also emphasize practical aspects of data analysis (e.g., with scripting for batch analysis and intuitive design of analysis pipelines) to promote research reproducibility and productivity. Brainstorm does not require a Matlab license: an executable version is available in the download package for all operating systems.

Since 2011, we have registered >27,000 user accounts and >1,500 published studies have used Brainstorm.

Extensive documentation is provided via online tutorials and user support via a forum. Consult our training pages for upcoming hands-on workshop opportunities!

Follow us on Twitter (@brainstorm2day) and Facebook (BrainstormSoftware) for news and tips!

brainCOLOR: human brain anatomical labeling protocols and colormaps

This resource was created to host descriptions of protocols, definitions, and "rules of thumb" for the reliable identification and localization of human brain anatomy and discussions of best practices in brain labeling. The projects related to this resource are on http://binarybottle.github.io/braincolor and https://www.synapse.org/Synapse:syn3207269/wiki/160639

Human Connectome Project (HCP)

The NIH Human Connectome Project is an effort to map the neural pathways that underlie human brain function. The purpose of the Project is to acquire and share data about the structural and functional connectivity of the human brain. It will advance the capabilities for imaging and analyzing brain connections, resulting in improved sensitivity, resolution, and utility.

The NIH Blueprint for Neuroscience (http://www.neuroscienceblueprint.nih.gov/) has funded two grants that have taken complementary approaches:

* A consortium led by Washington University in St. Louis and the University of Minnesota is using advanced rfMRI, tfMRI, dMRI and MEG methods to chart brain circuitry in 1200 healthy subjects. http://www.humanconnectome.org/

* A consortium led by Massachusetts General Hospital and the University of California at Los Angeles has enabled building a next-generation 3T MR scanner that improves the quality and spatial resolution of brain connectivity data. http://www.humanconnectomeproject.org/

CONN : functional connectivity toolbox

CONN is a Matlab-based cross-platform software for the computation, display, and analysis of functional connectivity in fMRI (fcMRI).

CONN includes a rich set of connectivity analyses (seed-based correlations, ROI-to-ROI graph analyses, ICA, masked ICA, fc-MVPA, generalized PPI, ALFF, ICC, GCOR, LCOR, etc.) in a simple-to-use and powerful software package

CONN is available for resting state data (rsfMRI) as well as task-related designs. It covers the entire pipeline from raw fMRI data to hypothesis testing.

Highlights:
Comprehensive quality assurance methods/measures/displays (scrubbing/ART, CompCor/ICA/GSR denoising, GCOR/FD QC covariates)
Connectome-wide analyses (ICA, fc-MVPA)
Dynamic connectivity analyses (dyn-ICA)
FWE-control of connectivity matrices (NBS)
Non-parametric cluster-level statistics (permutation tests)
ANOVA, regression, longitudinal, and mixed designs

Parallelization options (SGE/Grid Engine, PBS/Torque, LSF, Slurm)
Mac/Windows/Linux/HPC
SPM8/SPM12

https://www.conn-toolbox.org

BrainSuite

BrainSuite is a collection of open source software tools that enable largely automated processing of magnetic resonance images (MRI) of the human brain. The latest version of the BrainSuite software (v.21a) is available for download from the BrainSuite website (https://brainsuite.org/). This release includes a GUI for processing and visualization, tools to extract and parameterize cortex surface meshes, modules for automatic registration and labeling of brain volumes and surfaces, and distortion correction and coregistration of diffusion data with structural MRIs (available via the command line), for Windows, Mac OS X, and Linux platforms. BrainSuite source code is available under a GPLv2 license.

BrainSuite is produced as a collaborative project between David Shattuck's research group at the UCLA Brain Mapping Center (http://shattuck.bmap.ucla.edu) and Richard Leahy's Biomedical Imaging Group at USC (http://neuroimage.usc.edu).

Celeritas: Fiber Optic Response System

The Celeritas Fiber Optic Response System can be customized to fit your research needs by selecting the combination of Button Response Units (BRUs) and Joysticks. The BRUs and Joysticks comfortably fit small to large hand sizes and are constructed entirely of non-metallic components - completely eliminating all metal inside the magnet room. The units communicate button presses through fiber optic cabling which connects to an Interface Console located in the control room through an available wave guide. The Interface Console provides real-time feedback of participant responses via LED indicators and includes a set of buttons to make responses for the participant as needed. The system seamlessly integrates with E-Prime® through a USB connection. The new Interface Console comes with an Optical Connector and a BNC Connector.

Subject Library

UC Davis IDeA Lab Applications for Management Of Neuroimaging Data --

A collection of tools used for processing and organizing MRI data. The Dicom Importer allows you to to view, assemble, and organize dicom files. Subject Library is a filesystem-based search and reporting tool that can be configured to work with many different organization schemes. This package also contains a python library that can be used to write scripts for custom tasks.

HD Neuro-Informatics

Huntington Disease Nueroimaging Initiative (HDNI) is an international effort to establish resources necessary to study the application of neuroimaging measures as (surrogate) biomarkers in HD. The primary aims are to develop and apply software tools, imaging protocols, quality control procedures, data archiving, data distribution, and participation guidelines that will accelerate existing and prospective imaging studies.

NYU CSC TestRetest

The NYU CSC TestRetest resource includes EPI-images of 25 participants gathered during rest as well as anonymized anatomical images of the same participants.

The resting-state fMRI images were collected on several occasions:
1. the first resting-state scan in a scan session
2. 5-11 months after the first resting-state scan
3. about 30 (< 45) minutes after 2.

Each scan occasion is released as a new version release of the resource. Make sure to check all releases to get all files.

Data use is unrestricted, but users should reference our original publications and NITRC (see Documentations).

***Caution: Participants here are part of the NewYork_a contribution to the 1000 Functional Connectomes Project. DO NOT combine datasets.

Acquisition was funded by Stavros S. Niarchos Foundation, the Leon Lowenstein Foundation, NARSAD (The Mental Health Research Association) grants to F.Xavier Castellanos; and Linda and Richard Schaps, Jill and Bob Smith, and the Taubman Foundation gifts to F.Xavier Castellanos.

ASL data processing tool box

This tool box is for arterial spin labeled perfusion MRI data processing. It is based on SPM (copyright by Wellcome Department, London) and Matlab (MathWorks Inc). More detailed documentation can be found in the manual. The current version support 3D or 4D Analyze or Nifiti format and support PASL, CASL, and PCASL data. It contains the code for calculating CBF and a set of SPM batch scripts for preprocessing and statistical analysis. Sample data are also available from the webpage: http://cfn.upenn.edu/~zewang/ASLtbx.php. Please subscribe to the maillist here: http://www.nitrc.org/mailman/listinfo/asltbx-maillist.

TOADS-CRUISE Brain Segmentation Tools

The TOADS-CRUISE Brain Segmentation Tools are a collection of software plug-ins developed for the automatic segmentation of magnetic resonance brain images. The tools include multiple published algorithms developed at Johns Hopkins University. The SPECTRE algorithm performs brain extraction. The TOADS algorithm generates a topology-preserving tissue classification into cortical, subcortical, and cerebellar structures. The CRUISE algorithm produces inner, central, and outer cortical surfaces suitable for computing thickness and other geometric measures. Tools are also included for performing gyral labeling, lesion segmentation, thickness computation, surface visualization, and surface file conversion. All tools are released as plug-ins for the MIPAV software package and were developed using the Java Image Science Toolkit (both available on NITRC). They are therefore cross-platform and compatible with a wide variety of file formats.

QCQP

Quadratically constrained quadratic programing (QCQP) technique in medical image analysis. QCQP based tools are provided for classification, segmentation, and bias field correction.

OASIS

The Open Access Series of Imaging Studies (OASIS) is a project aimed at making neuroimaging data sets of the brain freely available to the scientific community. By compiling and freely distributing neuroimaging data sets, we hope to facilitate future discoveries in basic and clinical neuroscience. https://sites.wustl.edu/oasisbrains
OASIS currently contains 4 datasets (OASIS-1, OASIS-2, OASIS-3, and OASIS-4). For more information: https://sites.wustl.edu/oasisbrains

OASIS-1: Cross-sectional MRI Data in Young, Middle Aged, Nondemented and Demented Older Adults includes 416 subjects aged 18 to 96, 100 of whom were clinically diagnosed with Alzheimer's disease. (https://sites.wustl.edu/oasisbrains/home/oasis-1/ )
OASIS-2: Longitudinal MRI Data in Nondemented and Demented Older Adults includes 150 subjects aged 60 to 96, many diagnosed with Alzheimer's disease at some point during their participation. (https://sites.wustl.edu/oasisbrains/home/oasis-2/ )

factory_t1_dti

Tools to make easier on using spm, pipedream, dti-tk, and other softwares to analyze t1 or dti images.

BRAINSROIAuto

NOTE: All new development is being managed in a github repository. Please visit

https://github.com/BRAINSia/BRAINSTools

Automatically creates a mask based on the 'foreground' of an anatomical scan volume.

Camino

Camino is a free, open-source, object-oriented software package for analysis and reconstruction of Diffusion MRI data, tractography and connectivity mapping.

Please see the Camino home page (camino.org.uk) for documentation and tutorials. If you need support, join the Camino mailing list on this site.

NICE-SIGN

NICE-SIGN is a nice sign of bias field correction (nonuniformity) in medical images. This tool is fast and efficient. Technical details can be found at http://zheng.vision.googlepages.com/biasCorrection_miccai09_Zheng.pdf

Quantitative Diffusion Tools

The Quantitative Diffusion Tools provide Slicer3 modules for quantitative diffusion analysis. Modules include tools for clustering fiber tracts, summarizing measures over tract clusters, etc.

MRI Defacer

Tool to remove facial features from an MRI structural image for the purpose of de-identification.

