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http://mindboggle.info/data.html

Complete set of free, publicly accessible, downloadable atlases, templates, and individual manually labeled brain image data, the largest collection of publicly available, manually labeled human brains in the world! http://journal.frontiersin.org/article/10.3389/fnins.2012.00171/full

Proper citation: Mindboggle-101 atlases (RRID:SCR_002439) Copy   

•   Source: SciCrunch Registry

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

Symmetric atlas of the primate brain created using 18 cases of rhesus macaques aged 16-34 months. It includes the T1-weighted image (with and without skull), and also tissue segmentation probability maps (white matter, gray matter, CSF, rest), subcortical structures segmentation (amygdala, caudate, hippocampus, pallidus, putamen), and a lobar parcellation map. You can find more details about the creation of this atlas in the following paper : M. Styner, R. Knickmeyer, S. Joshi, C. Coe, S. J. Short, and J. Gilmore. Automatic brain segmentation in rhesus monkeys. In Proc SPIE Vol 6512, Medical Imaging, 2007, pp. 65122 L1-8

Proper citation: UNC Primate Brain Atlas (RRID:SCR_002570) Copy   

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http://www.med.unc.edu/bric/ideagroup/free-softwares/unc-infant-0-1-2-atlases

3 atlases dedicated for neonates, 1-year-olds, and 2-year-olds. Each atlas comprises a set of 3D images made up of the intensity model, tissue probability maps, and anatomical parcellation map. These atlases are constructed with the help of state-of-the-art infant MR segmentation and groupwise registration methods, on a set of longitudinal images acquired from 95 normal infants (56 males and 39 females) at neonate, 1-year-old, and 2-year-old.

Proper citation: UNC Infant 0-1-2 Atlases (RRID:SCR_002569) Copy   

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

A 3D stereoscopic (anaglyph method) full brain functional connectivity atlas created using a parcellation atlas published by Craddock et al. (2012). Using 3D Slicer 3.6.3 and the two hundred Region of Interest (ROI) version of the Craddock atlas, 200 grayscale surface models were created using a z-stat threshold > 2.3, and each surface model was processed with a surface decimation algorithm, smoothed with the Taubin algorithm and without surface normals. For improved visualization of the functional connectivity networks and their relative anatomical position, the surface model of five subcortical anatomical structures (corpus callosum, bilateral caudate, pallidum, putamen, thalamus, amygdala and hippocampus) were included in SAIBN. These surfaces were created with 3D Slicer using the segmentation computed with Freesurfer v. 5.1. The viewer should use red-cyan glasses to see the 3D stereoscopic effect using 3D Slicer (version 3.6.3, http://www.slicer.org/pages/Special:SlicerDownloads).

Proper citation: Stereoscopic Atlas of Intrinsic Brain Networks (RRID:SCR_002568) Copy   

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

A set of tools for deriving region of interest (ROI) atlases by whole brain clustering of task or resting state data. This resource also contains several atlases derived by parcellating publicly available resting state fMRI datasets. The initial release will include python scripts and ROI atlases developed to perform the analyses described in Craddock et. al., A whole brain fMRI atlas generated via spatially constrained spectral clustering, which is currently in revision in Human Brain Mapping. The scripts provide all of the tools necessary to derive an ROI atlases using spatially constrained Ncut spectral clustering. The scripts require python, numpy and scipy to run. Source code and parcellations now available! Go to http://ccraddock.github.io/cluster_roi/ for more information.

Proper citation: Spatially Constrained Parcellation (RRID:SCR_002198) Copy   

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http://www.nitrc.org/projects/colin3t7t

High-field extension of the Colin27 single-subject atlas with additional high-resolution, quantitative, averaged scans at both 3T and 7T.

Proper citation: Colin 3T/7T High-resolution Atlas (RRID:SCR_000160) Copy   

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http://neuromorphometrics.com/?page_id=23

Collection of neuroanatomically labeled MRI brain scans, created by neuroanatomical experts. Regions of interest include the sub-cortical structures (thalamus, caudate, putamen, hippocampus, etc), along with ventricles, brain stem, cerebellum, and gray and white matter and sub-divided cortex into parcellation units that are defined by gyral and sulcal landmarks.

