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http://www.med.unc.edu/bric/ideagroup/free-softwares/intergroup-image-registration
Software package that provides solutions for registering two groups of images, which are the necessary steps for many brain-related applications.
Proper citation: Inter-Group Registration Toolbox (RRID:SCR_002404) Copy
A free volume processing segmenting tool that combines a flexible manual interface with powerful image processing and segmentation algorithms. Users can explore and label image volumes using slice windows and 3D volume rendering.
Proper citation: Seg3D (RRID:SCR_002552) Copy
http://sig.biostr.washington.edu/projects/MindSeer/index.html
A cross-platform application for 3D brain visualization for multi-modality neuroimaging data written in Java/Java3D, that runs in both standalone and client-server mode. It supports basic data management capabilities, visualization of 3D surfaces (SPM's output or OFF files), volumes (Analyze, NIFTI or Minc) and label sets. MindSeer has 2 different modes: # Client/Server is designed to allow users to visualize data that is stored centrally and enhance collaboration. # Standalone mode is available to view local data and is built for more performance than Client/Server Both modes have the same interface and support the same features. It has a modular architecture and is designed to be extensible. Requirements: # Java 5.0 or above. # Java Web Start. # Java3D (installed automatically by Web Start).
Proper citation: MindSeer (RRID:SCR_003019) Copy
http://wiki.na-mic.org/Wiki/index.php/2010_Winter_Project_Week_Spine_Segmentation_Module_in_Slicer3
3D Slicer module for automated segmentation of the spine. This is an implementation of a novel model-based segmentation algorithm. This work was presented at the NA-MIC Week in Salt Lake City, Jan 2010.
Proper citation: SpineSegmentation module for 3DSlicer (RRID:SCR_002593) Copy
Software library for time-series analysis of data from neuroscience experiments. It contains a core of numerical algorithms for time-series analysis both in the time and spectral domains, a set of container objects to represent time-series, and auxiliary objects that expose a high level interface to the numerical machinery and make common analysis tasks easy to express with compact and semantically clear code.
Proper citation: Nitime (RRID:SCR_002504) Copy
Issue
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
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
https://neuroscienceblueprint.nih.gov/Resources-Tools/Blueprint-Resources-Tools-Library
THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 22, 2023. National initiative to advance biomedical research through data sharing and online collaboration that provides data sharing infrastructure, software tools, strategies and advisory services. Groups may choose whether to share data internally or with external audiences. Hardware and data remain under control of individual user groups.
Proper citation: Biomedical Informatics Research Network (RRID:SCR_005163) Copy
http://www.math.mcgill.ca/keith/surfstat
A Matlab toolbox for the statistical analysis of univariate and multivariate surface data using linear mixed effects models and random field theory.
Proper citation: SurfStat (RRID:SCR_007081) Copy
http://www.nitrc.org/projects/bnv/
Aa brain network visualization tool, which can help researchers to visualize structural and functional connectivity patterns from different levels in a quick, easy, and flexible way.
Proper citation: BrainNet Viewer (RRID:SCR_009446) Copy
A tool for automatic segmentation of 3D biological datasets, with emphasis on 3D electron microscopy. It works best for 3D blob shaped objects like mitochondria, lysosomes, etc. The project is written in Python and uses the pythonxy platform (which includes scipy and ITK image processing tools).
Proper citation: Cytoseg (RRID:SCR_009553) Copy
http://www.nitrc.org/projects/nihlungseg/
A segmentation tool for the segmentation of a lung from CT images. The sofware can be run in two modes: fully automatic and semi-automatic with manual seeding by the user. The software also allows the user to perform basic filtering operations and manual correction to the segmentation. The VTK-based rendering implementation, along with option to view in axial, coronal, and sagittal, provides the user with better visualization of the segmented lung.
Proper citation: NIH-CIDI Lung Segmentation Tool (RRID:SCR_014150) Copy
http://www.nitrc.org/projects/nutil/
Software toolbox to simplify and streamline mechanism of pre and post processing 2D brain image data. Neuroscience image processing and analysis utilities. Stand alone application that runs on all operating systems. Nutil is part of the QUINT workflow.
Proper citation: Nutil - Neuroimaging utilities (RRID:SCR_017183) Copy
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
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
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
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
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
Mindboggle (http://mindboggle.info) is open source software for analyzing the shapes of brain structures from human MRI data. The following publication in PLoS Computational Biology documents and evaluates the software: Klein A, Ghosh SS, Bao FS, Giard J, Hame Y, Stavsky E, Lee N, Rossa B, Reuter M, Neto EC, Keshavan A. (2017) Mindboggling morphometry of human brains. PLoS Computational Biology 13(3): e1005350. doi:10.1371/journal.pcbi.1005350
Proper citation: Mindboggle (RRID:SCR_002438) Copy
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
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