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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   

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http://www.neuron.yale.edu

NEURON is a simulation environment for modeling individual neurons and networks of neurons. It provides tools for conveniently building, managing, and using models in a way that is numerically sound and computationally efficient. It is particularly well-suited to problems that are closely linked to experimental data, especially those that involve cells with complex anatomical and biophysical properties. NEURON has benefited from judicious revision and selective enhancement, guided by feedback from the growing number of neuroscientists who have used it to incorporate empirically-based modeling into their research strategies. NEURON's computational engine employs special algorithms that achieve high efficiency by exploiting the structure of the equations that describe neuronal properties. It has functions that are tailored for conveniently controlling simulations, and presenting the results of real neurophysiological problems graphically in ways that are quickly and intuitively grasped. Instead of forcing users to reformulate their conceptual models to fit the requirements of a general purpose simulator, NEURON is designed to let them deal directly with familiar neuroscience concepts. Consequently, users can think in terms of the biophysical properties of membrane and cytoplasm, the branched architecture of neurons, and the effects of synaptic communication between cells. * helps users focus on important biological issues rather than purely computational concerns * has a convenient user interface * has a user-extendable library of biophysical mechanisms * has many enhancements for efficient network modeling * offers customizable initialization and simulation flow control * is widely used in neuroscience research by experimentalists and theoreticians * is well-documented and actively supported * is free, open source, and runs on (almost) everything

Proper citation: NEURON (RRID:SCR_005393) Copy   

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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   

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http://www.nitrc.org/projects/dkfz-diffusion/

This central project points to all open-source and open-data initiatives provided by the German Cancer Research Center in the field of diffusion MRI.

Proper citation: Diffusion MRI at DKFZ Heidelberg (RRID:SCR_009465) Copy   

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http://moose.sourceforge.net/

MOOSE is the Multiscale Object-Oriented Simulation Environment. It is the base and numerical core for large, detailed simulations including Computational Neuroscience and Systems Biology. MOOSE spans the range from single molecules to subcellular networks, from single cells to neuronal networks, and to still larger systems. it is backwards-compatible with GENESIS, and forward compatible with Python and XML-based model definition standards like SBML and MorphML. MOOSE is coordinating with the GENESIS-3 project towards the goals of developing educational resources for modeling. MOOSE is open source software, licensed under the LGPL (Lesser GNU Public License). It has absolutely no warranty. Sponsors: - National Center of Biological Sciences (NCBS) - National Institutes of Health (NIH) Collaboration - EU-India grid - Department of Atomic Energy Science Research Council (DAE/SRC) - Department of Biotechnology (DBT)

Proper citation: Multiscale Object Orientation Simulation Environment (RRID:SCR_008031) Copy   

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http://www.humanconnectomeproject.org/

A multi-center project comprising two distinct consortia (Mass. Gen. Hosp. and USC; and Wash. U. and the U. of Minn.) seeking to map white matter fiber pathways in the human brain using leading edge neuroimaging methods, genomics, architectonics, mathematical approaches, informatics, and interactive visualization. The mapping of the complete structural and functional neural connections in vivo within and across individuals provides unparalleled compilation of neural data, an interface to graphically navigate this data and the opportunity to achieve conclusions about the living human brain. The HCP is being developed to employ advanced neuroimaging methods, and to construct an extensive informatics infrastructure to link these data and connectivity models to detailed phenomic and genomic data, building upon existing multidisciplinary and collaborative efforts currently underway. Working with other HCP partners based at Washington University in St. Louis they will provide rich data, essential imaging protocols, and sophisticated connectivity analysis tools for the neuroscience community. This project is working to achieve the following: 1) develop sophisticated tools to process high-angular diffusion (HARDI) and diffusion spectrum imaging (DSI) from normal individuals to provide the foundation for the detailed mapping of the human connectome; 2) optimize advanced high-field imaging technologies and neurocognitive tests to map the human connectome; 3) collect connectomic, behavioral, and genotype data using optimized methods in a representative sample of normal subjects; 4) design and deploy a robust, web-based informatics infrastructure, 5) develop and disseminate data acquisition and analysis, educational, and training outreach materials.

