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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   

•   Source: SciCrunch Registry

http://www.kitware.com

A software repository which provides open source software and technology for visualization, computer vision, medical imaging, data publishing, and quality software process solutions. Kitware also provides services such as creating customized applications for clients, porting their open-source tools to specialized computing platforms, and supporting their open-source software tools with documentation, professional consulting services, and software training.

Proper citation: Kitware (RRID:SCR_013989) Copy   

•   Source: SciCrunch Registry

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://sccn.ucsd.edu/wiki/BCILAB

Open Source MATLAB toolbox and EEGLAB plugin for the design, prototyping, testing, experimentation with, and evaluation of Brain-Computer Interfaces (BCIs), and other systems in the same computational framework. It facilitates the design and development of new methods for cognitive state estimation and their use in both offline data analysis and real-time applications. BCILAB includes an easily extensible collection of currently over 100 methods from the literature (covering signal processing, machine learning and BCI-specific methods). Aside from supporting advanced BCI research, a special aim of BCILAB is to facilitate the adoption of machine learning and advanced statistical modeling for functional neuroimaging purposes in tandem with the EEGLAB platform. The toolbox offers multiple different interfaces which link to the same backend functionality, including a GUI, scripting support (MATLAB-based), APIs for real-time processing, and a variety of extension component interfaces. MATLAB programming is not strictly necessary, as most BCILAB features can be accessed from the GUI, although it is required for batch scripting and custom extensions. The strength of MATLAB-based software lies in its resources for leading-edge scientific computing, as well as in the good support for rapid prototyping, but BCI systems developed in it can be used for real-time out-of-lab experimentation, and can in principle be deployed without the need for a MATLAB license. However, due to the complexity and overhead of the MATLAB environment, the system is best used as a research platform, and not as a product development environment -- end-user software is ideally re-implemented in a compiled language, after a suitable approach has been identified and extensively tested. The process of identifying and testing an approach involves more than just computation, but also data exploration and investigation - an area which is helped by the deep integration with the EEGLAB platform. In the future, this integration will be further strengthened, bringing rich statistical learning and signal processing into routine EEG analysis workflows. The toolbox has been developed by C. Kothe at the Swartz Center, inspired by the preceding PhyPA BCI toolbox created by C. Kothe and T. Zander at the Chair for Human-Machine Systems, Berlin Institute of Technology.

Proper citation: BCILAB (RRID:SCR_007013) Copy   

•   Source: SciCrunch Registry

http://www.mricro.com

Software tool as a cross-platform NIfTI format image viewer. Used for viewing and exporting of brain images. MRIcroGL is a variant of MRIcron.

Proper citation: MRIcron (RRID:SCR_002403) Copy   

•   Source: SciCrunch Registry

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

This project is meant for planning the NITRC Grantee meetings. A website for organizing meetings for the Neuroimaging Informatics Tools and Resources Clearinghouse, to facilitate participants meeting one another, and promote discussion of common interests and collaboration.

Proper citation: Grantees Meeting for NITRC (RRID:SCR_000419) Copy   

•   Source: SciCrunch Registry

https://neuinfo.org/mynif/search.php?list=cover&q=*

Service that partners with the community to expose and simultaneously drill down into individual databases and data sets and return relevant content. This type of content, part of the so called hidden Web, is typically not indexed by existing web search engines. Every record links back to the originating site. In order for NIF to directly query these independently maintained databases and datasets, database providers must register their database or dataset with the NIF Data Federation and specify permissions. Databases are concept mapped for ease of sharing and to allow better understanding of the results. Learn more about registering your resource, http://neuinfo.org/nif_components/disco/interoperation.shtm Search results are displayed under the Data Federation tab and are categorized by data type and nervous system level. In this way, users can easily step through the content of multiple resources, all from the same interface. Each federated resource individually displays their query results with links back to the relevant datasets within the host resource. This allows users to take advantage of additional views on the data and tools that are available through the host database. The NIF site provides tutorials for each resource, indicated by the Professor Icon professor icon showing users how to navigate the results page once directed there through the NIF. Additionally, query results may be exported as an Excel document. Note: NIF is not responsible for the availability or content of these external sites, nor does NIF endorse, warrant or guarantee the products, services or information described or offered at these external sites. Integrated Databases: Theses virtual databases created by NIF and other partners combine related data indexed from multiple databases and combine them into one view for easier browsing. * Integrated Animal View * Integrated Brain Gene Expression View * Integrated Disease View * Integrated Nervous System Connectivity View * Integrated Podcasts View * Integrated Software View * Integrated Video View * Integrated Jobs * Integrated Blogs For a listing of the Federated Databases see, http://neuinfo.org/mynif/databaseList.php or refer to the Resources Listed by NIF Data Federation table below.

Proper citation: NIF Data Federation (RRID:SCR_004834) Copy   

•   Source: SciCrunch Registry

http://rfmri.org/DPARSF

A MATLAB toolbox forpipeline data analysis of resting-state fMRI that is based on Statistical Parametric Mapping (SPM) and a plug-in software within DPABI. After the user arranges the Digital Imaging and Communications in Medicine (DICOM) files and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data and results for functional connectivity, regional homogeneity, amplitude of low-frequency fluctuation (ALFF), fractional ALFF, degree centrality, voxel-mirrored homotopic connectivity (VMHC) results. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. In addition, users can also use DPARSF to extract time courses from regions of interest. DPARSF basic edition is very easy to use while DPARSF advanced edition (alias: DPARSFA) is much more flexible and powerful. DPARSFA can parallel the computation for each subject, and can be used to reorient images interactively or define regions of interest interactively. Users can skip or combine the processing steps in DPARSF advanced edition freely.

