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https://bbgre.brc.iop.kcl.ac.uk

A database and associated tools for investigating the genetic basis of neurodisability. It combines phenotype information from patients with neurodevelopmental and behavioral problems with clinical genetic data, and displays this information on the human genome map. Basic access to genetic information (deletions, duplications) relating to participants with neurodevelopmental disorders is provided without an account; access to the full dataset requires an account. The genetic information that is available to view comprises potentially pathogenic copy number variation across the genome, detected by array comparative genome hybridization (aCGH) using a customized 44K oligonucleotide array.

Proper citation: Brain and Body Genetic Resource Exchange (RRID:SCR_008959) Copy   

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http://www.strokecenter.org/radiology/

The Internet Stroke Center at Washington University is pleased to offer this module for viewing CT, MR, and angiogram images of cerebrovascular and neurological diseases. While this project is still being perfected -- and many more cases have yet to be added -- we hope that you will find this collection useful in your education and practice. The images presented here are for educational use only. This information may not be used for diagnosis or treatment. All images are protected property of the Internet Stroke Center at Washington University and may not be reproduced without permission. Permission may be granted to students and professionals to borrow images from this site for educational purposes and/or presentations; we just ask that an email be sent detailing both the desired material and the intended use. Please direct all comments, questions, and requests to the Site Editor of the Internet Stroke Center.

Proper citation: Neurology Image Library from The Internet Stroke Center (RRID:SCR_013633) Copy   

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http://www.cdtdb.neuroinf.jp/CDT/Top.jsp

A platform that allow users to visualize and analyze transcriptome data related to the genetics that underlie the development, function, and dysfunction stages and states of the brain. Users can search for cerebellar development genes by name, ID, keyword, expression, and tissue specificity. Search results include general information, links, temporal, spatial, and tissue information, and gene category.

Proper citation: Brain Transcriptome Database (RRID:SCR_014457) Copy   

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http://web.stanford.edu/group/barres_lab/brain_rnaseq.html

Database containing RNA-Seq transcriptome and splicing data from glia, neurons, and vascular cells of cerebral cortex. Collection of RNA-Seq transcriptome and splicing data from glia, neurons, and vascular cells of mouse cerebral cortex. RNA-Seq of cell types isolated from mouse and human brain.

Proper citation: Brain RNA-Seq (RRID:SCR_013736) Copy   

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http://netbio.bgu.ac.il/tissuenet/

Database of human tissue protein-protein interactions (PPIs) that associates each interaction with human tissues that express both pair mates. This was achieved by integrating current data of experimentally detected PPIs with extensive data of gene and protein expression across 16 main human tissues. Users can query TissueNet using a protein and retrieve its PPI partners per tissue, or using a PPI and retrieve the tissues expressing both pair mates. The graphical representation of the output highlights tissue-specific and tissue-wide PPIs. Thus, TissueNet provides a unique platform for assessing the roles of human proteins and their interactions across tissues.

Proper citation: TissueNet - The Database of Human Tissue Protein-Protein Interactions (RRID:SCR_002052) Copy   

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http://www.genes2cognition.org/db/Search

Database of protein complexes, protocols, mouse lines, and other research products generated from the Genes to Cognition project, a project focused on understanding molecular complexes involved in synaptic transmission in the brain.

Proper citation: Genes to Cognition Database (RRID:SCR_002735) Copy   

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https://bams1.org/cells/list.php, https://bams1.org/cells/search_bams_ref.php, https://bams1.org/cells/search_by_brain_region.php

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 6, 2023.BAMS is an online resource for information about neural circuitry. The BAMS Cell view focuses on the major brain regions and which cells are contained therein.

Proper citation: BAMS Cells (RRID:SCR_003531) Copy   

•   Source: SciCrunch Registry

https://confluence.crbs.ucsd.edu/display/NIF/DRG

Gene expression data from published journal articles that test hypotheses relevant to neuroscience of addiction and addictive behavior. Data types include effects of particular drug, strain, or knock out on particular gene, in particular anatomical region. Focuses on gene expression data and exposes data from investigations using DNA microarrays, polymerase chain reaction, immunohistochemistry and in-situ hybridizations. Data are available for query through NIF interface.Data submissions are welcome.

