Tools   Select Another Resource Report Type

http://sumsdb.wustl.edu/sums/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on May 11, 2016. Repository of brain-mapping data (surfaces and volumes; structural and functional data) derived from studies including fMRI and MRI from many laboratories, providing convenient access to a growing body of neuroimaging and related data. WebCaret is an online visualization tool for viewing SumsDB datasets. SumsDB includes: * data on cerebral cortex and cerebellar cortex * individual subject data and population data mapped to atlases * data from FreeSurfer and other brainmapping software besides Caret SumsDB provides multiple levels of data access and security: * Free (public) access (e.g., for data associated with published studies) * Data access restricted to collaborators in different laboratories * Owner-only access for work in progress Data can be downloaded from SumsDB as individual files or as bundles archived for offline visualization and analysis in Caret WebCaret provides online Caret-style visualization while circumventing software and data downloads. It is a server-side application running on a linux cluster at Washington University. WebCaret "scenes" facilitate rapid visualization of complex combinations of data Bi-directional links between online publications and WebCaret/SumsDB provide: * Links from figures in online journal article to corresponding scenes in WebCaret * Links from metadata in WebCaret directly to relevant online publications and figures

Proper citation: SumsDB (RRID:SCR_002759) Copy   

•   Source: SciCrunch Registry

http://fcon_1000.projects.nitrc.org/

Collection of resting state fMRI (R-fMRI) datasets from sites around world. It demonstrates open sharing of R-fMRI data and aims to emphasize aggregation and sharing of well-phenotyped datasets.

Proper citation: 1000 Functional Connectomes Project (RRID:SCR_005361) Copy   

•   Source: SciCrunch Registry

http://www.biological-networks.org/pubs/suppl/sinomo/

Analysis-tool which identifies singular node motifs in a network. Network nodes can be described by node-motifs. It is an improvement to the method described in Costa et al. (2009).

Proper citation: SINOMO (RRID:SCR_005286) Copy   

•   Source: SciCrunch Registry

http://fcon_1000.projects.nitrc.org/indi/adhd200/index.html#

A grassroots initiative dedicated to accelerating the scientific community''''s understanding of the neural basis of ADHD through the implementation of open data-sharing and discovery-based science. They believe that a community-wide effort focused on advancing functional and structural imaging examinations of the developing brain will accelerate the rate at which neuroscience can inform clinical practice. The ADHD-200 Global Competition invited participants to develop diagnostic classification tools for ADHD diagnosis based on functional and structural magnetic resonance imaging (MRI) of the brain. Applying their tools, participants provided diagnostic labels for previously unlabeled datasets. The competition assessed diagnostic accuracy of each submission and invited research papers describing novel, neuroscientific ideas related to ADHD diagnosis. Twenty-one international teams, from a mix of disciplines, including statistics, mathematics, and computer science, submitted diagnostic labels, with some trying their hand at imaging analysis and psychiatric diagnosis for the first time. The data for the competition was provided by the ADHD-200 Consortium. Consortium members from institutions around the world provided de-identified, HIPAA compliant imaging datasets from almost 800 children with and without ADHD. A phenotypic file including all of the test set subjects and their diagnostic codes can be downloaded. Winner is presented. The ADHD-200 consortium included: * Brown University, Providence, RI, USA (Brown) * The Kennedy Krieger Institute, Baltimore, MD, USA (KKI) * The Donders Institute, Nijmegen, The Netherlands (NeuroImage) * New York University Medical Center, New York, NY, USA (NYU) * Oregon Health and Science University, Portland, OR, USA (OHSU) * Peking University, Beijing, P.R.China (Peking 1-3) * The University of Pittsburgh, Pittsburgh, PA, USA (Pittsburgh) * Washington University in St. Louis, St. Louis, MO, USA (WashU)