NC-IGT Fast Imaging Library

This software provides algorithms for the reconstruction of raw MR data. In
particular, it supports the reconstruction of accelerated data acquisitions
where k-space is subsampled and the Fourier domain encoding is complemented
by temporal encoding, spatial encoding, or and/or a constrained
reconstruction. This library of functions provides a number of
reconstruction algorithms that accurately employ advanced MR imaging methods
including: UNFOLD; parallel imaging methods such as SENSE and GRAPPA;
Homodyne processing of partial-Fourier data, and gradient field
inhomogeneity correction (gradwarp); EPI Nyquist Ghost correction and
ramp-sampling gridding. The target audience is research groups who may be
interested in exploring or employing advanced MR reconstruction techniques,
but don't have the necessary expertise in-house.

Inquires may be directed to: ncigt-imaging-toolkit -at- bwh.harvard.edu

Cytoseg

The goal of the Cytoseg project is to produce tools for automatic segmentation of of 3D electron microscopy, specifically neuropil. The project is written in Python and uses the pythonxy platform (which includes scipy and ITK image processing tools).

E-Prime Extensions for fMRI

E-Prime® Extensions for fMRI (EEfMRI) software is designed to optimize E-Prime® experiments for fMRI research. EEfMRI allows you to synchronize the start of your experiment with the first scanner trigger pulse along with several valuable features to enhance the control you have over your experiment. Implementing EEfMRI into your current experiments is achieved by simply dragging and dropping the correct EEfMRI package calls into the E-Prime® experiment in the appropriate places. EEfMRI is designed to integrate with other PST hardware and software to increase usability for researchers while maintaining the millisecond accuracy of E-Prime®.

SpineSegmentation module for 3DSlicer

3D Slicer module for automated segmentation of the spine. This is an implementation of a novel model-based segmentation algorithm. This work was presented at the NA-MIC Week in Salt Lake City, Jan 2010.

FieldTrip

FieldTrip is the Matlab software toolbox for MEG and EEG analysis that is being developed at the Donders Institute for Brain, Cognition and Behaviour at the Radboud University Nijmegen, the Netherlands together with collaborating institutes.

The toolbox includes algorithms for simple and advanced analysis of MEG, EEG, and invasive electrophysiological data, such as time-frequency analysis, source reconstruction using dipoles, distributed sources and beamformers and non-parametric statistical testing. It supports the data formats of all major MEG systems (CTF, Neuromag, BTi) and of the most popular EEG systems, and new formats can be added easily. FieldTrip contains high-level functions that you can use to construct your own analysis protocols in Matlab. Furthermore, it easily allows developers to incorporate low-level algorithms for new EEG/MEG analysis methods.

Web Game for Collaborative Labeling

Statistical atlases of regional brain anatomy have proven to be extremely useful in characterizing the relationship between the structure and function of the human nervous system. Typically, an expert human rater manually examines each slice of a three-dimensional volume. This approach can be exceptionally time and resource intensive, so cost severely limits the clinical studies where subject-specific labeling is feasible. Methods for improved efficiency and reliability of manual labeling would be of immense benefit for clinical investigation into morphological correlates of brain function. The goal of the proposed work is to enable an alternative to expert raters for medical image labeling through statistical analysis of the collaborative efforts of many, minimally-trained raters. The proposed research investigates extension of established practices for volumetric labeling and web- based collaboration to create an innovative infrastructure for labeling.

Brain Connectivity Toolbox

The Brain Connectivity Toolbox (brain-connectivity-toolbox.net) is a MATLAB toolbox for complex-network (graph) analysis of structural and functional brain-connectivity data sets. Several people have contributed to the toolbox and users are welcome to contribute new functions with due acknowledgement.

All efforts have been made to avoid errors, but users are strongly urged to independently verify the accuracy and suitability of individual toolbox functions. Please report bugs or substantial improvements to Olaf Sporns or to Mika Rubinov.

iView X MRI-LR - Eye Tracking for fMRI

The iView X MRI-LR is a non-invasive, long-range eye tracking system for use in the fMRI environment. Some features of the system include:

- Elaborate faraday shielding and fiber optics to avoid noise in high-field magnets.

- Includes stimulus presentation software “Experiment Center” and is compatible with 3rd party products such as “Presentation” by NeuroBS.

- Utilizes mirror box customized for large field of view.

- Includes powerful analysis software “BeGaze2” for graphical and statistical analysis of eye movements.

- Includes fixation, saccade and blink detection, and area-of-interest based statistics

- Real-time data available via digital or analog output

Framework for Open Programmatic Access

The Framework for Open Programmatic Access (FOPA) aims to provide a standardized framework for communication and data exchange between medical imaging applications, with particular focus on neuroimaging technologies. FOPA is an attempt to design and implement a common protocol for network and command line communication with either file-system or imbedded data structures. Initial reference implementations will support interoperability between ITK, VTK, and Java platforms. Contributions are welcome from other neuroimaging development communities.

This project is an open community effort under the BSD license.

Presentation

Presentation® is a precise and powerful stimulus delivery and experimental control program for neuroscience. Presentation® runs on any Windows PC, and delivers auditory, visual and multimodal stimuli with sub-millisecond temporal precision. Presentation® includes special features for experiments using psychophysics, eye movements, fMRI, ERP, MEG, single neuron recording, and more.

Brede Toolbox

The Brede Toolbox is a package for neuroinformatics and neuroimaging analysis mostly programmed in Matlab with a few additional programs in Python and Perl. It allows coordinate-based meta-analysis and visualization, neuroimaging analysis of voxel or regional data. Among the algorithms implemented are kernel density estimation (for coordinate-based meta-analysis), independent component analysis, non-negative matrix factorization, k-means clustering, singular value decomposition, partial correlation analysis with permutation testing and partial canonical correlation analysis. Visualization of coordinate, surfaces and volumes are possible in 2D and 3D.
Generation of HTML for results are possible and algorithms can be accessed from the command line or via a flexible graphical interface. With the Brede Toolbox comes the Brede Database with a small coordinate database from published neuroimaging studies, and ontologies for, e.g., brain function and brain regions.

Spatial Analysis 3D

Spatial Analysis 3D is a user-friendly, graphical user interface (GUI) that allows statistical and visual manipulations of real and simulated three-dimensional spatial point patterns. The analyses use files containing sets of X, Y, Z coordinates. These point patterns are frequently coordinates of cells of specific cell classes within in volumes of tissue derived from microscopy analyses. The analyses are scale independent so spatial analyses of coordinates from larger and smaller scale distributions are possible. The software can also generate sample sets of X, Y, Z coordinates for program exploration and modeling purposes.

VAMCA Cortical Meta-analysis Toolbox

VAMCA: Visualization And Meta-analysis on Cortical Anatomy is a stand-alone, open source human cortical meta-analysis and visualization toolbox for MatLab. It projects stereotaxic coordinates to a mean cortical surface by using an anatomical database of 60 young adults to provide multiple mappings of normalized cortical surfaces into MNI space. VAMCA performs the following analyses:

1) Multi-Fiducial Projection Mapping: Map stereotaxic 3D coordinates to the normalized cortical location for each of 60 database subjects.
2) Computing Centroid Locations for groups of foci both on a mean cortical surface and in MNI space.
3) Comparing Two Groups of Foci for differences in location (surface or 3D) of their group centroids and computing the groups' overlap extent using permutation tests.
4) Detecting Significant Densities of Foci or Density Differences of Two Groups within anatomical ROIs on a mean cortical surface by using Monte Carlo analyses. Coordinate weights allow fixed or random effects type analyses.

HI-SPEED Software Packets

HI-SPEED Software Packets contain
(1) unconstrained and constrained nonlinear least squares diffusion tensor estimation techniques,
(2) 2-dimensional and 3-dimensional analytical (Shepp-Logan) magnetic resonance imaging phantoms in both the Fourier and image domains,
(3) techniques for reporting the underlying signal-to-noise ratio in magnetic resonance (MR) images,
(4) Probabilistic Identification and EStimation of NOise (PIESNO)---a technique for identifying noise-only pixels and estimating the underlying noise standard deviation in MR images, and
(5) a signal-transformational technique for breaking the noise floor in MR images.

Many more computational tools will be shared with users and developers as they become available.

Download info: http://sites.google.com/site/hispeedpackets/

ArtRepair for robust fMRI

ArtRepair is a toolbox for SPM to improve fMRI analysis of high motion pediatric and clinical subjects. The toolbox includes special algorithms for motion adjustment, data repair, and noise filtering, and methods to find outlier subjects in group studies. Visualization tools are included for quality checking the data, including a movie format for viewing all data and all contrast estimates on every voxel of every subject. Methods are included to quantify results into percent signal change.

Multi-fiber Reconstruction from DW-MRI

This program contains Python modules for modeling and reconstruction of diffusion weighted MRI data. It is a subset of the code internally used in the CVGMI lab at the University of Florida. Three different reconstruction methods are currently included in this program, namely, Mixture of Wisharts (MOW), Diffusion Orientation Transform (DOT) and Q-ball Imaging (QBI).

This program is mainly developed and maintained by Bing Jian, as part of his Ph.D. research, supervised by Prof. Baba Vemuri.

Please note that the source code of this program is hosted at Google Code, see the "Source Code" link on the left.

MoTrak: Head Motion Tracking System

Designed for use in an MRI simulator, MoTrak software uses Ascension Technology’s trakSTAR. The sensor attached to the subject’s head determines the position of the head in space relative to the transmitter. The sensor records angular rotations as well as positional displacements from an initially calibrated position. This information is displayed and logged by the program in real-time, allowing observation of head motion in an MRI simulator. The goal is to monitor how much the participant moves while the scan is in progress. If a participant moves too much, it can potentially result in unusable data and wasted resources.

In the simulator, the participant can simultaneously be habituated to the MRI environment, while being trained to remain still via feedback from the MoTrak system.

JIST: Java Image Science Toolkit

Java Image Science Toolkit (JIST) provides a native Java-based imaging processing environment similar to the ITK/VTK paradigm. Initially developed as an extension to MIPAV (CIT, NIH, Bethesda, MD), the JIST processing infrastructure provides automated GUI generation for application plug-ins, graphical layout tools, and command line interfaces.

