Tools   Select Another Resource Report Type

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

Software Python library that relies on the REST API provided by the XNAT platform since its 1.4 version. XNAT is an extensible database for neuroimaging data. The main objective is to ease communications with an XNAT server to plug-in external tools or python scripts to process the data.

Proper citation: pyxnat (RRID:SCR_002574) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/ncanda-datacore/

Manuals, training materials, and computational tools developed by the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) Data Component. The NCANDA consortium consists of an Administrative Component at UC San Diego, the Data Integration Component at SRI International, and five data collection sites, Duke University, Oregon Health & Sciences University, SRI International, University of Pittsburgh, and UC San Diego. Each collection site will collect data from about 150 adolescents, each of them seen for one baseline and three annual follow-up visits.

Proper citation: NCANDA: Data Integration Component (RRID:SCR_002447) Copy   

•   Source: SciCrunch Registry

http://nipy.org/nipy

A complete Python environment for the analysis of structural and functional neuroimaging data. It currently has a full system for general linear modeling of functional magnetic resonance imaging (fMRI).

Proper citation: NIPY (RRID:SCR_002489) Copy   

•   Source: SciCrunch Registry

http://www.neurobs.com/

Stimulus delivery and experiment control program. Stimuli include auditory, 2D and 3D visual, and multimodal and experimental data include fMRI, ERP, MEG, psychophysics, eye movements, single neuron recording, and reaction time measures.

Proper citation: Presentation (RRID:SCR_002521) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/parser_4d/

A tool for analyzing 4D images with pathology. Originally developed for processing longitudinal images of patients with traumatic brain injury, the tool contains new image analysis algorithms that combine registration and segmentation in a coherent framework, accounting for extreme changes due to extensive tissue damage.

Proper citation: 4D-PARSeR Pathological Anatomy Regression via Segmentation and Registration (RRID:SCR_002480) Copy   

•   Source: SciCrunch Registry

http://www.paraview.org/

Open source, multi platform data analysis and visualization application. ParaView users can quickly build visualizations to analyze their data using qualitative and quantitative techniques. The data exploration can be done interactively in 3D or programmatically using ParaView's batch processing capabilities. ParaView was developed to analyze extremely large datasets using distributed memory computing resources. It can be run on supercomputers to analyze datasets of terascale as well as on laptops for smaller data.

Proper citation: ParaView (RRID:SCR_002516) Copy   

•   Source: SciCrunch Registry

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

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 14, 2026. Software tools which can perform Stochastic Tractography and related analysis on DWMRI data. Stochastic Tractography applies a Bayesian approach towards the estimation of nerve fiber tracts from DWMRI images.

Proper citation: Stochastic Tractography System (RRID:SCR_002594) Copy   

•   Source: SciCrunch Registry

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

Software package representing Spatial Statistical Parametric Mapping that includes two tools presently: MAGEE and FADTTS. MAGEE represents the Multiscale Adaptive Generalized Estimating Equation. It was developed specifically for analyzing multivariate neuroimaging data in 3-dimensional volume (or on 2-dimensional surface) from longitudinal neuroimaging studies. FADTTS represents Functional Analysis of Diffusion Tensor Tract Statistics. The aim of this tool is to implement a functional analysis pipeline, for delineating the structure of the variability of multiple diffusion properties along major white matter fiber bundles and their association with a set of covariates of interest, in various diffusion tensor imaging studies.

Proper citation: Spatial Statistical Parametric Mapping (RRID:SCR_002592) Copy   

•   Source: SciCrunch Registry

http://mialab.mrn.org/software/eegift/index.html

Implements multiple algorithms for independent component analysis and blind source separation of group (and single subject) EEG data. This MATLAB toolbox is compatible with MATLAB 6.5 and higher.

Proper citation: Group ICA Of EEG Toolbox (RRID:SCR_002478) Copy   

•   Source: SciCrunch Registry

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

A brain imaging classification tool, which can help researchers to discriminate patients from normal controls. The M3 includes three steps: feature selection, maximum uncertainty linear discriminant analysis (MLDA)-based classification and multi-classifier. A leave-one-out cross-validation (LOOCV) is further used to estimate the performance of the M3. Finally, the most discriminative features are identified.

Proper citation: M3 (RRID:SCR_002475) Copy   

•   Source: SciCrunch Registry

http://theobjects.com/en/products/scientific/index.php

Software with advanced visualization techniques and state-of-the-art volume rendering provide unparalleled insight into the details and properties of neurological data acquired by CT, micro-CT, MRI, PET, SPECT, microscopy and other modalities. With data fusion tools, intramodality and multimodality registration of MR/CT or PET/CT is easily accomplished, while semi-automatic VOI delineation on fused datasets can improve analysis. Standard formats, such as DICOM, RAW, JPEG, NIFTI, Analyze are supported and 3D/4D sequences can be played. Other features include MPR, oblique, CPR, volume clipping, and surface visualization of cortex, skull, and scalp models. Also standard are easy-to-use tools for voxel-based delineation of features and the measurement of properties, including areas, volumes, counts, and intensity profiles. Present your findings by creating annotated animations or high-resolution images for posters. An SDK is also available to create plug-ins that provide new workflows or functionalities.

