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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   

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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   

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http://www.mbfbioscience.com/stereo-investigator

Stereo Investigator system includes microscope, computer, and Stereo Investigator software. Software works with Brightfield, Multi-Channel Fluorescence, Confocal, and Structured Illumination Microscopes. System used to provide estimates of number, length, area, and volume of cells or biological structures in tissue specimen in areas of neuroscience including neurodegenerative diseases, neuropathy, memory, and behavior, pulmonary research, spinal cord research, and toxicology.

Proper citation: MBF BioScience: Stereo Investigator (RRID:SCR_002526) Copy   

•   Source: SciCrunch Registry

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

A software toolkit that performs multiple imputation for group level, single sample t-tests. Whole brain group level statistic maps from fMRI rarely cover the entire brain as a result of missing data. Missingness between subjects in fMRI datasets can result from susceptibility artifacts, bounding box (acquisition parameters), and small differences in post-normalized morphology. The toolkit consists of several interactive command line scripts that guide the user to map the spatial distribution of missing data across contrast images, calculate spatial neighborhood averages that help impute values, perform conventional and multiple imputed t-statistics, save the results to brain maps, and create result tables. The toolkit contains an instruction manual (pdf), two Matlab scripts and one R-Statistics script, which depend on functions defined in the popular SPM toolbox and functions defined in the MICE package for (R).

Proper citation: Group Level Imputation of Statistic Maps (RRID:SCR_002397) Copy   

•   Source: SciCrunch Registry

http://sccn.ucsd.edu/wiki/MPT

This toolbox is an EEGLAB plugin for performing Measure Projection Analysis. Measure Projection Analysis (MPA) is a novel probabilistic multi-subject inference method that overcomes EEG Independent Component (IC) clustering issues by abandoning the notion of distinct IC clusters. Instead, it searches voxel by voxel for brain regions having event-related IC process dynamics that exhibit statistically significant consistency across subjects and/or sessions as quantified by the values of various EEG measures. Local-mean EEG measure values are then assigned to all such locations based on a probabilistic model of IC localization error and inter-subject anatomical and functional differences.

Proper citation: Measure Projection Toolbox (RRID:SCR_002429) Copy   

•   Source: SciCrunch Registry

https://pydicom.github.io/

Software Python package for working with DICOM files, made for inspecting and modifying DICOM data in an easy pythonic way. The modifications can be written again to a new file. As a pure python package, it should run anywhere python runs without any other requirements.

Proper citation: pydicom (RRID:SCR_002573) Copy   

•   Source: SciCrunch Registry

http://www.lead-dbs.org/

MATLAB toolbox for deep-brain-stimulation (DBS) electrode reconstructions and visualizations based on postoperative MRI and computed tomography (CT) imaging. The toolbox also facilitates visualization of localization results in 2D/3D, analysis of DBS-electrode placement's effects on clinical results, simulation of DBS stimulations, diffusion tensor imaging (DTI) based connectivity estimates, and fiber-tracking from the VAT to other brain regions (connectomic surgery).

Proper citation: LEAD-DBS (RRID:SCR_002915) Copy   

•   Source: SciCrunch Registry

http://www.brain-connectivity-toolbox.net

A large selection of complex network measures in Matlab that are increasingly used to characterize structural and functional brain connectivity datasets. Several people have contributed to the toolbox, and if you wish to contribute with a new function or set of functions, please contact Olaf Sporns. All efforts have been made to avoid errors, but users are strongly urged to independently verify the accuracy and suitability of toolbox functions for the chosen application. Please report bugs or substantial improvements.

Proper citation: Brain Connectivity Toolbox (RRID:SCR_004841) Copy   

•   Source: SciCrunch Registry

https://www.nitrc.org/projects/gmac_2012/

Open-source software toolbox implemented multivariate spectral Granger Causality Analysis for studying brain connectivity using fMRI data. Available features are: fMRI data importing, network nodes definition, time series preprocessing, multivariate autoregressive modeling, spectral Granger causality indexes estimation, statistical significance assessment using surrogate data, network analysis and visualization of connectivity results. All functions are integrated into a graphical user interface developed in Matlab environment. Dependencies: Matlab, BIOSIG, SPM, MarsBar.

Proper citation: GMAC: A Matlab toolbox for spectral Granger causality analysis of fMRI data (RRID:SCR_009581) Copy   

•   Source: SciCrunch Registry

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

An automated toolbox for a generalized form of psychophysiological interactions for SPM and FSFAST. The automated toolbox can do the following: (a1) produce identical results to the current implementation in SPM (a2) use the current implementation of PPI in SPM but using the regional mean instead of the eigenvariate (a3) use a generalized form that allows a PPI for each task to be in the same model using either the regional mean of eigenvariate (b) create the model using the output of one of the (a) options and the first level design (c) estimate the model (/results directory) (d) compute the contrasts specified.