This repository maintains the current multi-institutional JIST development tree and is recommended for public use and extension. To participate in this consortium or seek assistance in integrating JIST modules in your application, please contact us at jist-admin@www.nitrc.org.

JIST was originally developed at IACL and MedIC (Johns Hopkins University) and is now also supported by MASI (Vanderbilt University).

Please use the download nightly build link below to retrieve a currently supported version of JIST. The previous versions are available for archival access.

cbiNifti: Matlab/Octave Nifti library

cbiNifti: An I/O library for Matlab/Octave

Matlab and Octave library for reading and writing Nifti-1 files.

cbiNifti is intended to be a small, self-contained library that makes minimal assumptions about what Nifti files should look like and allow users easy access to the raw data.

cbiNifti handles compressed file formats for reading and writing, using Unix pipes for compression and decompression.

More information and code examples at:

http://www.pc.rhul.ac.uk/staff/J.Larsson/software.html

Internet Brain Volume Database (IBVD)

The IBVD provides a web-based searchable database of brain neuroanatomic volumetric observations. This is designed to access both group volumetric results as well as volume observations in individual cases. A major thrust effort is to enable electronic access to the results that exist in the published literature. Currently, there is quite limited electronic or searchable methods for the data observations that are contained in publications. This effort will facilitate the dissemination of volumetric observations by making a more complete corpus of volumetric observations findable to the neuroscience researcher. This also enhances the ability to perform comparative and integrative studies, as well as meta-analysis. Extensions that permit pre-published, non-published and other representation are planned, again to facilitate comparative analyses.

Neural ElectroMagnetic Ontologies

Neural ElectroMagnetic Ontologies (NEMO) is an NIH funded project that aims to create EEG and MEG ontologies and ontology based tools. Project documentation can be accessed on the NEMO website:

http://aimlab.cs.uoregon.edu/NEMO/web/

Project resources (ontologies, database, portal, and pattern analysis tools) can be downloaded from Sourceforge:

https://sourceforge.net/projects/nemoontologies/

These resources will be used to support representation, classification, and meta-analysis of brain electromagnetic data. The three pillars of NEMO are: DATA, ONTOLOGY, and DATABASE. NEMO data consist of raw EEG, averaged EEG (ERPs), and ERP data analysis results. NEMO ontologies include concepts related to ERP data (including spatial and temporal features of ERP patterns), data provenance, and the cognitive and linguistic paradigms that were used to collect the data. The NEMO database is a large repository that stores NEMO consortium data, data analysis results, and data provenance.

TractoR: Tractography with R

The TractoR (Tractography with R) project includes R packages for reading, writing and visualising magnetic resonance images stored in Analyze, NIfTI and DICOM file formats (DICOM support is read only). It also contains functions specifically designed for working with diffusion MRI, tractography and graph theory, including a standard implementation of the neighbourhood tractography approach to white matter tract segmentation. A shell interface is also provided, to run experiments with TractoR without interacting with R.

BRAINSConstellationDetector

NOTE: All active development of BRAINSConstellationDetector is now being stored in a Git repository at github.com. Please go to

https://github.com/BRAINSia/BRAINSTools

This program will find the mid-sagittal plane, the AC, PC, and mpj points in an image, and create an AC/PC aligned data set with the AC point at the center of the voxel lattice (la
beled at the origin of the image physical space.)

This work is an extention of the algorithms originally described by Dr. Babak A. Ardekani, Alvin H. Bachman, Model-based automatic detection of the anterior and posterior commissures on MRI scans, N
euroImage, Volume 46, Issue 3, 1 July 2009, Pages 677-682, ISSN 1053-8119, DOI: 10.1016/j.neuroimage.2009.02.030.
(http://www.sciencedirect.com/science/article/B6WNP-4VRP25C-4/2/8207b962a38aa83c822c6379bc43fe4c)

STAPLE

STAPLE is an algorithm for the Simultaneous Truth and Performance Level Estimation, which estimates a reference standard and segmentation generator performance from a set of segmentations. It has been widely applied for the validation of image segmentation algorithms, and to compare the performance of different algorithms and experts. It has also found application in the identification of a consensus segmentation, by combination of the output of a group of segmentation algorithms, and for segmentation by registration and template fusion. Further information is available at http://www.crl.med.harvard.edu

E-Prime

E-Prime® is a suite of applications to fulfill all of your computerized experiment needs. With more than 100,000 users in research institutions and laboratories in over 60 countries, E-Prime® provides a truly easy-to-use environment for computerized experiment design, data collection, and analysis. E-Prime® provides millisecond precision timing to ensure the accuracy of your data. E-Prime’s flexibility to create simple to complex experiments is ideal for both novice and advanced users.

The E-Prime® suite of applications includes:
•E-Studio – Drag and drop graphical interface for experiment design
•E-Basic – Underlying scripting language of E-Prime
•E-Run – Once the experiment is generated with a single click, E-Run affords you the millisecond precision of stimulus presentation, synchronizations, and data collection.
•E-Merge – Merges your single session data files for group analysis
•E-DataAid – Data management utility
•E-Recovery – Recovers data files

dinifti

Convert DICOM images to NIfTI format.

MindSeer

MindSeer is a cross-platform application for 3D brain visualization. It is written in Java/Java3D, and runs in both standalone and client-server mode. Click on the MindSeer Project Page for downloads, tutorials, source code, demos and a publication.

Anatomist

Anatomist is a software for interactive visualization of multimodal data and for manipulation of structured 3D objects. It allows to build scenes that merge or combine images, meshes, regions of interest, fibers, textures, color palettes, referential changes, etc. A user can interact in 3D and in real time with the objects of an Anatomist scene: change point of view, select objects, add/supress objects, change colors, draw regions of interests, do manual registration, etc.

BrainVISA

BrainVISA is a modular an customizable software platform built to host heterogeneous tools dedicated to neuroimaging research. Many toolboxes have already been developped for BrainVISA (T1 MRI, sulcal identification and morphometry, cortical surface analysis, diffusion imaging and tractography, fMRI, nuclear imaging, EEG and MEG, TMS, histology and autoradiography, etc.).

BrainVISA main features are:
- Harmonization of communications between different software. For instance, BrainVISA toolboxes are using home-made software but also third-party software such as FreeSurfer, FSL, SPM, nipy, R-project, Matlab, etc.
- Ontology-based data organization allowing database sharing and automation of mass of data analysis.
- Fusion and interactive visualization of multimodal data (using Anatomist software).
- Automatic generation of graphical user interfaces.
- Workflow monitoring and data quality checking.
- Full customization possible.
- Runs on Linux, Mac and Windows.

SRI24 Atlas: Normal Adult Brain Anatomy

SRI24 is an MRI-based atlas of normal adult human brain anatomy, generated by template-free nonrigid registration from images of 24 normal control subjects.

The atlas comprises T1, T2, and PD weighted structural MRI, tissue probability maps (GM, WM, CSF), maximum-likelihood tissue segmentation, DTI-based measures (FA, MD, longitudinal and transversal diffusivity), and two labels maps of cortical regions and subcortical structures.

The atlas is provided at 1mm isotropic image resolution in Analyze, NIFTI, and Nrrd format. We are also providing an experimental packaging for use with SPM8.

As per the CC-BY-SA license, please cite the following paper in publications using the SRI24 atlas:

T. Rohlfing, N. M. Zahr, E. V. Sullivan, and A. Pfefferbaum, “The SRI24 multichannel atlas of normal adult human brain structure,” Human Brain Mapping, vol. 31, no. 5, pp. 798-819, 2010. DOI: 10.1002/hbm.20906

Free article on PMC: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2915788

MarsBaR region of interest toolbox

MarsBaR (MARSeille Boîte À Région d'Intérêt) is a toolbox for SPM which provides routines for region of interest analysis. Features include region of interest definition, combination of regions of interest with simple algebra, extraction of data for regions with and without SPM preprocessing (scaling, filtering), and statistical analyses of ROI data using the SPM statistics machinery.

Computational Morphometry Toolkit (CMTK)

A software toolkit for computational morphometry of biomedical images, CMTK comprises a set of command line tools and a back-end general-purpose library for processing and I/O.

The command line tools primarily provide the following functionality: registration (affine and nonrigid; single and multi-channel; pairwise and groupwise), image correction (MR bias field estimation; interleaved image artifact correction; EPI unwarping), processing (filters; combination of segmentations via voting and STAPLE; shape-based averaging), statistics (t-tests; general linear model).

CMTK is implemented in C++ with parallel processing using POSIX Threads (SMP), OpenMP (SMP), Grand Central Dispatch (SMP), and CUDA (GPU).

Supported file formats include Analyze (r/w), NIFTI (r/w), Nrrd (r/w), DICOM (read), BioRad (read). Data exchange with other toolkits, such as ITK, FSL, AFNI, SPM, etc. is thus easily accomplished.

Journals

If you are seeking journals addressing functional and structural neuroimaging topics, this is the right place to start.

Books

If you are interested in finding books related to neuroscience, you have come to the right place! Books is a bibliography of books addressing the topic of functional and structural neuroimaging.

MRI Simulator

The Psychology Software Tools MRI Simulator provides a realistic approximation of an actual MRI scanner to allow habituation and training of participants in an environment less daunting than a real scanner. Special populations such as children, the elderly, and psychiatric patients, are often prone to claustrophobia and anxiety in the bore of a magnet, and consequently have a much higher rate of terminating the experiment or scan session before its completion. Some centers that have dealt with these populations estimate a 50%-80% failure rate. With the use of the MRI Simulator this failure rate can often be reduced below 5%, improving cost effectiveness.

DTI-TK

DTI-TK is a spatial normalization & atlas construction toolkit, designed from ground up to support the manipulation of diffusion-tensor images (DTI) with special cares taken to respect the tensorial nature of the data. It implements a state-of-the-art registration algorithm that drives the alignment of white matter (WM) tracts by matching the orientation of the underlying fiber bundle at each voxel. The algorithm has been shown to both improve WM tract alignment and to enhance the power of statistical inference in clinical settings.

