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The Virtual Brain
Simulates the dynamics of large-scale brain networks with biologically realistic connectivity. The Virtual Brain uses tractographic data (DTI/DSI) to generate connectivity matrices and build cortical and subcortical brain networks. The connectivity matrix defines the connection strengths and time delays via signal transmission between all network nodes. Various neural mass models are available in the repertoire of The Virtual Brain and define the dynamics of a network node. Together, the neural mass models at the network nodes and the connectivity matrix define the Virtual Brain. The Virtual Brain simulates and generates the time courses of various forms of neural activity including Local Field Potentials (LFP) and firing rate, as well as brain imaging data such as EEG, MEG and BOLD activations as observed in fMRI.
CAM-Java / Convex Analysis of Mixtures
Gathers several in vivo dynamic contrast-enhanced imaging subtools for analyzing various functional changes associated with disease initiation, progression and responses to therapy. CAM-Java incorporates three Convex Analysis of Mixtures (CAM) based algorithms: (1) compartment modeling (CAM-CM), (2) non-negative independent component analysis (CAM nICA) and (3) non-negative well-grounded component analysis (CAM-nWCA). These subtools address real-world blind source separation (BSS) problems.
Allows diffusion of imaging research in a clinical oncology setting and provides tools for end-to-end diffusion image analysis as well as interoperation with clinical imaging systems. SlicerDMRI is a suite of open-source software tools for diffusion magnetic resonance imaging (dMRI) research. The software is built upon and integrated with 3D Slicer, an NIH-supported open-source platform for medical image computing. SlicerDMRI is used for both neuroscience research and cancer imaging research.
Dipy / Diffusion Imaging in Python
Allows to study diffusion Magnetic Resonance Imaging (MRI) data. Dipy is a program allowing users to share their code and experiments. One of its objectives is to provide transparent implementations for all the different steps of the dMRI analysis with a uniform programming interface. It implements two interfaces for probabilistic Markov fiber tracking: (1) it allows the user to provide the distribution evaluated on a discrete set of possible tracking directions, and (2) it accommodates tracking methods where the fiber orientation distribution function (fODF) cannot be easily computed.
Allows the analysis and visualization of structural and functional neuroimaging data from cross-sectional or longitudinal studies. FreeSurfer proposes a suite of tools that provide extensive and automated analysis of key features in the human brain. This includes skull stripping, image registration, subcortical segmentation, cortical surface reconstruction, cortical segmentation, cortical thickness estimation, longitudinal processing, fMRI analysis, tractography, FreeView Visualization GUI, etc... FreeSurfer is freely available, runs on a wide variety of hardware and software platforms, and is open source.
PANDA / Pipeline for Analyzing braiN Diffusion imAges
A MATLAB toolbox for a comprehensive pipeline processing of dMRI dataset, aiming to facilitate image processing for the across-subject analysis of diffusion metrics and brain network constructions. Of note, the processing pipelines in this toolbox have been completely set up, allowing the end-users of dMRI to process the data immediately. After the user sets the input/output and processing parameters through the friendly graphical user interface (GUI), PANDA fully automates all processing steps for datasets of any number of subjects, and results in data pertaining to many diffusion metrics that are ready for statistical analysis at three levels (Voxel-level, ROI-level, and TBSS-level). Additionally, anatomical brain networks can be automatically generated using either deterministic or probabilistic tractography techniques. Particularly, PANDA can run processing jobs in parallel with multiple cores either in a single computer or within a distributed computing environment using a Sun Grid Engine (SGE) system, thus allowing for maximum usage of the available computing resources.
GAT / Graph-Analysis Toolbox
Facilitates analysis and comparison of structural and functional network brain networks. GAT provides a graphical user interface (GUI) that facilitates construction and analysis of brain networks, comparison of regional and global topological properties between networks, analysis of network hub and modules, and analysis of resilience of the networks to random failure and targeted attacks. Area under a curve (AUC) and functional data analyses (FDA), in conjunction with permutation testing, is employed for testing the differences in network topologies; analyses that are less sensitive to the thresholding process.
Amira 3D Software for Life Sciences
Allows users to visualize, manipulate, and understand data from imaging modalities such as computed tomography, microscopy or Magnetic resonance imaging (MRI). Amira 3D Software for Life Sciences provides features to import and process 2D and 3D images data, visualization techniques and tools for visual analysis. Users can also create and share presentations. The base product can be customized by adding functional extensions to fit special needs in different application areas.
TORTOISE / Tolerably Obsessive Registration and Tensor Optimization Indolent Software Ensemble
Processes the diffusion of magnetic resonance imaging (MRI) data. TORTOISE contains three main modules: DIFF_PREP-software for image resampling, motion, eddy current distortion, and EPI distortion correction using a structural image as target, and for re-orientation of data to a common space; DIFF_CALC-software for tensor fitting, error analysis, directionally encoded color map visualization and ROI analysis; DR-BUDDI-software for EPI distortion correction using pairs of diffusion data sets acquired with opposite phase encoding (blip-up blip-down acquisitions).
A “profilometry” framework for the multimetric analysis of white matter tracts. Mrpipe is a statistical framework that combine a multivariate analysis of covariance (MANCOVA) and a linear discriminant analysis (LDA) both accounting for age and gender, with multiple comparison corrections. This methodology can not only combine tract profile analyses in a single statistical model but can also take into account covariates such as age and gender which are of particular importance in most clinical investigations. This project is still a skeleton, the tool is not ready for use.
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