Proper citation: Manually Labeled MRI Brain Scan Database (RRID:SCR_009604) Copy   

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http://pingstudy.ucsd.edu/

A large multi-site pediatric MRI and genetics data resource to facilitate studies of the genomic landscape of the developing human brain. It includes information about the developing mental and emotional functions of the children to understand the genetic basis of individual differences in brain structure and connectivity, cognition, and personality. Investigators on the project are studying 1400 children between the ages of 3 and 20 years so that links between genetic variation and developing patterns of brain connectivity can be examined. Investigators interested in the effects of a particular gene will be able to search the database for any brain areas or connections between areas that differ as a function of variation in a particular gene, and also to determine if the genes appear to affect the course of brain development at some point during childhood. A data exploration tool has been created for mapping and analyzing MRI data sets collected for PING and related developmental studies. Approved investigators will be able to view raw image sets and derived 3D brain maps of MRI and DTI data, conduct hypothesis testing, and graph brain area measures as they change across the time course of development. PING Cores * Coordinating Core: Functions include project management, screening of participants and maintaining the database * Neuroimaging Core: applying a standardized high-resolution structural MRI protocol involving 3-D T1-weighted scans, a T2-weighted volume, and a set of diffusion-weighted scans with multiple b values and diffusion directions, scans to estimate MRI relaxation rates, and gradient echo EPI scans for resting state fMRI. Importantly, adaptive motion compensation, using ����??PROMO����??, a novel real-time motion correction algorithm will be used. Specific PING protocols for each scanner manufacturer: ** PING MRI Protocol - GE ** PING MRI Protocol - Philips ** PING MRI Protocol - Siemens * Assessment Core: Cognitive assessments for the PING project are conducted using the NIH Toolbox for Cognition. * Genomics Core: functions as a central repository for receipt of saliva samples collected for each study participant. Once received, samples are catalogued, maintained, and DNA is extracted using state-of-the-field laboratory techniques. Ultimately, genome-wide genotyping is performed on the extracted DNA using the Illumina Human660W-Quad BeadChip. PING involves 10 sites throughout the country including UCSD, University of Hawaii, Scripps Genomics, UCLA, UC Davis, Kennedy Krieger Institute/Johns Hopkins, Sacker Institute/Cornell University, University of Massachusetts, Massachusetts General Hospital/Harvard, and Yale. Families who may want to participate in the study, or others who want to know more about it, may email questions to ping (at) ucsd.edu.

Proper citation: Pediatric Imaging Neurocognition and Genetics (RRID:SCR_008953) Copy   

•   Source: SciCrunch Registry

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

An effort to provide a set of quasi-probabilistic atlases for established functional ROIs in the human neuroimaging literature. Many atlases exist for various anatomical parcellation schemes, such as the Brodmann areas, the structural atlases, tissue segmentation atlases, etc. To date, however, there is no atlas for so-called functional ROIs. Such fROIs are typically associated with an anatomical label of some kind (e.g. the _fusiform_ face area), but these labels are only approximate and can be misleading inasmuch as fROIs are not constrained by anatomical landmarks, whether cytoarchitectonic or based on sulcal and gyral landmarks. The goal of this project is to provide quasi-probabilistic atlases for fROIs that are based on published coordinates in the neuroimaging literature. This is an open-ended enterprise and the atlas can grow as needed. Members of the neuroscience and neuroimaging community interested in contributing to the project are encouraged to do so.

Proper citation: Functional ROI Atlas (RRID:SCR_009481) Copy   

•   Source: SciCrunch Registry

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

This atlas takes advantage of ultra-high resolution 7T MRI to provide unprecedented levels of detail on structures of the basal ganglia in-vivo. The atlas includes probability maps of the Subthalamic Nucleus (STh) using T2*-imaging. For now it has been created on 13 young healthy participants with a mean age of 24.38 (range: 22-28, SD: 2.36). We recently also created atlas STh probability maps from 8 middle-aged participants with a mean age of 50.67 (range: 40-59, SD: 6.63), and 9 elderly participants with a mean age of 72.33 (range: 67-77, SD: 2.87). You can find more details about the creation of these maps in the following papers: Young: http://www.ncbi.nlm.nih.gov/pubmed/22227131 Middle-aged & Elderly: http://www.ncbi.nlm.nih.gov/pubmed/23486960 Participating institutions are the Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, and the Cognitive Science Center Amsterdam, University of Amsterdam, the Netherlands.