The key features include:

- NIfTI support for scalar, vector and DTI volumes

- tool chains for manipulating DTI volumes: resampling, smoothing, warping, registration & visualization

- pipelines for WM morphometry: spatial normalization & atlas construction for population-based studies

- built-in cluster-computing support

- Interoperability with other major DTI tools: AFNI, Camino, DTIStudio & FSL

Brede Wiki

The Brede Wiki is a semantic wiki with structured information from neuroscience. It contains listing of results from neuroimaging studies such as Talairach coordinates and brain volume measurements as well as items for researchers, software packages and brain regions. SQL dumps of the structured information in the wiki is available so complex queries can be formed.
Search on Talairach coordinates in the database is available from an external specialized search engine.

UNC 0-1-2 Infant Atlases

Dedicated atlases for neonates, 1-year-olds, and 2-year-olds are included. Each atlas comprises an intensity model, tissue probability maps, and an anatomical parcellation map. These atlases are constructed with state-of-the-art infant MR segmentation and groupwise registration methods using a set of longitudinal images of 95 normal infants (56 males and 39 females) acquired at birth and 1 year, and 2 years of age.

Please cite the following article if you use our atlases in your research:
Feng Shi, Pew-Thian Yap, Guorong Wu, Hongjun Jia, John H. Gilmore, Weili Lin, Dinggang Shen,"Infant Brain Atlases from Neonates to 1- and 2-year-olds", PLoS ONE, 6(4): e18746, 2011.

For more information, please visit:
http://www.med.unc.edu/bric/ideagroup/free-softwares/unc-infant-0-1-2-atlases

fMRI-CPCA

Constrained Principal Component Analysis (CPCA) combines regression analysis and principal component analysis into a unified framework. This method derives images of functional neural networks from singular-value decomposition of BOLD signal time series, and allows derivation of images when the analyzed BOLD signal is constrained to the scans occurring in peristimulus time, using all other scans as baseline.

CPCA provides allows (1) determination of multiple functional networks involved in a task, (2) estimation of the pattern of BOLD changes associated with each functional network over peristimulus time points, (3) quantification of the degree of interaction between these multiple functional networks, and (4) a statistical test of the degree to which experimental manipulations affect each functional network.

fMRI CPCA provides all results in matlab.mat file format, as well as writing images in analyze format for all components, rotated and unrotated.

CIGAL

CIGAL is a program that provides accurate real-time stimulus control, behavioral and physiological recording, and synchronization with external devices. It can also provide continuous real-time feedback of task performance and physiological responses. Task programming typically involves a simple text file specifying basic parameter settings (e.g. screen color) and a list of stimulus events, which can include images, animated movies, sound files, text stimuli, video graphics, or commands that communicate with external hardware devices. Multiple video and auditory stimuli can be presented simultaneously. Multi-channel response recording and real-time feedback features require no user programming. Advanced users can add customized stimulus events using CIGAL’s real-time programming capabilities. Output files can be automatically created in a variety of output formats (e.g. FSL 3-column files, XML Events files, CSV trial tables).

BXH/XCEDE Tools

A collection of data processing and image analysis tools for data in BXH or XCEDE format. This includes data format encapsulation/conversion, event-related analysis, QA tools, and more. These tools form the basis of the fBIRN QA procedures and are also distributed as part of the fBIRN Data Upload Scripts.

R-package for adaptive DWI analysis

The package dti provides methods for structural adaptive smoothing of diffusion weighted data in the context of the diffusion tensor model. Through its edge preserving properties they reduce data noise without compromizing significant structures.

R-package for adaptive fMRI analysis

The package fmri provides fMRI analysis with R using structural adaptive smoothing methods. They allow smoothing especially at low SNR avoiding the apparent blurring of non-adapative smoothing and thus without reducing the effective spatial resolution.

UNC Primate Brain Atlas

This symmetric atlas of the primate brain has been created using 18 cases of rhesus macaques aged 16-34 months. It includes the T1-weighted image (with and without skull), and also tissue segmentation probability maps (white matter, gray matter, CSF, rest), subcortical structures segmentation (amygdala, caudate, hippocampus, pallidus, putamen), and a lobar parcellation map.

You can find more details about the creation of this atlas in the following paper :
M. Styner, R. Knickmeyer, S. Joshi, C. Coe, S. J. Short, and J. Gilmore. Automatic brain segmentation in rhesus monkeys. In Proc SPIE Vol 6512, Medical Imaging, 2007, pp. 65122 L1-8

VMTK in 3D Slicer

This project provides a series of modules which enable functions of the Vascular Modeling Toolkit (http://www.vmtk.org) in 3D Slicer (http://www.slicer.org).

The functionality includes vessel enhancement filtering, level set segmentation, centerline computation, network extraction and branch splitting.

Installation notes and documentation are available at the official project page:

http://www.vmtk.org/Main/VmtkIn3DSlicer

WFU_Pipeline

The WFU SPM5 Pipeline is a fully automated method for the processing of fMRI data using SPM. It is fully automated from the point of data acquisition at the MRI scanner. It incorporates tools for automated data transfer, archiving, real-time SPM5 batch script generation with distributed grid processing, automated error-recovery procedures, full data-provenance, email notifications, optional conversion back to DICOM (Digital Imaging and Communications in Medicine), and picture archiving and communications systems (PACS) insertion. The architecture allows for an infinite number of easily definable analyses that are fully automated from the point of acquisition.

Landman NeuroImaging Tools

Project to provide long-term hosting and release for small tools related to medical image analysis. Source repository contains highly experimental code intended for collaborative development. However, any interested parties are welcome to browse/reuse code. Stable/evolved projects will be moved to independent projects.

TAPIR

TAPIR (Tools for Advanced Parameterized Image Registration) is a set of command line tools allowing 2D and 3D image registration, mainly for medical imaging (although also relevant to other image registration problems).

DicomBrowser

DicomBrowser is a platform-independent desktop tool for inspecting DICOM header fields, editing DICOM header fields, viewing DICOM images, and transferring DICOM files to a DICOM receiver. DicomBrowser includes scriptable header editing to support various de-identification protocols. Dicomrowser is written in Java and uses ImageJ for image viewing and the dcm4che toolkit for much of its DICOM implementation. For more information: http://nrg.wustl.edu/software/dicom-browser

ProbabilisticBiasCorrection

A multichannel capable tool for probabilistic inhomogeneity correction implemented as both a standalone command line tool and a Slicer3 module.

CBICA: WMLS (White Matter Lesion Segmentation)

Brain lesions appear in different brain diseases as well as in normal aging; they can be due to small vessel disease, infarcts, MS, or other reasons. MRI is used as surrogate imaging marker, as MRI signal changes reﬂect certain aspects of brain pathology.Computer algorithms have started to complement expert-readings of MRI as they may improve throughput and consistency, in addition to providing more accurate quantitative measures of lesion type and volume. Computerized segmentation methods can also offer more precise measurements of longitudinal change of a lesion with disease progression or treatment response.
The brain lesion segmentation tool uses image analysis and machine learning techniques (Support Vector Machines). Image intensities from multiple MR acquisition protocols, after coregistration, are used to form a voxel-wise attribute vector which is used to perform the segmentation.
Primary Contact: Christos Davatzikos
Contributors: Zhiqiang Lao ,Dinggang Shen ,Eva Zacharaki,Nick Bryan

Hammer And WML Modules for 3D Slicer

HAMMER is an acronym for Hierarchical Attribute Matching Mechanism for Elastic Registration (Dinggang Shen, Christos Davatzikos, HAMMER: Hierarchical Attribute Matching Mechanism for Elastic Registration, IEEE Trans. on Medical Imaging, 21(11):1421-1439, Nov 2002) - an elastic registration algorithm for medical images, matching morphological signatures of images in a hierarchical multi-scale regime.

White matter lesion (WML) segmentation is a novel multi-spectral WML segmentation protocol via incorporating information from T1-w, T2-w, PD-w and FLAIR MR brain images. (Zhiqiang Lao, Dinggang Shen, Dengfeng Liu, Abbas F Jawad, Elias R Melhem, Lenore J Launer, Nick R Bryan, Christos Davatzikos, Computer-Assisted Segmentation of White Matter Lesions in 3D MR images, Using Pattern Recognition, Academic Radiology, 15(3):300-313, March 2008).

ARCTIC

ARCTIC (Automatic Regional Cortical ThICkness) is an end-to-end application developped at UNC-Chapel Hill allowing individual regional analysis of cortical thickness. This cross-platform tool can be run within Slicer3 as an external module, or directly as a command line.

xjView, a viewing tool for SPM

xjView is a viewing program for SPM2 and SPM5. p-value slider, displays multiple images at a time and can be used to build ROI masks. For a given region you can find the anatomical name and search the selected region in online database (wiki, google scholar and pubmed).

Segmentation Validation Engine

The Segmentation Validation Engine (SVE) provides an automated online framework for performing validation studies of skull-stripping methods. Registered users may download 40 T1 MRI volumes, skull-strip them with the algorithm of their choice, and upload their segmentation results to the SVE website (http://sve.bmap.ucla.edu), hosted at the UCLA Brain Mapping Center (http://www.bmap.ucla.edu). The server will then compare the 40 skull-stripped results against a set of manually generated brain masks. The server computes a series of measures for the uploaded data, including Jaccard and Dice measures, and produces images of the spatial location of segmentation errors relative to a common space. Results are archived on the server, and the measures are viewable by visitors to the site. Details on the methodology can be found in:

Shattuck DW, Prasad G, Mirza M, Narr KL, and Toga AW (2009) Online Resource for Validation of Brain Segmentation Algorithms, NeuroImage(doi:10.1016/j.neuroimage.2008.10.066).