Proper citation: ORS Visual SI (RRID:SCR_002509) Copy   

•   Source: SciCrunch Registry

http://sites.google.com/site/marcocongedo/software/nica

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 13, 2026. Software program, executable under any Windows32 OS, performs Group BSS (Blind Source Separation) analysis comparing two groups of individuals and it performs NICA (Normative ICA) analysis where individuals are compared individually to a (normative) group. All analysis is performed in the frequency domain, that is, for all frequencies. The program also performs all these analysis for qEEG, that is, at the electrode level, without any BSS. The program does all computations, saves and displays results. The rationale and methods used in this program are explained in all details in the following paper: Congedo M, John ER, De Ridder D, Prichep L (2010) Group Independent Component Analysis of Resting-State EEG in Large Normative Samples International Journal of Psychophysiology 78, 89-99.

Proper citation: Normative Independent Component Analysis (RRID:SCR_002506) Copy   

•   Source: SciCrunch Registry

http://connectir.projects.nitrc.org

An R-based package to conduct brain connectivity analyses with a focus on a novel approach to conducting Connectome-Wide Association Studies (CWAS) using functional connectivity.

Proper citation: Connectir (RRID:SCR_000863) Copy   

•   Source: SciCrunch Registry

http://www.egi.com/clinical-division/clinical-division-geodesic-eeg-components/clinical-division-net-station

APIs for Net Station data files. APIs are available for C++, C#, and Java.

Proper citation: Net Station API (RRID:SCR_000867) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/mni2orfromxyz/

Input either normalized MNI coordinates from a 3D image, or input real world XYZ matrix coordinates, and this code will convert coordinates of one type to the other.

Proper citation: Convert MNI coordinates to or from XYZ (RRID:SCR_000406) Copy   

•   Source: SciCrunch Registry

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

Vervet (Chlorocebus aethiops sabaeus) probabilistic atlas that defines an anatomical space (template) with associated tissue and regional prior probability maps. The atlas was produced from whole head MRI of 10 normal adult animal subjects. The package consists of two atlases. The Biased directory contains the average template and probabilistic atlases for selected tissue classes constructed by registering the training population to one subject. The Unbiased directory contains the atlas constructed using unbiased estimation. The atlas is suitable for use in any segmentation tool using a probabilistic atlas, for example those in Slicer.

Proper citation: Vervet Probabilistic Atlas (RRID:SCR_000426) Copy   

•   Source: SciCrunch Registry

http://physics.ucsd.edu/neurophysics/links.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 16,2023. Software suite for custom-built multiphoton microscopes available as freeware for the Wintel platform. The MPScope package features the acquisition software MPScan, analysis program MPView and several software utilities.

Proper citation: MPScope (RRID:SCR_000065) Copy   

•   Source: SciCrunch Registry

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

Slicer3 module to provide a capability for performing white matter lesion classification and summary.

Proper citation: 3DSlicerLupusLesionModule (RRID:SCR_000853) Copy   

•   Source: SciCrunch Registry

http://ccb.loni.usc.edu/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 31, 2022. Center focused on the development of computational biological atlases of different populations, subjects, modalities, and spatio-temporal scales with 3 types of resources: (1) Stand-alone computational software tools (image and volume processing, analysis, visualization, graphical workflow environments). (2) Infrastructure Resources (Databases, computational Grid, services). (3) Web-services (web-accessible resources for processing, validation and exploration of multimodal/multichannel data including clinical data, imaging data, genetics data and phenotypic data). The CCB develops novel mathematical, computational, and engineering approaches to map biological form and function in health and disease. CCB computational tools integrate neuroimaging, genetic, clinical, and other relevant data to enable the detailed exploration of distinct spatial and temporal biological characteristics. Generalizable mathematical approaches are developed and deployed using Grid computing to create practical biological atlases that describe spatiotemporal change in biological systems. The efforts of CCB make possible discovery-oriented science and the accumulation of new biological knowledge. The Center has been divided into cores organized as follows: - Core 1 is focused on mathematical and computational research. Core 2 is involved in the development of tools to be used by Core 3. Core 3 is composed of the driving biological projects; Mapping Genomic Function, Mapping Biological Structure, and Mapping Brain Phenotype. - Cores 4 - 7 provide the infrastructure for joint structure within the Center as well as the development of new approaches and procedures to augment the research and development of Cores 1-3. These cores are: (4)Infrastructure and Resources, (5) Education and Training, (6) Dissemination, and (7) Administration and Management. The main focus of the CCB is on the brain, and specifically on neuroimaging. This area has a long tradition of sophisticated mathematical and computational techniques. Nevertheless, new developments in related areas of mathematics and computational science have emerged in recent years, some from related application areas such as Computer Graphics, Computer Vision, and Image Processing, as well as from Computational Mathematics and the Computational Sciences. We are confident that many of these ideas can be applied beneficially to neuroimaging.

Proper citation: Center for Computational Biology at UCLA (RRID:SCR_000334) Copy   

•   Source: SciCrunch Registry

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

A software toolbox that can automatically identify many of the artifact components that are often present in independent component analysis (ICA) of functional MRI (fMRI). The method: * Does not require temporal information about the fMRI paradigm. * Does not require the user to train the algorithm. * Requires only the EPI images (additional acquisition of anatomical images is not required). * Is able to identify a high proportion of artifact-related ICs without removing components that are likely to be of neuronal origin. * Can be applied to resting-state fMRI. * Is automated, requiring minimal or no human intervention.

Proper citation: SOCK (RRID:SCR_002544) Copy   

•   Source: SciCrunch Registry