Proper citation: MGH-USC Human Connectome Project (RRID:SCR_003490) Copy   

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http://www.pediatricmri.nih.gov/

Data sets of clinical / behavioral and image data are available for download by qualified researchers from a seven year, multi-site, longitudinal study using magnetic resonance technologies to study brain maturation in healthy, typically-developing infants, children, and adolescents and to correlate brain development with cognitive and behavioral development. The information obtained in this study is expected to provide essential data for understanding the course of normal brain development as a basis for understanding atypical brain development associated with a variety of developmental, neurological, and neuropsychiatric disorders affecting children and adults. This study enrolled over 500 children, ranging from infancy to young adulthood. The goal was to study each participant at least three times over the course of the project at one of six Pediatric Centers across the United States. Brain MR and clinical/behavioral data have been compiled and analyzed at a Data Coordinating Center and Clinical Coordinating Center. Additionally, MR spectroscopy and DTI data are being analyzed. The study was organized around two objectives corresponding to two age ranges at the time of enrollment, each with its own protocols. * Objective 1 enrolled children ages 4 years, 6 months through 18 years (total N = 433). This sample was recruited across the six Pediatric Study Centers using community based sampling to reflect the demographics of the United States in terms of income, race, and ethnicity. The subjects were studied with both imaging and clinical/behavioral measures at two year intervals for three time points. * Objective 2 enrolled newborns, infants, toddlers, and preschoolers from birth through 4 years, 5 months, who were studied three or more times at two Pediatric Study Centers at intervals ranging from three months for the youngest subjects to one year as the children approach the Objective 1 age range. Both imaging and clinical/behavioral measures were collected at each time point. Participant recruitment used community based sampling that included hospital venues (e.g., maternity wards and nurseries, satellite physician offices, and well-child clinics), community organizations (e.g., day-care centers, schools, and churches), and siblings of children participating in other research at the Pediatric Study Centers. At timepoint 1, of those enrolled, 114 children had T1 scans that passed quality control checks. Staged data release plan: The first data release included structural MR images and clinical/behavioral data from the first assessments, Visit 1, for Objective 1. A second data release included structural MRI and clinical/behavioral data from the second visit for Objective 1. A third data release included structural MRI data for both Objective 1 and 2 and all time points, as well as preliminary spectroscopy data. A fourth data release added cortical thickness, gyrification and cortical surface data. Yet to be released are longitudinally registered anatomic MRI data and diffusion tensor data. A collaborative effort among the participating centers and NIH resulted in age-appropriate MR protocols and clinical/behavioral batteries of instruments. A summary of this protocol is available as a Protocol release document. Details of the project, such as study design, rationale, recruitment, instrument battery, MRI acquisition details, and quality controls can be found in the study protocol. Also available are the MRI procedure manual and Clinical/Behavioral procedure manuals for Objective 1 and Objective 2.

Proper citation: NIH MRI Study of Normal Brain Development (RRID:SCR_003394) Copy   

•   Source: SciCrunch Registry

http://signalml.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 5, 2023.An XML-based language designed for metadescription of formats, used for digital storage of biomedical time series. Using SignalML, information on the structure of binary data files can be simply and efficiently coded. Once written, this information can be used by any software, which - owing to this metadescription - can read data files in the original format. This eliminates the need for conversions and duplication of data. signalml.org provides the following resources for interchange of relevant information and ideas: * SignalML wiki * Newsgroup / mailing list The main current software project is Svarog - a SignalML-compliant signal viewer, annotator, analyzer and (future) recorder. Svarog is written in Java and is currently best fitted for display of EEG and MEG signals. Also open platform for implementing advanced signal processing methods in user-friendly environment, at the moment interfacs for Java code, standalone executables and Matlab code via Matlab Builder for Java.

