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http://pubbrain.org/

A literature search and visualization tool that allows end users to enter any PubMed query and see that query rendered as a heatmap illustrating which regions of interest are most commonly mentioned within the search results. To use PubBrain, simply enter any valid PubMed search in the search box.

Proper citation: PubBrain (RRID:SCR_005387) Copy   

•   Source: SciCrunch Registry

http://www.neurobureau.org/

A neuroscience collaboratory that supports open neuroscience, which basically encompasses the unrestricted sharing of: analytic tools, computational resources, data, and knowledge. Its goal is to establish a spirit and forum for open neuroscience, and to facilitate the translation of that ethos into action by conducting successful large open interdisciplinary collaborative efforts such as releasing the preprocessed version of the ADHD-200 competition dataset. The Brain-Art Competition is likewise an effort to bring attention to the more aesthetically-oriented aspects of their field, and to publicize and encourage creative developments taking place at the nexus of art and neuroimaging.

Proper citation: Neuro Bureau (RRID:SCR_005357) Copy   

•   Source: SciCrunch Registry

http://afni.nimh.nih.gov/afni/

Set of (mostly) C programs that run on X11+Unix-based platforms (Linux, Mac OS X, Solaris, etc.) for processing, analyzing, and displaying functional MRI (FMRI) data defined over 3D volumes and over 2D cortical surface meshes. AFNI is freely distributed as source code plus some precompiled binaries.

Proper citation: Analysis of Functional NeuroImages (RRID:SCR_005927) Copy   

•   Source: SciCrunch Registry

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

EPI-images of 25 participants gathered during rest as well as anonymized anatomical images of the same participants. The resting-state fMRI images were collected on several occasions: # the first resting-state scan in a scan session # 5-11 months after the first resting-state scan # about 30 (< 45) minutes after 2. Each scan occasion is released as a new version release of the resource. ---Caution: Participants here are part of the NewYork_a contribution to the 1000 Functional Connectomes Project. DO NOT combine datasets.

Proper citation: NYU CSC TestRetest (RRID:SCR_005975) Copy   

•   Source: SciCrunch Registry

http://martinos.org/mne/

Software suite for processing magnetoencephalography and electroencephalography data. Open source Python software for exploring, visualizing, and analyzing human neurophysiological data including MEG, EEG, sEEG, ECoG . Implements all functionality of MNE Matlab tools in Python and extends capabilities of MNE Matlab tools to, e.g., frequency-domain and time-frequency analyses and non-parametric statistics.

Proper citation: MNE software (RRID:SCR_005972) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/toads-cruise/

A collection of software plug-ins developed for the automatic segmentation of magnetic resonance brain images. The tools include multiple published algorithms developed at Johns Hopkins University. The SPECTRE algorithm performs brain extraction. The TOADS algorithm generates a topology-preserving tissue classification into cortical, subcortical, and cerebellar structures. The CRUISE algorithm produces inner, central, and outer cortical surfaces suitable for computing thickness and other geometric measures. Tools are also included for performing gyral labeling, lesion segmentation, thickness computation, surface visualization, and surface file conversion. All tools are released as plug-ins for the MIPAV software package and were developed using the Java Image Science Toolkit (both available at NITRC: http://nitrc.org). They are therefore cross-platform and compatible with a wide variety of file formats.

Proper citation: TOADS-CRUISE Brain Segmentation Tools (RRID:SCR_005977) Copy   

•   Source: SciCrunch Registry

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

ART ''''acpcdetect'''' program for automatic detection of the AC and PC landmarks and the mid-sagittal plane on 3D structural MRI scans. ART ''''brainwash'''' program for automatic multi-atlas skull-stripping of 3D structural MRI scans. ART ''''3dwarper'''' program of non-linear inter-subject registration of 3D structural MRI scans. Software (art2) for linear rigid-body intra-subject inter-modality (MRI-PET) image registration. Data resource: The ART projects makes available corpus callosum segmentations of 316 normal subjects from the OASIS cross-sectional database. ART ''''yuki'''' program for fast, robust, and fully automatic segmentation of the corpus callosum on 3D structural MRI scans.