Proper citation: Drug Related Gene Database (RRID:SCR_003330) Copy   

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http://hendrix.imm.dtu.dk/services/jerne/brede/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 4th, 2023. A database of human data from functional neuroimaging scientific articles containing Talairach coordinates that provides data for novel information retrieval techniques and automated meta-analyses. Each article in this database is identified by a unique number: A WOBIB. Some of the structure of the Brede database is similar to the structure of the BrainMap database (Research Imaging Center, San Antonio). The database is inspired by the hierarchical structure of BrainMap with scientific articles (bib structures) on the highest level containing one or more experiments (exp structure, corresponding to a contrast in general linear model analyses), these in turn comprising one or more locations (loc structures). The information on the bib level (author, title, ...) is setup automatically from PubMed while the rest of the information is entered manually in a Matlab graphical user interface. On the loc level this includes the 3D stereotactic coordinates in either Talairach or MNI space, the brain area (functional, anatomical or cytoarchitectonic area) and magnitude values such as Z-score and P-value. On the exp level information such as modality, scanner and behavioral domain are recorded with external components (such as face recognition or kinetic boundaries) organized in a directed graph and marked up with Medical Subject Headings (MeSH) where possible. The database is distributed as part of the Brede neuroinformatics toolbox (hendrix.imm.dtu.dk/software/brede/) which also provides the functions to manipulate and analyze the data. The Brede Toolbox is a program package primarily written in Matlab. As of 2006/11, 186 papers with 586 experiments.

Proper citation: Brede Database (RRID:SCR_003327) Copy   

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http://neuroinformatics.usc.edu/

The USC Brain Project is engaged in the effort to develop new tools and methodologies for neuroinformatics in modeling neural mechanisms of visuomotor coordination and exploring the evolution of the human language-ready brain, as well as conducting work in both neural modeling and database construction in relation to rehabilitation after stroke. Sponsors: USCBP is funded by the University of Southern California.

Proper citation: University of Southern California Brain Project (RRID:SCR_008044) Copy   

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

Software toolkit for analysis of MR brain imaging data. MRTool runs on Apple computers and PCs and requires SPM12.

Proper citation: MRTool (RRID:SCR_015956) Copy   

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http://fcon_1000.projects.nitrc.org/indi/pro/Berlin.html

Dataset consisting of a community sample of individuals ranging in age from 18 to 60 years old with at least two 7.5-minute resting state fMRI scans. During the resting state scan participants were instructed to relax while keeping their eyes open. In part of the sample eye status was randomized between scans. The particular eye status for each scan is indicated in the phenotypic information. No visual stimulus was presented. A subset of participants completed the ICS and PANAS affective behavior scales. The following data are released for every participant: * Scanner Type: Siemens, 3T Trio Tim * 7.5-minute resting state fMRI scan (R-fMRI) * MPRAGE anatomical scan, defaced to protect patient confidentiality * Demographic information, inluding ICS and PANAS scores (included in the release file).

Proper citation: Neuro Bureau - Berlin Mind and Brain Sample (RRID:SCR_003537) Copy   

•   Source: SciCrunch Registry

http://braintrap.inf.ed.ac.uk/braintrap/

This database contains information on protein expression in the Drosophila melanogaster brain. It consists of a collection of 3D confocal datasets taken from EYFP expressing protein trap Drosophila lines from the Cambridge Protein Trap project. Currently there are 884 brain scans from 535 protein trap lines in the database. Drosophila protein trap strains were generated by the St Johnston Lab and the Russell Lab at the University of Cambridge, UK. The piggyBac insertion method was used to insert constructs containing splice acceptor and donor sites, StrepII and FLAG affinity purification tags, and an EYFP exon (Venus). Brain images were acquired by Seymour Knowles-Barley, in the Armstrong Lab at the University of Edinburgh. Whole brain mounts were imaged by confocal microscopy, with a background immunohistochemical label added to aid the identification of brain structures. Additional immunohistochemical labeling of the EYFP protein using an anti-GFP antibody was also used in most cases. The trapped protein signal (EYFP / anti-GFP), background signal (NC82 label), and the merged signal can be viewed on the website by using the corresponding channel buttons. In all images the trapped protein / EYFP signal appears green and the background / NC82 channel appears magenta. Original .lsm image files are also available for download.

Proper citation: BrainTrap: Fly Brain Protein Trap Database (RRID:SCR_003398) Copy   

•   Source: SciCrunch Registry

http://www.uzh.ch/keyinst/loreta

Software application which computes cortical three-dimensional distribution of current density of the brain based on the scalp-recorded electric potential distribution. The exact low resolution brain electromagnetic tomography method has the property of exact localization to test point sources, yielding images of current density with exact localization, albeit with low spatial resolution. eLORETA has no localization bias even in the presence of structured noise. Deep structures, such as the anterior cingulate cortex and mesial temporal lobes, can be correctly localized with these methods.

Proper citation: exact Low Resolution Electromagnetic Tomography (RRID:SCR_013830) Copy   

•   Source: SciCrunch Registry

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3520032/

Algorithm for computational imaging of MRI data that detects and quantifies ischemic core–penumbra using only a single MRI modality (T2- or diffusion-weighted imaging, T2WI/DWI). It can be used with 3D data on lesions and normal-appearing brain matter (NABM) volumes.

