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

Software Matlab toolbox for directed functional connectivity analysis of fMRI BOLD signal from predefined regions of interest. It recovers true structure of connections and estimates weights attributed to each connection. Obtains patterns at group and individual levels.

Proper citation: GIMME (RRID:SCR_014115) Copy   

•   Source: SciCrunch Registry

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/kwyk/

Software tool as deep neural network for predicting FreeSurfer segmentations of structural MRI volumes. This tool is implemented as both Docker and Singularity containers. Used for brain parcellation and uncertainty estimation.

Proper citation: Knowing what you know (kwyk) - Bayesian Brain Parcellation (RRID:SCR_017470) Copy   

•   Source: SciCrunch Registry

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

Software tool to simplify creation and management of computing environments in Neuroimaging.

Proper citation: ReproMan (RRID:SCR_018468) 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://connectir.projects.nitrc.org

An R-based package to conduct brain connectivity analyses with a focus on a novel approach to conducting Connectome-Wide Association Studies (CWAS) using functional connectivity.

Proper citation: Connectir (RRID:SCR_000863) Copy   

•   Source: SciCrunch Registry

http://www.egi.com/clinical-division/clinical-division-geodesic-eeg-components/clinical-division-net-station

APIs for Net Station data files. APIs are available for C++, C#, and Java.

Proper citation: Net Station API (RRID:SCR_000867) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/mni2orfromxyz/

Input either normalized MNI coordinates from a 3D image, or input real world XYZ matrix coordinates, and this code will convert coordinates of one type to the other.

Proper citation: Convert MNI coordinates to or from XYZ (RRID:SCR_000406) Copy   

•   Source: SciCrunch Registry

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

Vervet (Chlorocebus aethiops sabaeus) probabilistic atlas that defines an anatomical space (template) with associated tissue and regional prior probability maps. The atlas was produced from whole head MRI of 10 normal adult animal subjects. The package consists of two atlases. The Biased directory contains the average template and probabilistic atlases for selected tissue classes constructed by registering the training population to one subject. The Unbiased directory contains the atlas constructed using unbiased estimation. The atlas is suitable for use in any segmentation tool using a probabilistic atlas, for example those in Slicer.

Proper citation: Vervet Probabilistic Atlas (RRID:SCR_000426) Copy   

•   Source: SciCrunch Registry

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

Slicer3 module to provide a capability for performing white matter lesion classification and summary.

Proper citation: 3DSlicerLupusLesionModule (RRID:SCR_000853) Copy   

•   Source: SciCrunch Registry

http://ccb.loni.usc.edu/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 31, 2022. Center focused on the development of computational biological atlases of different populations, subjects, modalities, and spatio-temporal scales with 3 types of resources: (1) Stand-alone computational software tools (image and volume processing, analysis, visualization, graphical workflow environments). (2) Infrastructure Resources (Databases, computational Grid, services). (3) Web-services (web-accessible resources for processing, validation and exploration of multimodal/multichannel data including clinical data, imaging data, genetics data and phenotypic data). The CCB develops novel mathematical, computational, and engineering approaches to map biological form and function in health and disease. CCB computational tools integrate neuroimaging, genetic, clinical, and other relevant data to enable the detailed exploration of distinct spatial and temporal biological characteristics. Generalizable mathematical approaches are developed and deployed using Grid computing to create practical biological atlases that describe spatiotemporal change in biological systems. The efforts of CCB make possible discovery-oriented science and the accumulation of new biological knowledge. The Center has been divided into cores organized as follows: - Core 1 is focused on mathematical and computational research. Core 2 is involved in the development of tools to be used by Core 3. Core 3 is composed of the driving biological projects; Mapping Genomic Function, Mapping Biological Structure, and Mapping Brain Phenotype. - Cores 4 - 7 provide the infrastructure for joint structure within the Center as well as the development of new approaches and procedures to augment the research and development of Cores 1-3. These cores are: (4)Infrastructure and Resources, (5) Education and Training, (6) Dissemination, and (7) Administration and Management. The main focus of the CCB is on the brain, and specifically on neuroimaging. This area has a long tradition of sophisticated mathematical and computational techniques. Nevertheless, new developments in related areas of mathematics and computational science have emerged in recent years, some from related application areas such as Computer Graphics, Computer Vision, and Image Processing, as well as from Computational Mathematics and the Computational Sciences. We are confident that many of these ideas can be applied beneficially to neuroimaging.