Proper citation: signalml.org (RRID:SCR_003383) Copy   

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http://fcon_1000.projects.nitrc.org/indi/CoRR/html/

Consortium that has aggregated resting state fMRI (R-fMRI) and diffusion imaging data from laboratories around the world, creating an open science resource for the imaging community, that facilitates the assessment of test-retest reliability and reproducibility for functional and structural connectomics. Given that this was a retrospective data collection, they have focused on basic phenotypic measures that are relatively standard in the neuroimaging field, as well as fundamental for analyses and sample characterization. Their phenotypic key is organized to reflect three classifications of variables: 1) core (i.e., minimal variables required to characterize any dataset), 2) preferred (i.e., variables that were strongly suggested for inclusion due to their relative import and/or likelihood of being collected by most sites), and 3) optional (variables that are data-set specific or only shared by a few sites). CoRR includes 33 datasets consisting of: * 1629 Subjects * 3357 Anatomical Scans * 5093 Resting Functional Scans * 1302 Diffusion Scans * 300 CBF and ASL Scans

Proper citation: Consortium for Reliability and Reproducibility (RRID:SCR_003774) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/ir/

Data repository for neuroimaging data in DlCOM and NIFTI formats. It allows users to search for and freely download publicly available data sets relating to normal subjects and those with diagnoses such as: schizophrenia, ADHD, autism, and Parkinson's disease.XNAT-based image registry that supports both NIfTI and DICOM images to promote re-use and integration of NIH funded data.

Proper citation: NITRC-IR (RRID:SCR_004162) Copy   

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https://nda.nih.gov/

The National Institute of Mental Health Data Archive (NDA) makes available human subjects data collected from hundreds of research projects across many scientific domains. Research data repository for data sharing and collaboration among investigators. Used to accelerate scientific discovery through data sharing across all of mental health and other research communities, data harmonization and reporting of research results. Infrastructure created by National Database for Autism Research (NDAR), Research Domain Criteria Database (RDoCdb), National Database for Clinical Trials related to Mental Illness (NDCT), and NIH Pediatric MRI Repository (PedsMRI).

Proper citation: NIMH Data Archive (RRID:SCR_004434) Copy   

•   Source: SciCrunch Registry

https://www.fieldtriptoolbox.org

Software toolbox for analysis of MEG, EEG, and other electrophysiological data. Used by experimental neuroscientists.

Proper citation: FieldTrip (RRID:SCR_004849) Copy   

•   Source: SciCrunch Registry

http://trackvis.org/

TrackVis is software tool that can visualize and analyze fiber track data from diffusion MR imaging (DTI/DSI/HARDI/Q-Ball) tractography. It does NOT perform actual fiber tracking. Diffusion Toolkit is a set of tools that reconstruct diffusion imaging data and generate fiber track data for TrackVis to visualize. Because these two sets of tools were developed and maintained separately and each has distinguished funtionalities, they decided to distribute them as two separate programs for the ease of maintenance and upgrade. You do need both of them to perform complete diffusion data processing and analysis. Features of TrackVis include: * Cross-platform. Works on Windows, Mac OS X and Linux with native look and feel. * A variety of track filters (track selecting methods) allowing users to explore and locate specific bundles with ease. * Multiple rendering modes with customizable scalar-driven color codes. * Real-time parameter adjustment and 3D render. * Open format of the track data file allowing users to integrate customized scalar data into the track file and visualize and analyze it. Save and restore scenes in XML style scene file. * Statistical scalar analysis of tracks and ROIs. * Synchronized real-time multiple dataset analysis and display allowing time-point and/or subject comparison. Synchronized analysis and display on same dataset can also be performed in real-time remotely over the network. * Upfront in-line parameter adjustment in real-time. No tedious pop-up dialogs. TrackVis works with Track File created by Diffusion Toolkit. Diffusion Toolkit processes raw DICOM, Nifti format and ANALYZE images. TrackVis and Diffusion Toolkit are cross-platform software. They can run on Windows XP, Mac OS X as well as Linux.

