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  <copyright>Copyright 1999-2000 VA Linux Systems, Inc.</copyright>
  <title>Neuroinformatics - The Journal News</title>
  <link>http://www.nitrc.org</link>
  <description>Neuroinformatics - The Journal Latest News</description>
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 <rdf:li rdf:resource="http://www.nitrc.org/forum/forum.php?forum_id=8973" />
 <rdf:li rdf:resource="http://www.nitrc.org/forum/forum.php?forum_id=8915" />
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 <rdf:li rdf:resource="http://www.nitrc.org/forum/forum.php?forum_id=8890" />
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 <item rdf:about="http://www.nitrc.org/forum/forum.php?forum_id=8973">
   <title>Automated Neuron Detection in High-Content Fluorescence Microscopy Images Using Machine Learning</title>
   <link>http://www.nitrc.org/forum/forum.php?forum_id=8973</link>
   <description>&lt;br /&gt;
                  &lt;h3 class=&quot;a-plus-plus&quot;&gt;Abstract&lt;/h3&gt;&lt;br /&gt;
                  &lt;p class=&quot;a-plus-plus&quot;&gt;The study of neuronal morphology in relation to function, and the development of effective medicines to positively impact this relationship in patients suffering from neurodegenerative diseases, increasingly involves image-based high-content screening and analysis. The first critical step toward fully automated high-content image analyses in such studies is to detect all neuronal cells and distinguish them from possible non-neuronal cells or artifacts in the images. Here we investigate the performance of well-established machine learning techniques for this purpose. These include support vector machines, random forests, k-nearest neighbors, and generalized linear model classifiers, operating on an extensive set of image features extracted using the compound hierarchy of algorithms representing morphology, and the scale-invariant feature transform. We present experiments on a dataset of rat hippocampal neurons from our own studies to find the most suitable classifier(s) and subset(s) of features in the common practical setting where there is very limited annotated data for training. The results indicate that a random forests classifier using the right feature subset ranks best for the considered task, although its performance is not statistically significantly better than some support vector machine based classification models.&lt;/p&gt;&lt;br /&gt;
                </description>
   <dc:subject>Neuroinformatics - The Journal</dc:subject>
   <dc:creator>NITRC ADMIN</dc:creator>
  <dc:date>Thu, 13 Sep 2018 7:00:00 GMT</dc:date>
  </item>

 <item rdf:about="http://www.nitrc.org/forum/forum.php?forum_id=8915">
   <title>Characterizing Regularization Techniques for Spatial Filter Optimization in Oscillatory EEG Regression Problems</title>
   <link>http://www.nitrc.org/forum/forum.php?forum_id=8915</link>
   <description>&lt;br /&gt;
                  &lt;h3 class=&quot;a-plus-plus&quot;&gt;Abstract&lt;/h3&gt;&lt;br /&gt;
                  &lt;p class=&quot;a-plus-plus&quot;&gt;We report on novel supervised algorithms for single-trial brain state decoding. Their reliability and robustness are essential to efficiently perform neurotechnological applications in closed-loop. When brain activity is assessed by multichannel recordings, spatial filters computed by the source power comodulation (SPoC) algorithm allow identifying oscillatory subspaces. They regress to a known continuous trial-wise variable reflecting, e.g. stimulus characteristics, cognitive processing or behavior. In small dataset scenarios, this supervised method tends to overfit to its training data as the involved recordings via electroencephalogram (EEG), magnetoencephalogram or local field potentials generally provide a low signal-to-noise ratio. To improve upon this, we propose and characterize three types of regularization techniques for SPoC: approaches using Tikhonov regularization (which requires model selection via cross-validation), combinations of Tikhonov regularization and covariance matrix normalization as well as strategies exploiting analytical covariance matrix shrinkage. All proposed techniques were evaluated both in a novel simulation framework and on real-world data. Based on the simulation findings, we saw our expectations fulfilled, that SPoC regularization generally reveals the largest benefit for small training sets and under severe label noise conditions. Relevant for practitioners, we derived operating ranges of regularization hyperparameters for cross-validation based approaches and offer open source code. Evaluating all methods additionally on real-world data, we observed an improved regression performance mainly for datasets from subjects with initially poor performance. With this proof-of-concept paper, we provided a generalizable regularization framework for SPoC which may serve as a starting point for implementing advanced techniques in the future.&lt;/p&gt;&lt;br /&gt;
                </description>
   <dc:subject>Neuroinformatics - The Journal</dc:subject>
   <dc:creator>NITRC ADMIN</dc:creator>
  <dc:date>Mon, 20 Aug 2018 7:00:00 GMT</dc:date>
  </item>

