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http://www.nitrc.org/projects/xfsl/

A set of many useful automation scripts to facilitate the neuroimaging data analysis process. It contains BASH scripts for MRI data management, FSL automation and web application.

Proper citation: XFSL: An FSL toolbox (RRID:SCR_014181) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/psics

Software for efficient generation and simulation of models containing stochastic ion channels distributed across dendritic and axonal membranes. It computes the behavior of neurons taking account of the stochastic nature of ion channel gating and the detailed positions of the channels themselves. It is designed as a complement for existing tools.

Proper citation: Parallel Stochastic Ion Channel Simulator (RRID:SCR_014159) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/longitudinal_ms/

The Longitudinal MS Lesion Imaging Archive provides Training data consisting of longitudinal images from five patients and Testing data consisting of 15 patients. Each longitudinal dataset includes T1-weighted, T2-weighted, PD-weighted, and T2-weighted FLAIR MRI with 3-5 time points acquired on a 3T MR scanner. T1-weighted images have approximately a 1mm cubic voxel resolution, while the other scans are 1mm in plane with 3mm sections. Accounting for the multiple time points, this constitutes approximately 80 individual data sets. The Training data contains manual segmentations of the MS lesions from two different raters for each of the time points provided.

Proper citation: Longitudinal MS Lesion Imaging Archive (RRID:SCR_014136) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/cluster_correct/

This script will take any .img file and correct it based on a cluster extent, cluster definition and voxelwise threshold. The threshold entered will be applied to positive and negative values separately, and separate pos and neg corrected images will be output. This script requires a license for the matlab image processing toolbox.

Proper citation: Cluster Extent Correction (RRID:SCR_002226) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/sock/

A software toolbox that can automatically identify many of the artifact components that are often present in independent component analysis (ICA) of functional MRI (fMRI). The method: * Does not require temporal information about the fMRI paradigm. * Does not require the user to train the algorithm. * Requires only the EPI images (additional acquisition of anatomical images is not required). * Is able to identify a high proportion of artifact-related ICs without removing components that are likely to be of neuronal origin. * Can be applied to resting-state fMRI. * Is automated, requiring minimal or no human intervention.

Proper citation: SOCK (RRID:SCR_002544) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/quantitativedti/

Slicer3 modules for quantitative diffusion analysis. Modules include tools for clustering fiber tracts, summarizing measures over tract clusters, etc.

Proper citation: Quantitative Diffusion Tools (RRID:SCR_002527) Copy   

•   Source: SciCrunch Registry

https://www.nitrc.org/projects/threedicsi/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 13,2026. Software program for multi-dimensional CSI data visualization, spectral processing, localization, quantification and multi-variate analysis.

Proper citation: 3D Interactive Chemical Shift Imaging (RRID:SCR_002581) Copy   

•   Source: SciCrunch Registry

http://fmri.wfubmc.edu/cms/software#WFU_Pipeline

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 14, 2026. A fully automated software application for the processing of fMRI data using SPM. It is fully automated from the point of data acquisition at the MRI scanner. It incorporates tools for automated data transfer, archiving, real-time SPM5 batch script generation with distributed grid processing, automated error-recovery procedures, full data-provenance, email notifications, optional conversion back to DICOM (Digital Imaging and Communications in Medicine), and picture archiving and communications systems (PACS) insertion. The architecture allows for an infinite number of easily definable analyses that are fully automated from the point of acquisition. Requirements: * MATLAB 7.3 or greater with the Image Processing Toolbox * SPM5

Proper citation: WFU Pipeline (RRID:SCR_002614) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/valmap/

A command line voxel-wise statistical analysis software program for images. Images can be gray matter density, jacobian images, etc. The linear model is implemented, i.e. designs that can be modeled as Y=AB, where Y is a vector or matrix of dependent variables, B is a vector or matrix of parameters to be estimated, and A is a design matrix. Why use valmap? # Do not need a Matlab license to run. # Can incorporate a spatially varying independent variable (e.g., you have a perfusion map as your dependent variable, and you want to co-vary for gray matter at each voxel, so use a gray matter map as an independent variable). # Can use spatially invariant independent variables (e.g., you can have a cognitive test score as the dependent variable, and use jacobian maps as the independent variable). # Can have multiple dependent variables and do multivariate analyses (e.g., want to know the overall effect of disease on perfusion and structure, so use perfusion maps and jacobian maps as dependent variables).