Proper citation: Center for Computational Biology at UCLA (RRID:SCR_000334) 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

https://www.rad.upenn.edu/sbia/software/index.html#hammer

Software package that performs high-dimensional warping of brain images. Standard voxel-based analysis can be applied to these tissue density maps, in order to examine regional volumetrics, effects of disease, or correlations with clinical measurements. In order to make HAMMER as robust as possible to different acquisition protocols and conditions, they provide a distribution that assumes that images have been skull-stripped and segmented into gray matter, white matter, and ventricular CSF. We have other software tools that can perform these steps, including skull stripping, reorientation and reslicing, and segmentation tools. Importantly, they use 250 for WM, 150 for GM, 50 for Ventricles and 10 for CSF in the tissue-segmented brain images. Current modules used for group analysis: Labeling subject brain using a manually-labeled brain Model; Generating RAVENS map for each tissue (WM, GM, VN); Normalizing subject brain images

Proper citation: Hierarchical Attribute Matching Mechanism for Elastic Registration (RRID:SCR_001960) Copy   

•   Source: SciCrunch Registry

http://www.math.mcgill.ca/keith/fmristat/

A Matlab toolbox for the statistical analysis of fMRI data. The fMRI data was first converted to percentage of whole volume. The statistical analysis of the percentages was based on a linear model with correlated errors. The design matrix of the linear model was first convolved with a hemodynamic response function modelled as a difference of two gamma functions timed to coincide with the acquisition of each slice. Temporal drift was removed by adding a cubic spline in the frame times to the design matrix (one covariate per 2 minutes of scan time), and spatial drift was removed by adding a covariate in the whole volume average. The correlation structure was modelled as an autoregressive process of degree 1. At each voxel, the autocorrelation parameter was estimated from the least squares residuals using the Yule-Walker equations, after a bias correction for correlations induced by the linear model. The autocorrelation parameter was first regularized by spatial smoothing, then used to "whiten" the data and the design matrix. The linear model was then re-estimated using least squares on the whitened data to produce estimates of effects and their standard errors. In a second step, runs, sessions and subjects were combined using a mixed effects linear model for the effects (as data) with fixed effects standard deviations taken from the previous analysis. This was fitted using ReML implemented by the EM algorithm. A random effects analysis was performed by first estimating the the ratio of the random effects variance to the fixed effects variance, then regularizing this ratio by spatial smoothing with a Gaussian filter. The variance of the effect was then estimated by the smoothed ratio multiplied by the fixed effects variance. The amount of smoothing was chosen to achieve 100 effective degrees of freedom. The resulting T statistic images were thresholded using the minimum given by a Bonferroni correction and random field theory, taking into account the non-isotropic spatial correlation of the errors.

Proper citation: FMRISTAT - A general statistical analysis for fMRI data (RRID:SCR_001830) 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

http://www.bic.mni.mcgill.ca/software/minc/

A medical imaging data format and an associated set of tools and libraries including a 3 level API for medical image analysis with a particular focus on the needs of research. There are also a number of tools including Registration and Non-Uniformity correction.

Proper citation: MINC (RRID:SCR_002391) 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://www.nitrc.org/projects/cluster_report/

This matlab script and associated files will take resultant statistical images and essentially output everything you could ever want to know. It can work off of images that were previously corrected for multiple comparisons, but it can actually do the correction itself. This is because the cluster_correct script is incorporated within. It will iterate through atlases (borrowed from other software) to tell you the location of significant results. It outputs an extremely detailed report as well as a summary table for quick investigation. In addition, it will output statistics for each surviving cluster, and the image as a whole. Feedback would be much appreciated.

Proper citation: Cluster reporter (RRID:SCR_002218) 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