 <item rdf:about="http://www.nitrc.org/forum/forum.php?forum_id=8895">
   <title>Intracerebral EEG Artifact Identification Using Convolutional Neural Networks</title>
   <link>http://www.nitrc.org/forum/forum.php?forum_id=8895</link>
   <description>&lt;br /&gt;
                  &lt;h3 class=&quot;a-plus-plus&quot;&gt;Abstract&lt;/h3&gt;&lt;br /&gt;
                  &lt;p class=&quot;a-plus-plus&quot;&gt;Manual and semi-automatic identification of artifacts and unwanted physiological signals in large intracerebral electroencephalographic (iEEG) recordings is time consuming and inaccurate. To date, unsupervised methods to accurately detect iEEG artifacts are not available. This study introduces a novel machine-learning approach for detection of artifacts in iEEG signals in clinically controlled conditions using convolutional neural networks (CNN) and benchmarks the method’s performance against expert annotations. The method was trained and tested on data obtained from St Anne’s University Hospital (Brno, Czech Republic) and validated on data from Mayo Clinic (Rochester, Minnesota, U.S.A). We show that the proposed technique can be used as a generalized model for iEEG artifact detection. Moreover, a transfer learning process might be used for retraining of the generalized version to form a data-specific model. The generalized model can be efficiently retrained for use with different EEG acquisition systems and noise environments. The generalized and specialized model F1 scores on the testing dataset were 0.81 and 0.96, respectively. The CNN model provides faster, more objective, and more reproducible iEEG artifact detection compared to manual approaches.&lt;/p&gt;&lt;br /&gt;
                </description>
   <dc:subject>Neuroinformatics - The Journal</dc:subject>
   <dc:creator>NITRC ADMIN</dc:creator>
  <dc:date>Mon, 13 Aug 2018 7:00:00 GMT</dc:date>
  </item>

 <item rdf:about="http://www.nitrc.org/forum/forum.php?forum_id=8890">
   <title>An Uncertainty Visual Analytics Framework for fMRI Functional Connectivity</title>
   <link>http://www.nitrc.org/forum/forum.php?forum_id=8890</link>
   <description>&lt;br /&gt;
                  &lt;h3 class=&quot;a-plus-plus&quot;&gt;Abstract&lt;/h3&gt;&lt;br /&gt;
                  &lt;p class=&quot;a-plus-plus&quot;&gt;Analysis and interpretation of functional magnetic resonance imaging (fMRI) has been used to characterise many neuronal diseases, such as schizophrenia, bipolar disorder and Alzheimer’s disease. Functional connectivity networks (FCNs) are widely used because they greatly reduce the amount of data that needs to be interpreted and they provide a common network structure that can be directly compared. However, FCNs contain a range of data uncertainties stemming from inherent limitations, e.g. during acquisition, as well as the loss of voxel-level data, and the use of thresholding in data abstraction. Additionally, human uncertainties arise during interpretation due to the complexity in understanding the data. While existing FCN visual analytics tools have begun to mitigate the human ambiguities, reducing the impact of data limitations is an open problem. In this paper, we propose a novel visual analytics framework with three linked, purpose-designed components to evoke deeper interpretation of the fMRI data: (i) an enhanced FCN abstraction; (ii) a temporal signal viewer; and (iii) the anatomical context. Each component has been specifically designed with novel visual cues and interaction to expose the impact of uncertainties on the data. We augment this with two methods designed for comparing subjects, by using a small multiples and a marker approach. We demonstrate the enhancements enabled by our framework on three case studies of common research scenarios, using clinical schizophrenia data, which highlight the value in interpreting fMRI FCN data with an awareness of the uncertainties. Finally, we discuss our framework in the context of fMRI visual analytics and the extensibility of our approach.&lt;/p&gt;&lt;br /&gt;
                </description>
   <dc:subject>Neuroinformatics - The Journal</dc:subject>
   <dc:creator>NITRC ADMIN</dc:creator>
  <dc:date>Sat, 11 Aug 2018 7:00:00 GMT</dc:date>
  </item>