Proper citation: ValMap: simple statistical mapping tool (RRID:SCR_002610) Copy   

•   Source: SciCrunch Registry

http://www.softpedia.com/get/Science-CAD/MeshValmet.shtml

A tool that measures surface to surface distance between two triangle meshes using user-specified uniform sampling. Thus, users can choose finer sampling level to calculate errors to gain more accuracy in theerror space, or sparser sampling to gain speed and get an approximate feeling of error distribution between boundaries. Besides its pleasant visualization using the VTK library, MeshValmet also provides useful histogram and statistical information based on the sample errors, such as mean and median distance, root mean square distance, mean square distance, mean absolute distance, Hausdorff distance, 95 percentile, 68 percentile, etc. MeshValmet is based on the work of Nicolas Aspert, etc.: MESH: Measuring Errors between Surfaces using the Hausdorff distance in the proceedings of the IEEE Int. Conf. on Multimedia and Expo 2002 (ICME), vol. I, pp. 705-708. The calculation of the Dice's Coefficient is calculated by Joshua Stough using the concept of a Riemannian sum.

Proper citation: MeshValmet: Validation Metric for Meshes (RRID:SCR_006622) Copy   

•   Source: SciCrunch Registry

http://www.bic.mni.mcgill.ca/ServicesAtlases/Macaque

A reference atlas of standard macaque monkey magnetic resonance images. The template brain volume that offers a common stereotaxic reference frame to localize anatomical and functional information in an organized and reliable way for comparison across individual macaque monkeys and studies. We have used MRI volumes from a group of 25 normal adult macaque monkeys (18 Macaca fascicularis, 7 Macaca mulatta) to create the individual atlas. Thus, the atlas does not rely on the anatomy of a single subject, but instead depends on nonlinear normalization of numerous macaque brains mapped to an average template image that is faithful to the location of anatomical structures. Tools for registering a native MRI to the MNI macaque atlas can be found in the Software section. Viewing the atlas and associated volumes online requires Java browser support. Additionally, you may download the atlas and associated files in your chosen format.

Proper citation: McConnell Brain Imaging Center MNI Macaque Atlas (RRID:SCR_005265) Copy   

•   Source: SciCrunch Registry

http://eeg.sourceforge.net/

Software toolbox to facilitate quick and easy import, visualization and measurement for Event Related Potential (ERP) data. The toolbox can open and visualise ERP averaged data (Neuroscan, ascii formats), 2D/3D electrode coordinates and 3D cerebral tissue tesselations (meshes). All the features can be explored quickly and easily using the example data provided in the toolbox. The GUI interface is simple and intuitive.

Proper citation: Bioelectromagnetism Matlab Toolbox (RRID:SCR_006090) Copy   

•   Source: SciCrunch Registry

http://neurosynth.org

Platform for large-scale, automated synthesis of functional magnetic resonance imaging (fMRI) data extracted from published articles. It''s a website wrapped around a set of open-source Python and JavaScript packages. Neurosynth lets you run crude but useful analyses of fMRI data on a very large scale. You can: * Interactively visualize the results of over 3,000 term-based meta-analyses * Select specific locations in the human brain and view associated terms * Browse through the nearly 10,000 studies in the database Their ultimate goal is to enable dynamic real-time analysis, so that you''ll be able to select foci, tables, or entire studies for analysis and run a full-blown meta-analysis without leaving your browser. You''ll also be able to do things like upload entirely new images and obtain probabilistic estimates of the cognitive states most likely to be associated with the image.

Proper citation: NeuroSynth (RRID:SCR_006798) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/jist/

A native Java-based imaging processing environment similar to the ITK/VTK paradigm. Initially developed as an extension to MIPAV (CIT, NIH, Bethesda, MD), the JIST processing infrastructure provides automated GUI generation for application plug-ins, graphical layout tools, and command line interfaces. This repository maintains the current multi-institutional JIST development tree and is recommended for public use and extension. JIST was originally developed at IACL and MedIC (Johns Hopkins University) and is now also supported by MASI (Vanderbilt University).

Proper citation: JIST: Java Image Science Toolkit (RRID:SCR_008887) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/eeglab/