BRAINSMush

NOTE: All active development of BRAINSMush is now being stored in a Git repository at github.com. Please go to

https://github.com/BRAINSia/BRAINSTools

Tool to generate brain volume mask from input of T1 and T2-weighted images alongside a region of interest brain mask. This volume mask omits dura, skull, eyes, etc. The program is built upon ITK and uses the Slicer3 execution model framework to define the command line arguments and can be fully integrated with Slicer3 using the module discovery capabilities of Slicer3.

3DSlicerLupusLesionModule

The purpose of this Slicer3 module is to provide a capability for performing white matter lesion classification and summary.

BrainMask Volume Processing Tool

Segmentation of the brain from three-dimensional MR images is a crucial pre-processing step in morphological and volumetric brain studies. BrainMask implements a fully automatic brain segmentation algorithm that uses advanced "thresholding with morphology" and 3D edge detection algorithms. BrainMask demonstrates high segmentation accuracy. For a representative 26 datasets, the segmentation error averaged 3.4% ± 1.3% (Mikheev A et al. J Magn Reson Imag 27(6):1235-41;2008).

BrainMask includes NNN - a tool based on the algorithm developed by John Sled for correcting the intensity non-uniformity in MR data (Sled JG et al. IEEE Trans Med Imag 17(1):87-97;1998).

BrainMask also includes a versatile DICOM wiewer and allows to selectively load and organize DICOM images into 3D and 4D datasets.

Statistics Online Computational Resource

The Statistics Online Computational Resources (SOCR) include the following suite of web-based Java applets:
* Distributions (interactive graphs and calculators)
* Experiments (virtual computer-generated games and processes)
* Analyses (collection of common web-accessible tools for statistical data analysis)
* Games (interfaces and simulations to real-life processes)
* Modeler (tools for distribution, polynomial and spectral model-fitting and simulation)
* Graphs, Plots and Charts (comprehensive web-based tools for exploratory data analysis),
* Additional Tools (other statistical tools and resources)
* SOCR Java-based Statistical Computing Libraries.

In addition, SOCR provides a suite of tools for volume-based statistical mapping (http://wiki.stat.ucla.edu/socr/index.php/SOCR_EduMaterials_AnalysesCommandLine) via command-line execution and via the LONI Pipeline workflows (http://www.nitrc.org/projects/pipeline).

INCF

The INCF is a professional organization devoted to advancing the field of neuroinformatics. One of its aims is to develop an international neuroinformatics infrastructure, which promotes the sharing of data and computing resources to the international research community. A second objective of INCF is to help develop scalable, portable, and extensible applications that can be used by neuroscience laboratories worldwide.

Mouse BIRN

Mouse BIRN has ended. The next phase of this project is the Mouse Connectome Project (https://www.nitrc.org/projects/mcp/).

Textpresso - literature search engine

Textpresso is a text-mining system for scientific literature. Textpresso's two major elements are (1) access to full text, so that entire articles can be searched, and (2) introduction of categories of biological concepts and classes that relate two objects (e.g., association, regulation, etc.) or describe one (e.g., methods, etc). A search engine enables the user to search for one or a combination of these categories and/or keywords within an entire literature.
Textpresso is useful as a search engine for researchers as well as a curation tool. It was developed as a part of WormBase and is used extensively by C. elegans curators. Textpresso has currently been implemented for 17 different literatures, among them Neuroscience, and can readily be extended to other corpora of text.
Textpresso can be used online (http://www.textpresso.org), but also installed
locally via a downloadable software package (http://www.textpresso.org/downloads.html)

POLGUI- Matlab GUI for Polhemus Fastrak

POLGUI is an interface between MATLAB and the Polhemus Fastrak digitizer used to digitize fiducial locations and scalp EEG electrode locations.

There are 5 versions all of which work under MATLAB R14 (on both linux and windows platforms),
1. polgui_ver1_r14 : works with 1 receiver (stylus pen)
2. polgui_ver2_r14 : works with 2 receivers (including the pen)
3. polgui_ver3_r14 : works with 3 receivers(including the pen)
4. polgui_ver4_r14 : works with 4 receivers (including the pen)
5. polgui_ver5_r14 : Generic version which works with 1/2/3/4 receivers [WARNING: Ver 5 might be buggy; not fully tested]

Requirements: MATLAB R14 (Linux/Windows)

The project is hosted on Sourceforge at http://sourceforge.net/projects/polgui/

BrainVoyager Brain Tutor

The free BrainVoyager Brain Tutor teaches you knowledge about the human brain through interactive exploration of rotatable 3D models. The models have been computed with BrainVoyager using original data from magnetic resonance imaging (MRI) scans. Besides having fun with the rotatable 3D models, the program contains information about the major lobes, gyri, sulci and Brodmann areas of the cerebral cortex.

BrainVoyager Viewer

BrainVoyager Brain Viewer allows to browse and inspect essential BrainVoyager data files. In addition, the program supports viewing the header and content of DICOM files. The Viewer also supports standard image files (JPEG, GIF, PNG, TIFF, BMP) allowing to inspect snapshots, figures or photos. The program offers an elegant user interface with fluid navigation abilities inspired by Apple's "Cover Flow" and the iPhone interface. The program can be downloaded and distributed freely.

N3 - MINC B0 nonuniformity correction

nu_correct implements a novel approach to correcting for intensity non-uniformity in MR data that achieves high performance without requiring supervision. By making relatively few assumptions about the data, the method can be applied at an early stage in an automated data analysis, before a tissue intensity or geometric model is available. Described as Non-parametric Non-uniform intensity Normalization (N3), the method is independent of pulse sequence and insensitive to pathological data that might otherwise violate model assumptions. To eliminate the dependence of the field estimate on anatomy, an iterative approach is employed to estimate both the multiplicative bias field and the distribution of the true tissue intensities. Preprocessing of MR data using N3 has been shown to substantially improve the accuracy of anatomical analysis techniques such as tissue classification and cortical surface extraction.

Level-set Segmentation for Slicer3

The modules in the framework support different tasks in the segmentation realization in 3DSlicer.

A module called Level-set label map evolver was developed, which takes an initial label image and a feature image as input and performs a Geodesic Active Contours evolution on the label image according to the feature image and to a different terms in the level-set equation. The evolution takes place for a customizable number of iterations.

The output is a label image that can be used to produce a model.

Other modules were developed to accompany the main module as can be seen in http://www.slicer.org/slicerWiki/index.php/Slicer3:Module:Level-Set_Segmentation_Framework-Documentation

MNE - Minimum Norm Current Estimates

The MNE software has been developed to compute cortically-constrained L2 minimum-norm current estimates and associated dynamic statistical parametric maps from MEG and EEG data, optionally constrained by fMRI. This software includes MEG and EEG preprocessing tools, interactive and batch-mode modules for the forward and inverse calculations, as well as various data conditioning and data conversion utilities.

This is a subproject of the Center for Functional Neuroimaging Techniques (http://www.nitrc.org/projects/cfnt/)

JHU Proj. in Applied Medical Imaging

This project is used for students enrolled in courses using the JIST framework. Content in this CVS is freely available, but it is not intended for any specific purpose.

Optseq - fMRI Event Scheduler

Optseq is a tool for automatically scheduling events for rapid-presentation event-related (RPER) fMRI experiments (the schedule is the order and timing of events). Events in RPER are presented closely enough in time that their hemodynamic responses will overlap. This requires that the onset times of the events be jittered in order to remove the overlap from the estimate of the hemodynamic response. RPER is highly resistant to habituation, expectation, and set because the subject does not know when the next stimulus will appear or which stimulus type it will be. RPER is also more efficient than fixed-interval event related (FIER) because more stimuli can be presented within a given scanning interval at the cost of assuming that the overlap in the hemodynamic responses will be linear. In SPM parlance, RPER is referred to as 'stochastic design'.

This is a subproject of the Center for Functional Neuroimaging Techniques (http://www.nitrc.org/projects/cfnt/)

Center for Functional Neuroimaging Tech

The Center for Functional Neuroimaging Technologies (CFNT) is a Regional Resource located at the Athinoula A. Martinos Center for Biomedical Imaging at the Massachusetts General Hospital. The primary mission of the Center is to expand understanding of the human brain in health and disease through the development and dissemination of innovative multimodal Magnetic Resonance (MR)-based neuroimaging techniques and technologies. The Resource is sponsored by the National Center for Research Resources of the NIH.

Neuroinformatics - The Journal

Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. Coverage extends to theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; descriptions of developed databases and software tools, and of the methods for their distribution; relevant experimental results, such as reports accompanied by the release of massive data sets; computational simulations of models integrating and organizing complex data; and neuroengineering approaches, including hardware, robotics, and information theory studies. Neuroinformatics also publishes independent "tests and evaluations" of available neuroscience databases and software tools, and fosters a commitment to the principles of tool and data sharing.

PyMVPA

PyMVPA is a Python package intended to ease statistical learning analyses of large datasets. It offers an extensible framework with a high-level interface to a broad range of algorithms for classification, regression, feature selection, data import and export. While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is truly free software (in every respect) and additionally requires nothing but free-software to run.

RapidArt

RapidArt provides software for detecting artifacts and performing individual region-of-interest (ROI) based statistical analysis of fMRI data and enables users of fMRI technology to produce more detailed, consistent and reliable results.

NeuroWeb - NeuroImaging Database

NeuroWeb provides infrastructure for data aggregation, processing, and management in multi-dimensional medical imaging research (i.e., MRI, CT, PET). NeuroWeb is designed for rapid deployment on a small/moderate scale with limited hardware.

Maps4Mipav (Exploratory JIST)

Java Image Science Toolkit (JIST) is an extension to the MIPAV (Medical Image Processing, Analysis, and Visualization) plug-in framework that allows the user to design and execute pipelines, which are multi-stage processing tasks.

JIST was formerly known as the MedIC Automated Pipeline Scheduler (MAPS).

This is the exploratory development tree. New features and designs are tested here before general release into the JIST project.

