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http://free-d.versailles.inra.fr/html/freed.html

Free-D allows the reconstruction of 3D models from image stacks (segmentation, registration, surface reconstruction, 3D rendering). It is designed in the goal of non-linear spatial normalization and averaging of collections of individual 3D models (this module is currently in alpha version only and not included in the distributed version)., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Free-D (RRID:SCR_009578) Copy   

•   Source: SciCrunch Registry

http://neurospaces.sourceforge.net/

The GEneral NEural SImulation System (GENESIS) started as a very advanced software package in the late eighties, for biologically accurate neuronal modeling. Besides being used as a neuronal simulator, it was also applied to various domains outside computational neuroscience. The Neurospaces project is a departure from the monolithic software system design of the original GENESIS system. It is a development center for software components of computational neuroscience simulators. There are many advantages of developing independent software components: - Interfacing to an individual component is obviously more simple than interfacing to a do-all monolithic system. The compartmental solver developed for the Neurospaces project can be connected to Matlab fi. - It simplifies the individual components and encourages other developers to get involved. - It allows for separate testing of the components. More than 1000 use case tests been defined for these software components, including integration tests. - Integrating different component, gives different flavours of the same simulator, and enhances the user experienced consistency when doing multilevel simulations. - A component based software system avoids vendor-lockin. Its life-cycle is more smooth than that of a monolithic system, because software components can be upgraded one at a time. The Neurospaces project embodies many software components that all have been developed in full isolation. The core of the most important components is finished. The current development focus has shifted from component integration to the support of specific use case with an emphasis on single neuron modeling. This is a list of software components that have been developed or are under construction. Together, these tools give the core for the upcoming GENESIS 3 GUI. - GShell: a simple replacement for the Genesis 2 SLI. - Heccer: a fast compartmental solver, a backend. - Dash: a second compartmental solver faster than Heccer, for simpler models. - Neurospaces Model Container: provides a solver independent internal and external storage format for models. - Discrete event system: consists of a discrete event distributor and queuer. This is used for abstract modeling of an action potential traveling inside an axon as a ''discrete event''. - SSP: a flexible scheduler written in perl, to run simulations with the Neurospaces model container and Heccer. - The Neurospaces Studio: some tools for graphical browsing and command line usage. - The Genesis Script Language Interface: a scripting component that reads Genesis 2 scripts and feeds them to the Neurospaces model container. - The Geometry Library is a general purpose geometry library, with some essential geometrical operators, not commonly found in other geometrical libraries. - Using the Geometry Library, a Reconstruct Interface has been written. This interface supports the conversion of contours exported by the Reconstruct software to the Neurospaces declarative NDF format. - The Neurospaces project browser for browsing projects and inspecting simulation results. - The Installer package contains the Neurospaces installer and developer tools that have emerged from developing Neurospaces software components. - The Configurator package contains configuration utilities for the other tools. It is not needed for the other tools to work properly. Rather, it allows to set up model database and simulation servers in a convenient way. - There is also a Neurospaces blog and a wiki at googlecode for the Neurospaces project, with information for developers.

Proper citation: GEneral NEural SImulation System: The Neurospaces Project (RRID:SCR_008035) Copy   

•   Source: SciCrunch Registry

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

A GPU-accelerated library for simulating large-scale spiking neural network (SNN) models with a high degree of biological detail. CARLsim allows execution of networks of Izhikevich spiking neurons with realistic synaptic dynamics on both generic x86 CPUs and standard off-the-shelf GPUs. The simulator provides a PyNN-like programming interface in C/C++, which allows for details and parameters to be specified at the synapse, neuron, and network level.

Proper citation: CARLsim: a GPU-accelerated SNN Simulator (RRID:SCR_014095) Copy   

•   Source: SciCrunch Registry

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

A Matlab-based tool for computational neuroscience-based analysis and data visualization. Its features include: surface mesh visualization in PLY, PIAL, NV, STL,VTK, and GIFTI formats; conversion of NIfTI voxel images to surface meshes and saving as PLY or VTK; track (TRK files) visualization; connectome data (BrainNet Viewer .node and .edge files) visualization; intuitive GUI; that availability of all functions available in the GUI through scripting (automated scripts can be created); and exporting of rendered image as bitmap.

Proper citation: MRIcroS (RRID:SCR_014142) Copy   

•   Source: SciCrunch Registry

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

A statistical analysis tool for manifold-valued data. The SPD manifold for diffusion tensor images (DTI) and the Hilbert unit sphere for square root representation of orientation distribution functions (ODF) can be used.

Proper citation: Multivariate General Linear Models (MGLM) on Riemannian Manifolds (RRID:SCR_014143) 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/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.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://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.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

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.nitrc.org/projects/frats/

Software for the analysis of multiple diffusion properties along fiber bundle as functions in an infinite dimensional space and their association with a set of covariates of interest, such as age, diagnostic status and gender, in real applications. The resulting analysis pipeline can be used for understanding normal brain development, the neural bases of neuropsychiatric disorders, and the joint effects of environmental and genetic factors on white matter fiber bundles.

Proper citation: Functional Regression Analysis of DTI Tract Statistics (RRID:SCR_002293) 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://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://trackvis.org/dtk/

Software as set of commandline tools with GUI frontend that performs data reconstruction and fiber tracking on diffusion MR images. It does preparation work for TrackVis. Software Package for diffusion imaging data processing and tractography.

Proper citation: Diffusion Toolkit (RRID:SCR_017345) Copy   

•   Source: SciCrunch Registry

https://github.com/bheAI/MonkeyCBP_CLI

Software toolbox for connectivity based parcellation of monkey brain. Integrated pipeline realizing tractography based brain parcellation with automatic processing and massive parallel computing. Highly automated process and high throughput performance supported by GPU option makes toolbox ready to be used by research community.

Proper citation: MonkeyCBP (RRID:SCR_017640) 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.sci.utah.edu/cibc-software/scirun.html

A Problem Solving Environment (PSE) for modeling, simulation and visualization of scientific problems. SCIRun now includes the biomedical components formally released as BioPSE, as well as BioMesh3D. BioMesh3D is a free, easy to use program for generating quality meshes for the use in biological simulations. The most recent stable release is version 4.6.

Proper citation: SCIRun (RRID:SCR_002541) Copy   

•   Source: SciCrunch Registry