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https://github.com/bids-standard/bids-validator

Software validation tool that checks submitted folder structure for compliance to BIDS data standard. Validates Brain Imaging Data Structure.

Proper citation: BIDS Validator (RRID:SCR_017255) Copy   

•   Source: SciCrunch Registry

https://www.drugabuse.gov

Portal provides list of genetic resources such as Brain Atlases and genomes for various species provided by National Institute of Drug Abuse.

Proper citation: Compilation of Genetics Resource Databases (RRID:SCR_017501) Copy   

•   Source: SciCrunch Registry

https://github.com/nipy/heudiconv

Software tool as flexible DICOM converter for organizing brain imaging data into structured directory layouts.

Proper citation: HeuDiConv: a heuristic-centric DICOM converter (RRID:SCR_017427) Copy   

•   Source: SciCrunch Registry

https://cic.ini.usc.edu/

Project to create complete mesoscale connectivity atlas of the C57Black/6 mouse brain and to subsequently generate its global neural networks.

Proper citation: Mouse Connectome Project (RRID:SCR_017313) Copy   

•   Source: SciCrunch Registry

http://brancusi.usc.edu/bkms/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 7th, 2019. BAMS is an online resource for information about neural circuitry. The BAMS Nested Regions view focuses on the major brain regions and their relationships.

Proper citation: BAMS Nested Regions (RRID:SCR_000238) Copy   

•   Source: SciCrunch Registry

http://brainevolutionnews.blogspot.com/

Brain Evolution in the News pulls in blogs from a variety of resources on topic.

Proper citation: Brain Evolution in the News (RRID:SCR_000592) Copy   

•   Source: SciCrunch Registry

http://www.eideneurolearningblog.blogspot.com/

Weekly articles related to brain-based learning and learning styles, problem-solving and creativity, kids, families, and parenting, gifted and visual learners, dyslexia, attention deficit disorders, autism, and more.

Proper citation: Eide Neurolearning Blog (RRID:SCR_000680) Copy   

•   Source: SciCrunch Registry

http://gemma-doc.chibi.ubc.ca/neurocarta/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Neurocarta is a knowledgebase that consolidates information on genes and phenotypes across multiple resources and allows tracking and exploring of the associations. The system enables automatic and manual curation of evidence supporting each association, as well as user-enabled entry of their own annotations. Phenotypes are recorded using controlled vocabularies such as the Disease Ontology to facilitate computational inference and linking to external data sources. The gene-to-phenotype associations are filtered by stringent criteria to focus on the annotations most likely to be relevant. Neurocarta is constantly growing and currently holds more than 30,000 lines of evidence linking over 6,800 genes to 1,800 different phenotypes. Neurocarta is a one-stop shop for researchers looking for candidate genes for any disorder of interest. In Neurocarta, they can review the evidence linking genes to phenotypes and filter out the evidence they're not interested in. In addition, researchers can enter their own annotations from their experiments and analyze them in the context of existing public annotations. Neurocarta's in-depth annotation of neurodevelopmental disorders makes it a unique resource for neuroscientists working on brain development.

Proper citation: Neurocarta (RRID:SCR_000617) Copy   

•   Source: SciCrunch Registry

http://gbrowse.csbio.unc.edu/cgi-bin/gb2/gbrowse/slep/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Database of genetic and gene expression data from the published literature on psychiatric disorders. Users can search the accumulated data to find the evidence in support of the involvement of a particular genomic region with a set of important psychiatric disorders, ADHD, autism, bipolar disorder, eating disorder, major depressive disorder, schizophrenia, and smoking behavior. It contains findings from manual reviews of 144 papers in psychiatric genetics, 136 primary reports and 8 meta-analyses. Disorders covered include schizophrenia (44 papers), autism (24 papers), bipolar disorder (24 papers), smoking behavior (24 papers), major depressive disorder and neuroticism (14 papers), ADHD (8 papers), eating disorders (3 papers), and a combined schizophrenia-bipolar phenotype (3 papers). The unbiased searches integrated into SLEP include genomewide linkage (117 papers), genomewide association (15 papers), copy number variation (9 papers), and gene expression studies of post-mortem brain tissue (3 meta-analyses courtesy of the Stanley Foundation). In total, SLEP captures 3,741 findings from these 144 papers. SLEP also contains over 70,000 SignPosts. These annotations derive from many different sources and are designed to try to capture current state of knowledge about disease associations in the human genome. SignPosts can be searched simultaneously with the psychiatric genetics literature in order to integrate these two bodies of knowledge. The SignPosts include: accumulated GWAS findings from the human genetics literature, the OMIM database, candidate gene association study literature, CNV location and frequency data, SNPs that influence gene expression in brain, genes expressed in brain, genes with evidence of imprinting and random monoalleleic expression, genes mutated in breast or colorectal cancer, and pathway data from BioCyc.