Access to the source code is freely given. However, registration is required so that we can notify users of critical changes. For unrestricted access to the stable release, see the JIST project. http://www.nitrc.org/projects/jist/

MedINRIA

MedINRIA allows to process and analyze a wide range of magnetic resonance (MR) images including anatomical MRI, functional MRI (fMRI), and diffusion tensor MRI (DT-MRI). MedINRIA is intended to be used by anyone curious about medical images!

DW-MRI Random Walk Simulator

The DW-MRI Random Walk Simulator provides a simple interface to simulate Brownian motion in arbitrary, complex environments. The analysis routines enable visualization of these models with DTI, q-space, and higher order diffusion weighted MRI..

Jim

Jim is a medical image display package that allows easy viewing and analysis of Magnetic Resonance, x-ray CT and other types of medical image. Jim is an up-to-the-minute design with a familiar user-interface.

ODIN

ODIN is a C++ software framework to develop, simulate and run magnetic resonance sequences on different platforms.

MRIcron

MRIcron is a cross-platform NIfTI format image viewer. It can load multiple layers of images, generate volume renderings and draw volumes of interest. It also provides dcm2nii for converting DICOM images to NIfTI format and NPM for statistics. MRIcron is a mature and useful tool, however you may want to consider the more recent MRIcroGL as an alternative.

CoCoMac-Paxinos3D viewer

CoCoMac-Paxinos3D viewer represents an interactive interface of macaque stereotaxic atlas with a connectivity database, allowing integrated data analysis and mapping between 3D structures and database vocabularies.

BrainMaps.org

BrainMaps.org is an interactive multiresolution next-generation brain atlas that is based on over 20 million megapixels of sub-micron resolution, annotated, scanned images of serial sections of both primate and non-primate brains.

CoCoMac Brain Connectivity Database

CoCoMac.org gives online access (html or xml) to structural connectivity ('wiring') data on the Macaque brain. The database has become by far the largest of its kind, with data extracted from more than four hundred published tracing studies.

Conferences, Workshops and Meetings

This project assists the community in the support of information about upcoming Conferences, Workshops and Meetings. Such support may be documents, news, files, etc.

To see a listing of upcoming Events, please use the NITRC Community Events Page at http://www.nitrc.org/incf/event_list.php.

All users are encouraged to check this site for upcoming meetings, and promote future meetings here.

Insight Toolkit

ITK is an open-source, cross-platform system that provides developers with an extensive suite of software tools for image analysis. The Insight Toolkit is based on generic programming, with a data pipeline architecture, supports for multi-threads, and it is tested nightly on multiple platforms including Linux, Windows, and Mac.

PubBrain Database

PubBrain is a literature search and visualization tool that allows end users to enter any PubMed query and see that query rendered as a heatmap illustrating which regions of interest are most commonly mentioned within the search results.

JIST Resources for Algorithm Development

This repository stores plugins, tutorial code, and examples demonstrating MRI manipulation within the MIPAV plugin environment.

This project is separate from JIST so that we can provide WRITE access to any interested party without overly exposing the infrastructure to unplanned modification. Please contact the administrators if you would like to join this project - open use is encouraged.

SumsDB Database

SumsDB is a repository of neuroimaging data (surfaces & volumes; structural & functional data), mainly from humans and macaques but also mouse, rat, and great apes. WebCaret is an online visualization tool for viewing SumsDB datasets. Visit the Tool/Resource Home Page for more information.

map3d

Map3d is a scientific visualization application developed at the CVRTI to display and edit complex, three-dimensional geometric models and the scalar data associated with those models. map3d was originally written in ANSI-C using the Graphics Library (GL) from Silicon Graphics Inc. The map3d interface provides an interactive display of both geometry and data assigned to elements of that geometry. The program can read multiple surfaces, each with multiple associated potential/current data files.

Seg3D

Seg3D is a free volume processing sgementing tool that combines a flexible manual interface with powerful image processing and segmentation algorithms. Users can explore and label image volumes using slice windows and 3D volume rendering.

cmrep

The continuous medial representation (cm-rep) is a set of deformable modeling algorithms for shape analysis and structure-specific normalization. Applications of cm-reps include structure-specific fMRI analysis, DTI analysis, and structural brain mor

DICOM_UploadGUI

UploadGUI is a Java tool that takes an unorganized collection of DICOM scans, sorts and categorizes them according to user-customizable rules, gathers metadata about the scans, and saves out this information to help facilitate data uploads. Batch pr

Numerical Fibre Generator (NFG)

NFG is a collection of tools that generate numerical fiber structures with the complexity of human white matter and simulate Diffusion-Weighted MR images that would arise from them. Its primary use is to enable the testing of tracking algorithms

VectorValuedHistogramNormalizer

Implement the methods defined in http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2006/Jaeger06-ANM.pdf as and ITK filter.

Advanced Normalization Tools

Advanced Normalization Tools (ANTS) : Image registration with variable transformation models (elastic, diffeomorphic, unbiased) and similarity metrics (landmarks, cross-correlation, mutual information, optical flow). Designed for neuroscience and medical imaging researchers and users. Special capabilities include symmetric diffeomorphic normalization, optimal template creation and user-guided normalization.

libminc - Medical Image NetCDF library

MINC is primarily a file format and a 3 level API for medical image analysis with a particular focus on the needs of research.

MRtrix

NOTE: this is the legacy version of MRtrix, and is no longer supported or maintained. To use the latest official version of MRtrix, please visit https://www.mrtrix.org

MRtrix provides a set of tools to perform diffusion-weighted MR white-matter tractography in a manner robust to crossing fibres, using constrained spherical deconvolution (CSD) and probabilistic streamlines.

BrainVisa Morphologist extensions

BrainVisa Morphologist is collaborative BrainVisa extension library that provides computational tools for performing Object Based Morphology measurements to assess groupwise differences and track morphological changes to study aging, development, neurological and neurodegenerative disorders and genetic factors that affect them. This library includes source codes and methods for computation of regional GM thickness (multi-threaded and single thread), 3D gyrification index, sulcal lenght and depth and sulcal span and gyral white matter span.
These tools are distributed as plugins for a popular analysis package BrainVisa

ITools Resourceome

iTools is an infrastructure for managing of diverse computational biology resources - data, software tools and web-services. The iTools design, implementation and meta-data content reflect the broad NCBC needs and expertise (www.NCBCs.org).

iTools is distributed by the Laboratory of Neuro Imaging (http://www.loni.usc.edu/Software/iTools) at USC.

fMRIstat

A Matlab toolbox for the statistical analysis of fMRI data.

SurfStat

SurfStat is a Matlab toolbox for the statistical analysis of univariate and multivariate surface data using linear mixed effects models and random field theory.

FBIRN Image Processing Scripts (FIPS)

FBIRN Imaging Processing Scripts (FIPS) is a FSL package for the comprehensive management of large-scale multi-site fMRI projects, including data storage, retrieval, calibration, analysis, multi-modal integration, and quality control.

XCEDE Schema

The XML-Based Clinical Experiment Data Exchange Schema provides an extensive metadata hierarchy for describing and documenting research and clinical studies. XCEDE was originally designed in the context of neuroimaging studies and complements the BIRN-HID

XCEDE Schema

XML-Based Clinical Experiment Data Exchange Schema (XCEDE)provides an extensive metadata hierarchy for describing and documenting research and clinical studies.

Diffusion Toolkit / TrackVis

Diffusion Toolkit (with TrackVis) is a cross-platform software package that does reconstruction, fiber tracking, visualization and analysis on various diffusion imaging data. Features of the software include:

• Handles DTI, DSI, Q-Ball and HARDI imaging techniques.
• Works on Windows, Mac OS X and Linux with native look and feel. Native 64-bit support on Mac OS X and Linux.
• Fast streamlined data processing.
• Intuitive GUI front-end with command-line driven back-end. Thus, allows advanced user to write their own scripts for automated multiple dataset processing.
• Real-time fiber track visualization and analysis. Parameter adjustment applied to 3D render on the fly. Various tracking selection methods (filters) allows locating specific track bundle with ease.

NUTMEG

NUTMEG (Neurodynamic Utility Toolbox for Magnetoencephalography) is an open-source MATLAB toolbox for reconstructing the spatiotemporal dynamics of neural activations and overlaying them onto structural MR images.

UPDATE 10/01/2023 -- NUTMEG 4.8 is now available, with updated compatibility with newer versions of MATLAB (2022)

ITK-SNAP

ITK-SNAP is an open-source software application for medical image segmentation. Its primary use is for delineating anatomical structures and regions in MRI, CT and other 3D biomedical imaging data. ITK-SNAP is actively developed by Paul Yushkevich, Hui Zhang, and colleagues at the Penn Image Computing and Science Laboratory at the University of Pennsylvania.

Penn Hippocampus Atlas

The Penn Hippocampus Atlas is a resource consisting of segmented and normalized high-resolution ex vivo MRI and histology of the human hippocampus. The current atlas, consisting of MRI scans of 31 specimens and serial histology of 9 specimens is described in the 2018 paper by Adler, Wisse, et al. in PNAS. Imaging data, source code, and statistics for the 2018 paper can be accessed using the Download link on this page.

A prior version of the atlas consisting of MRI scans of 5 specimens was described in Yushkevich et al., Neuroimage 44(2):385-398, 2009. Data for this atlas are also available on the Download page.

BASH4RfMRI

BASH Scripts for a resting-state functional MRI study. The functions include seed-based correlation analysis, amplitude analysis and independent component analysis. Note: this tool is just a plug-in for FSL, AFNI and FreeSurfer. Thus, you need have them before you use BASH4RfMRI. Of note: most functions have been integrated in 1000 Functional Connectomes Project (www.nitrc.org/projects/fcon_1000). I do not update this package, thus please visit 1000 Functional Connectomes Project site and download relevant bash scripts.

