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

A statistical analysis tool for manifold-valued data. The SPD manifold for diffusion tensor images (DTI) and the Hilbert unit sphere for square root representation of orientation distribution functions (ODF) can be used.

Proper citation: Multivariate General Linear Models (MGLM) on Riemannian Manifolds (RRID:SCR_014143) Copy   

•   Source: SciCrunch Registry

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

Software Matlab toolbox for directed functional connectivity analysis of fMRI BOLD signal from predefined regions of interest. It recovers true structure of connections and estimates weights attributed to each connection. Obtains patterns at group and individual levels.

Proper citation: GIMME (RRID:SCR_014115) Copy   

•   Source: SciCrunch Registry

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

A set of many useful automation scripts to facilitate the neuroimaging data analysis process. It contains BASH scripts for MRI data management, FSL automation and web application.

Proper citation: XFSL: An FSL toolbox (RRID:SCR_014181) Copy   

•   Source: SciCrunch Registry

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

Software for efficient generation and simulation of models containing stochastic ion channels distributed across dendritic and axonal membranes. It computes the behavior of neurons taking account of the stochastic nature of ion channel gating and the detailed positions of the channels themselves. It is designed as a complement for existing tools.

Proper citation: Parallel Stochastic Ion Channel Simulator (RRID:SCR_014159) Copy   

•   Source: SciCrunch Registry

https://scicrunch.org/scicrunch/data/source/nlx_154697-10/search?q=*&l=

A virtual database currently indexing software and tools from the SciCrunch Registry, Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC), Visiome Platform, Cerebellar Platform, Brain Machine Interface Platform, and Genetic Analysis Software (GAS).

Proper citation: Integrated Software (RRID:SCR_004745) 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.nitrc.org/projects/nitrcext/

Software repository of custom extensions to the GForge collaborative environment.

Proper citation: NITRC GForge Extensions (RRID:SCR_002495) Copy   

•   Source: SciCrunch Registry

http://www.pymvpa.org

A Python package intended to ease statistical learning analyses of large datasets. It offers an extensible framework with a high-level interface to a broad range of algorithms for classification, regression, feature selection, data import and export. While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is truly free software (in every respect) and additionally requires nothing but free-software to run. Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. This Python-based, cross-platform, open-source software toolbox software toolbox for the application of classifier-based analysis techniques to fMRI datasets makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages.

Proper citation: PyMVPA (RRID:SCR_006099) Copy   

•   Source: SciCrunch Registry

http://nifti.nimh.nih.gov/

Coordinated and targeted service, training, and research to speed the development and enhance the utility of informatics tools related to neuroimaging. The initial focus will be on tools that are used in fMRI. If NIfTI proves useful in addressing informatics issues in the fMRI research community, it may be expanded to address similar issues in other areas of neuroimaging. Objectives of NIfTI * Enhancement of existing informatics tools used widely in neuroimaging research * Dissemination of neuroimaging informatics tools and information about them * Community-based approaches to solving common problems, such as lack of interoperability of tools and data * Unique training activities and research career development opportunities to those in the tool-user and tool-developer communities * Research and development of the next generation of neuroimaging informatics tools

Proper citation: Neuroimaging Informatics Technology Initiative (RRID:SCR_003141) Copy   

•   Source: SciCrunch Registry

https://github.com/BRAINSia/BRAINSTools

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 23,2023. A suite of tools to generate the cortical surface of the brain. The surface is generated in the middle of grey matter and can be used to measure surface features including cortical depth and curvature.

Proper citation: BRAINSCortex (RRID:SCR_001082) Copy   

•   Source: SciCrunch Registry

http://software.incf.org/

Software repository that makes it easy for neuroscientists to find, use and share software tools. The Software Center is accessible to everyone: you can browse and download available software tools without registering. However, by creating an account, you will be able to post comments, and request to join development teams. The INCF Software Center and the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) are sharing content. Software tools hosted by NITRC also appear at the INCF Software Center. Your software tool will be available to all users of the Software Center. You will be able to upload documentation, executables and related files; track use of your software; create a wiki; and establish a development team. Registered Software Center users will be able to comment on and post reviews about your software, and can request to join your development team. INCF's vision of the Software Center is that it will become a communication enabler for software users as well as developers. Accordingly, future system features to be added include communication and collaboration functions. We also plan to include support services to allow software developers organize their software, track the use, and receive feedback for further improvement and development. Further development of the Software Center will be strongly driven by the user needs. Please let us know what features you would like to see added.

Proper citation: INCF Software Center (RRID:SCR_002347) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/camino-trackvis/

Software package that allows interoperability between CAMINO and TRACKVIS. CAMINO is a leading software package in DTI processing. The package is from University of College London. TRACKVIS is a tract visualizing utility with capability of visualizing up to and over a million white matter tracts seamlessly. The package is from Massachusetts General Hospital. With increasing efforts on brain connectivity analyses it becomes important to have tools that can allow increased interoperability among different tractography tools. The tools in this package allow conversion of tracts from one format to another in a very effective way with ability to handle over a million tracts.

Proper citation: CAMINO-TRACKVIS (RRID:SCR_009450) Copy   

•   Source: SciCrunch Registry

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

A reference for modifications, extensions, and utilities for the FMRIB Software Library (FSL).

Proper citation: FSL extensions (RRID:SCR_009472) Copy   

•   Source: SciCrunch Registry

http://www.montefiore.ulg.ac.be/~phillips/FASST.html

An EEG toolbox developed to help users with 3 specific types of data and problems: simulatenous EEG-fMRI recording, continuous EEG scoring (e.g. sleep) and handling (visualisation, cutting, power spectrum, etc.) multi-channel recording of spontaneous EEG. The toolbox is written in Matlab and is specifically compatible with the BrainAmp family of EEG recorders (from BrainProducts GmbH) Three other data formats are now also supported: the edf "European Data Format", exported raw-EGI data (from Electrical Geodesics, Inc.) and the BCI2000 format.The results are directly compatible with SPM8 and are saved with SPM8 EEG data format.

Proper citation: fMRI Artefact rejection and Sleep Scoring Toolbox (RRID:SCR_009620) 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://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.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.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