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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   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/cmind_2014/

A database that contains brain imaging data collected on 3T MRI scanners from over 200 normally developing healthy children from birth to 18 years. The imaging data stored in the C-MIND database are DTI, HARDI, 3DT1W, 3DT2W, concurrent ASL-BOLD scans during two language tasks (Stories and Sentence-Picture Matching), Resting State fMRI and Baseline ASL scans.

Proper citation: C-MIND Database (RRID:SCR_014094) Copy   

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

Stereotactic atlases with probabilistic values describing the location of the main cerebral arteries. The data collected are from the COBRA study as described in "COBRA: A prospective multimodal imaging study of dopamine, brain structure and function, and cognition" by Nevalainen et al.

Proper citation: Umea Brain Arteries (RRID:SCR_014752) Copy   

•   Source: SciCrunch Registry

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

A diffusion MRI service that handles the processing of diffusion data from raw data to structural connectivity. They provide high angular resolution (HARDI) reconstruction from DTI data with at least 20 gradient directions acquisitions.

Proper citation: Imeka Tractography Service (RRID:SCR_014124) Copy   

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

Anatomical atlases constructed by Computational Anatomy of Johns Hopkins University for analysis of shape vectors. The atlases were generated from segmented hippocampal and amygdala structures in acquired populations of children, adolescents and young adults in neuroimaging studies of major depression disorder (MDD) at Washington University at St Louis.

Proper citation: Atlases of amygdala and hippocampus for pediatric populations (RRID:SCR_014085) Copy   

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

An atlas of of the fetal brain from MRI of normal fetuses scanned prenatally generated using a mathematical framework. The atlas shows the inter-subject anatomic variability of the fetal brain over the fetal brain growth period and is currently available between 27 weeks gestational age to 35 weeks. It has been constructed following an unbiased minimum distance template estimation approach which utilizes symmetric diffeomorphic deformation and the cross-correlation (CC) similarity metric integrated with kernel regression in age.

Proper citation: CRL Unbiased and Deformable Spatiotemporal Atlas of the Fetal Brain (RRID:SCR_014176) Copy   

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http://www.nitrc.org/projects/whs-sd-atlas/

Open access volumetric atlas of anatomical delineations of rat brain based on structural contrast in isotropic magnetic resonance and diffusion tensor images acquired ex vivo from 80 day old male Sprague Dawley rat at Duke Center for In Vivo Microscopy. Spatial reference is provided by Waxholm Space coordinate system. Location of bregma and lambda are identified as anchors towards stereotaxic space. Application areas include localization of signal in non structural images. Atlas, MRI and DTI volumes, and diffusion tensor data are shared in NIfTI format.

Proper citation: Waxholm Space Atlas of the Sprague Dawley Rat Brain (RRID:SCR_017124) Copy   

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

Data collected from subjects scanned 3 times (V1, V2, V3), with V1 and V2 on a scanner, V3 on another scanner in another site. Resting state blood oxygenation level dependent functional MRI (BOLD fMRI), pseudo continuous arterial spin labeling (pCASL), and high resolution 3D T1 imaging were performed under eyes open (EO) and eyes closed (EC) conditions.

Proper citation: Intra- and inter-scanner reliability of RS-fMRI BOLD and ASL with eyes closed vs. eyes open (RRID:SCR_016935) Copy   

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

Atlas for studies of older adult brain. Includes T1-weighted template of older adult brain and tissue probability maps. Exhibits high image sharpness, provides higher inter-subject spatial normalization accuracy compared to other standardized templates and similar normalization accuracy to well-constructed study-specific templates.

Proper citation: MIITRA atlas (RRID:SCR_017566) Copy   

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http://www.nitrc.org/projects/onprc18_atlas/

Atlas includes co-registered templates constructed from MR images frequently used to characterize macroscopic brain structure T2/SPACE and T1/MP-RAGE, and diffusion tensor imaging template.

