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https://sites.google.com/site/functionalconnectivitytoolbox/

MATLAB toolbox for performing functional connectivity analyses includes many of the most commonly-used approaches researchers have utilized to date for the identification of condition-dependent functional interactions between fMRI time-series obtained from two or more brain regions. The approaches are either bivariate or multivariate methods defined in time or frequency domains that emphasize distinct features of relationships among the time-series.

Proper citation: Functional Connectivity Toolbox (RRID:SCR_006394) Copy   

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http://www.mitre.org/news/digest/archives/2002/neuroinformatics.html

This resource''s long-term goal is to develop informatics methodologies and tools that will increase the creativity and productivity of neuroscience investigators, as they work together to use shared human brain mapping data to generate and test ideas far beyond those pursued by the data''s originators. This resource currently has four major projects supporting this goal: * Database tools: The goal of the NeuroServ project is to provide neuroscience researchers with automated information management tools that reduce the effort required to manage, analyze, query, view, and share their imaging data. It currently manages both structural magnetic resonance image (MRI) datasets and diffusion tensor image (DTI) datasets. NeuroServ is fully web-enabled: data entry, query, processing, reporting, and administrative functions are performed by qualified users through a web browser. It can be used as a local laboratory repository, to share data on the web, or to support a large distributed consortium. NeuroServ is based on an industrial-quality query middleware engine MRALD. NeuroServ includes a specialized neuroimaging schema and over 40 custom Java Server Pages supporting data entry, query, and reporting to help manage and explore stored images. NeuroServ is written in Java for platform independence; it also utilizes several open source components * Data sharing: DataQuest is a collaborative forum to facilitate the sharing of neuroimaging data within the neuroscience community. By publishing summaries of existing datasets, DataQuest enables researchers to: # Discover what data is available for collaborative research # Advertise your data to other researchers for potential collaborations # Discover which researchers may have the data you need # Discover which researchers are interested in your data. * Image quality: The approach to assessing the inherent quality of an image is to measure how distorted the image is. Using what are referred to as no-reference or blind metrics, one can measure the degree to which an image is distorted. * Content-based image retrieval: NIRV (NeuroImagery Retrieval & Visualization) is a work environment for advanced querying over imagery. NIRV will have a Java-based front-end for users to issue queries, run processing algorithms, review results, visualize imagery and assess image quality. NIRV interacts with an image repository such as NeuroServ. Users can also register images and will soon be able to filter searches based on image quality.

Proper citation: MITRE Neuroinformatics (RRID:SCR_006508) Copy   

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https://www.phenxtoolkit.org/

Set of measures intended for use in large-scale genomic studies. Facilitate replication and validation across studies. Includes links to standards and resources in effort to facilitate data harmonization to legacy data. Measurement protocols that address wide range of research domains. Information about each protocol to ensure consistent data collection.Collections of protocols that add depth to Toolkit in specific areas.Tools to help investigators implement measurement protocols.

Proper citation: Phenotypes and eXposures Toolkit (RRID:SCR_006532) Copy   

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http://www.schizophreniaforum.org/

The mission of the SRF is to help in the search for causes, treatments, and understanding of the devastating disease of schizophrenia. Our goal is to foster collaboration among researchers by providing an international online forum where ideas, research news, and data can be presented and discussed. The website is intended to bring together scientists working specifically on schizophrenia, scientists researching related diseases, and basic scientists whose work can shed light on these diseases. In this way, we hope that the Schizophrenia Research Forum will be a catalyst for creative thinking in the quest to understand a deeply complex disease. It is our goal to create and maintain up-to-date content of the highest quality. The website is free of charge to users, independent of industry sponsorship, and open to the public. Though geared toward researchers, we welcome other visitorspeople with mental illnesses, families, the media, and others who need accurate information on research into schizophrenia. We do, however, require that users who wish to post comments and other materials be registered members. All such materials are subject to approval by the editorial team. As a forum, we encourage participation and welcome feedback from the community.

Proper citation: Schizophrenia Research Forum (RRID:SCR_002899) Copy   

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http://trans.nih.gov/bmap/resources/resources.htm

As part of BMAP gene discovery efforts, mouse brain cDNA libraries and Expressed Sequence Tags (ESTs) have been generated. Through this project a BMAP mouse brain UniGene set consisting of over 24,000 non-redundant members of unique clusters has been developed from EST sequencing of more than 50,000 cDNA clones from 10 regions of adult mouse brain, spinal cord, and retina (http://brainEST.eng.uiowa.edu/). In 2001, NIMH along with NICHD, NIDDK, and NIDA, awarded a contract to the University of Iowa ( M.B. Soares, PI) to isolate full-length cDNA clones corresponding to genes expressed in the developing mouse nervous system and determine their full-coding sequences. The BMAP mouse brain EST sequences can be accessed at NCBI's dbEST database (http://www.ncbi.nlm.nih.gov/dbEST/). Arrayed sets of BMAP mouse brain UniGenes and cDNA libraries, and individual BMAP cDNA clones can be purchased from Open Biosystems, Huntsville, AL (http://www.openbiosystems.com

Proper citation: BMAP cDNA Resources (RRID:SCR_002973) Copy   

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http://rsb.info.nih.gov/nih-image/index.html

Public image processing and analysis program for Macintosh.

