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

A software package written in OpenCL (Open Computing Language) that can be used for parallel analysis of fMRI data on a large variety of hardware configurations. If BROCCOLI is running on a GPU, it can perform non-linear spatial normalization to a 1 mm brain template in 4-6 s and run a second level permutation test with 10,000 permutations.

Proper citation: BROCCOLI (RRID:SCR_014093) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/afni_3dsvm/

A command-line program and plugin for AFNI built around SVM-Light. It performs support vector machine (SVM) analysis on fMRI data and runs on Unix+X11+Motif systems, including SGI, Solaris, Linux, and Mac OS X.

Proper citation: 3dsvm (RRID:SCR_014083) Copy   

•   Source: SciCrunch Registry

http://www.softpedia.com/get/Science-CAD/BrainCSI.shtml

A tool for analysis of Magnetic Resonance Spectroscopy (MRS) data by registering it to anatomical images. BrainCSI imports LCModel results to calculate absolute metabolite concentrations using tissue water. Corrections to LCModel metabolite concentrations for partial volume of tissues are accomplished by tissue classification of the anatomical images.

Proper citation: BrainCSI (RRID:SCR_013244) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/ntu-dsi-122/

A diffusion spectrum imaging (DSI) template constructed in the standard ICBM-152 space from 122 healthy adults. The template was built through incorporating the macroscopic anatomical information using high-resolution T1-weighted images and the microscopic structural information obtained from DSI datasets, rendering it to achieve a high anatomical matching to the ICBM-152 space. This template can serve as a representative DSI dataset for a healthy adult population. It is released in its original DWI format.

Proper citation: NTU-DSI-122: a DSI template in ICBM-152 space (RRID:SCR_014155) Copy   

•   Source: SciCrunch Registry

http://www.cis.hut.fi/projects/ica/fastica/

General-purpose unsupervised data-analysis tool, most often used for brain imaging data.

Proper citation: FastICA (RRID:SCR_013110) Copy   

•   Source: SciCrunch Registry

https://github.com/NIRALUser/DTIAtlasBuilder

This tool creates an Atlas image as an average of several DTI images that will be registered. The registration will be done in two steps : - Affine Registration with BRAINSFit - Non Linear Registration with GreedyAtlas A final step will apply the transformations to the original DTI images so that the final average can be computed. The main function writes a python script that will be executed to compute the Atlas. By running DTIAtlasBuilder, you will need to fill in informations in a Graphical User Interface, and then compute the Atlas. You can also run the tool in command line without using the GUI. Using the GUI, you will be able to save or load a dataset file or a parameter file. The tool needs these other tools to work, so be sure to have these installed on your computer: - ImageMath - ResampleDTIlogEuclidean - CropDTI - dtiprocess - BRAINSFit - GreedyAtlas - dtiaverage - DTI-Reg - unu - MriWatcher If you download the package, be sure to have the glut library installed.

Proper citation: DTI Atlas Builder (RRID:SCR_013112) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/r2r_prf/

Software tool as a framework for fitting and describing connectivity patterns between regions that is a simple extension from the current population receptive field models in the visual neuroscience literature. The connectivity from each voxel in a designated seed region to a mapping region is modeled as a 3-dimensional Gaussian, providing location parameters and spread parameters. This allows the direct description of the relative mapping from one region to another.

Proper citation: Region to Region (RRID:SCR_016995) 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

http://www.hitachi-medical.co.jp/english/

An instrumental supplier which provides researchers and clinicians with sophisticated All-in-One solutions in the field of neuroscience.

