Tools   Select Another Resource Report Type

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

A software package for computational modeling of neural maps developed as part of the NIMH Human Brain Project. Topographica focuses on the large-scale structure and function that is visible only when many thousands of such neurons are connected into topographic maps containing millions of connections. The software package provides a general-purpose framework for building models at this level, at an appropriate level of detail and complexity, as determined by the available computing power, phenomena of interest, and amount of biological data available for validation. It is intended to complement low-level neuron simulators that are available, such as General Neural Simulation System and NEURON.

Proper citation: Topographica (RRID:SCR_014174) Copy   

•   Source: SciCrunch Registry

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

A GUI-based software for real-time fMRI processing, multivoxel pattern decoding and neurofeedback. The package integrates routines for image preprocessing in real-time, ROI-based feedback and brain decoding-based feedback using the FSL and libSVM libraries. Users can create or employ pre-specified visual stimuli for neurofeedback experiments. FRIEND can be used as Windows standalone software or as a multiplatform toolbox (called FRIEND Engine).

Proper citation: Functional Real-time Interactive Endogenous Neuromodulation and Decoding (FRIEND) (RRID:SCR_014186) Copy   

•   Source: SciCrunch Registry

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

A full featured and extensible application which simulates biological neural networks using graphic tools which edit neurons and networks, run the simulation and analyze results. It is written in C and runs on Unix and Windows. It is specifically targeted for neuroscientists who are less experienced with computer programming.

Proper citation: XNBC (RRID:SCR_014182) Copy   

•   Source: SciCrunch Registry

http://www.theuais.org

A topical portal for the UAIS Lab of Lanzhou University which researches predicting depression and schizophrenia based on demographics and physiological information (EEG, ERPs, Genetics, MRI, fMRI, etc.). It also researches wearable bio-signal sensors and antennas, bio-signal processing, speech analysis, pervasive mental health, psycho-physiological computing, bioinformatics and multimodal data fusion and modeling.

Proper citation: Prediction and Diagnosis for Depression and Schizophrenia (RRID:SCR_014161) Copy   

•   Source: SciCrunch Registry

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

A tool which implements a functional analysis pipeline for the joint analysis of longitudinally measured functional data and clinical data (for example age, gender and disease status). FMPM consists of a functional mixed effects model for characterizing the association of functional response with covariates of interest by incorporating complex spatial–temporal correlation structure, an efficient method for spatially smoothing varying coefficient functions, an estimation method for estimating the spatial– temporal correlation structure, a test procedure with local and global test statistics for testing hypotheses of interest associated with functional response, and a simultaneous confidence band for quantifying the uncertainty in the estimated coefficient functions.

Proper citation: Functional Mixed Processes Models (RRID:SCR_014187) Copy   

•   Source: SciCrunch Registry

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

Graphical user interface that has been implemented as a 3D Slicer plugin (scripted module). It serves to display a corresponding set of cortical regions from functional connectivity matrix in an explorable 3D scene that represents brain anatomical environment. In addition to grey matter regions, MultiXplore automatically finds and extracts deterministic fiber bundles which exist between selected region(s) and adds them to the 3D environment. This feature helps in generating region-based fiber bundles given a desired whole-brain tractography data.

Proper citation: MultiXplore (RRID:SCR_014814) Copy   

•   Source: SciCrunch Registry

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

Integrated pipeline for tractography-based brain parcellation with automatic processing and massive parallel computing. ATPP offers a CLI version for parcellating multiple brain regions and a GUI version for parcellating a specific brain region. " ATPP completely follows the scientific cultural shift to open science, which aims at making scientific research including journal papers, lab notes, data, and, of course, workflow tools, accessible and transparent to all levels of society. ATPP is publicly accessible in Neuroimaging Informatics Tools and Resources Clearinghouse8 (NITRC) (https://www.nitrc.org/projects/atpp). Its source codes are hosted in GitHub9 (https://github.com/haililihai/ATPP_CLI; https://github.com/haililihai/ATPP_GUI), under the GNU generic purpose license version 310 (GPLv3), and are welcome to download and fork. The Digital Object Identifiers (DOIs) providing a persistent way to make digital data easily and uniquely citable was created from Zenodo11 platform with those GitHub repositories (ATPP CLI v2.0.0, doi: https://doi.org/10.5281/zenodo.239702; ATPP GUI v2.0.0, doi: https://doi.org/10.5281/zenodo.239705). "