Proper citation: Hierarchical Region Splitting (RRID:SCR_016398) Copy   

•   Source: SciCrunch Registry

http://www.neuroinf.jp/

The Japan Node of the INCF coordinates neuroinformatics activities within Japan and represents Japanese efforts in INCF. This site provides information about Japanese neuroinformatics platforms (NI Platforms) and the techniques and tools available from the International Neuroinformatics Coordinating Facility (INCF). The Neuroinformatics Japan Center (NIJC) will also supply techniques and tools developed at RIKEN BSI and at other research groups in Japan. INCF expects each national node to: 1. Actively formulate and implement the INCF Work Programs, 2. Coordinate and facilitate local neuroinformatics research activities at the national level, 3. Encourage neuroinformatics data sharing that conforms with INCF standards, and 4. Promote neuroinformatics development that supports the goals of INCF. The Neuroinformatics Japan Center (NIJC) represents the Japan Node. Together with the Japan Node Committee and the Platform Subcommittees, we promote domestic activities of neuroinformatics. Platform Subcommittee members collaborate to develop databases that are available for use on the website. Standing at the intersection of neuroscience and information science, the field of neuroinformatics develops the tools to house, share and analyze neuroscientific data, and to create computational models of brain. NIJC supports researchers developing and maintaining neuroscience databases, provides a portal for these databases and Neuroinformatics, and is designing the infrastructure for Neuroinformatics. It is also developing database technologies, and facilitates cooperation and distribution of the information stored in those databases. The activities of the Japan Node * Shaping domestic neuroinformatics research and directions (Japan Node Committee) * Advising on Intellectual Property Rights and protecting experimental subjects (Japan Node Committee) * Developing and publishing brain science databases (Platform Subcommittee) * Coordinating database management (Platform Subcommittee) * Disseminating neuroinformatics information via the web portal * Developing the infrastructure for brain science information and neuroinformatics * Supporting the development and diffusion of neuroinformatics technology

Proper citation: INCF Japan Node (RRID:SCR_006569) Copy   

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http://www.hbpp.org/

An open international project under the patronage of the Human Proteome Organisation (HUPO) that aims: To analyze the brain proteome of human as well as mouse models in healthy, neurodiseased and aged status with focus on Alzheimer's and Parkinson's Disease; To perform quantitative proteomics as well as complementary gene expression profiling on disease-related brain areas and bodily fluids; To advance knowledge of neurodiseases and aging in order to push new diagnostic approaches and medications; To exchange knowledge and data with other HUPO projects and national / international initiatives in the neuroproteomic field; To make neuroproteomic research and its results available in the scientific community and society. Recent work has shown that standards in proteomics and especially in bioinformatics are mandatory to allow comparable analyses, but still missing. To address this challenge, the HUPO BPP is closely working together with the HUPO Proteome Standards Initiative (HUPO PSI).

Proper citation: HUPO Brain Proteome Project (RRID:SCR_007302) 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   

•   Source: SciCrunch Registry

http://kymata-atlas.org

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 31,2025. An online atlas of neural function, maintained by Cambridge University and the MRC Cognition and Brain Sciences Unit (CBSU).

Proper citation: Kymata Atlas (RRID:SCR_000269) Copy   

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http://www.cabiatl.com/mricro/anatomy/home.html

Annotated magnetic resonance brain images, both slices and surface views, normalized to Talairach space, along with annotations and a nice tutorial on image normalization. A viewer for MRI images (MRicro) is available and is described in a separate entry. Series of coronal, axial and sagittal brain slices along with some rendered volumes with major brain structures delineated. Slices are presented as static series with partial overlap of slices, so they are not suitable for 3d reconstruction. This neuroanatomy atlas shows regions on normalized MRI scans. Normalization is the process of warping a brain to match a standard size, orientation and shape of other brains. You can normalize MRI scans using programs like AIR, FLIRT or SPM. Once normalized, the overall shape of your MRI scan will approximately match those in this atlas. However, normalization preserves the unique sulcal features of each brain, so there will be some variation between your image and the images shown in this atlas. There is a great deal of individual variability even after normalization, so any atlas is only a rough guide to the shape and location of structures in an individuals brain. As I have noted before, secondary and tertiary sulci are not found in all individuals (Ono et al. 1990, Atlas of Cerebral Sulci). Another benefit of normalizing brains is it makes it easy to complete an accurate "scalp stripping" with brain extracting software (my MRIcro software implements Steve Smith's BET for this task). You can then create a useful volume rendering of the cortical surface. Typically, it is much easier to identify cortical sulci and gyri by looking at a rendered image of the brain's surface. This atlas shows you how to recognize these landmarks on a rendered MRI scan.

Proper citation: Neuroanatomy Atlas (RRID:SCR_002402) Copy   

•   Source: SciCrunch Registry