Proper citation: NIH Image (RRID:SCR_003073) Copy   

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https://neurobiobank.nih.gov/

National resource for investigators utilizing human post-mortem brain tissue and related biospecimens for their research to understand conditions of the nervous system. Federated network of brain and tissue repositories in the United States that collects, evaluates, stores, and makes available to researchers, brain and other tissues in a way that is consistent with the highest ethical and research standards. The NeuroBioBank ensures protection of the privacy and wishes of donors. Provides information to the public about the need for tissue donation and how to register as a donor.

Proper citation: NIH NeuroBioBank (RRID:SCR_003131) Copy   

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https://bioimagesuiteweb.github.io/webapp/index.html

Web applications for analysis of multimodal/multispecies neuroimaging data. Image analysis software package. Has facilities for DTI and fMRI processing. Capabilities for both neuro/cardiac and abdominal image analysis and visualization. Many packages are extensible, and provide functionality for image visualization and registration, surface editing, cardiac 4D multi-slice editing, diffusion tensor image processing, mouse segmentation and registration, and much more. Can be intergrated with other biomedical image processing software, such as FSL, AFNI, and SPM.

Proper citation: BioImage Suite (RRID:SCR_002986) Copy   

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http://www.loni.usc.edu/Software/Pipeline

A free workflow application primarily aimed at neuroimaging researchers that allows users to easily describe their executables in a graphical user interface (ie. create a module) and connect them together to create complex analyses all without having to code a single line in a scripting language. The Pipeline Client runs on your PC/Mac/Linux computer upon which you can create sophisticated processing workflows using a variety of commonly available executable tools (e.g. FSL, AIR, FreeSurfer, AFNI, Diffusion Toolkit, etc). The Distributed Pipeline Server can be installed on your Linux cluster and you can submit processing jobs directly to your own compute systems. Once you����??ve created a module for use in the LONI Pipeline, you can save it into your personal library and reuse it in other workflows you create by simply dragging and dropping it in. Because the LONI Pipeline is written in Java, you can work in whatever operating system suits you best. If there are tools that you need that can only work on another operating system, you can install a Pipeline server on that computer and connect from your client to do processing and analysis remotely.

Proper citation: LONI Pipeline Processing Environment (RRID:SCR_001161) Copy   

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http://mbl.org

Collection of high resolution images and databases of brains from many genetically characterized strains of mice with aim to systematically map and characterize genes that modulate architecture of mammalian CNS. Includes detailed information on genomes of many strains of mice. Consists of images from approximately 800 brains and numerical data from just over 8000 mice. You can search MBL by strain, age, sex, body or brain weight. Images of slide collection are available at series of resolutions. Apple's QuickTime Plugin is required to view available MBL Movies.

Proper citation: Mouse Brain Library (RRID:SCR_001112) Copy   

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http://clarityresourcecenter.org/

Protocols and other training materials related to the CLARITY protocol, a technique for the transformation of intact tissue into a nanoporous hydrogel-hybridized form (crosslinked to a three-dimensional network of hydrophilic polymers) that is fully assembled but optically transparent and macromolecule-permeable.

Proper citation: Clarity resources (RRID:SCR_001387) Copy   

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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   

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http://neuromorphometrics.org:8080/nvm/index.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 6, 2023. Software tool for quantitative neuroanatomical measurements in volumetric image data. Used to draw regions of interest for subsequent fMRI analysis.

Proper citation: NVM (RRID:SCR_000600) Copy   

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http://www.loni.usc.edu/Software/LOVE