Proper citation: ONPRC18 Multimodal MRI Atlas (RRID:SCR_019073) Copy   

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

Software to manage the conversion of imaging data from one file format and convention to another. It consists of a graphical user interface to visually program the translations, and a data translation engine to read, sort and translate the input files, and write the output files to disk. The data translation engine: (1) Reads metadata from a set of image files on disk to identify the source that produced each file; (2) Groups the image files into user-defined collections using image metadata values; (3) Translates each image file collection by reading metadata and pixel data and mapping the data into the appropriate output file format through a programmable set of connected modules. The Debabeler uses the Java Image I/O Plugin Architecture to read and write a wide variety of common medical image file formats, including ANALYZE, MINC, and most variations of DICOM.

Proper citation: LONI Debabeler (RRID:SCR_001160) Copy   

•   Source: SciCrunch Registry

http://www.megimaging.com/

A software program for source imaging Magnetoencephalographic data. Now MEG tools has added Imaged Coherence mapping, Talairach and MNI coordinates, Grainger Causality. MEG Tools also includes MR-FOCUSS, ECD, Beamformers and many other useful MEG tools. This is a Matlab-based software module that is used to image MEG data onto a patient's MRI. This software imports all MEG manufacture's data (4D-Neuroimaging/BTi, CTF and Neuromag/Elekta).

Proper citation: MEG Tools (RRID:SCR_000661) Copy   

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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://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   

•   Source: SciCrunch Registry

http://labs.nri.ucsb.edu/reese/benjamin/SA3D.html

A user-friendly, graphical user interface (GUI) that allows statistical and visual manipulations of real and simulated three-dimensional spatial point patterns. The analyses use files containing sets of X, Y, Z coordinates. These point patterns are frequently coordinates of cells of specific cell classes within in volumes of tissue derived from microscopy analyses. The analyses are scale independent so spatial analyses of coordinates from larger and smaller scale distributions are possible. The software can also generate sample sets of X, Y, Z coordinates for program exploration and modeling purposes.

Proper citation: Spatial Analysis 3D (RRID:SCR_002563) Copy   

•   Source: SciCrunch Registry

http://www.cise.ufl.edu/~tichen/ShapeComplexAtlas.zip

A Matlab demo for constructing a neuro-anatomical shape complex atlas from 3D MRI brain structures, based on the paper Ting Chen, Anand Rangarajan, Stephan J. Eisenschenk and Baba C. Vemuri, Construction of a Neuroanatomical Shape Complex Atlas from 3D MRI Brain Structures. In NeuroImage, Volume 60, Page 1778-1787, 2012

Proper citation: ShapeComplexAtlas (RRID:SCR_002553) Copy   

•   Source: SciCrunch Registry

http://www.na-mic.org/Wiki/index.php/UNC_SPHARM-PDM_Tutorial

Software tool that computes point-based models using a parametric boundary description for the computing of Shape analysis. The point-based models computed with the SPHARM-PDM tool can be used in combination with the also UNC designed statistical tool shapeAnalysisMANCOVA to perform quantitative morphological assessment of structural changes at speci?c locations. Shape analysis has become of increasing interest to the medical community due to its potential to precisely locate morphological changes between healthy and pathological structures.

Proper citation: SPHARM-PDM Toolbox (RRID:SCR_002546) Copy   

•   Source: SciCrunch Registry

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

A stand-alone axecutable under all main Windows OS for training the working memory. The WM Trainer looks and behaves a little bit like a video-game and has been specifically conceived for children attending the primary school. However, it can be used purposefully by people of any age, including adult and elderly. This application features highest graphic quality, a powerful adaptive engine for the difficulty level, a database of users and statistical tools to evaluate the progress. Currently English, French and Italian are supported, but any language can be easily supported.

Proper citation: Working Memory Trainer (RRID:SCR_002617) Copy   

•   Source: SciCrunch Registry

http://scikit-learn.org/

scikit-learn: machine learning in Python

Proper citation: scikit-learn (RRID:SCR_002577) 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