Proper citation: ADHD-200 Sample (RRID:SCR_005358) Copy   

•   Source: SciCrunch Registry

http://neurolex.org/wiki/Main_Page

A freely editable semantic wiki for community-based curation of the terms used in Neuroscience. Entries are curated and eventually incorporated into the formal NIFSTD ontology. NeuroLex also includes a Resource branch for community members to freely add neuroscience relevant resources that do not become part of NIFSTD ontology but rather make up the NIF Registry. As part of the NIF, we provide a simple search interface to many different sources of neuroscience information and data. To make this search more effective, we are constructing ontologies to help organize neuroscience concepts into category hierarchies, e.g., neuron is a cell. These categories provide the means to perform more effective searches and also to organize and understand the information that is returned. But an important adjunct to this activity is to clearly define all of the terms that we use to describe our data, e.g., anatomical terms, techniques, organism names. Because wikis provide an easy interface for communities to contribute their knowledge, we started the NeuroLex.

Proper citation: NeuroLex (RRID:SCR_005402) Copy   

•   Source: SciCrunch Registry

http://cbrain.mcgill.ca/

A flexible software platform for distributed processing, analysis, exchange and visualization of brain imaging data. The expected result is a middleware platform that will render the processing environment (hardware, operating systems, storage servers, etc...) transparent to a remote user. Interaction with a standard web browser allows application of complex algorithm pipelines to large datasets stored at remote locations using a mixture of network available resources such as small clusters, neuroimaging tools and databases as well as Compute Canada's High Performance Computing Centers (HPC). Though the focus of CBRAIN is providing tools for use by brain imaging researchers, the platform is generalizable to other imaging domains, such as radiology, surgical planning and heart imaging, with profound consequences for Canadian medical research. CBRAIN expanded its concept to include international partners in the US, Germany and Korea. As of December 2010, GBRAIN has made significant progress with the original three partners and has developed new partners in Singapore, China, India, and Latin America. CBRAIN is currently deployed on 6 Compute Canada HPC clusters, one German HPC cluster and 3 clusters local to McGill University Campus, totaling more than 80,000 potential CPU cores.

Proper citation: CBRAIN (RRID:SCR_005513) Copy   

•   Source: SciCrunch Registry

http://www.cma.mgh.harvard.edu/iatr/

A centrally available listing of all image analysis tools that are available to the neuroscience community in order to facilitate the development, identification, and sharing of tools. It is hoped that this helps the tool developers to get their tools to a larger user community and to reduce redundancy (or at least utilize tool redundancy to facilitate optimal tool design) in tool development. This also helps tool users in identification of the existing tools for specific problems as they arise. The registry is designed to be self-moderated. This means that all tool entries are owned by some responsible party who enters the tool information, and keeps it up to date via the Web.

Proper citation: Internet Analysis Tools Registry (RRID:SCR_005638) Copy   

•   Source: SciCrunch Registry

http://slicer.org/

A free, open source software package for visualization and image analysis including registration, segmentation, and quantification of medical image data. Slicer provides a graphical user interface to a powerful set of tools so they can be used by end-user clinicians and researchers alike. 3D Slicer is natively designed to be available on multiple platforms, including Windows, Linux and Mac Os X. Slicer is based on VTK (http://public.kitware.com/vtk) and has a modular architecture for easy addition of new functionality. It uses an XML-based file format called MRML - Medical Reality Markup Language which can be used as an interchange format among medical imaging applications. Slicer is primarily written in C++ and Tcl.

