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http://www.sci.utah.edu/software/fluorender.html

Interactive rendering tool for confocal microscopy data visualization. Combines rendering of multi-channel volume data and polygon mesh data, where properties of each dataset can be adjusted independently and quickly. Designed for neurobiologists, allowing them to better visualize confocal data from fluorescently-stained brains, but it is also useful for other biological samples. Features include feature tracking, 3D measurement tools, multiple render modes for multi-channel confocal data, and volume paint selection and segmentation.

Proper citation: FluoRender (RRID:SCR_014303) Copy   

•   Source: SciCrunch Registry

http://knowledge-space.org/

A community encyclopaedia that links brain research concepts with data, models and literature from around the world. It is an open project where users can participate and contribute to the global research community.

Proper citation: KnowledgeSpace (RRID:SCR_014539) Copy   

•   Source: SciCrunch Registry

http://www.linkrbrain.eu

An online tool for managing and viewing datasets. Data can be viewed in 2D or 3D with activation points as points clouds or projections on the cortex surface. Data can be imported as a NIfTI file or a list of activation peaks and results can be exported as a PDF file.

Proper citation: linkRbrain (RRID:SCR_014562) Copy   

•   Source: SciCrunch Registry

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

Graphical user interface that has been implemented as a 3D Slicer plugin (scripted module). It serves to display a corresponding set of cortical regions from functional connectivity matrix in an explorable 3D scene that represents brain anatomical environment. In addition to grey matter regions, MultiXplore automatically finds and extracts deterministic fiber bundles which exist between selected region(s) and adds them to the 3D environment. This feature helps in generating region-based fiber bundles given a desired whole-brain tractography data.

Proper citation: MultiXplore (RRID:SCR_014814) Copy   

•   Source: SciCrunch Registry

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

Integrated pipeline for tractography-based brain parcellation with automatic processing and massive parallel computing. ATPP offers a CLI version for parcellating multiple brain regions and a GUI version for parcellating a specific brain region. " ATPP completely follows the scientific cultural shift to open science, which aims at making scientific research including journal papers, lab notes, data, and, of course, workflow tools, accessible and transparent to all levels of society. ATPP is publicly accessible in Neuroimaging Informatics Tools and Resources Clearinghouse8 (NITRC) (https://www.nitrc.org/projects/atpp). Its source codes are hosted in GitHub9 (https://github.com/haililihai/ATPP_CLI; https://github.com/haililihai/ATPP_GUI), under the GNU generic purpose license version 310 (GPLv3), and are welcome to download and fork. The Digital Object Identifiers (DOIs) providing a persistent way to make digital data easily and uniquely citable was created from Zenodo11 platform with those GitHub repositories (ATPP CLI v2.0.0, doi: https://doi.org/10.5281/zenodo.239702; ATPP GUI v2.0.0, doi: https://doi.org/10.5281/zenodo.239705). "

Proper citation: Automatic Tractography-based Parcellation Pipeline (RRID:SCR_014815) Copy   

•   Source: SciCrunch Registry

https://github.com/BlueBrain/BluePyOpt

An extensible framework for data-driven model parameter optimization that wraps and standardizes several existing open-source tools. BluePyOpt abstracts the optimization and evaluation tasks into various reusable and flexible discrete elements according to established best-practices. It also provides methods for setting up both small- and large-scale optimizations on a variety of platforms.

Proper citation: BluePyOpt (RRID:SCR_014753) Copy   

•   Source: SciCrunch Registry

http://brainmap.org/software.html#GingerALE

Software available from brainmap.org to perform meta-analyses via the activation likelihood estimation (ALE) method. It also includes the transforms for icbm2tal conversions.