Proper citation: Sullivan Lab Evidence Project (RRID:SCR_000753) Copy   

•   Source: SciCrunch Registry

http://bodymap.genes.nig.ac.jp/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. A taxonomical and anatomical database of latest cross species animal EST data, clustered by UniGene and inter connected by Inparanoid. Users can search by Unigene, RefSeq, or Entrez Gene ID, or search for Gene Name or Tissue type. Data is also sortable and viewable based on qualities of normal, Neoplastic, or other. The last data import appears to be from 2008

Proper citation: BodyMap-Xs (RRID:SCR_001147) Copy   

•   Source: SciCrunch Registry

http://www.madsci.org/cgi-bin/cgiwrap/~lynn/image?return=http://www.google.com/custom?domains&name=a_vm1110&show_all=1&search

Portal for learning resources about the brain. It includes information and interactive images of transverse brain sections.

Proper citation: Visible Human Transverse Section Through the Head (RRID:SCR_001966) Copy   

•   Source: SciCrunch Registry

http://mouse.brain-map.org/static/atlas

Allen Mouse Brain Atlas includes full color, high resolution anatomic reference atlas accompanied by systematic, hierarchically organized taxonomy of mouse brain structures. Enables interactive online exploration of atlas and to provide deeper level of 3D annotation for informatics analysis and viewing in Brain Explorer 3D viewer.

Proper citation: Allen Mouse Brain Reference Atlas (RRID:SCR_002978) Copy   

•   Source: SciCrunch Registry

http://database.hudsen.eu/

Interactive digital atlas and movies comprising 3-D reconstructions at all stages of human development from Carnegie Stage 12 (CS12; ~26 days post conception (dpc)) to CS23 (~ 56 dpc) and anatomical annotations of the 3-D models linked to an anatomical database. The 3D models are generated using Optical Projection Tomography (OPT; Sharpe et al 2002). The digital atlas is also linked to a gene expression database that has been developed from the Edinburgh Mouse Atlas Project gene expression database (EMAGE). In the future, the HUDSEN EADHB aims to provide the wider scientific and medical communities with a dynamic tool for documenting and analyzing gene expression patterns and morphological changes in the developing human brain.

Proper citation: HUDSEN Electronic Atlas of the Developing Human Brain (RRID:SCR_002056) Copy   

•   Source: SciCrunch Registry

http://brain.io/

A startup research, development and innovation company based in The Grand Duchy of Luxembourg working on four major areas: Open Research, as Information Hub; Information Technology, as The Common Brain; Collective Awareness, as Manifesto; and Biophysics, as Human Extensibility. The Information Hub researches a methodology to conduct open research using a collaborative approach designed for multi-disciplinary interventions, multi-scope goal alignment, advanced analytics and a unified research experience for international cooperation. The Common Brain researches an open source intelligent architecture for future internet, one that is deeply sustainable over a highly distributed hybrid network, self-governed, heterogenous, and logical. Manifesto researches a methodology for a collaborative approach for policy making, open standardization, accreditation, verification and compliance. Human Extensibility researches the establishment of the scientific ground for a field of science concerned with the study of the physics and physiology of the human being, to provide techniques and genetic algorithms for human extensibility.

Proper citation: Brain.io (RRID:SCR_014527) Copy   

•   Source: SciCrunch Registry

http://www.tbi-impact.org/

Project focused on advancing knowledge of prognosis, trial design and treatment in Traumatic Brain Injury. IMPACT has developed and validated prognostic models for classification and characterization of TBI series, and participated in development of standardization of data collection in TBI studies.

Proper citation: IMPACT: International Mission for Prognosis and Analysis of Clinical Trials in TBI (RRID:SCR_000539) 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://mindhacks.com/

Mind Hacks: Neuroscience and psychology tricks to find out what's going on inside your brain. Mind Hacks is also a book by Tom Stafford and Matt Webb.