Interactive Matlab toolbox for processing continuous and event-related EEG, MEG and other electrophysiological data incorporating independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data. First developed on Matlab 5.3 under Linux, EEGLAB runs on Matlab v5 and higher under Linux, Unix, Windows, and Mac OS X (Matlab 7+ recommended). EEGLAB provides an interactive graphic user interface (GUI) allowing users to flexibly and interactively process their high-density EEG and other dynamic brain data using independent component analysis (ICA) and/or time/frequency analysis (TFA), as well as standard averaging methods. EEGLAB also incorporates extensive tutorial and help windows, plus a command history function that eases users'' transition from GUI-based data exploration to building and running batch or custom data analysis scripts. EEGLAB offers a wealth of methods for visualizing and modeling event-related brain dynamics, both at the level of individual EEGLAB ''datasets'' and/or across a collection of datasets brought together in an EEGLAB ''studyset.'' For experienced Matlab users, EEGLAB offers a structured programming environment for storing, accessing, measuring, manipulating and visualizing event-related EEG data. For creative research programmers and methods developers, EEGLAB offers an extensible, open-source platform through which they can share new methods with the world research community by publishing EEGLAB ''plug-in'' functions that appear automatically in the EEGLAB menu of users who download them. For example, novel EEGLAB plug-ins might be built and released to ''pick peaks'' in ERP or time/frequency results, or to perform specialized import/export, data visualization, or inverse source modeling of EEG, MEG, and/or ECOG data. EEGLAB Features * Graphic user interface * Multiformat data importing * High-density data scrolling * Defined EEG data structure * Open source plug-in facility * Interactive plotting functions * Semi-automated artifact removal * ICA & time/frequency transforms * Many advanced plug-in toolboxes * Event & channel location handling * Forward/inverse head/source modeling

Proper citation: EEGLAB (RRID:SCR_007292) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/dti_rat_atlas/

3D DTI anatomical rat brain atlases have been created by the UNC- Chapel Hill Department of Psychiatry and the CAMID research collaboration. There are three age groups, postnatal day 5, postnatal day 14, and postnatal day 72. The subjects were Sprague-Dawley rats that were controls in a study on cocaine abuse and development. The P5 and P14 templates were made from scans of twenty rats each (ten female, ten male); the P72, from six females. The individual cases have been resampled to isotropic resolution, manually skull-stripped, and deformably registered via an unbiased atlas building method to create a template for each age group. Each template was then manually segmented using itk-SNAP software. Each atlas is made up of 3 files, a template image, a segmentation, and a label file.

Proper citation: 3D DTI Atlas of the Rat Brain In Postnatal Day 5 14 and Adulthood (RRID:SCR_009437) Copy   

•   Source: SciCrunch Registry

http://www.nbtwiki.net/

An open source Matlab toolbox for the computation and integration of neurophysiological biomarkers. NBT offers a pipeline from data storage to statistics including artifact rejection, signal visualization, biomarker computation, and statistical testing. NBT allows for easy implementation of new biomarkers, and incorporates an online wiki that facilitates collaboration among NBT users including extensive help and tutorials. NBT is specialized in analyzing EEG data, however it allows the processing of any kind of signal. NBT can, e.g., be used to analyze ongoing oscillation between: * Eyes-closed rest of subject populations (e.g., healthy subjects and patients, males vs. females, young vs. old, etc.). * Two experimental condition (e.g., classical eyes-closed rest vs. meditation, or before vs. after consumption of a CNS-active substance (a drug, coffee, nicotine, alcohol, etc.)., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Neurophysiological Biomarker Toolbox (RRID:SCR_009612) Copy   

•   Source: SciCrunch Registry

http://www.epilepsiae.eu/project_outputs/epilab_software

A Matlab-based software package developed for supporting researchers in performing studies on the prediction of epileptic seizures. It provides an intuitive and convenient graphical user interface. Fundamental concepts that are crucial for epileptic seizure prediction studies were implemented.This includes, for example, the development and statistical validation of prediction methodologies in long-term continuous recordings. Seizure prediction is usually based on electroencephalography (EEG) and electrocardiography (ECG) signals. EPILAB is able to process both EEG and ECG data stored in different formats. More than 35 time and frequency domain measures (features) can be extracted based on univariate and multivariate data analysis. These features can be post-processed and used for prediction purposes. The predictions may be conducted based on optimized thresholds or by applying classifications methods such as artificial neural networks, cellular neuronal networks, and support vector machines.

Proper citation: EPILAB (RRID:SCR_009573) Copy   

•   Source: SciCrunch Registry

http://marsbar.sourceforge.net/

A toolbox for SPM which provides routines for region of interest analysis. Features include region of interest definition, combination of regions of interest with simple algebra, extraction of data for regions with and without SPM preprocessing (scaling, filtering), and statistical analyses of ROI data using the SPM statistics machinery.

Proper citation: MarsBaR region of interest toolbox for SPM (RRID:SCR_009605) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/brainvox/

An interactive 3D rendering and neuroanatomical analysis package developed for analyzing focal brain lesions and functional brain imaging data (Damasio and Frank, 1991; Frank, Damasio and Grabowski, 1997).

Proper citation: Brainvox (RRID:SCR_009448) Copy   

•   Source: SciCrunch Registry