A versatile 1D, 2D and 3D data viewer geared for cross-platform visualization of stereotactic brain data. It is a 3-D viewer that allows volumetric data display and manipulation of axial, sagittal and coronal views. It reads Analyze, Raw-binary and NetCDF volumetric data, as well as, Multi-Contour Files (MCF), LWO/LWS surfaces, atlas hierarchical brain-region labelings ( Brain Trees). It is a portable Java-based software, which only requires a Java interpreter and a 64 MB of RAM memory to run on any computer architecture. LONI_Viz allows the user to interactively overlay and browse through several data volumes, zoom in and out in the axial, sagittal and coronal views, and reports the intensities and the stereo-tactic voxel and world coordinates of the data. Expert users can use LONI_Viz to delineate structures of interest, e.g., sulcal curves, on the 3 cardinal projections of the data. These curves then may be use to reconstruct surfaces representing the topological boundaries of cortical and sub-cortical regions of interest. The 3D features of the package include a SurfaceViewer and a full real-time VolumeRenderer. These allow the user to view the relative positions of different anatomical or functional regions which are not co-planar in any of the axial, sagittal or coronal 2D projection planes. The interactive part of LONI_Viz features a region drawing module used for manual delineation of regions of interest. A series of 2D contours describing the boundary of a region in projection planes (axial, sagittal or coronal) could be used to reconstruct the surface-representation of the 3D outer shell of the region. The latter could then be resliced in directions complementary to the drawing-direction and these complementary contours could be loaded in all tree cardinal views. In addition the surface object could be displayed using the SurfaceViewer. A pre-loading data crop and sub-sampling module allows the user to load and view practically data of any size. This is especially important when viewing cryotome, histological or stained data-sets which may reach 1GB (109 bytes) in size. The user could overlay several pre-registered volumes, change intensity colors and ranges and the inter-volume opacities to visually inspect similarities and differences between the different subjects/modalities. Several image-processing aids provide histogram plotting, image-smoothing, etc. Specific Features: * Region description DataBase * Moleculo-genetic database * Brain anatomical data viewer * BrainMapper tool * Surface (LightWave objects/scenes) and Volume rendering tools * Interactive Contour Drawing tool Implementation Issues: * Applet vs. Application - the software is available as both an applet and a standalone application. The former could be used to browse data from within the LONI database, however, it imposes restrictions on file-size, Internet connection and network-bandwidth and client/server file access. The later requires a local install and configuration of the LONI_Viz software * Extendable object-oriented code (Java), computer architecture independent * Complete online software documentation is available at http://www.loni.ucla.edu/LONI_Viz and a Java-Class documentation is available at http://www.loni.ucla.edu/~dinov/LONI_Vis.dir/doc/LONI_Viz_Java_Docs.html

Proper citation: LONI Visualization Tool (RRID:SCR_000765) Copy   

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http://www.hedtags.org/

Strategy guide for HED Annotation. Framework for systematically describing laboratory and real world events.HED tags are comma separated path strings. Organized in forest of groups with roots Event, Item, Sensory presentation, Attribute, Action, Participant, Experiment context, and Paradigm. Used for preparing brain imaging data for automated analysis and meta analysis. Applied to brain imaging EEG, MEG, fNIRS, multimodal mobile brain or body imaging, ECG, EMG, GSR, or behavioral data. Part of Brain Imaging Data Structure standard for brain imaging.

Proper citation: HED Tags (RRID:SCR_014074) Copy   

•   Source: SciCrunch Registry

http://wings-workflows.org

A software application which assists scientists with designing computational experiments. WINGS is a semantic workflow system which incorporates semantic constraints about datasets and workflow components into its workflow representations. The workflow system has an open modular design and can be easily integrated with other existing workflow systems and execution frameworks to extend them with semantic reasoning capabilities. WINGS also allows users to express high-level descriptions of their analysis goals, and assists them by automatically and systematically generating possible workflows that are consistent with that request. In cases where privacy or off-line use are important, WINGS can submit workflows in a scripted format for execution in the local host. It uses Pegasus or OODT as the execution engine for large-scale distributed workflow execution.

Proper citation: WINGS (RRID:SCR_013997) Copy   

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

A database which contains longitudinal structural MRIs, spectroscopy, DTI and correlated clinical/behavioral data from approximately 500 healthy, normally developing children, ages newborn to young adult.

Proper citation: NIH Pediatric MRI Data Repository (RRID:SCR_014149) Copy   

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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   

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http://nunda.northwestern.edu/nunda/app

A resource for managing study data collected by the Northwestern University neuroimaging community. It includes a secure database, automated pipelines for processing managed data, and tools for exploring and accessing the data. Access to data in the NUNDA is restricted to users authorized by the specific study's investigators. The NUNDA is hosted by the Neuroimaging & Applied Computational Anatomy Lab, and it is modeled after the Washington University's Central Neuroimaging Data Archive (CNDA). The NUNDA is powered by XNAT, an open source software package for managing neuroimaging and related data.

Proper citation: NUNDA (RRID:SCR_013664) Copy   

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https://imagej.net/

Open source Java based image processing software program designed for scientific multidimensional images. ImageJ has been transformed to ImageJ2 application to improve data engine to be sufficient to analyze modern datasets.

Proper citation: ImageJ (RRID:SCR_003070) Copy   

•   Source: SciCrunch Registry