Featured tool/resource
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
Featured tool/resource
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.
Featured tool/resource
BrainEffeX
Effect size estimation is crucial for power analysis and experiment deisgn, but poses unique challenges in fMRI research due to the complexity of the data and analysis techniques. Here, we utalized large fMRI datasets to obtain precise univariate and multivariate effect size estimates from "typical" fMRI study designs: brain-behavior correlation, task vs. rest, and between-group analyses of functional connecitivity and task-based activation maps. This is an interactive web application for exploring these effect maps.
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