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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   

•   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

http://mcx.sourceforge.net/

A Monte Carlo simulation software for photon migration in 3D turbid media. It uses Graphics Processing Units (GPU) based massively parallel computing techniques and is extremely fast compared to the traditional single-threaded CPU-based simulations. Using an nVidia 8800GT graphics card (14MP/114Cores), the acceleration is about 300x~400x compared to a single core of Xeon 5120 CPU; this ratio can be as high as 700x with a GTX 280 GPU and 1400x with a GTX 470.

Proper citation: Monte Carlo eXtreme (RRID:SCR_007001) Copy   

•   Source: SciCrunch Registry

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.birncommunity.org/collaborators/function-birn/

The FBIRN Federated Informatics Research Environment (FIRE) includes tools and methods for multi-site functional neuroimaging. This includes resources for data collection, storage, sharing and management, tracking, and analysis of large fMRI datasets. fBIRN is a national initiative to advance biomedical research through data sharing and online collaboration. BIRN provides data-sharing infrastructure, software tools, strategies and advisory services - all from a single source.

Proper citation: Function BIRN (RRID:SCR_007291) Copy   

•   Source: SciCrunch Registry

http://www.brainvisa.info/

BrainVISA is a modular an customizable software platform built to host heterogeneous tools dedicated to neuroimaging research. Many toolboxes have already been developed for BrainVISA (T1 MRI, sulcal identification and morphometry, cortical surface analysis, diffusion imaging and tractography, fMRI, nuclear imaging, EEG and MEG, TMS, histology and autoradiography, etc.). Anatomist is a software for interactive visualization of multimodal data and for manipulation of structured 3D objects. It allows to build scenes that merge or combine images, meshes, regions of interest, fibers, textures, color palettes, referential changes, etc. A user can interact in 3D and in real time with the objects of an Anatomist scene: change point of view, select objects, add/suppress objects, change colors, draw regions of interests, do manual registration, etc. BrainVISA main features are: * Harmonization of communications between different software. For instance, BrainVISA toolboxes are using home-made software but also third-party software such as FreeSurfer, FSL, SPM, nipy, R-project, Matlab, etc. * Ontology-based data organization allowing database sharing and automation of mass of data analysis. * Fusion and interactive visualization of multimodal data (using Anatomist software). * Automatic generation of graphical user interfaces. * Workflow monitoring and data quality checking. * Full customization possible. * Runs on Linux, Mac and Windows. * Programming Language: C++, Python * Supported Data Format: ANALYZE, DICOM, GIfTI, MINC, NIfTI-1, Other Format

Proper citation: BrainVISA / Anatomist (RRID:SCR_007354) Copy   

•   Source: SciCrunch Registry

http://fmri.wfubmc.edu/software/PickAtlas

A software toolbox that provides a method for generating Region of Interest (ROI) masks based on the Talairach Daemon database. The atlases include Brodmann area, Lobar, Hemisphere, Anatomic Label (gyral anatomy), and Tissue type. The atlases have been extended to the vertex in MNI space, and corrected for the precentral gyrus anomaly. Additional atlases (including non-human atlases) can be added without difficulty., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: WFU PickAtlas (RRID:SCR_007378) Copy   

•   Source: SciCrunch Registry

http://mipav.cit.nih.gov

A Java-based application that enables quantitative analysis and visualization of medical images of numerous modalities such as DTI, PET, MRI, CT, or microscopy. Using MIPAV's standard user-interface and analysis tools, researchers at remote sites (via the internet) can easily share research data and analyses, thereby enhancing their ability to research, diagnose, monitor, and treat medical disorders. MIPAV can be run on any Java-enabled platform such as Windows, UNIX, or Macintosh OS X. Functionality includes segmentation, inter- and intra multi-modality registration, surface rendering, volume rendering and reading and writing a large number of biomedical file formats including: DICOM 3.0, Analyze, NIFTI, SPM, MINC, Phillips, GE, Zeiss, Biorad, jpeg, png, tiff, mrc, fits, interfile, and many more.

Proper citation: MIPAV: Medical Image Processing and Visualization (RRID:SCR_007371) Copy   

•   Source: SciCrunch Registry

http://www.harvard.edu/

Institution of higher education in the United States. Private Ivy League research university in Cambridge, Massachusetts.

Proper citation: Harvard University; Cambridge; United States (RRID:SCR_011273) Copy   

•   Source: SciCrunch Registry

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

A fast, scalable tool developed at the Johns Hopkins University to automatically segment the major anatomical fiber tracts within the human brain from clinical quality diffusion tensor MR imaging. With an atlas-based Markov Random Field representation, DOTS directly estimates the tract probabilities, bypassing tractography and associated issues. Overlapping and crossing fibers are modeled and DOTS can also handle white matter lesions. DOTS is released as a plug-in for the MIPAV software package and as a module for the JIST pipeline environment. They are therefore cross-platform and compatible with a wide variety of file formats.

