Tools   Select Another Resource Report Type

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://www.mri-resource.kennedykrieger.org/

Biomedical technology research center that provides expertise for the design of quantitative magnetic resonance imaging (MRI) and spectroscopy (MRS) data acquisition and processing technologies that facilitate the biomedical research of a large community of clinicians and neuroscientists in Maryland and throughout the USA. These methods allow noninvasive assessment of changes in brain anatomy as well as in tissue metabolite levels, physiology, and brain functioning while the brain is changing size during early development and during neurodegeneration, i.e. the changing brain throughout the life span. The Kirby Center has 3 Tesla and 7 Tesla state of the art scanners equipped with parallel imaging (8, 16, and 32-channel receive coils) and multi-transmit capabilities. CIS has an IBM supercomputer that is part of a national supercomputing infrastructure. Resources fall into the following categories: * MRI facilities, image acquisition, and processing * Computing facilities and image analysis * Novel statistical methods for functional brain imaging * Translating laboratory discoveries to patient treatment

Proper citation: National Resource for Quantitative Functional MRI (RRID:SCR_006716) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   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://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://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   

•   Source: SciCrunch Registry

http://www.mrc-cbu.cam.ac.uk/Imaging

Portal where neuroimaging studies are carried out using a Siemens 3T Tim Trio Magnetic Resonance Imaging (or MRI) scanner that is wholly dedicated to studies in Cognitive Neuroscience. From emotions and memories to language and learning, functional neuroimaging is being applied in many different areas of Cognitive Neuroscience. In many cases, this research relies upon support from healthy volunteers although neuroimaging studies are also being conducted in various clinical populations, including depression, anxiety, Parkinson's disease and Alzheimer's disease.

Proper citation: CBU Imaging Wiki (RRID:SCR_003014) Copy   

•   Source: SciCrunch Registry

http://bluebrain.epfl.ch/

A Swiss-led project with the aim of reverse engineering the mammalian brain and achieving a complete virtual human brain. The researchers have demonstrated the validity of their method by developing a realistic model of a rat cortical column, consisting of about 10,000 neurons. The eventual goal is to simulate systems of millions and hundreds of millions of neurons. The virtual brain will be an exceptional tool giving neuroscientists a new understanding of the brain and a better understanding of neurological diseases. In five years of work, Henry Markram's team has perfected a facility that can create realistic models of one of the brain's essential building blocks. This process is entirely data driven and essentially automatically executed on the supercomputer. Meanwhile the generated models show a behavior already observed in years of neuroscientific experiments. These models will be basic building blocks for larger scale models leading towards a complete virtual brain.

Proper citation: Blue Brain Project (RRID:SCR_002994) Copy   

•   Source: SciCrunch Registry

http://neurovault.org/

Data repository where researchers can publicly store and share unthresholded statistical brain activation maps produced by MRI and PET studies.

Proper citation: NeuroVault (RRID:SCR_003806) Copy   

•   Source: SciCrunch Registry

http://cibsr.stanford.edu/

The Center for Interdisciplinary Brain Sciences Research (CIBSR) at the Stanford University School of Medicine is dedicated to research that will improve the lives and well-being of individuals with disorders of the brain and improve knowledge of healthy brain and behavioral development. CIBSR research staff are dedicated to identifying biological and environmental risk factors, understanding disease pathophysiology and developmental outcomes, and developing new treatments for neurodevelopmental, neurogenetic and neuropsychiatric disorders of childhood onset. Our research studies are truly multi/interdisciplinary as they bring together experts from the fields of psychiatry, neurology, psychology, computer science, biostatistics and genetics to explore and seek answers for complex questions related to brain-behavior relationships. Active research at CIBSR includes: * Mutlimodal imaging of the brain utilizing anatomical and functional magnetic resonance imaging (MRI), diffusion tensor imaging (DTI) and magnetic resonance spectroscopy (MRS). * Behavioral, cognitive, and physiological assessment to address questions concerning the influence of biological and environmental factors on outcome. * The development of new biological and cognitive-behavioral treatments. * Development of brain image analysis methods and software.

Proper citation: Stanford University, Center for Interdisciplinary Brain Sciences Research (RRID:SCR_004134) Copy   

•   Source: SciCrunch Registry

https://cnmdp.atlassian.net/wiki/spaces/CNMDP/overview

Platform to store, analyze, and share large amounts of various types of data to facilitate research, and ultimately, unravel the complex connections between the brain and the heart.

Proper citation: Cardio-Neuro-Mind Data Platform (RRID:SCR_027008) Copy   

•   Source: SciCrunch Registry

https://github.com/TonnesenLab/Diffusion-Model/

Software code for simulating diffusion in brain extracellular space images.

Proper citation: Diffusion-Model (RRID:SCR_027942) Copy   

•   Source: SciCrunch Registry

https://www.synapse.org/

Sage Bionetworks, Mount Sinai School of Medicine (MSSM), University of Pennsylvania (Penn), the National Institute of Mental Health (NIMH), and Takeda Pharmaceuticals Company Limited (TAKEDA) have launched a Public-Private Pre-Competitive Consortium, the CommonMind Consortium, to generate and analyze large-scale genomic data from human subjects with neuropsychiatric disease and to make this data and the associated analytical results broadly available to the public. This collaboration brings together disease area expertise, large scale and well curated brain sample collections, and data management and analysis expertise from the respective institutions. As many as 450 million people worldwide are believed to be living with a mental or behavioral disorder: schizophrenia and bipolar disorder are two of the top six leading causes of years lived with disability according to the World Health Organization. The burden on the individual as well as on society is significant with estimates for the health care costs for these individuals as high as four percent GNP. This highlights a grave need for new therapies to alleviate this suffering. Researchers from MSSM including Dr. Pamela Sklar, Dr. Joseph Buxbaum and Dr. Eric Schadt will join with Dr. Raquel Gur and Dr. Chang-Gyu Hahn from Penn to combine their extensive brain bank collections for the generation of whole genome scale RNA and DNA sequence data. Dr.Pamela Sklar, Professor of Psychiatry and Neuroscience at MSSM commented this is an exciting opportunity for us to use the newest genomic methods to really expand our understanding of the molecular underpinnings of neuropsychiatric disease, while Dr Raquel Gur, Professor of Psychiatry from Penn observed this will be a great complement to some of the large-scale genetic analyses that have been carried out to date because it will give a more complete mechanistic picture. The CommonMind Consortium is committed to generating an open resource for the community and invites others with common goals to contact us at info (at) CommonMind.org.

Proper citation: CommonMind Consortium (RRID:SCR_000139) 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