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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   

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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   

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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   

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http://www.nirx.net

Commercial technology solutions for NIRS neuroscience imaging applications.

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

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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   

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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   

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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   

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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   

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http://www.psychopy.org

Open source application to allow the presentation of stimuli and collection of data for a wide range of neuroscience, psychology and psychophysics experiments. It is intended as a free, powerful alternative to Presentation or e-Prime.

Proper citation: PsychoPy (RRID:SCR_006571) Copy   

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http://www.uzh.ch/keyinst/loreta

Software package for functional imaging of human brain. Used to compute three dimensional distribution of electric neuronal activity from non-invasive measurements of scalp electric potential differences with high time resolution in millisecond range. Non-invasive intracranial time series are used for studying functional dynamic connectivity.. Current software version includes two new, improved variants of the original method: standardized (sLORETA) and exact (eLORETA). The new methods are characterized by exact localization when tested with point sources. Due to the fact that these methods are multivariate tomographies that are solutions to the inverse EEG problem, and that they are linear in nature, they will produce a low spatial resolution image for any distribution of activity. This property is not shared by naive one-at-a-time single dipole techniques.

Proper citation: Low Resolution Electromagnetic Tomography (RRID:SCR_007077) Copy   

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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   

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http://www.ppmi-info.org/

An observational longitudinal clinical study partnership to identify and validate biomarkers of Parkinson disease (PD) progression and provide easy and open web-based access to the comprehensive set of correlated clinical data and biospecimens, information, and biosamples acquired from PD and age and gender matched healthy control subjects to the research community. The data and specimens have been collected in a standardized manner under strict protocols and includes clinical (demographic, motor and non-motor, cognitive and neurobehavioral), imaging (raw and processed MRI, SPECT and DAT), and blood chemistry and hematology subject assessments and biospecimen inventories (serum, plasma, whole blood, CSF, DNA, RNA and urine). All data are de-identified to protect patient privacy. PPMI will be carried out over five years at 21 clinical sites in the United States and Europe and requires the participation of 400 Parkinson's patients and 200 control participants. The PPMI database provides researchers with access to correlated clinical and imaging data, along with annotated biospecimens, all available within an open access system that encourages data sharing (http://www.ppmi-info.org/access-data-specimens/). The website hosts an Ongoing Analysis section to keep the scientific community apprised of analyses being completed, in hopes of stimulating collaborations between researchers who are using PPMI data and specimens.

Proper citation: Parkinson's Progression Markers Initiative (RRID:SCR_006431) Copy   

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

A Java tool that takes an unorganized collection of DICOM scans, sorts and categorizes them according to user-customizable rules, gathers metadata about the scans, and saves out this information to help facilitate data uploads. Batch pr

Proper citation: DICOM UploadGUI (RRID:SCR_009458) Copy   

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

This package is a set of three slicer modules which together are able to compute a DWI Atlas given a set of DWI''s. The modules included are: # A Groupwise Registration module -> compute''s a deformation field for each DWI, using, for example, FA maps as input # Warp DWI module -> used to warp each DWI using the deformation from (1) # DWI Averager -> used to average the set of warped DWI''s producing the final DWI Atlas

Proper citation: Create DWI Atlas (RRID:SCR_009455) Copy   

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http://fmripower.org

An easy to use matlab-based graphical user interface that calculates power for future studies based on older analyses or pilot data.

Proper citation: FMRIpower (RRID:SCR_009576) Copy   

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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   

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http://www.ant-neuro.com/products/eeprobe

A complete software package for the study of event-related brain activity with high-resolution EEG/MEG. This package has been designed to suit the high standards of neuroscience research. The software has been developed originally at the Max Planck Institute for Cognitive Neuroscience in Leipzig, Germany, and is available for other institutions through ANT Neuro B.V., The Netherlands, enhanced with the EEProbe Databrowser. ERP investigations, both in psychophysiology research and clinical applications require a multitude of processing steps. Analysis of large data sets is made efficient through advanced scripting possibilities. All different aspects of data handling are efficiently available in the EEProbe Databrowser. Alternatively, external data can be imported from a multitude of formats. Processing in EEProbe makes use of open file formats (see LIBEEP) and is designed to integrate with ASA for advanced source analysis. EEProbe is available for Linux and Mac OS X.

Proper citation: EEProbe (RRID:SCR_009570) Copy   

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http://www.pstnet.com/software.cfm?ID=101

Software designed to optimize E-Prime experiments for fMRI research. EEfMRI allows you to synchronize the start of your experiment with the first scanner trigger pulse along with several valuable features to enhance the control you have over your experiment. Implementing EEfMRI into your current experiments is achieved by simply dragging and dropping the correct EEfMRI package calls into the E-Prime experiment in the appropriate places. EEfMRI is designed to integrate with other PST hardware and software to increase usability for researchers while maintaining the millisecond accuracy of E-Prime.

Proper citation: E-Prime Extensions for fMRI (RRID:SCR_009568) Copy   

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

A Matlab toolbox that allows computation of task-related functional connectivity between multiple pairs of regions. Task-related functional connectivity is computed using the correlational psychophysiological interaction (cPPI) methodology described in Fornito et al. (2012) PNAS, 109: 12788-12793. The toolbox assumes that first-level design matrices have been specified and estimated using SPM5 or later. It takes as input these design matrices as well as user-extracted regional time courses and returns a matrix of pair-wise, task-related functional connectivity for each participant. The method is scalable to large networks comprising hundreds of regions and is well-suited to graph theoretic analyses and functional connectomics. One modifiable script, cPPI_master.m, can be used to run the analysis for an entire sample of participants.

Proper citation: cPPI Toolbox for fMRI (RRID:SCR_009518) Copy   

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

This module implements the Finsler tractography method with HARDI data described by J. Melonakos et al. From a set of seeding and target points, the paths are estimated as the shortest path taking into account a local, directional dependent cost. The output provided is the connectivity map from each voxel in the volume to the seeding points, plus a vector volume with the directions tangent to the fiber bundles at each point. If the Backtracing module within is built, these directions can be traced back to actually compute the fiber bundles (VTK required). The software can be built as either a stand-alone or a CLI plugin for 3D Slicer.

Proper citation: Finsler tractography module for Slicer (RRID:SCR_009477) Copy   

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