Proper citation: Hitachi Optical Topography System (RRID:SCR_000865) Copy   

•   Source: SciCrunch Registry

http://neurobureau.projects.nitrc.org/ADHD200/Introduction.html

Preprocessed versions of the ADHD-200 Global Competition data including both preprocessed versions of structural and functional datasets previously made available by the ADHD-200 consortium, as well as initial standard subject-level analyses. The ADHD-200 Sample is pleased to announce the unrestricted public release of 776 resting-state fMRI and anatomical datasets aggregated across 8 independent imaging sites, 491 of which were obtained from typically developing individuals and 285 in children and adolescents with ADHD (ages: 7-21 years old). Accompanying phenotypic information includes: diagnostic status, dimensional ADHD symptom measures, age, sex, intelligence quotient (IQ) and lifetime medication status. Preliminary quality control assessments (usable vs. questionable) based upon visual timeseries inspection are included for all resting state fMRI scans. In accordance with HIPAA guidelines and 1000 Functional Connectomes Project protocols, all datasets are anonymous, with no protected health information included. They hope this release will open collaborative possibilities and contributions from researchers not traditionally addressing brain data so for those whose specialties lay outside of MRI and fMRI data processing, the competition is now one step easier to join. The preprocessed data is being made freely available through efforts of The Neuro Bureau as well as the ADHD-200 consortium. They ask that you acknowledge both of these organizations in any publications (conference, journal, etc.) that make use of this data. None of the preprocessing would be possible without the freely available imaging analysis packages, so please also acknowledge the relevant packages and resources as well as any other specific release related acknowledgements. You must be logged into NITRC to download the ADHD-200 datasets, http://www.nitrc.org/projects/neurobureau

Proper citation: ADHD-200 Preprocessed Data (RRID:SCR_000576) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/maps4mipav/

The exploratory development tree of Java Image Science Toolkit (JIST), an extension to the MIPAV (Medical Image Processing, Analysis, and Visualization) plug-in framework that allows the user to design and execute pipelines, which are multi-stage processing tasks.. New features and designs are tested here before general release into the JIST project. JIST was formerly known as the MedIC Automated Pipeline Scheduler (MAPS).

Proper citation: Maps4Mipav (Exploratory JIST) (RRID:SCR_000613) Copy   

•   Source: SciCrunch Registry

http://www.nirx.net

Commercial technology solutions for NIRS neuroscience imaging applications.

Proper citation: NIRx NIRS Neuroimaging (RRID:SCR_002491) Copy   

•   Source: SciCrunch Registry

http://fcon_1000.projects.nitrc.org/indi/retro/BeijingEOEC.html

Data set of 48 healthy controls from a community (student) sample from Beijing Normal University in China with 3 resting state fMRI scans each. During the first scan participants were instructed to rest with their eyes closed. The second and third resting state scan were randomized between resting with eyes open versus eyes closed. In addition this dataset contains a 64-direction DTI scan for every participant. The following data are released for every participant: * 6-minute resting state fMRI scan (R-fMRI) * MPRAGE anatomical scan, defaced to protect patient confidentiality * 64-direction diffusion tensor imaging scan (2mm isotropic) * Demographic information and information on the counterbalancing of eyes open versus eyes closed.

Proper citation: Beijing: Eyes Open Eyes Closed Study (RRID:SCR_001507) Copy   

•   Source: SciCrunch Registry

http://www.nmr.mgh.harvard.edu/CFNT/index

Biomedical technology research center that develops and applies innovative neuroimaging technologies and techniques to enable closer examination of the human brain, and thereby contribute to better understanding of the brain in health and disease. They develop new techniques and advance existing technologies for acquisition and analysis of functionally specific images of the working brain, with unprecedented physiological precision and spatiotemporal resolution. The research and development aims to improve and extend existing methods for non-invasive magnetic resonance image analysis and acquisition, electromagnetic source imaging, optical neuroimaging, and most recently, combined MR-PET neuroimaging. The Resource provides an essential interactive environment, within which an interdisciplinary team of highly skilled scientists, engineers, and clinicians with diverse expertise in multiple modalities and disciplines. The resource supports service use of the Center's facilities by neuroscientists throughout the country, provide extensive training opportunities for students, fellows, and staff scientists, and seek to advance the field of brain mapping through active dissemination of new knowledge and technology.

Proper citation: Center for Functional Neuroimaging Technologies (RRID:SCR_001423) Copy   

•   Source: SciCrunch Registry

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

Sample data in MINC format and collection of scripts to verify correct installation of minc-toolkit.

Proper citation: minc-toolkit-testsuite (RRID:SCR_014139) Copy   

•   Source: SciCrunch Registry

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

A project which contains data and analysis pipelines for a set of 53 subjects in a cross-sectional Parkinsons disease (PD) study. The dataset contains diffusion-weighted images (DWI) of 27 PD patients and 26 age, sex, and education-matched control subjects. The DWIs were acquired with 120 unique gradient directions, b=1000 and b=2500 s/mm2, and isotropic 2.4 mm3 voxels. The acquisition used a twice-refocused spin echo sequence in order to avoid distortions induced by eddy currents.