Proper citation: Generalized PPI Toolbox (RRID:SCR_009489) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/dti-denoising/

A Matlab package which contains six denoising filters and a noise estimation method for 4D DWI. The package includes nonlocal means, local PCA and Oracle DCT methods. Based on image redundancy and/or sparsity, the proposed filters provide efficient denoising while preserving fine structures.

Proper citation: DTI denoising (RRID:SCR_014102) Copy   

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http://cmic.cs.ucl.ac.uk/mig/index.php?n=Tutorial.NODDImatlab

This MATLAB toolbox implements a data fitting routine for Neurite Orientation Dispersion and Density Imaging (NODDI). NODDI is a new diffusion MRI technique for imaging brain tissue microstructure. Compared to DTI, it has the advantage of providing measures of tissue microstructure that are much more direct and hence more specific. It achieves this by adopting the model-based strategy which relates the signals from diffusion MRI to geometric models of tissue microstructure. In contrast to typical model-based techniques, NODDI is much more clinically feasible and can be acquired on standard MR scanners with an imaging time comparable to DTI.

Proper citation: NODDI Matlab Toolbox (RRID:SCR_006826) Copy   

•   Source: SciCrunch Registry

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

Biomedical technology resource center specializing in novel approaches and tools for neuroimaging. It develops novel strategies to investigate brain structure and function in their full multidimensional complexity. There is a rapidly growing need for brain models comprehensive enough to represent brain structure and function as they change across time in large populations, in different disease states, across imaging modalities, across age and sex, and even across species. International networks of collaborators are provided with a diverse array of tools to create, analyze, visualize, and interact with models of the brain. A major focus of these collaborations is to develop four-dimensional brain models that track and analyze complex patterns of dynamically changing brain structure in development and disease, expanding investigations of brain structure-function relations to four dimensions.

Proper citation: Laboratory of Neuro Imaging (RRID:SCR_001922) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/sri24/

An MRI-based atlas of normal adult human brain anatomy, generated by template-free nonrigid registration from images of 24 normal control subjects. The atlas comprises T1, T2, and PD weighted structural MRI, tissue probability maps (GM, WM, CSF), maximum-likelihood tissue segmentation, DTI-based measures (FA, MD, longitudinal and transversal diffusivity), and two labels maps of cortical regions and subcortical structures. The atlas is provided at 1mm isotropic image resolution in Analyze, NIFTI, and Nrrd format. We are also providing an experimental packaging for use with SPM8.

Proper citation: SRI24 Atlas: Normal Adult Brain Anatomy (RRID:SCR_002551) Copy   

•   Source: SciCrunch Registry

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

Human brain atlases for adult, pediatric and elderly populations, by iterative joint deformable registration of training datasets into a single unbiased average image. Atlases packages include T1-weighted images, tissue priors (WM,GM,CSF), lobar parcellation maps and subcortical structures. Current available atlases: * Adult atlas: Symmetric atlas generated from 50+ healthy adult subjects (20-59 year old). * UNC-MNI Pediatric 1-year-old atlas: Symmetric atlas generated from 104 1-year-old subjects, combining children at high familial risk of autism and controls. * Pediatric 4-year-old atlas: Symmetric atlas generated from 10 4-year-old healthy subjects. * Elderly atlas: Atlas generated from 27 healthy elderly subjects (60+ years old). Additional information and acknowledgment for their usage can be found by clicking on the release notes.

Proper citation: UNC Human Brain Atlas (RRID:SCR_002606) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://ibvd.virtualbrain.org/

A database of brain neuroanatomic volumetric observations spanning various species, diagnoses, and structures for both individual and group results. A major thrust effort is to enable electronic access to the results that exist in the published literature. Currently, there is quite limited electronic or searchable methods for the data observations that are contained in publications. This effort will facilitate the dissemination of volumetric observations by making a more complete corpus of volumetric observations findable to the neuroscience researcher. This also enhances the ability to perform comparative and integrative studies, as well as metaanalysis. Extensions that permit pre-published, non-published and other representation are planned, again to facilitate comparative analyses. Design strategy: The principle organizing data structure is the "publication". Publications report on "groups" of subjects. These groups have "demographic" information as well as "volume" information for the group as a whole. Groups are comprised of "individuals", which also have demographic and volume information for each of the individuals. The finest-grained data structure is the "individual volume record" which contains a volume observation, the units for the observation, and a pointer to the demographic record for individual upon which the observation is derived. A collection of individual volumes can be grouped into a "group volume" observation; the group can be demographically characterized by the distribution of individual demographic observations for the members of the group.

Proper citation: Internet Brain Volume Database (RRID:SCR_002060) Copy   

•   Source: SciCrunch Registry

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