Proper citation: GingerALE (RRID:SCR_014921) Copy   

•   Source: SciCrunch Registry

http://projectreporter.nih.gov/project_info_description.cfm?aid=8662111&icde=19363315&ddparam=&ddvalue=&ddsub=&cr=2&csb=default&cs=ASC

A multi-center and multi-disciplinary study designed to dramatically increase understanding of chronic traumatic encephalopathy (CTE) and other late effects of traumatic brain injury (TBI). Overlapping clinical features, postmortem pathologies and patterns of involvement exist in TBI, CTE, and Alzheimer''s disease pose challenges to accurate diagnosis. Premortem diagnosis of CTE is currently impossible. The neuropathological consequences of single mild or moderate-severe TBI and its relationship with CTE and known dementias are unclear. The proposed project will leverage extensive resources from an ongoing population-based prospective cohort study of brain aging (Adult Changes in Thought; ACT, n=2,305) which includes excellent medical, behavioral, and genetic characterization of a cohort (20% of whom have a history of mild-moderate TBI) in addition to state-of-the-art neuropathology workup upon death. Neuropathological study of TBI effects can begin immediately in the existing ACT autopsy sample (n=489, 20% with TBI exposure). Additional cohorts of TBI- exposed individuals will come from the Brain Injury Research Center at Mount Sinai (n=150 individuals with moderate-severe TBI), the University of Texas Southwestern (n=50 retired boxers with repetitive TBI exposure), and the National Football League (n=76 retired players with repetitive TBI exposure). All participants in the proposed study (ACT and other sites) will undergo uniform harmonized neurobehavioral assessment (chosen to maximize correspondence with existing large-scale TBI and dementia studies), MRI scan, and genomic analysis. Those individuals who expire during the course of the study will undergo ex-vivo neuroimaging and extensive neuropathological exam using state-of-the-art techniques (such as Histelide) designed to quantify tau and A�� in whole brain specimens. Only by examining postmortem pathology in a sample of individuals with varying levels of TBI exposure who are well characterized during life (as proposed herein) can postmortem pathology facilitate identification of in-vivo biomarkers that can act as diagnostic tools. This project represents the most systematic and scientifically rigorous effort to date to develop a more complete understanding of the long-term clinical and neuropathological sequelae of single and multiple TBI.

Proper citation: Neuropathology of CTE and Delayed Effects of TBI: Toward In-Vivo Diagnostics (RRID:SCR_012951) Copy   

•   Source: SciCrunch Registry

http://www.brain.northwestern.edu/research/for-researchers/index.html

Tissue bank for collecting, cataloging and storing postmortem brain tissue samples from subjects with and without neurological disorders. Specimens are available for research on cognitive impairment, Alzheimer's, dementia and other disorders along with clinical data such as demographic information, health and family history and neuropsychological test scores. The bank provides services to distribute postmortem brain tissue and other samples to investigators for use in research that will provide qualitative and quantitative diagnostic information to physicians, families, and researchers.

Proper citation: Northwestern CNADC Tissue Bank / Neuropathology Core (RRID:SCR_013178) Copy   

•   Source: SciCrunch Registry

http://brain.ubc.ca/

Research facility for research on neurological and psychiatric disorders on the learning brain and the aging brain. The Centre utilizes a multidisciplinary approach to explore the causes and potential treatments of disorders like Alzheimer's disease, mental health and addiction, stroke and neurotrauma. The Centre focuses on translating research into patient care and therapies.

Proper citation: Djavad Mowafaghian Centre for Brain Health (RRID:SCR_013149) Copy   

•   Source: SciCrunch Registry

http://www.fz-juelich.de/ime/spm_anatomy_toolbox

A MATLAB toolbox which uses three dimensional probabilistic cytoarchitechtonic maps to correlate microscopic, anatomic and functional data of the cerebral cortex. Correlating the activation foci identified in functional imaging studies of the human brain with structural (e.g., cytoarchitectonic) information on the activated areas is a major methodological challenge for neuroscience research. We here present a new approach to make use of three-dimensional probabilistic cytoarchitectonic maps, as obtained from the analysis of human post-mortem brains, for correlating microscopical, anatomical and functional imaging data of the cerebral cortex. We introduce a new, MATLAB based toolbox for the SPM2 software package which enables the integration of probabilistic cytoarchitectonic maps and results of functional imaging studies. The toolbox includes the functionality for the construction of summary maps combining probability of several cortical areas by finding the most probable assignment of each voxel to one of these areas. Its main feature is to provide several measures defining the degree of correspondence between architectonic areas and functional foci. The software, together with the presently available probability maps, is available as open source software to the neuroimaging community. This new toolbox provides an easy-to-use tool for the integrated analysis of functional and anatomical data in a common reference space.

Proper citation: SPM Anatomy Toolbox (RRID:SCR_013273) 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://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://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://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   

•   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://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://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.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