 <item rdf:about="http://www.nitrc.org/forum/forum.php?forum_id=8882">
   <title>Multi-Objective Cognitive Model: a Supervised Approach for Multi-subject fMRI Analysis</title>
   <link>http://www.nitrc.org/forum/forum.php?forum_id=8882</link>
   <description>&lt;br /&gt;
                  &lt;h3 class=&quot;a-plus-plus&quot;&gt;Abstract&lt;/h3&gt;&lt;br /&gt;
                  &lt;p class=&quot;a-plus-plus&quot;&gt;In order to decode human brain, Multivariate Pattern (MVP) classification generates cognitive models by using functional Magnetic Resonance Imaging (fMRI) datasets. As a standard pipeline in the MVP analysis, brain patterns in multi-subject fMRI dataset must be mapped to a shared space and then a classification model is generated by employing the mapped patterns. However, the MVP models may not provide stable performance on a new fMRI dataset because the standard pipeline uses disjoint steps for generating these models. Indeed, each step in the pipeline includes an objective function with independent optimization approach, where the best solution of each step may not be optimum for the next steps. For tackling the mentioned issue, this paper introduces Multi-Objective Cognitive Model (MOCM) that utilizes an integrated objective function for MVP analysis rather than just using those disjoint steps. For solving the integrated problem, we proposed a customized multi-objective optimization approach, where all possible solutions are firstly generated, and then our method ranks and selects the robust solutions as the final results. Empirical studies confirm that the proposed method can generate superior performance in comparison with other techniques.&lt;/p&gt;&lt;br /&gt;
                </description>
   <dc:subject>Neuroinformatics - The Journal</dc:subject>
   <dc:creator>NITRC ADMIN</dc:creator>
  <dc:date>Thu, 09 Aug 2018 7:00:00 GMT</dc:date>
  </item>

 <item rdf:about="http://www.nitrc.org/forum/forum.php?forum_id=8842">
   <title>FMST: an Automatic Neuron Tracing Method Based on Fast Marching and Minimum Spanning Tree</title>
   <link>http://www.nitrc.org/forum/forum.php?forum_id=8842</link>
   <description>&lt;br /&gt;
                  &lt;h3 class=&quot;a-plus-plus&quot;&gt;Abstract&lt;/h3&gt;&lt;br /&gt;
                  &lt;p class=&quot;a-plus-plus&quot;&gt;Neuron reconstruction is an important technique in computational neuroscience. Although there are many reconstruction algorithms, few can generate robust results. In this paper, we propose a reconstruction algorithm called fast marching spanning tree (FMST). FMST is based on a minimum spanning tree method (MST) and improve its performance in two aspects: faster implementation and no loss of small branches. The contributions of the proposed method are as follows. Firstly, the Euclidean distance weight of edges in MST is improved to be a more reasonable value, which is related to the probability of the existence of an edge. Secondly, a strategy of pruning nodes is presented, which is based on the radius of a node’s inscribed ball. Thirdly, separate branches of broken neuron reconstructions can be merged into a single tree. FMST and many other state of the art reconstruction methods were implemented on two datasets: 120 Drosophila neurons and 163 neurons with gold standard reconstructions. Qualitative and quantitative analysis on experimental results demonstrates that the performance of FMST is good compared with many existing methods. Especially, on the 91 fruitfly neurons with gold standard and evaluated by five metrics, FMST is one of two methods with best performance among all 27 state of the art reconstruction methods. FMST is a good and practicable neuron reconstruction algorithm, and can be implemented in Vaa3D platform as a neuron tracing plugin.&lt;/p&gt;&lt;br /&gt;
                </description>
   <dc:subject>Neuroinformatics - The Journal</dc:subject>
   <dc:creator>NITRC ADMIN</dc:creator>
  <dc:date>Mon, 23 Jul 2018 7:00:00 GMT</dc:date>
  </item>