Proper citation: 3D Slicer (RRID:SCR_005619) Copy   

•   Source: SciCrunch Registry

http://neuroscienceblueprint.nih.gov/

Collaborative framework that includes the NIH Office of the Director and the 14 NIH Institutes and Centers that support research on the nervous system. By pooling resources and expertise, the Blueprint identifies cross-cutting areas of research, and confronts challenges too large for any single Institute or Center. The Blueprint makes collaboration a day-to-day part of how the NIH does business in neuroscience, complementing the basic missions of Blueprint partners. During each fiscal year, the partners contribute a small percentage of their funds to a common pool. Since the Blueprint's inception in 2004, this pool has comprised less than 1 percent of the total neuroscience research budget of the partners. In 2009, the Blueprint Grand Challenges were launched to catalyze research with the potential to transform our basic understanding of the brain and our approaches to treating brain disorders. * The Human Connectome Project is an effort to map the connections within the healthy brain. It is expected to help answer questions about how genes influence brain connectivity, and how this in turn relates to mood, personality and behavior. The investigators will collect brain imaging data, plus genetic and behavioral data from 1,200 adults. They are working to optimize brain imaging techniques to see the brain's wiring in unprecedented detail. * The Grand Challenge on Pain supports research to understand the changes in the nervous system that cause acute, temporary pain to become chronic. The initiative is supporting multi-investigator projects to partner researchers in the pain field with researchers in the neuroplasticity field. * The Blueprint Neurotherapeutics Network is helping small labs develop new drugs for nervous system disorders. The Network provides research funding, plus access to millions of dollars worth of services and expertise to assist in every step of the drug development process, from laboratory studies to preparation for clinical trials. Project teams across the U.S. have received funding to pursue drugs for conditions from vision loss to neurodegenerative disease to depression. Since its inception in 2004, the Blueprint has supported the development of new resources, tools and opportunities for neuroscientists. For example, the Blueprint supports several training programs to help students pursue interdisciplinary areas of neuroscience, and to bring students from underrepresented groups into the neurosciences. The Blueprint also funds efforts to develop new approaches to teaching neuroscience through K-12 instruction, museum exhibits and web-based platforms. From fiscal years 2007 to 2009, the Blueprint focused on three major themes of neuroscience - neurodegeneration, neurodevelopment, and neuroplasticity. These efforts enabled unique funding opportunities and training programs, and helped establish new resources including the Blueprint Non-Human Primate Brain Atlas.

Proper citation: NIH Blueprint for Neuroscience Research (RRID:SCR_003670) Copy   

•   Source: SciCrunch Registry

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

An end-to-end pipeline for corpus callosum processing that provides automated midsagittal alignment, CC segmentation with a quality control tool, and thickness profile generation. Groupwise analysis is facilitated by permutation testing with FWER and FDR multiple comparison correction. Results display is facilitated by a display script that shows p-values on a 3D pipe representation of a CC. This pipeline is implemented in MATLAB and requires the Image Processing Toolbox. There are plans to implement it completely in Python.

Proper citation: Corpus Callosum Thickness Profile Analysis Pipeline (RRID:SCR_003575) Copy   

•   Source: SciCrunch Registry

http://incf.org/programs/atlasing/projects/waxholm-space

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 1st, 2023. Coordinate based reference space for the mapping and registration of neuroanatomical data. Users can download image volumes representing the canonical Waxholm Space (WHS) adult C57BL/6J mouse brain, which include T1-, T2*-, and T2-Weighted MR volumes (generated at the Duke Center for In-Vivo Microscopy), Nissl-stained optical histology (acquired at Drexel University), and a volume of labels. All volumes are represented at 21.5μ isotropic resolution. Datasets are provided as gzipped NIFTI files.

Proper citation: Waxholm Space (RRID:SCR_001592) Copy   

•   Source: SciCrunch Registry

Issue

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

Open source whole genome association analysis toolset, designed to perform range of basic, large scale analyses in computationally efficient manner. Used for analysis of genotype/phenotype data. Through integration with gPLINK and Haploview, there is some support for subsequent visualization, annotation and storage of results. PLINK 1.9 is improved and second generation of the software.