Proper citation: DPARSF (RRID:SCR_002372) Copy   

•   Source: SciCrunch Registry

http://niftyrec.scienceontheweb.net/

Software toolbox that includes reconstruction tools for emission and transmission imaging modalities, including Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), cone-beam X-Ray CT and parallel-beam X-Ray CT. At the core of NiftyRec are efficient, GPU accelerated, projection, back-projection and iterative reconstruction algorithms. The easy to use Matlab and Python interfaces of NiftyRec enable fast prototyping and development of reconstruction algorithms. NiftyRec includes standard iterative reconstruction algorithms such as Maximum Likelihood Expectation Maximisation (MLEM), Ordered Subsets Expectation Maximisation (OSEM) and One Step Late Maximum A Posteriori Expectation Maximisation (OSL-MAPEM), for multiple imaging modalities.

Proper citation: NiftyRec (RRID:SCR_002499) Copy   

•   Source: SciCrunch Registry

http://www.warwick.ac.uk/snpm

A toolbox for Statistical Parametric Mapping (SPM) that provides an extensible framework for voxel level non-parametric permutation/randomization tests of functional Neuroimaging experiments with independent observations. SnPM uses the General Linear Model to construct pseudo t-statistic images, which are then assessed for significance using a standard non-parametric multiple comparisons procedure based on randomization/permutation testing. It is most suitable for single subject PET/SPECT analyses, or designs with low degrees of freedom available for variance estimation. In these situations the freedom to use weighted locally pooled variance estimates, or variance smoothing, makes the non-parametric approach considerably more powerful than conventional parametric approaches, as are implemented in SPM. Further, the non-parametric approach is always valid, given only minimal assumptions. The SnPM toolbox provides an alternative to the Statistics section of SPM.

Proper citation: Statistical non-Parametric Mapping (RRID:SCR_002092) Copy   

•   Source: SciCrunch Registry

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

Matlab toolbox that implements the wavelet-based image fusion technique for orthogonal images, introduced in (Aganj et al, MRM 2012).

Proper citation: Wavelet-based Image Fusion (RRID:SCR_002007) Copy   

•   Source: SciCrunch Registry

http://icatb.sourceforge.net/fusion/fusion_startup.php

A MATLAB toolbox which implements the joint Independent Component Analysis (ICA), parallel ICA and CCA with joint ICA methods. It is used to to extract the shared information across modalities like fMRI, EEG, sMRI and SNP data. * Environment: Win32 (MS Windows), Gnome, KDE * Operating System: MacOS, Windows, Linux * Programming Language: MATLAB * Supported Data Format: ANALYZE, NIfTI-1

Proper citation: Fusion ICA Toolbox (RRID:SCR_003494) 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://datalad.org/

Project to adapt model of open source software distributions to address technical limitations of data sharing and develop all components of data distribution. Builds on top of git-annex and extends it with intuitive command line interface. Enables users to operate on data using familiar concepts, such as files and directories, while transparently managing data access and authorization with underlying hosting providers. Can create DataLad datasets using any data files published on the web.

Proper citation: DataLad (RRID:SCR_003931) 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://aimlab.cs.uoregon.edu/NEMO/web/

THIS RESOURCE IS NO LONGER IN SERVICE. NIH tombstone webpage lists Project Period : 2009 - 2013. NIH funded project to create EEG and MEG ontologies and ontology based tools. These resources will be used to support representation, classification, and meta-analysis of brain electromagnetic data. Three pillars of NEMO are: DATA, ONTOLOGY, and DATABASE. NEMO data consist of raw EEG, averaged EEG (ERPs), and ERP data analysis results. NEMO ontologies include concepts related to ERP data (including spatial and temporal features of ERP patterns), data provenance, and cognitive and linguistic paradigms that were used to collect data. NEMO database portal is large repository that stores NEMO consortium data, data analysis results, and data provenance. EEG and MEG ontologies and ontology-based tools to support representation, classification, and meta-analysis of brain electromagnetic data. Raw EEG and ERP data may be uploaded to the NEMO FTP site., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Neural ElectroMagnetic Ontologies (NEMO) Project (RRID:SCR_002001) Copy   

•   Source: SciCrunch Registry

Issue

https://github.com/spm

Software package for analysis of brain imaging data sequences. Sequences can be a series of images from different cohorts, or time-series from same subject. Current release is designed for analysis of fMRI, PET, SPECT, EEG and MEG.

Proper citation: SPM (RRID:SCR_007037) Copy   

•   Source: SciCrunch Registry

http://humanconnectome.org/connectome/connectomeDB.html

Data management platform that houses all data generated by the Human Connectome Project - image data, clinical evaluations, behavioral data and more. ConnectomeDB stores raw image data, as well as results of analysis and processing pipelines. Using the ConnectomeDB infrastructure, research centers will be also able to manage Connectome-like projects, including data upload and entry, quality control, processing pipelines, and data distribution. ConnectomeDB is designed to be a data-mining tool, that allows users to generate and test hypotheses based on groups of subjects. Using the ConnectomeDB interface, users can easily search, browse and filter large amounts of subject data, and download necessary files for many kinds of analysis. ConnectomeDB is designed to work seamlessly with Connectome Workbench, an interactive, multidimensional visualization platform designed specifically for handling connectivity data. De-identified data within ConnectomeDB is publicly accessible. Access to additional data may be available to qualified research investigators. ConnectomeDB is being hosted on a BlueArc storage platform housed at Washington University through the year 2020. This data platform is based on XNAT, an open-source image informatics software toolkit developed by the NRG at Washington University. ConnectomeDB itself is fully open source.

Proper citation: ConnectomeDB (RRID:SCR_004830) Copy   

•   Source: SciCrunch Registry