Proper citation: TrackVis (RRID:SCR_004817) Copy   

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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   

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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   

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http://www.ebire.org/hcnlab/software/vamca.html

A stand-alone, open source human cortical meta-analysis and visualization toolbox for MatLab. It projects stereotaxic coordinates to a mean cortical surface by using an anatomical database of 60 young adults to provide multiple mappings of normalized cortical surfaces into MNI space. VAMCA performs the following analyses: # Multi-Fiducial Projection Mapping: Map stereotaxic 3D coordinates to the normalized cortical location for each of 60 database subjects. # Computing Centroid Locations for groups of foci both on a mean cortical surface and in MNI space. # Comparing Two Groups of Foci for differences in location (surface or 3D) of their group centroids and computing the groups' overlap extent using permutation tests. # Detecting Significant Densities of Foci or Density Differences of Two Groups within anatomical ROIs on a mean cortical surface by using Monte Carlo analyses. Coordinate weights allow fixed or random effects type analyses.

Proper citation: VAMCA (RRID:SCR_007028) Copy   

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http://humanconnectome.org/consortia/

Project to map the neural pathways that underlie human brain function for several modalities of neuroimaging data including fMRI. The purpose of the Project is to acquire and share data about the structural and functional connectivity of the human brain. It will greatly advance the capabilities for imaging and analyzing brain connections, resulting in improved sensitivity, resolution, and utility, thereby accelerating progress in the emerging field of human connectomics. Altogether, the Human Connectome Project will lead to major advances in the understanding of what makes us uniquely human and will set the stage for future studies of abnormal brain circuits in many neurological and psychiatric disorders. The sixteen institutes and centers of the NIH Blueprint for Neuroscience have funded two major grants that will take complementary approaches to deciphering the brain's amazingly complex wiring diagram. An 11-institution consortium led by Washington University in St. Louis and the University of Minnesota received a 5-year grant to enable development and utilization of advanced Magnetic Resonance Imaging (MRI) methods to chart brain circuitry. A consortium led by Massachusetts General Hospital and the University of California at Los Angeles received a grant to enable building and refining a next-generation 3T MR scanner that improves the quality and spatial resolution with which brain connectivity data can be acquired at this field strength.

Proper citation: NIH Human Connectome Project (RRID:SCR_006942) Copy   

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http://ncmir.ucsd.edu/downloads/manual_align_rts2000.shtm

Software program to adjust the alignment of two adjacent images. Allows to correct for any misalignment that may occur during auto-alignment step. Serves as a bootstrap to get the images in approximately the right place.

Proper citation: Manual Align RTS2000 (RRID:SCR_007107) Copy   

•   Source: SciCrunch Registry

http://www.wholebraincatalog.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 26, 2016. An open source, downloadable, 3d atlas of the mouse brain and its cellular constituents that allows multi-scale data to be visualized in a seamless way, similar to Google earth. Data within the Catalog is marked up with annotations and can link out to additional data sources via a semantic framework. This next generation open environment has been developed to connect members of the neuroscience community to facilitate solutions for today's intractable challenges in brain research through cooperation and crowd sourcing. The client-server platform provides rich 3-D views for researchers to zoom in, out, and around structures deep in a multi-scale spatial framework of the mouse brain. An open-source, 3-D graphics engine used in graphics-intensive computer gaming generates high-resolution visualizations that bring data to life through biological simulations and animations. Within the Catalog, researchers can view and contribute a wide range of data including: * 3D meshes of subcellular scenes or brain region territories * Large 2D image datasets from both electron and light level microscopy * NeuroML and Neurolucida neuronal reconstructions * Protein Database molecular structures Users of the Whole Brain Catalog can: * Fit data of any scale into the international standard atlas coordinate system for spatial brain mapping, the Waxholm Space. * View brain slices, neurons and their animation, neuropil reconstructions, and molecules in appropriate locations * View data up close and at a high resolution * View their own data in the Whole Brain Catalog environment * View data within a semantic environment supported by vocabularies from the Neuroscience Information Framework (NIF) at http://www.neuinfo.org. * Contribute code and connect personal tools to the environment * Make new connections with related research and researchers 5 Easy Ways to Explore: * Explore the datasets across multiple scales. * View data closely at high resolution. * Observe accurately simulated neurons. * Readily search for content. * Contribute your own research.

Proper citation: Whole Brain Catalog (RRID:SCR_007011) Copy   

•   Source: SciCrunch Registry