Proper citation: High-quality diffusion-weighted imaging of Parkinsons disease (RRID:SCR_014121) Copy   

•   Source: SciCrunch Registry

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

A longitudinal study of late-life depression at Duke University. There are 281 depressed subjects and 154 controls included. An MR scan of each subject was obtained every 2 years for up to 8 years (total of 1093 scans). Clinical assessments occurred more frequently and consists of a battery of psychiatric tests, including several depression-specific tests.

Proper citation: Efficient Longitudinal Upload of Depression in the Elderly (ELUDE) (RRID:SCR_014103) Copy   

•   Source: SciCrunch Registry

http://www.chibi.ubc.ca/WhiteText/

Freely available corpus of manually annotated brain region mentions created to facilitate text mining of neuroscience literature. The corpus contains 1,377 abstracts with 18,242 brain region annotations. Interannotator agreement was evaluated for a subset of the documents, and was 90.7% and 96.7% for strict and lenient matching respectively. We observed a large vocabulary of over 6,000 unique brain region terms and 17,000 words. For automatic extraction of brain region mentions we evaluated simple dictionary methods and complex natural language processing techniques. The dictionary methods based on neuroanatomical lexicons recalled 36% of the mentions with 57% precision. The best performance was achieved using a conditional random field (CRF) with a rich feature set. Features were based on morphological, lexical, syntactic and contextual information. The CRF recalled 76% of mentions at 81% precision, by counting partial matches recall and precision increase to 86% and 92% respectively. We suspect a large amount of error is due to coordinating conjunctions, previously unseen words and brain regions of less commonly studied organisms. We found context windows, lemmatization and abbreviation expansion to be the most informative techniques. We encourage you to test new methods and applications of the dataset. Please contact us if you do, we would like to hear about and link to your work. The abstracts are from PubMed/Medline, specifically The Journal of Comparative Neurology.

Proper citation: Automated recognition of brain region mentions in neuroscience literature. (RRID:SCR_002731) Copy   

•   Source: SciCrunch Registry

http://biodev.ece.ucsb.edu/projects/bisquik/wiki

A scalable web-based system for biological image analysis, management and exploration. The Bisque system incorporates many features useful to imaging researchers from image capture to extensible image analysis and querying. At the core, bisque maintains a flexible database of images and experimental metadata. Image analyses can be incorporated into the system and deployed on clusters and desktops. Search and comparison of datasets by image data and content is supported. Novel semantic analyses are integrated into the system allowing high level semantic queries and comparison of image content. New features and testing of Bisque version: 0.5.1, among many others are: # Parallel execution of datasets # Rich interfaces for autogenerated module UI # Abstracted storage system for local, irods, etc.. They are using Mercurial for their source control system. This should be installed before proceeding. Browse source on-line, http://biodev.ece.ucsb.edu/projects/bisquik/browser Bisque Installation, http://biodev.ece.ucsb.edu/projects/bisquik/wiki/InstallationInstructions05 Bisque DOWNLOAD, http://biodev.ece.ucsb.edu/projects/bisquik/wiki/download, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Bisque (RRID:SCR_005564) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/cs_schizbull08/

This project hosts data for CANDI Share Schizophrenia Bulletin 2008 (reference below) as part of the CANDI Neuroimaging Access Point. This set includes preprocessed MRI images and segmentation results of all 4 diagnostic groups (Healthy Controls, N=29; Schizophrenia Spectrum, N=20; Bipolar Disorder with Psychosis, N=19; and Bipolar Disorder without Psychosis, N=35). Frazier JA, Hodge SM, Breeze JL, Giuliano AJ, Terry JE, Moore CM, Kennedy DN, Lopez-Larson MP, Caviness VS, Seidman LJ, Zablotsky B, Makris N. Diagnostic and sex effects on limbic volumes in early-onset bipolar disorder and schizophrenia. Schizophr Bull. 2008 Jan;34(1):37-46.

Proper citation: CANDI Share: Schizophrenia Bulletin 2008 (RRID:SCR_009451) Copy   

•   Source: SciCrunch Registry