Proper citation: Automatic Registration Toolbox (RRID:SCR_005993) Copy   

•   Source: SciCrunch Registry

https://analyzedirect.com/

Software toolkit for the visualization, segmentation, registration and analysis of multidimensional biomedical imaging data. With Analyze, both anatomic structure and associated function can be studied and fused together for synergistic display and measurement of important structure-to-function relationships.

Proper citation: Analyze Software System (RRID:SCR_005988) Copy   

•   Source: SciCrunch Registry

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

An end-to-end application allowing individual regional analysis of cortical thickness. This cross-platform tool can be run within Slicer3 as an external module, or directly as a command line. * Operating System: MacOS, Linux * Programming Language: C++ * Supported Data Format: ANALYZE, Nrrd, Other Format * build requires: Insight Toolkit

Proper citation: ARCTIC (RRID:SCR_005989) Copy   

•   Source: SciCrunch Registry

http://www.humanconnectomeproject.org/

A multi-center project comprising two distinct consortia (Mass. Gen. Hosp. and USC; and Wash. U. and the U. of Minn.) seeking to map white matter fiber pathways in the human brain using leading edge neuroimaging methods, genomics, architectonics, mathematical approaches, informatics, and interactive visualization. The mapping of the complete structural and functional neural connections in vivo within and across individuals provides unparalleled compilation of neural data, an interface to graphically navigate this data and the opportunity to achieve conclusions about the living human brain. The HCP is being developed to employ advanced neuroimaging methods, and to construct an extensive informatics infrastructure to link these data and connectivity models to detailed phenomic and genomic data, building upon existing multidisciplinary and collaborative efforts currently underway. Working with other HCP partners based at Washington University in St. Louis they will provide rich data, essential imaging protocols, and sophisticated connectivity analysis tools for the neuroscience community. This project is working to achieve the following: 1) develop sophisticated tools to process high-angular diffusion (HARDI) and diffusion spectrum imaging (DSI) from normal individuals to provide the foundation for the detailed mapping of the human connectome; 2) optimize advanced high-field imaging technologies and neurocognitive tests to map the human connectome; 3) collect connectomic, behavioral, and genotype data using optimized methods in a representative sample of normal subjects; 4) design and deploy a robust, web-based informatics infrastructure, 5) develop and disseminate data acquisition and analysis, educational, and training outreach materials.

Proper citation: MGH-USC Human Connectome Project (RRID:SCR_003490) Copy   

•   Source: SciCrunch Registry

http://www.pediatricmri.nih.gov/

Data sets of clinical / behavioral and image data are available for download by qualified researchers from a seven year, multi-site, longitudinal study using magnetic resonance technologies to study brain maturation in healthy, typically-developing infants, children, and adolescents and to correlate brain development with cognitive and behavioral development. The information obtained in this study is expected to provide essential data for understanding the course of normal brain development as a basis for understanding atypical brain development associated with a variety of developmental, neurological, and neuropsychiatric disorders affecting children and adults. This study enrolled over 500 children, ranging from infancy to young adulthood. The goal was to study each participant at least three times over the course of the project at one of six Pediatric Centers across the United States. Brain MR and clinical/behavioral data have been compiled and analyzed at a Data Coordinating Center and Clinical Coordinating Center. Additionally, MR spectroscopy and DTI data are being analyzed. The study was organized around two objectives corresponding to two age ranges at the time of enrollment, each with its own protocols. * Objective 1 enrolled children ages 4 years, 6 months through 18 years (total N = 433). This sample was recruited across the six Pediatric Study Centers using community based sampling to reflect the demographics of the United States in terms of income, race, and ethnicity. The subjects were studied with both imaging and clinical/behavioral measures at two year intervals for three time points. * Objective 2 enrolled newborns, infants, toddlers, and preschoolers from birth through 4 years, 5 months, who were studied three or more times at two Pediatric Study Centers at intervals ranging from three months for the youngest subjects to one year as the children approach the Objective 1 age range. Both imaging and clinical/behavioral measures were collected at each time point. Participant recruitment used community based sampling that included hospital venues (e.g., maternity wards and nurseries, satellite physician offices, and well-child clinics), community organizations (e.g., day-care centers, schools, and churches), and siblings of children participating in other research at the Pediatric Study Centers. At timepoint 1, of those enrolled, 114 children had T1 scans that passed quality control checks. Staged data release plan: The first data release included structural MR images and clinical/behavioral data from the first assessments, Visit 1, for Objective 1. A second data release included structural MRI and clinical/behavioral data from the second visit for Objective 1. A third data release included structural MRI data for both Objective 1 and 2 and all time points, as well as preliminary spectroscopy data. A fourth data release added cortical thickness, gyrification and cortical surface data. Yet to be released are longitudinally registered anatomic MRI data and diffusion tensor data. A collaborative effort among the participating centers and NIH resulted in age-appropriate MR protocols and clinical/behavioral batteries of instruments. A summary of this protocol is available as a Protocol release document. Details of the project, such as study design, rationale, recruitment, instrument battery, MRI acquisition details, and quality controls can be found in the study protocol. Also available are the MRI procedure manual and Clinical/Behavioral procedure manuals for Objective 1 and Objective 2.