Proper citation: PLINK (RRID:SCR_001757) Copy   

•   Source: SciCrunch Registry

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

An international working group dedicated to improving access to neuroimaging results in a free and open-access manner. It seeks to establish a universal coordinate database, including both past papers and future studies. Their current project involves the creation of a comprehensive database of neuroimaging results searchable based on standardized coordinates. Once complete, this will allow anyone to find all of the articles that report a coordinate, or set of coordinates, easily and without cost. Eventually, they hope to expand this database to include not only coordinates, but statistical parametric maps as well. Formation of such a database will increase the likelihood of relevant papers being found and cited, and also be a very useful tool for those interested in meta-analysis, and hopefully clarify structure-function relationships. They are interested in hearing from people who might be willing to contribute to their projects, particularly those with programming experience. The number of published neuroimaging studies is increasing rapidly and it is not feasible to read them all. If a computer database could store key information from published fMRI papers and make that information easier to search or share, this would have substantial benefits for the neuroimaging community. Projects like AMAT, Brainmap, Brede and SumsDB have started to tackle this problem. NIDAG wants to formalize and improve these databases so that they meet the needs of the neuroimaging community. Formal meta-analysis of published data is a valuable way to assess the consistency and reliability of experimental results. A database of neuroimaging results would facilitate meta-analyses, in conjunction with tools like GingerALE and Multi-level Kernel Density Analysis.

Proper citation: NIDAG: Neuroimaging Data Access Group (RRID:SCR_001674) Copy   

•   Source: SciCrunch Registry

http://surfer.nmr.mgh.harvard.edu/

Open source software suite for processing and analyzing human brain MRI images. Used for reconstruction of brain cortical surface from structural MRI data, and overlay of functional MRI data onto reconstructed surface. Contains automatic structural imaging stream for processing cross sectional and longitudinal data. Provides anatomical analysis tools, including: representation of cortical surface between white and gray matter, representation of the pial surface, segmentation of white matter from rest of brain, skull stripping, B1 bias field correction, nonlinear registration of cortical surface of individual with stereotaxic atlas, labeling of regions of cortical surface, statistical analysis of group morphometry differences, and labeling of subcortical brain structures.Operating System: Linux, macOS.

Proper citation: FreeSurfer (RRID:SCR_001847) Copy   

•   Source: SciCrunch Registry

http://www.nesys.uio.no/Atlas3D/

A multi-platform visualization tool which allows import and visualization of 3-D atlas structures in combination with tomographic and histological image data. The tool allows visualization and analysis of the reconstructed atlas framework, surface modeling and rotation of selected structures, user-defined slicing at any chosen angle, and import of data produced by the user for merging with the atlas framework. Tomographic image data in NIfTI (Neuroimaging Informatics Technology Initiative) file format, VRML and PNG files can be imported and visualized within the atlas framework. XYZ coordinate lists are also supported. Atlases that are available with the tool include mouse brain structures (3-D reconstructed from The Mouse Brain in Stereotaxic Coordinates by Paxinos and Franklin (2001)) and rat brain structures (3-D reconstructed from The Rat Brain in Stereotaxic Coordinates by Paxinos and Watson (2005)). Experimental data can be imported in Atlas3D and warped to atlas space, using manual linear registration, with the possibility to scale, rotate, and position the imported data. This facilitates assignment of location and comparative analysis of signal location in tomographic images.

Proper citation: Atlas3D (RRID:SCR_001808) Copy   

•   Source: SciCrunch Registry

https://itunes.apple.com/gb/app/neuropub-visualizer/id405721542?mt=8

A NIfTI visualizer for statistical brain images (fMRI, VBM, etc) the iPad. The visualizer displays these images as overlay on the MNI standard brain. You can use it to store all your statistical images from your fMRI / VBM / TBSS studies and visualise them in 2D and 3D. Use NeuroPub as a library for your statistical images. It's the perfect app to bring to meetings, conferences, etc, and show your latest results.