Proper citation: Atlasing of the basal ganglia (RRID:SCR_009431) Copy   

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

An atlas intended to provide accurate data in terms of specific uptake location to make the BP quantitation. The VOIs were manually drawn with software Analyze 9.0 (Mayo Clinic) in 18F-DOPA brain image after spatial normalization with a 18F-DOPA Template. Each striatum was divided into 6 sub-regions: ventral caudate, anterior dorsal caudate, posterior dorsal caudate, ventral putamen, anterior dorsal putamen and posterior dorsal putamen.

Proper citation: Striatal Subregional VOImap (RRID:SCR_014173) Copy   

•   Source: SciCrunch Registry

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

The Brain Coactivation Map describes all the coactivation networks in the human brain based on the meta-analysis of more than 5,400 neuroimaging articles (from NeuroSynth) containing more than 16,000 individual experiments. The map can be browsed interactively (CoactivationMap.app on GitHub) or queried from a shell using a command line tool (cmtool on GitHub).

Proper citation: Brain Coactivation Map (RRID:SCR_014172) Copy   

•   Source: SciCrunch Registry

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

A project which systematically preprocess the data from the 1000 Functional Connectomes Project (FCP) and International Neuroimaging Data-sharing Initiative (INDI) and openly share the results. Data is currently hosted in an Amazon Web Services Public S3 Bucket and at NITRC.

Proper citation: Preprocessed Connectomes Project (RRID:SCR_014162) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/pd3/

THIS RESOURCE IS NO LONGER IN SERVICE, documented Jan. 5, 2016. Tools will be available for biomedical data mining and visualization as well as linkages to Google Maps and other online resources.

Proper citation: Parkinsons Disease Discovery Database (RRID:SCR_014160) Copy   

•   Source: SciCrunch Registry

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

Database as an open science framework with a scientific data extracted from scientific literature about various altered states of consciousness assessed with questionnaires. Used to compare what experiences are elicited by different drugs and non-pharmacological methods that induce altered states to help to understand human consciousness functions. Is listed by Neuroimaging Informatics Tools.

Proper citation: Altered States Database (RRID:SCR_016350) Copy   

•   Source: SciCrunch Registry

https://as.nyu.edu/research-centers/cbi/resources/Software.html

Software which converts DICOM images to NIfTI format.

Proper citation: dinifti (RRID:SCR_000303) Copy   

•   Source: SciCrunch Registry

http://www.sci.utah.edu/cibc/software/131-shapeworks.html

THIS RESOURCE IS NO LONGER IN SERVICE.Documented on September 2, 2022. Software that is an open-source distribution of a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wide range of shape analysis problems, including nonmanifold surfaces and objects of arbitrary topology. The proposed correspondence point optimization uses an entropy-based minimization that balances the simplicity of the model (compactness) with the accuracy of the surface representations. The ShapeWorks software includes tools for preprocessing data, computing point-based shape models, and visualizing the results.

Proper citation: ShapeWorks (RRID:SCR_000424) Copy   

•   Source: SciCrunch Registry

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

Standardizes file formats for the storage of connectivity data. These formats are developed by the Human Connectome Project and other interested parties. Use the MEDIAWIKI entry in the menu on the left for more information about the CIFTI file formats. Access the CIFTI discussion forum using the Forums entry in the menu on the left. Subscribe to the discussion forum and you will be informed about issues involving the CIFTI file formats via email.

Proper citation: CIFTI Connectivity File Format (RRID:SCR_000852) Copy   

•   Source: SciCrunch Registry

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

So far there is a lack for Random Field Theory (RFT) -based multiple comparison correction for surfaces generated in Freesurfer software package. This set of Matlab-based functions can be used for that purpose. They are based on Worsley?s SurfStat toolbox. You also need to have installed Freesurfer software package and included the Freesurfer?s matlab subdirectory in the Matlab?s search path. In addition, this tool implements the RFT-FDR hierarchical correction that can be used for optimizing the amount of smoothing in cortical thickness analyses (Neuroimage 52, 158-171).

Proper citation: RFT FDR (RRID:SCR_002533) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/nirx2nirs/

A matlab script which takes near-infrared spectroscopy data recorded by NIRx system(s) and converts it to a .nirs file format for use with the HOMER2 NIRS processing pacakge.

Proper citation: NIRx2nirs: A NIRx to .nirs data converter (RRID:SCR_002492) Copy   

•   Source: SciCrunch Registry