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PRoNTo / Pattern Recognition for Neuroimaging Toolbox
Provides a method for multivariate analysis based on machine learning models for neuroimaging data. PRoNTo is open-source, cross-platform, MATLAB-based and Statistical Parametric Mapping (SPM) compatible, therefore being suitable for both cognitive and clinical neuroscience research. It can also be extended via the addition of new feature selection and extraction approaches, validation procedures or classification/regression models.
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.
Nipype / Neuroimaging in Python: Pipelines and Interfaces
Interfaces with existing software for analysis of neuroimaging data and comparative development of algorithms. Nipype is an open-source, community-developed, Python-based software package that consists of three components: (1) interfaces to external tools providing a unified way for setting inputs, executing, and retrieving outputs; (2) a workflow engine for creating analysis pipelines; and (3) plug-ins that execute workflows either locally or in a distributed processing environment.
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.
An open-source MATLAB toolbox with user-friendly graphical user interfaces, implementing both dynamic functional and effective connectivity for tracking brain dynamics from functional MRI. We provided two strategies for dynamic analysis: (1) the commonly utilized sliding-window analysis and (2) the flexible least squares based time-varying parameter regression strategy. The toolbox also implements multiple functional measures including seed-to-voxel analysis, region of interest (ROI)-to-ROI analysis, and voxel-to-voxel analysis.
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