Proper citation: NeuroPub Visualizer (RRID:SCR_006797) Copy   

•   Source: SciCrunch Registry

http://scalablebrainatlas.incf.org/

A web-based, interactive brain atlas viewer, containing a growing number of atlas templates for various species, including mouse, macaque and human. Standard features include fast brain region lookup, point and click to select a region and view its full 3D extent, mark a stereotaxic coordinate and view all regions in a hierarchy. Built-in extensions are the CoCoMac plugin, which provides a spatial display of Macaque connectivity, and a service to transform stereotaxic coordinates to and from the INCF Waxholm space for the mouse. Three dimensional renderings of brain regions are available through a Matlab interface (local installation of Matlab required). The SBA is designed to be customizable. External users can create plugins, hosted on their own servers, to interactively attach images or data to spatial atlas locations. This fully web-based display engine for brain atlases and topologies allows client websites to show brain region related data in a 3D interactive context. Currently available atlases are: * Macaque: The Paxinos Rhesus Monkey atlas (2000) * Macaque: Various templates available through Caret, registered to F99 space: Felleman and Van Essen (1991), Lewis and Van Essen (2000), Regional Map from K��tter and Wanke (2005), Paxinos Rhesus Monkey (2000) * Macaque: The NeuroMaps Macaque atlas (2008) * Mouse: The INCF Waxholm Space for the mouse (2011). Previous versions available. * Mouse: The Allen Mouse Brain volumetric atlas (ABA07) * Human: The LPBA40 parcellation, registered to SRI24 space A variety of services are being developed around the templates contained in the Scalable Brain Atlas. For example, you can include thumbnails of brain regions in your own webpage. Other applications include: * Analyze atlas templates in Matlab * List all regions belonging to the given template * List of supported atlas templates * Find region by coordinate * Color-coded PNG (bitmap) or SVG (vector) image of a brain atlas slice * Region thumbnail in 2D (slice) or 3D (stack of slices) The Scalable Brain Atlas is created by Rembrandt Bakker and Gleb Bezgin, under supervision of Rolf K��tter in the NeuroPhysiology and -Informatics group of the Donders Institute, Radboud UMC Nijmegen.

Proper citation: Scalable Brain Atlas (RRID:SCR_006934) Copy   

•   Source: SciCrunch Registry

http://bric.unc.edu/ideagroup/free-softwares/ABSORB/

This software package implements an algorithm for effective groupwise registration. The required input is a set of 3D MR intensity images (in Analyze format with paired .hdr and .img files) with a text file (.txt) listing all header file (.hdr) names. The output is the set of registered images together with the corresponding dense deformation fields. This software has been tested on Windows XP (32-bit) and Linux (64-bit, kernel version 2.6.18-194.el5). The images should be pre-processed before applying ABSORB: * All brain MR images used as inputs to ABSORB should be in the same situation (e.g., skull-stripped or not, cerebellum removed or not, etc.). * The input images should be in Analyze format with paired header and image files. This software was developed in IDEA group in UNC-Chapel Hill.

Proper citation: ABSORB: Atlas Building by Self-Organized Registration and Bundling (RRID:SCR_007018) Copy   

•   Source: SciCrunch Registry

http://www.cns.atr.jp/dni/en/downloads/tools-for-brain-behavior-data-sharing/

This is MATLAB library to create Neuroshare data format. You can convert your own data into Neuroshare format file.

Proper citation: Matlab Neuroshare Library (RRID:SCR_006957) Copy   

•   Source: SciCrunch Registry

http://www.neuroconstruct.org/

Software for simulating complex networks of biologically realistic neurons, i.e. models incorporating dendritic morphologies and realistic cell membrane conductance, implemented in Java and generates script files for the NEURON and GENESIS simulators, with support for other simulation platforms (including PSICS and PyNN) in development. neuroConstruct is being developed in the Silver Lab in the Department of Neuroscience, Physiology and Pharmacology at UCL and uses the latest NeuroML specifications, including MorphML, ChannelML and NetworkML. Some of the key features of neuroConstruct are: Creation of networks of biologically realistic neurons, positioned in 3D space. Complex connectivity patterns between cell groups can be specified for the networks. Can import morphology files in GENESIS, NEURON, Neurolucida, SWC and MorphML format for inclusion in network models. Simulations can be run on the NEURON or GENESIS platforms. Cellular processes (synapses/channel mechanisms) can be imported from native script files or created in ChannelML. Recording of simulation data generated by the simulation and visualization/analysis of data. Stored simulation runs can be viewed and managed through the Simulation Browser interface.

Proper citation: neuroConstruct (RRID:SCR_007197) Copy   

•   Source: SciCrunch Registry