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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.
ANTs / Advanced Normalization Tools
Computes high-dimensional mappings to capture the statistics of brain structure and function. ANTs allows users to organize, visualize and statistically explore large biomedical image sets. It integrates imaging modalities and related information in space and time, and works across species or organ systems with minimal customization. ANTs depends on the Insight ToolKit (ITK), a widely used medical image processing library to which ANTs developers contribute. ANTs can be used paired with ANTsR, an emerging tool supporting standardized multimodality image analysis. ANTs is popularly considered a state-of-the-art medical image registration and segmentation toolkit.
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Provides a set of python scripts for deriving a whole brain parcellation of functional magnetic resonance imaging (fMRI) data. The resulting regions are suitable for use as regions of interest (ROIs) in fMRI data analysis. The pyClusterROI method employs a spatially-constrained normalized-cut spectral clustering algorithm to generate individual-level and group-level parcellations. The spatial constraint is imposed to ensure that the resulting ROIs are spatially coherent, i.e. the voxels in the resulting ROIs are connected. Using this package, clustering can be performed based on either the temporal correlation between voxel time courses, the spatial correlation between whole brain functional connectivity maps generated from each voxel time course, or a by spatial distance.
MANTiS / Morphologically Adaptive Neonatal Tissue Segmentation
Extends the unified segmentation approach to tissue classification implemented in Statistical Parametric Mapping (SPM) software to neonates. MANTiS utilizes a combination of unified segmentation, template adaptation via morphological segmentation tools and topological filtering, to segment the neonatal brain into eight tissue classes: cortical gray matter, white matter, deep nuclear gray matter, cerebellum, brainstem, cerebrospinal fluid (CSF), hippocampus and amygdala. It is able to segment neonatal brain tissues well, even in images that have brain abnormalities common in preterm infants.
Permits to analyze functional magnetic resonance imaging (fMRI) data. HeteroscedasticfMRI is based on a Markov Chain Monte Carlo (MCMC) algorithm. It allows for Bayesian variable selection among the regressors to model both the mean and variance. The tool can be used for estimating functional connectivity; for example by using a seed time series as a covariate in the design matrix. The general linear model with autoregressive noise and heteroscedastic noise innovations tends to detect more brain activity, compared to its homoscedastic counterpart.
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