Proper citation: NIH MRI Study of Normal Brain Development (RRID:SCR_003394) Copy   

•   Source: SciCrunch Registry

http://signalml.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 5, 2023.An XML-based language designed for metadescription of formats, used for digital storage of biomedical time series. Using SignalML, information on the structure of binary data files can be simply and efficiently coded. Once written, this information can be used by any software, which - owing to this metadescription - can read data files in the original format. This eliminates the need for conversions and duplication of data. signalml.org provides the following resources for interchange of relevant information and ideas: * SignalML wiki * Newsgroup / mailing list The main current software project is Svarog - a SignalML-compliant signal viewer, annotator, analyzer and (future) recorder. Svarog is written in Java and is currently best fitted for display of EEG and MEG signals. Also open platform for implementing advanced signal processing methods in user-friendly environment, at the moment interfacs for Java code, standalone executables and Matlab code via Matlab Builder for Java.

Proper citation: signalml.org (RRID:SCR_003383) Copy   

•   Source: SciCrunch Registry

http://fcon_1000.projects.nitrc.org/indi/CoRR/html/

Consortium that has aggregated resting state fMRI (R-fMRI) and diffusion imaging data from laboratories around the world, creating an open science resource for the imaging community, that facilitates the assessment of test-retest reliability and reproducibility for functional and structural connectomics. Given that this was a retrospective data collection, they have focused on basic phenotypic measures that are relatively standard in the neuroimaging field, as well as fundamental for analyses and sample characterization. Their phenotypic key is organized to reflect three classifications of variables: 1) core (i.e., minimal variables required to characterize any dataset), 2) preferred (i.e., variables that were strongly suggested for inclusion due to their relative import and/or likelihood of being collected by most sites), and 3) optional (variables that are data-set specific or only shared by a few sites). CoRR includes 33 datasets consisting of: * 1629 Subjects * 3357 Anatomical Scans * 5093 Resting Functional Scans * 1302 Diffusion Scans * 300 CBF and ASL Scans

Proper citation: Consortium for Reliability and Reproducibility (RRID:SCR_003774) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/ir/

Data repository for neuroimaging data in DlCOM and NIFTI formats. It allows users to search for and freely download publicly available data sets relating to normal subjects and those with diagnoses such as: schizophrenia, ADHD, autism, and Parkinson's disease.XNAT-based image registry that supports both NIfTI and DICOM images to promote re-use and integration of NIH funded data.

Proper citation: NITRC-IR (RRID:SCR_004162) Copy   

•   Source: SciCrunch Registry

https://nda.nih.gov/

The National Institute of Mental Health Data Archive (NDA) makes available human subjects data collected from hundreds of research projects across many scientific domains. Research data repository for data sharing and collaboration among investigators. Used to accelerate scientific discovery through data sharing across all of mental health and other research communities, data harmonization and reporting of research results. Infrastructure created by National Database for Autism Research (NDAR), Research Domain Criteria Database (RDoCdb), National Database for Clinical Trials related to Mental Illness (NDCT), and NIH Pediatric MRI Repository (PedsMRI).