Brainscape

Brainscape is a database for resting state functional connectivity studies. Functional connectivity has shown tremendous promise in mapping the intrinsic functional topography of the brain, evaluating neuroanatomical models, and investigating neurological and psychiatric disease. Brainscape includes a repository of public and private data and an analysis engine for exploring the correlation structure of spontaneous fluctuations in the fMRI BOLD signal.

Fusion ICA Toolbox (FIT)

FIT is a MATLAB toolbox which uses Independent Component Analysis (ICA) to extract the shared information across modalities like fMRI, EEG, sMRI and SNP data.

Artifact Detection Tools (ART)

Toolbox for post-processing fMRI data. Includes software for comprehensive analysis of sources of artifacts in timeseries data including spiking and motion. Most compatible with SPM processing, but adaptable for FSL as well.

Biomedical Informatics Research Network

BIRN is a geographically distributed virtual community of shared resources to advance the diagnosis and treatment of disease. It's emphasis is on enabling the sharing of biomedical data through a suite of software capabilities. It currently consists of expertise in Data Movement, Data Security, Mediation Across Data Sources, Knowledge Engineering, Workflows, and Genetics. The main BIRN site is at the Information Sciences Institute at USC with collborators at the University of Chicago, the Massachusetts General Hospital, University of California, Irvine and University of California, Los Angeles.

Diffusion Warp

This project contains tools appropriate for the registration of diffusion tensor images to an average coordinate system. The tools include image registration methods and algorithms for the correct alignment of the diffusion tensor when applying the resulting transformation. The program uses the Slicer3 execution model framework to define the command line arguments, and can be fully integrated using the module discovery capabilities of Slicer3.

B0 and eddy current correction for DTI

Software tool (excecutable and source code in C and C++) to correct distortions in diffusion MR images that are generated by main magnetic field inhomogeneities and eddy current induced fields generated from the direction-dependent diffusion encoding

Mango

Mango is a viewer for medical research images. It provides analysis tools and a user interface to navigate image volumes. There are three versions of Mango, each geared for a different platform:

* Mango – Desktop – Mac OS X, Windows, and Linux
* webMango – Browser – Safari, Firefox, Chrome, and Internet Explorer
* iMango – Mobile – Apple iPad

Key Features:
- Built-in support for DICOM, NIFTI, Analyze, and NEMA-DES formats
- Customizable: Create plugins, custom filters, color tables, file formats, and atlases
- ROI Editing: Threshold and component-based tools for painting and tracing ROIs
- Surface Rendering: Interactive surface models supporting cut planes and overlays
- Image Registration: Semi-automatic image coregistration and manual transform editing
- Image Stacking: Threshold and transparency-based image overlay stacking
- Analysis: Histogram, cross-section, time-series analysis, image and ROI statistics
- Processing: Kernel and rank filtering, arithmetic/logic image and ROI calculators

Lumina LP- 400 Response System

The Lumina LP-400 is a reliable patient response system designed specifically for use in an fMRI. Lumina was developed to satisfy the requirements of both the clinical and research fields.

NIH Pediatric MRI Data Repository

The NIH Pediatric MRI Data Repository contains longitudinal structural MRIs, spectroscopy, DTI and correlated clinical/behavioral data from approximately 500 healthy, normally developing children, ages newborn to young adult.

BrainCSI

BrainCSI is a tool for analysis of Magnetic Resonance Spectroscopy (MRS) data by registering it to anatomical images. BrainCSI imports LCModel results to calculate absolute metabolite concentrations using tissue water. Corrections to LCModel metabolite concentrations for partial volume of tissues are accomplished by tissue classification of the anatomical images.

MRI Studio

MRI Studio is an image processing program running under Windows. It is suitable for such tasks as tensor calculation, color mapping, fiber tracking, and 3D visualization. Most of operations can be done with only a few clicks. This tool evolved from DTI Studio. (May also be called mristudio or dtistudio)

Tools in the program can be grouped in the following way:

* Image Viewer
* Diffusion Tensor Calculations
* Fiber Tracking and Editing
* 3D Visualization
* Image File Management
* Region of Interesting (ROI) Drawing and Statistics
* Image Registration

Gradient Non-linearity Unwarping Tool

The Brain Morphometry testbed of the BIRN provides a tool to correct gradient non-linearity distortions in MR structural images. This correction improves test-retest reproducibility crucial for multi-site studies.

IDeA Lab brain image processing suite

Suite of tools for brain image analysis. Image manipulation, 2D visualization, linear alignment, BBSI, template-based bias correction, skullstrip. GUI Image analysis tools. Now modified to read/write single file nifti (.nii) format. Other packages to be added.

Functional Connectivity Community

The purpose of this project is to provide a community for the discussion of functional connectivity and all related topics. This includes discussion of related tools, data sets, methodological discussion, related websites and publications, etc.

Automatic Registration Toolbox

ATRA software for fully automated, fast, reliable, unbiased and inverse-consistent registration of within-subject multiple T1-weighted structural MRI brain volumes.

ART `acpcdetect' program for automatic detection of the AC and PC landmarks and the mid-sagittal plane on 3D structural MRI scans.

ART `brainwash' program for automatic multi-atlas skull-stripping of 3D structural MRI scans.

ART `3dwarper' program of non-linear inter-subject registration of 3D structural MRI scans.

Software (art2) for linear rigid-body intra-subject inter-modality (MRI-PET) image registration.

Data resource: The ART projects makes available corpus callosum segmentations of 316 normal subjects from the OASIS cross-sectional database

ART `yuki' program for fast, robust, and fully automatic segmentation of the corpus callosum on 3D structural MRI scans.

ART 'kaiba' program: estimates the hippocampal parenchymal fraction (HPF) from 3D T1W MRI.

LDDMM

The Large Deformation Diffeomorphic Metric Mapping (LDDMM) tool is an application which aims to assign metric distances on the space of anatomical images in Computational Anatomy thereby allowing for the direct comparison and quantization of morphometric changes in shapes. As part of these efforts the Center for Imaging Science at Johns Hopkins University developed techniques to not only compare images, but also to visualize the changes and differences.

For additional information please refer to:

Faisal Beg, Michael Miller, Alain Trouve, and Laurent Younes. Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms. International Journal of Computer Vision, Volume 61, Issue 2; February 2005.

M.I. Miller and A. Trouve and L. Younes, On the Metrics and Euler-Lagrange Equations of Computational Anatomy, Annual Review of biomedical Engineering, 4:375-405, 2002.

Software developed with support from National Institutes of Health NCRR grant P41 RR15241.

MIView

MIview is an OpenGL based medical image viewer that contains useful tools such as a DICOM anonymizer and format conversion utility. MIView can read DICOM, Analyze/Nifti, and raster images, and can write Analyze/Nifti and raster images.

BrainGraph Editor 1.0 Beta

The BrainGraph Editor 1.0 Beta is a JAVA application designed to create taxonomies or hierarchies in order to classify and organize information.

MouseBIRN Atlasing Toolkit (MBAT)

MBAT provides a workflow environment bringing together heterogenous, online biological image resources, a user’s image data and biological atlases in a concise, unified and intuitive workspace. The MBAT viewer displays multiple images on a single virtual canvas allowing easy side-by-side comparisons and image compositing. MBAT is written in Java so it is platform independent and is highly extensible through it’s plugin architecture.

MBAT integrates three distinct workspaces for online search, image alignment (registration) and image display:

Search Workspace: able to submit a query to multiple databases simultaneously and online literature searches.

Registration Workspace: performs 2D landmark based registration.

Viewer Workspace: displays & composites images and image volumes using high performance graphics hardware.

Atlas Viewer: allows navigation and interrogation of volumetric atlases.

Hierarchy Editor: create logical groupings of atlas labels.

Task Independent Fluctuations Discussion

The methodology and applications of task independent fluctuation measures including: connectivity maps of fMRI resting state scans, research using EEG/MEG/PET etc, methods to remove non-neural fluctuations, and applications to clinical populations.

Slicer3 Example Modules

Example Slicer3 plugins that can be built against a Slicer3 build or a Slicer3 installation.

Note: these are for 3D Slicer version 3. There is now a version 4 of 3D Slicer available. Information about extensions for version 4 can be found at the following links:

http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/SlicerApplication/ExtensionsManager

http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Developers/Tutorials/BuildTestPackageDistributeExtensions

MPScope

MPScope is a comprehensive software suite for custom-built multiphoton microscopes available as freeware for the Wintel platform. The MPScope package features the acquisition software MPScan, the analysis program MPView and several software utilities

Human Imaging Database (HID)

The Human Imaging Database (HID) is an extensible database management system developed to handle the increasingly large and diverse datasets collected as part of the MBIRN and FBIRN collaboratories and throughout clinical imaging communities at large.

Grantees Meeting for NITRC

This project is meant for planning the NITRC Grantee meetings. The meetings introduce NITRC participants to one another, promote discussion of common interests, and identify opportunities for collaboration and interoperability.

Plans for the 2009 Grantee Meeting are underway. Click on the MediaWiki link (in gray menu on left) to learn more.

NPTK

NPTK provides non-rigid registration/distortion correction tools for enhanced functional localization through the registration of EPI fMRI to high-resolution anatomical MRI.

GTRACT

NOTE: All new development is being managed in a github repository. Please visit

https://github.com/BRAINSia/BRAINSTools

GTRACT is a Diffusion Tensor fiber tracking suite that includes streamline tracking tools. The fiber tracking includes a guided tracking tool that integrates apriori information into a streamlines algorithm. This suite of programs is built using the NA-MIC toolkit and uses the Slicer3 execution model framework to define the command line arguments. These tools can be fully integrated with Slicer3 using the module discovery capabilities of Slicer3.

GIFTI

Geometry format under the Neuroimaging Informatics Technology Initiative (NIfTI).

BRAINSTools

DO NOT USE THIS PAGE -- GOTO --> https://github.com/BRAINSia/BRAINSTools
DO NOT USE THIS PAGE
DO NOT USE THIS PAGE

NOTE: All active development of BRAINS is now being stored in a Git repository at github.com. Please go to

https://github.com/BRAINSia/BRAINSTools

The BRAINS (Brain Research: Analysis of Images, Networks, and Systems) image analysis software has been developed to study the brain thus providing a better understand of psychiatric and neurological disorders.