Proper citation: DOTS WM tract segmentation (RRID:SCR_009459) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/c8c8/

A small, stand-alone MatLab toolbox that measures sagittal cross-section thickness and area of the human corpus callosum from high-resolution T1 in vivo MR images. C8 takes as input affine normalized white matter segmentations derived from high-resolution (in-plane) T1 images and outputs both regional callosal thicknesses in three different formats and geometrically-defined regional areas in three different configurations. It is a small package that is easily configurable and modifiable and it measures callosa at the rate of several per minute.

Proper citation: C8: Corpus Callosum Computations (RRID:SCR_009449) Copy   

•   Source: SciCrunch Registry

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

A tutorial that took place as part of MICCAI 2010 is the 13th International Conference on Medical Image Computing and Computer Assisted Intervention, September 20-24, 2010 in Beijing, China. See http://www.miccai2010.org/ This project supporedt community outreach and dialog between presenters and audience, both before and after the tutorial session, and is intended to engage the broader community in the deliberations on this important topic. ''Best Practices'' covered software engineering practices as well end-user installation and support practices.

Proper citation: Best Practices for Software Development (RRID:SCR_009441) Copy   

•   Source: SciCrunch Registry

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

Software toolkit developed for the fbrain project that consists of several image processing tools: image reconstruction, image denoising, image segmentation, tractography etc., for a better understanding of fetal brain development.

Proper citation: Baby Brain Toolkit (RRID:SCR_009440) Copy   

•   Source: SciCrunch Registry

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

A standardized framework for communication and data exchange between medical imaging applications, with particular focus on neuroimaging technologies. FOPA is an attempt to design and implement a common protocol for network and command line communication with either file-system or imbedded data structures. Initial reference implementations will support interoperability between ITK, VTK, and Java platforms. Contributions are welcome from other neuroimaging development communities.

Proper citation: Framework for Open Programmatic Access (RRID:SCR_009479) Copy   

•   Source: SciCrunch Registry

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

An efficient framework for building and analyzing graphs called epsilon radial networks (ERNs) using tractography data in a normalized space. Currently there is no agreed-upon method for constructing the brain anatomical connectivity graphs out of large number of white matter tracts. The key challenge in defining brain networks is node delineation and their method defines nodes in the graph using tract-end points clustered in a sphere of a given radius (epsilon). Using a kd-tree based search algorithm they can identify the nodes computationally efficiently and in a fully automatic way. These networks can be used not only to analyze topo-physical properties of the structural brain networks but also to perform classical region-of-interest (ROI) analyses in a very efficient way. Thus ERNs can be used as a novel image processing lens for statistical and machine learning based analyses.

Proper citation: Epsilon Radial Networks (RRID:SCR_009470) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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

Software tools appropriate for the registration of diffusion tensor images to an average coordinate system. The tools include image registration methods and algorithms for the correct alignment of the diffusion tensor when applying the resulting transformation. The program uses the Slicer3 execution model framework to define the command line arguments, and can be fully integrated using the module discovery capabilities of Slicer3.

Proper citation: Diffusion Warp (RRID:SCR_009466) Copy   

•   Source: SciCrunch Registry

http://www.cancerimagingarchive.net/

Archive of medical images of cancer accessible for public download. All images are stored in DICOM file format and organized as Collections, typically patients related by common disease (e.g. lung cancer), image modality (MRI, CT, etc) or research focus. Neuroimaging data sets include clinical outcomes, pathology, and genomics in addition to DICOM images. Submitting Data Proposals are welcomed.

Proper citation: Cancer Imaging Archive (TCIA) (RRID:SCR_008927) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/gpu-areg/

This tool can be used as a command line module with 3D Slicer (version 3 and above) for the affine registration of image volumes. The registration toolbox has 2 options: 1) a Mutual Information based registration, 2) a Sum-of-Square differences registration method. The final output is in the same space as the fixed image. You do require to have CUDA v2.2 or greater installed on your system with atleast 256MB Nvidia GPU memmory card. All operating systems are supported, but take a look at the CMakeLists.txt file for how to compile for you system.

Proper citation: GPU based affine registration (RRID:SCR_009486) Copy   

•   Source: SciCrunch Registry

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

Journals addressing functional and structural neuroimaging topics.

Proper citation: Functional and Structural Neuroimaging Journals Listing (RRID:SCR_009482) Copy   

•   Source: SciCrunch Registry