Proper citation: NIMH Data Archive (RRID:SCR_004434) Copy   

•   Source: SciCrunch Registry

http://www.brainvisa.info/

BrainVISA is a modular an customizable software platform built to host heterogeneous tools dedicated to neuroimaging research. Many toolboxes have already been developed for BrainVISA (T1 MRI, sulcal identification and morphometry, cortical surface analysis, diffusion imaging and tractography, fMRI, nuclear imaging, EEG and MEG, TMS, histology and autoradiography, etc.). Anatomist is a software for interactive visualization of multimodal data and for manipulation of structured 3D objects. It allows to build scenes that merge or combine images, meshes, regions of interest, fibers, textures, color palettes, referential changes, etc. A user can interact in 3D and in real time with the objects of an Anatomist scene: change point of view, select objects, add/suppress objects, change colors, draw regions of interests, do manual registration, etc. BrainVISA main features are: * Harmonization of communications between different software. For instance, BrainVISA toolboxes are using home-made software but also third-party software such as FreeSurfer, FSL, SPM, nipy, R-project, Matlab, etc. * Ontology-based data organization allowing database sharing and automation of mass of data analysis. * Fusion and interactive visualization of multimodal data (using Anatomist software). * Automatic generation of graphical user interfaces. * Workflow monitoring and data quality checking. * Full customization possible. * Runs on Linux, Mac and Windows. * Programming Language: C++, Python * Supported Data Format: ANALYZE, DICOM, GIfTI, MINC, NIfTI-1, Other Format

Proper citation: BrainVISA / Anatomist (RRID:SCR_007354) Copy   

•   Source: SciCrunch Registry

http://mipav.cit.nih.gov

A Java-based application that enables quantitative analysis and visualization of medical images of numerous modalities such as DTI, PET, MRI, CT, or microscopy. Using MIPAV's standard user-interface and analysis tools, researchers at remote sites (via the internet) can easily share research data and analyses, thereby enhancing their ability to research, diagnose, monitor, and treat medical disorders. MIPAV can be run on any Java-enabled platform such as Windows, UNIX, or Macintosh OS X. Functionality includes segmentation, inter- and intra multi-modality registration, surface rendering, volume rendering and reading and writing a large number of biomedical file formats including: DICOM 3.0, Analyze, NIFTI, SPM, MINC, Phillips, GE, Zeiss, Biorad, jpeg, png, tiff, mrc, fits, interfile, and many more.

Proper citation: MIPAV: Medical Image Processing and Visualization (RRID:SCR_007371) Copy   

•   Source: SciCrunch Registry

http://moose.sourceforge.net/

MOOSE is the Multiscale Object-Oriented Simulation Environment. It is the base and numerical core for large, detailed simulations including Computational Neuroscience and Systems Biology. MOOSE spans the range from single molecules to subcellular networks, from single cells to neuronal networks, and to still larger systems. it is backwards-compatible with GENESIS, and forward compatible with Python and XML-based model definition standards like SBML and MorphML. MOOSE is coordinating with the GENESIS-3 project towards the goals of developing educational resources for modeling. MOOSE is open source software, licensed under the LGPL (Lesser GNU Public License). It has absolutely no warranty. Sponsors: - National Center of Biological Sciences (NCBS) - National Institutes of Health (NIH) Collaboration - EU-India grid - Department of Atomic Energy Science Research Council (DAE/SRC) - Department of Biotechnology (DBT)

Proper citation: Multiscale Object Orientation Simulation Environment (RRID:SCR_008031) Copy   

•   Source: SciCrunch Registry

http://www.alivelearn.net/xjview8/

A viewing program for Statistical Parametric Mapping (SPM2, SPM5 and SPM8). p-value slider, displays multiple images at a time and can be used to build Region of Interest (ROI) masks. For a given region you can find the anatomical name and search the selected region in online database (wiki, Google scholar and PubMed).

Proper citation: xjView: A Viewing Program For SPM (RRID:SCR_008642) Copy   

•   Source: SciCrunch Registry

https://www.humanconnectome.org/software/connectome-workbench

Software brain visualization, analysis and discovery tool for fMRI and dMRI brain imaging data, including functional and structural connectivity data generated by the Human Connectome Project. Used to map brain imaging data. Allows for visualization of outputs from HCP pipelines from single subject, or average data from group of subjects and register that data onto standard brain atlas.

Proper citation: Connectome Workbench (RRID:SCR_008750) Copy   

•   Source: SciCrunch Registry