 <item rdf:about="http://www.nitrc.org/forum/forum.php?forum_id=8819">
   <title>Special Issue on High Performance Computing in Bio-medical Informatics</title>
   <link>http://www.nitrc.org/forum/forum.php?forum_id=8819</link>
   <description>[DESCRIPTION UNAVAILABLE]</description>
   <dc:subject>Neuroinformatics - The Journal</dc:subject>
   <dc:creator>NITRC ADMIN</dc:creator>
  <dc:date>Wed, 18 Jul 2018 7:00:00 GMT</dc:date>
  </item>

 <item rdf:about="http://www.nitrc.org/forum/forum.php?forum_id=8817">
   <title>Towards Differential Connectomics with NeuroVIISAS</title>
   <link>http://www.nitrc.org/forum/forum.php?forum_id=8817</link>
   <description>&lt;br /&gt;
                  &lt;h3 class=&quot;a-plus-plus&quot;&gt;Abstract&lt;/h3&gt;&lt;br /&gt;
                  &lt;p class=&quot;a-plus-plus&quot;&gt;The comparison of connectomes is an essential step to identify changes in structural and functional neuronal networks. However, the connectomes themselves as well as the comparisons of connectomes could be manifold. In most applications, comparisons of connectomes are applied to specific sets of data. In many studies collections of scripts are applied optimized for certain species (non-generic approaches) or diseases (control versus disease group connectomes). These collections of scripts have a limited functionality which do not support functional and topographic mappings of connectomes (hemispherical asymmetries, peripheral nervous system). The platform-independent and generic &lt;em class=&quot;a-plus-plus&quot;&gt;neuroVIISAS&lt;/em&gt; framework is built to circumvent limitations that come with variants of nomenclatures, connectivity lists and connectional hierarchies as well as restrictions to structural connectome analyses. A new analytical module is introduced into the framework to compare different types of connectomes and different representations of the same connectome within a unique software environment. As an example a differential analysis of the partial connectome of the laboratory rat that is based on virus tract tracing with the same regions of non-virus tract tracing has been performed. A relatively large connectional coherence between the two different techniques was found. However, some detected connections are described by virus tract-tracing only.&lt;/p&gt;&lt;br /&gt;
                </description>
   <dc:subject>Neuroinformatics - The Journal</dc:subject>
   <dc:creator>NITRC ADMIN</dc:creator>
  <dc:date>Mon, 16 Jul 2018 7:00:00 GMT</dc:date>
  </item>

