Processes and investigates large anatomical and functional magnetic resonance imaging (MRI) data sets. AnalyzeFMRI can be used for temporal and spatial independent component (IC) analysis. It aims to study the intrinsic structure of data and alleviate the need for explicit a priori about the neural responses. This tool supports the computation of contiguous clusters of locations in a 3D array.
Aims to facilitate data processing. BRANT is a MATLAB-based toolbox that integrates (i) batch processing pipelines for resting-state functional magnetic resonance imaging (rs-fMRI) data preprocessing, (ii) voxel-wise spontaneous activity analysis, (iii) functional connectivity analysis, (iv) complex network analysis, as well as (v) statistical analysis and results visualization. It was designed using dynamically generated GUIs. Users can generate their own GUIs by adding a few lines of MATLAB code.
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.
Reduces significantly the effort required to construct specifically tailored, interactive applications for medical image analysis. MITK allows an easy combination of algorithms developed by ITK with visualizations created by VTK and extends these two toolkits with those features, which are outside the scope of both. It adds support for complex interactions with multiple states as well as undo-capabilities, a very important prerequisite for convenient user interfaces.
Facilitates the utilization of the scikit-learn package for neuroimaging. Nilearn is useful for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis. It plots brain volumes and employs different heuristics to find cutting coordinates. This tool enables researchers to automatically download reference datasets and atlases.
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