VoxBo

VoxBo is a highly modular and interoperable collection of free tools for brain image manipulation and analysis, focusing on fMRI and lesion analysis. VoxBo can be used independently or in conjunction with other packages.

Stochastic Tractography System

Stochastic Tractography applies a Bayesian approach towards the estimation of nerve fiber tracts from DWMRI images. This project provides tools which can perform Stochastic Tractography and related analysis on DWMRI data.

MIPAV

The MIPAV (Medical Image Processing, Analysis, and Visualization) application enables quantitative analysis and visualization of medical images of numerous modalities such as PET, MRI, CT, or microscopy.

Slicer3 Module Rician noise filter

Two Slicer3 modules removing rician noise in diffusion tensor MRI

NeuroElf

NeuroElf is a Matlab-based toolbox initially created for reading, writing, and processing of BrainVoyager (QX) files in Matlab. Some additional routines are available, such as the scripting of preprocessing using SPM or second-level statistics. The toolbox is freely available.

HAMMER: Deformable Registration

The HAMMER package performs high-dimensional warping of brain images. Standard voxel-based analysis can be applied to these tissue density maps, in order to examine regional volumetrics, effects of disease, or correlations with clinical measurements.

FSL extensions

FSL extensions is a reference for modifications, extensions, and utilities for the FMRIB Software Library (FSL).

NVM

NVM is an open-source software tool for locating and measuring neuroanatomy in structural volumetric image data. It is used to draw regions of interest for subsequent fMRI analysis.

Functional Imaging BIRN

The FBIRN Federated Informatics Research Environment (FIRE) includes tools and methods for multi-site functional neuroimaging. This includes resources for data collection, storage, sharing and management, tracking, and analysis of large fMRI datasets.

NeuroLens

NeuroLens is an integrated environment for the analysis and visualization of functional neuroimages.

FreeSurfer

FreeSurfer is a set of automated tools for reconstruction of the brain’s cortical surface from structural MRI data, and overlay of functional MRI data onto the reconstructed surface.

XNAT

XNAT is an open source imaging informatics platform designed to facilitate management and exploration of medical imaging and related data. XNAT includes a DICOM workflow, a secure database backend, and a rich web-based user interface. XNAT's web services interface enables external applications to easily access XNAT-hosted data.

Morphometry BIRN

The Morphometry Testbed (MBIRN) of the Biomedical Informatics Research Network (BIRN) focuses on pooling and analyzing of neuroimaging data acquired at multiple sites. Specific applications include potential relationships between anatomical differences and specific memory dysfunctions, such as Alzheimer’s disease. With the completion of the initial BIRN testbed phase, each of the original BIRN testbeds have now been retired in order to focus on new users in other biomedical domains. The BIRN project has ended. Information on the entire BIRN project can be found at https://en.wikipedia.org/wiki/Biomedical_Informatics_Research_Network

Scribe

Scribe encodes papers to populate the BrainMap Database

REX

REX is a stand-alone MATLAB-based toolkit for the rapid and flexible exploration of ROI response waveforms and other signals from across large fMRI datasets. An alpha-release is currently available for use with an example dataset and tutorial.

Group ICA Toolbox (GIFT and EEGIFT)

Group ICA Toolbox is a MATLAB toolbox which implements multiple algorithms for independent component analysis of group magneto resonance imaging data (GIFT) and electro encephalogram data (EEGIFT) to group studies.

Talairach Daemon

The Talairach Daemon database contains anatomical names for brain areas using x-y-z coordinates defined by the 1988 Talairach atlas.

BrainMap Database

BrainMap is a community database of published functional neuroimaging studies (mainly PET and fMRI). BrainMap contains both metadata descriptions of experimental design and activation locations in the form of stereotactic coordinates.

BRAINSFit

NOTE: All active development of BRAINSFit is now being stored in a Git repository at github.com. Please go to

https://github.com/BRAINSia/BRAINSTools

A program for registering images with with mutual information based metric. Several registration options are given for 3,6, 9,12,16 parameter (i.e. translate, rigid, scale, scale/skew, full affine) based constraints for the registration. The program uses the Slicer3 execution model framework to define the command line arguments and can be fully integrated with Slicer3 using the module discovery capabilities of Slicer3

BioImage Suite

BioImage Suite is a web-based medical image analysis software package with image processing , image registration and visualization capabiliies.

See https://bioimagesuiteweb.github.io/webapp/

3D Slicer

An extensible, cross-platform, totally open research platform for image computing.

IATR

IATR is the Image Analysis Tools Registry. The goal for this site is to provide a centrally available listing of all image analysis tools that are available to the neuroscience community.

IBSR

The Internet Brain Segmentation Repository (IBSR) provides manually-guided expert segmentation results along with magnetic resonance brain image data. Its purpose is to encourage the evaluation and development of segmentation methods. Please see the MediaWiki for more information.

WFU_BPM

The WFU Biological Parametric Mapping (WFU_BPM) toolbox performs SPM analysis with voxel-wise imaging covariates.

WFU_PickAtlas

WFU_PickAtlas provides a method for generating ROI masks based on the Talairach Daemon database. The atlases include Brodmann area, Lobar, Hemisphere, Anatomic Label and Tissue Type.

LONI Inspector

The LONI Inspector is an application for examining medical image files. The Inspector focuses on reading, displaying, searching, comparing, and exporting "metadata" (e.g., patient name, the model of scanner).

LONI De-identification Debablet

The LONI de-identification Debablet is an application for removing patient-identifying information from medical image files. Removing this information is often necessary for enabling investigators to share image files in a HIPAA compliant manner.

LONI Debabeler

The LONI Debabeler manages the conversion of imaging data from one file format and convention to another. It consists of a GUI to visually program the translations, and a data translation engine to read, sort and translate the input files.

BrainFX

A developer tool to provide batch processing capability for pipelines. Users input data into a input table and run analysis with it. It is used to power CamBA's and Brainwaver's User interface.

Brainwaver

Characterisation of small-world networks constructed from wavelet analysis of resting fMRI. This package is currently available as an R library. Futrure development will take place within the CamBA software repository

CamBA

Software respository containing pipelines of chained modules and GUI for batch processing. Current pipelines include fMRI analysis, statistical testing based on randomisation methods and fractal spectral analysis. Pipelines are continually being added.

MEG Tools

MEG Tools© is a software program for source imaging Magnetoencephalographic data. Now MEG tools has added Coherence Source Imaging (CSI), Talairach and MNI coordinates, Grainger Causality. MEG Tools also includes MR-FOCUSS, ECD, Beamformers and many other useful MEG tools. You may download the MEG tools Matlab implementation files.

This is a Matlab-based software module that is used to image MEG data onto a patient’s MRI. This software imports all MEG manufacture’s data (4D-Neuroimaging/BTi, CTF and Neuromag/Elekta).

SnPM

The Statistical nonParametric Mapping toolbox provides an extensible framework for voxel level non-parametric permutation/randomisation tests of functional Neuroimaging experiments with independent observations.

Caret

Caret is free, open source software used to visualize and analyze the structural and functional characteristics of cerebral and cerebellar cortex in humans, nonhuman primates, and rodents. Visit the Tool/Resource Home Page for more information.

Mindboggle: open source software for analyzing the shapes of brain structures from human MRI data

Mindboggle (http://mindboggle.info and https://osf.io/ydyxu/) is open source software for analyzing the shapes of brain structures from human MRI data. The primary reference documenting and evaluating Mindboggle was published in PLoS Computational Biology:

Klein A, Ghosh SS, Bao FS, Giard J, Hame Y, Stavsky E, Lee N, Rossa B, Reuter M, Neto EC, Keshavan A. (2017) Mindboggling morphometry of human brains. PLoS Computational Biology 13(3): e1005350. doi:10.1371/journal.pcbi.1005350

LONI Pipeline Environment

The LONI Pipeline is a free workflow application primarily aimed at neuroimaging researchers, but is useful for many other fields of science. The Pipeline Client runs on your PC/Mac/Linux computer upon which you can create sophisticated processing workflows using a variety of commonly available executable tools (e.g. FSL, AIR, FreeSurfer, AFNI, Diffusion Toolkit, etc). The Distributed Pipeline Server can be installed on your Linux cluster and you can submit processing jobs directly to your own compute systems. Visit http://pipeline.loni.usc.edu for more info.

The Pipeline is distributed by the Laboratory of Neuro Imaging (http://www.loni.usc.edu/Software/Pipeline).

FIV

The Functional Image Viewer, also known as FIV, is a tool for visualizing functional and anatomic MRI data.

NIfTI

The Neuroimaging Informatics Technology Initiative (NIfTI) and NIfTI Data Format Working Group, creator and maintainer of the NIfTI-1 and NIfTI-2 data formats.

FSL

FSL is a comprehensive library of image analysis and statistical tools for FMRI, MRI and DTI brain imaging data. FSL is written mainly by members of the Analysis Group, FMRIB, Oxford, UK.

SPM

The SPM software package has been designed for the analysis of brain imaging data sequences. The sequences can be a series of images from different cohorts, or time-series from the same subject. The current release is designed for the analysis of fMRI, PET, SPECT, EEG and MEG.

AFNI

AFNI is a set of C programs for processing, analyzing, and displaying FMRI data. It runs on Unix+X11+Motif systems, including SGI, Solaris, Linux, and Mac OS X. It is available free for research purposes.

Resource Ontology Discussion Group

This project will discuss, debate, develop and deploy ontological practices for the fMRI community.

SVV

This project is a simple Surface and Volumetric Visualization application that has been designed to facilitate rapid and flexible visualization from neuroanatomically segmented results.

NITRC Community

This resource provides NITRC-wide facilities: Forums, Wiki, Tracker, and News.