 <item rdf:about="http://www.nitrc.org/forum/forum.php?forum_id=8808">
   <title>Morphological Neuron Classification Based on Dendritic Tree Hierarchy</title>
   <link>http://www.nitrc.org/forum/forum.php?forum_id=8808</link>
   <description>&lt;br /&gt;
                  &lt;h3 class=&quot;a-plus-plus&quot;&gt;Abstract&lt;/h3&gt;&lt;br /&gt;
                  &lt;p class=&quot;a-plus-plus&quot;&gt;The shape of a neuron can reveal interesting properties about its function. Therefore, morphological neuron characterization can contribute to a better understanding of how the brain works. However, one of the great challenges of neuroanatomy is the definition of morphological properties that can be used for categorizing neurons. This paper proposes a new methodology for neuron morphological analysis by considering different hierarchies of the dendritic tree for characterizing and categorizing neuronal cells. The methodology consists in using different strategies for decomposing the dendritic tree along its hierarchies, allowing the identification of relevant parts (possibly related to specific neuronal functions) for classification tasks. A set of more than 5000 neurons corresponding to 10 classes were examined with supervised classification algorithms based on this strategy. It was found that classification accuracies similar to those obtained by using whole neurons can be achieved by considering only parts of the neurons. Branches close to the soma were found to be particularly relevant for classification.&lt;/p&gt;&lt;br /&gt;
                </description>
   <dc:subject>Neuroinformatics - The Journal</dc:subject>
   <dc:creator>NITRC ADMIN</dc:creator>
  <dc:date>Sat, 14 Jul 2018 7:00:00 GMT</dc:date>
  </item>

 <item rdf:about="http://www.nitrc.org/forum/forum.php?forum_id=8801">
   <title>A Web-Based Atlas Combining MRI and Histology of the Squirrel Monkey Brain</title>
   <link>http://www.nitrc.org/forum/forum.php?forum_id=8801</link>
   <description>&lt;br /&gt;
                  &lt;h3 class=&quot;a-plus-plus&quot;&gt;Abstract&lt;/h3&gt;&lt;br /&gt;
                  &lt;p class=&quot;a-plus-plus&quot;&gt;The squirrel monkey (&lt;em class=&quot;a-plus-plus&quot;&gt;Saimiri sciureus&lt;/em&gt;) is a commonly-used surrogate for humans in biomedical research. In the neuroimaging community, MRI and histological atlases serve as valuable resources for anatomical, physiological, and functional studies of the brain; however, no digital MRI/histology atlas is currently available for the squirrel monkey. This paper describes the construction of a web-based multi-modal atlas of the squirrel monkey brain. The MRI-derived information includes anatomical MRI contrast (i.e., T2-weighted and proton-density-weighted) and diffusion MRI metrics (i.e., fractional anisotropy and mean diffusivity) from data acquired both in vivo and ex vivo on a 9.4 Tesla scanner. The histological images include Nissl and myelin stains, co-registered to the corresponding MRI, allowing identification of cyto- and myelo-architecture. In addition, a bidirectional neuronal tracer, biotinylated dextran amine (BDA) was injected into the primary motor cortex, enabling highly specific identification of regions connected to the injection location. The atlas integrates the results of common image analysis methods including diffusion tensor imaging glyphs, labels of 57 white-matter tracts identified using DTI-tractography, and 18 cortical regions of interest identified from Nissl-revealed cyto-architecture. All data are presented in a common space, and all image types are accessible through a web-based atlas viewer, which allows visualization and interaction of user-selectable contrasts and varying resolutions. By providing an easy to use reference system of anatomical information, our web-accessible multi-contrast atlas forms a rich and convenient resource for comparisons of brain findings across subjects or modalities. The atlas is called the Combined Histology-MRI Integrated Atlas of the Squirrel Monkey (CHIASM). All images are accessible through our web-based viewer (&lt;a href=&quot;https://chiasm.vuse.vanderbilt.edu&quot; class=&quot;a-plus-plus&quot;&gt;https://chiasm.vuse.vanderbilt.edu&lt;/a&gt;/), and data are available for download at (&lt;a href=&quot;https://www.nitrc.org/projects/smatlas&quot; class=&quot;a-plus-plus&quot;&gt;https://www.nitrc.org/projects/smatlas/&lt;/a&gt;).&lt;/p&gt;&lt;br /&gt;
                </description>
   <dc:subject>Neuroinformatics - The Journal</dc:subject>
   <dc:creator>NITRC ADMIN</dc:creator>
  <dc:date>Fri, 13 Jul 2018 7:00:00 GMT</dc:date>
  </item>
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