Proper citation: Mind Hacks (RRID:SCR_000170) Copy   

•   Source: SciCrunch Registry

http://www.pc.rhul.ac.uk/staff/J.Larsson/software.html

Set of programs and Tk/Tcl scripts, with a GUI wrapper, for extracting the inner and outer cortical surfaces from a T1-weighted MR image of the human brain. It is based on the TFI C++ library and is written for a Unix-based environment (specifically 64-bit and 32-bit versions of Ubuntu). As a courtesy to Apple users a version for Apple/X11 is maintained but the OS X version will always lag the Linux version; also, because some of the third-party libraries SurfRelax relies on may not be supported in or compatible with future versions of OS X, there is no guarantee that the OS X version will be supported indefinitely. Although in principle SurfRelax could be run under Windows (using Cygwin), because of the difficulties of maintaining multiple platforms and the limited support libraries available under Cygwin, there will no longer be support for SurfRelax on this platform. The surfaces are guaranteed to be topologically equivalent to a sphere, thereby obviating the need for handle removal. SurfRelax requires no user intervention, although minor manual editing is recommended for optimal results (normally less than 10 minutes per hemisphere). SurfRelax has several properties that compare favorably with other software packages for surface reconstruction: * Free software - The binaries (written in C++ and Tcl/Tk) are in the public domain. The source code will be released once legacy code issues have been resolved (i.e. replacing with GPL code). * Uses standard public file formats: Analyze file format (SPM/FSL-compatible) for volumes and OOGL OFF binary format for surfaces (see www.geomview.org (http://www.geomview.org/docs/html/geomview_26.html#OOGLRef)) * Combines advantages of volumetric and surface-based methods for surface generation * Correct topology of output surface guaranteed * Requires little or no user intervention - no need for manual handle removal * Relatively robust to noise - multi-scale method compensates for partial volume effects and intensity inhomogeneities * Relatively fast (an entire brain is segmented, extracted and unfolded in less than 2 hours of CPU time) * Powerful editing and visualization tools for volumes and surfaces * Readily extendable - for instance for use with monkey brains or children's brains * Can be used to visualize functional data from SPM or FSL * Includes tools for integration with Stanford's VISTASOFT tools for FMRI data analysis (white.stanford.edu)

Proper citation: SurfRelax (RRID:SCR_002139) Copy   

•   Source: SciCrunch Registry

http://www.cogsci.ucsd.edu/index.php

This UCSD department, the first Cognitive Science Department in the world, provides a focus for the continued evolution of the discipline of cognitive science. Cognitive science is a diverse field unifying three broad categories: the brain, behavior and computation. It's the study of how people, animals and computers think, act and learn. In order to understand the mind/brain, cognitive science brings together the methods and discoveries from neuroscience, psychology, linguistics, anthropology, philosophy and computer science. The interdisciplinary aspects of cognitive science continue to flourish, and the participation of the broader cognitive science community on campus continues to be fundamental to cognitive science as practiced at UCSD. The interdisciplinary Ph.D. program continues to be offered as a degree option (in addition to the departmental Ph.D.), with participation by members of the Departments of Anthropology, Biology, Cognitive Science, Communication, Computer Science and Engineering, Linguistics, Music, Neuroscience, Philosophy, Psychiatry, Psychology, and Sociology. Cognitive Science is a relatively young field that focuses on conducting new research into the field or applying existing knowledge into new applications. This Department contains several research labs each specializing in different areas and the Department's faculty has also been published many times in various publications. Sponsors: This department is supported by the University of California at San Diego.

Proper citation: UCSD Cognitive Science: The Future of Cognitive Science (RRID:SCR_001926) Copy   

•   Source: SciCrunch Registry

http://flybrain.stanford.edu/

Project content including raw image data, neuronal tracings, image registration tools and analysis scripts covering three manuscripts: Comprehensive Maps of DrosophilaHigher Olfactory Centres : Spatially Segregated Fruit and Pheromone Representation which uses single cell labeling and image registration to describe the organization of the higher olfactory centers of Drosophila; Diversity and wiring variability of olfactory local interneurons in the Drosophila antennal lobe which uses single cell labeling to describe the organization of the antennal lobe local interneurons; and Sexual Dimorphism in the Fly Brain which uses clonal analysis and image registration to identify a large number of sex differences in the brain and VNC of Drosophila. Data * Raw Data of Reference Brain (pic, amira) (both seed and average) * Label field of LH and MB calyx and surfaces for these structures * Label field of neuropil of Reference Brain * Traces (before and after registration). Neurolucida, SWC and AmiraMesh lineset. * MB and LH Density Data for different classes of neuron. In R format and as separate amira files. * Registration files for all brains used in the study * MBLH confocal images for all brains actually used in the study (Biorad pic format) * Sample confocal images for antennal lobe of every PN class * Confocal stacks of GABA stained ventral PNs Programs * ImageJ plugins (Biorad reader /writer/Amira reader/writer/IGS raw Reader) * Binary of registration, warp and gregxform (macosx only, others on request) * Simple GUI for registration tools (macosx only at present) * R analysis/visualization functions * Amira Script to show examples of neuronal classes The website is a collaboration between the labs of Greg Jefferis and Liqun Luo and has been built by Chris Potter and Greg Jefferis. The core Image Registration tools were created by Torsten Rohlfing and Calvin Maurer.

Proper citation: Flybrain at Stanford (RRID:SCR_001877) Copy   

•   Source: SciCrunch Registry