Proper citation: Automatic Tractography-based Parcellation Pipeline (RRID:SCR_014815) Copy   

•   Source: SciCrunch Registry

https://github.com/BlueBrain/BluePyOpt

An extensible framework for data-driven model parameter optimization that wraps and standardizes several existing open-source tools. BluePyOpt abstracts the optimization and evaluation tasks into various reusable and flexible discrete elements according to established best-practices. It also provides methods for setting up both small- and large-scale optimizations on a variety of platforms.

Proper citation: BluePyOpt (RRID:SCR_014753) 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

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

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://users.loni.ucla.edu/~shattuck/brainsuite/

Suite of image analysis tools designed to process magnetic resonance images (MRI) of the human head. BrainSuite provides an automatic sequence to extract genus-zero cortical surface mesh models from the MRI. It also provides a set of viewing tools for exploring image and surface data. The latest release includes graphical user interface and command line versions of the tools. BrainSuite was specifically designed to guide its users through the process of cortical surface extraction. NITRC has written the software to require minimal user interaction and with the goal of completing the entire process of extracting a topologically spherical cortical surface from a raw MR volume within several minutes on a modern workstation. The individual components of BrainSuite may also be used for soft tissue, skull and scalp segmentation and for surface analysis and visualization. BrainSuite was written in Microsoft Visual C using the Microsoft Foundation Classes for its graphical user interface and the OpenGL library for rendering. BrainSuite runs under the Windows 2000 and Windows XP Professional operating systems. BrainSuite features include: * Sophisticated visualization tools, such as MRI visualization in 3 orthogonal views (either separately or in 3D view), and overlayed surface visualization of cortex, skull, and scalp * Cortical surface extraction, using a multi-stage user friendly approach. * Tools including brain surface extraction, bias field correction, voxel classification, cerebellum removal, and surface generation * Topological correction of cortical surfaces, which uses a graph-based approach to remove topological defects (handles and holes) and ensure a tessellation with spherical topology * Parameterization of generated cortical surfaces, minimizing a harmonic energy functional in the p-norm * Skull and scalp surface extraction

Proper citation: BrainSuite (RRID:SCR_006623) Copy   

•   Source: SciCrunch Registry

http://www.brainvoyager.com/products/braintutor.html

A free award-winning educational program that teaches you knowledge about the human brain through interactive exploration of rotatable 3D models. The models have been computed with BrainVoyager QX using original data from magnetic resonance imaging (MRI) scans. Besides having fun with the rotatable 3D models, the program contains information about the major lobes, gyri, sulci and Brodmann areas of the cerebral cortex. The program runs on Windows XP, Vista and Windows 7.

Proper citation: BrainVoyager Brain Tutor (RRID:SCR_006737) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/niftyreg/

Software tools for global and local image registration. The algorithm used for global registration is based on a block matching approach enabling robust registration (outliers rejection). The local registration implementation uses a cubic B-Spline parametrisation (Free-Form Deformation). All registration algorithms are based on symmetric approaches where forward and backward transformations can be optimised concurrently. NiftyReg has been implemented for both CPU and GPU (through the use of CUDA).

Proper citation: NiftyReg (RRID:SCR_006593) Copy   

•   Source: SciCrunch Registry

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

A simple interface to simulate Brownian motion in arbitrary, complex environments. The analysis routines enable visualization of these models with DTI, q-space, and higher order diffusion weighted MRI.

