Tools   Select Another Resource Report Type

http://www.mouseconnectome.org/

Three-dimensional digital connectome atlas of the C57Black/6J mouse brain and catalog of neural tracer injection cases, which will eventually cover the entire brain. Serial sections of each case are available to view at 10x magnification in the interactive iConnectome viewer. The Image Gallery provides a glimpse into some of the highlights of their data set. Representative images of multi-fluorescent tracer labeling can be viewed, while more in depth examination of these and all other cases can be performed in the iConnectome viewer. Phase 1 of this project involves generating a physical map of the basic global wiring diagram by applying proven, state of the art experimental circuit tracing methods systematically, uniformly, and comprehensively to the structural organization of all major neuronal pathways in the mouse brain. Connectivity imaging data for the whole mouse brain at cellular resolution will be presented within a standard 3D anatomic frame available through the website and accompanied by a comprehensive searchable online database. A Phase 2 goal for the future will allow users to view, search, and generate driving direction-like roadmaps of neuronal pathways linking any and all structures in the nervous system. This could be looked on as a pilot project for more ambitious projects in species with larger brains, such as human, and for providing a reliable framework for more detailed local circuitry mapping projects in the mouse.

Proper citation: Mouse Connectome Project (RRID:SCR_004096) Copy   

•   Source: SciCrunch Registry

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

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 31,2025. 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/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

http://neuro.debian.net/pkgs/cmtk.html

A software toolkit for computational morphometry of biomedical images, CMTK comprises a set of command line tools and a back-end general-purpose library for processing and I/O. The command line tools primarily provide the following functionality: registration (affine and nonrigid; single and multi-channel; pairwise and groupwise), image correction (MR bias field estimation; interleaved image artifact correction; EPI unwarping), processing (filters; combination of segmentations via voting and STAPLE; shape-based averaging), statistics (t-tests; general linear model). CMTK is implemented in C++ with parallel processing using POSIX Threads (SMP), OpenMP (SMP), Grand Central Dispatch (SMP), and CUDA (GPU). Supported file formats include Analyze (r/w), NIFTI (r/w), Nrrd (r/w), DICOM (read), BioRad (read). Data exchange with other toolkits, such as ITK, FSL, AFNI, SPM, etc. is thus easily accomplished.

Proper citation: Computational Morphometry Toolkit (RRID:SCR_002234) Copy   

•   Source: SciCrunch Registry

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

Project hosting binary packaged distributions, scripts, example datasets, and corresponding results of analysis using their UNC/Utah NAMIC DTI Fiber Analysis Framework. This project can be seens as a master project encompassing several current NITRC projects into a coherent set. Their workflow utilizes tools already available on NITRC including: * DTIPrep * DTIAtlasBuilder * FiberViewerLight * DTIAtlasFiberAnalyzer * FADTTS

Proper citation: UNC/Utah NAMIC DTI Fiber Analysis Framework (RRID:SCR_009615) Copy   

•   Source: SciCrunch Registry

http://www.brainvoyager.com/products/brainvoyagerqx.html

Commercial neuroimaging software package for multi-modal data analysis and management. It has been programmed in C++ with efficient statistical, numerical, and image processing routines. It supports parallelized basic math routines on all platforms and uses modern multi-core, multi-processor hardware for demanding computational routines.

Proper citation: BrainVoyager (RRID:SCR_013057) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/fiber-sig/

Used to analyze the fibers produced by ukf tractography

Proper citation: Fiber Tracking Tool (RRID:SCR_009474) Copy   

•   Source: SciCrunch Registry

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

These are the scripts used for the analyses reported in: Craddock RC, Holtzheimer PE, 3rd, Hu XP, Mayberg HS. (2009): Disease state prediction from resting state functional connectivity. Magn Reson Med 62(6):1619-28. Specifically included are scripts for performing t-test filter, reliability filter, recursive feature elimination, and reliability recursive feature elimination feature selection methods. These make use of wrappers that perform .632 bootstrap and k-fold cross validation strategies. The scripts are written in matlab and require the Bioinformatics toolbox. If you do not have the bioinformatics toolbox, the scripts can be easily modified to run with other matlab SVM toolboxes (i.e., libsvm, svmlight, shogun, etc.).

Proper citation: Disease State Prediction (RRID:SCR_009467) Copy   

•   Source: SciCrunch Registry

http://www.nmr.mgh.harvard.edu/DOT/resources/homer2/home.htm

Software matlab scripts used for analyzing fNIRS data to obtain estimates and maps of brain activation. Graphical user interface (GUI) for visualization and analysis of functional near-infrared spectroscopy (fNIRS) data.

Proper citation: Homer2 (RRID:SCR_009586) Copy   

•   Source: SciCrunch Registry

http://www.iit.edu/~mri/

Atlas that contains new anatomical, DTI, HARDI templates and probabilistic gray matter labels of the adult human brain in ICBM-152 space. Artifact-free MRI data from 72 human subjects was used in the development of the atlas. All diffusion MRI data collection was conducted using Turboprop, and spatial normalization was accomplished in a population-based fashion. A description of the contents of the atlas can be found in the Downloads link. NOTE: The files of the older IIT2 DTI Brain Template are still available. However, the new DTI template of the IIT Human Brain Atlas (v.3) is of superior quality and allows more accurate registration across subjects.

Proper citation: IIT Human Brain Atlas (RRID:SCR_009587) Copy   

•   Source: SciCrunch Registry

http://hermes.ctb.upm.es/

A toolbox for the Matlab environment designed to study functional and effective brain connectivity from neurophysiological data such as multivariate EEG and/or MEG records. It includes also visualization tools and statistical methods to address the problem of multiple comparisons. This toolbox may be very helpful to all the researchers working in the emerging field of brain connectivity analysis.

Proper citation: HERMES (RRID:SCR_009584) Copy   

•   Source: SciCrunch Registry

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

This is a command line tool which allows the user to study the behavior of water diffusion (using DTI data) along the length of the white matter fiber-tracts. Various tract-oriented scalar diffusion measures obtained from DTI brain images, are treated as a continuous function of white matter fibers'' arc-length. To analyze the trend along a given fiber tract, a command line tool performs kernel regression on this data. The idea is to try out different noise models and maximum likelihood estimates within kernel windows (along the tract), such that they best represent the data and are robust to noise and Partial Volume effect. The package contains several command line based modules and an GUI based tool called DTIAtlasFiberAnalyzer to access most functions. The features available in the tool currently, its use and input / output formats and other relevant details are provided in the first draft of the documentation. (http://www.na-mic.org/Wiki/index.php/Projects:dtistatisticsfibers).

Proper citation: DTI Fiber Tract Statistics (RRID:SCR_009460) Copy   

•   Source: SciCrunch Registry