Proper citation: DW-MRI Random Walk Simulator (RRID:SCR_006652) Copy   

•   Source: SciCrunch Registry

http://www.med.unc.edu/bric/ideagroup/free-softwares/mabmis

This software package implements an algorithm for accurate and consistent segmentation / labeling on a group of images. The images should be in Analyze format with paired header and image files. All images should be preprocessed so that they have been affinely aligned together.

Proper citation: MABMIS: Multi-Atlas Based Multi-Image Segmentation (RRID:SCR_006975) Copy   

•   Source: SciCrunch Registry

http://www.brain.org.au/software/mrtrix/

A set of tools to perform diffusion-weighted MRI white matter tractography in the presence of crossing fibres, using Constrained Spherical Deconvolution (Tournier et al.. 2004; Tournier et al. 2007), and a probabilisitic streamlines algorithm (e.g. Behrens et al., 2003; Parker et al., 2003). These applications have been written from scratch in C++, using the functionality provided by the GNU Scientific Library, and gtkmm. The software is currently capable of handling DICOM, NIfTI and AnalyseAVW image formats, amongst others. Installation * Unix/Linux * Microsoft Windows * Mac Os X, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: MRtrix (RRID:SCR_006971) Copy   

•   Source: SciCrunch Registry

http://www.mitk.org/DiffusionImaging

A selection of image analysis algorithms for the processing of diffusion-weighted MR images. Features & Highlights * Tensor and q-ball reconstruction * Glyph visualization * Quantification and partial volume clustering of tensor and q-ball images * Global fiber tractography, visualization, and tract post-processing * Brain network statistics and visualization (connectomics) * Interactive exploration of Tract-based spatial statistics (TBSS) results * Intra-voxel incoherent motion (IVIM) estimation * Synthetic data generation Additional system specific requirements: * Windows: If you have problems running the Windows application, please install the Microsoft Redistributable Packages for VS 2008: 32 bit or 64 bit * Linux: the Qt framework, version 4.6.2 or later Tested systems: Windows 7, Windows Vista; Ubuntu 12.04 and newer; OS X 10.6 (Snow Leopard), OS X 10.8 (Mountain Lion) The OS X 10.6 installer is compatible with OS X 10.7 (Lion) so there is no dedicated disk image build under 10.7. The MITK Diffusion application is based on the MITK research platform and the most of it is open-source. The available code is embedded into the source code of MITK as a module and can be accessed through the public git repository.

Proper citation: MITK Diffusion (RRID:SCR_006846) Copy   

•   Source: SciCrunch Registry

http://brainmaps.org

An interactive multiresolution brain atlas that is based on over 20 million megapixels of sub-micron resolution, annotated, scanned images of serial sections of both primate and non-primate brains and integrated with a high-speed database for querying and retrieving data about brain structure and function. Currently featured are complete brain atlas datasets for various species, including Macaca mulatta, Chlorocebus aethiops, Felis catus, Mus musculus, Rattus norvegicus, Tyto alba and many other vertebrates. BrainMaps is currently accepting histochemical, immunocytochemical, and tracer connectivity data, preferably whole-brain. In addition, they are interested in EM, MRI, and DTI data.

Proper citation: BrainMaps.org (RRID:SCR_006878) Copy   

•   Source: SciCrunch Registry

http://www.mlnl.cs.ucl.ac.uk/pronto/

A software toolbox based on pattern recognition techniques for the analysis of neuroimaging data. Statistical pattern recognition is a field within the area of machine learning which is concerned with automatic discovery of regularities in data through the use of computer algorithms, and with the use of these regularities to take actions such as classifying the data into different categories. In PRoNTo, brain scans are treated as spatial patterns and statistical learning models are used to identify statistical properties of the data that can be used to discriminate between experimental conditions or groups of subjects (classification models) or to predict a continuous measure (regression models).

Proper citation: PRoNTo (RRID:SCR_006908) Copy   

•   Source: SciCrunch Registry