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http://www.pstnet.com/software.cfm?ID=94

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 14,2026. NOTE: VR Worlds 2 is no longer available as a standalone software. Software application designed for the creation and execution of neurobehavioral studies. The VR Worlds 2 platform allows accurate, real-time data collection of the navigation and interactions within a simulated environment. You are free to design and perform custom or predefined experiments tailored to your personal research or clinical needs. Create complex environments with meaningful content using VR Worlds 2?s user-friendly, drag-and-drop interface. Include triggers to launch simple or intricate events, such as a conversation between characters or a customizable rating scale. VR Worlds 2 provides several realistic simulations to immerse your subjects in context appropriate environments, including: * Residential/Urban Area * Hotel Lobby * Medical Office * Grocery Store * Neighborhood for drug and alcohol cue extinction treatment (featured on GMA, motion capture process) * Casino for gambling addiction research (featured on Canadian Discovery Channel) * Driving simulation for mild cognitive impairment research * Phobia scenarios * Spatial navigation mazes

Proper citation: VR Worlds 2 (RRID:SCR_002608) Copy   

•   Source: SciCrunch Registry

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

The Longitudinal MS Lesion Imaging Archive provides Training data consisting of longitudinal images from five patients and Testing data consisting of 15 patients. Each longitudinal dataset includes T1-weighted, T2-weighted, PD-weighted, and T2-weighted FLAIR MRI with 3-5 time points acquired on a 3T MR scanner. T1-weighted images have approximately a 1mm cubic voxel resolution, while the other scans are 1mm in plane with 3mm sections. Accounting for the multiple time points, this constitutes approximately 80 individual data sets. The Training data contains manual segmentations of the MS lesions from two different raters for each of the time points provided.

Proper citation: Longitudinal MS Lesion Imaging Archive (RRID:SCR_014136) Copy   

•   Source: SciCrunch Registry

http://www.pymvpa.org

A Python package intended to ease statistical learning analyses of large datasets. It offers an extensible framework with a high-level interface to a broad range of algorithms for classification, regression, feature selection, data import and export. While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is truly free software (in every respect) and additionally requires nothing but free-software to run. Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. This Python-based, cross-platform, open-source software toolbox software toolbox for the application of classifier-based analysis techniques to fMRI datasets makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages.

Proper citation: PyMVPA (RRID:SCR_006099) Copy   

•   Source: SciCrunch Registry

http://www.ppmi-info.org/

An observational longitudinal clinical study partnership to identify and validate biomarkers of Parkinson disease (PD) progression and provide easy and open web-based access to the comprehensive set of correlated clinical data and biospecimens, information, and biosamples acquired from PD and age and gender matched healthy control subjects to the research community. The data and specimens have been collected in a standardized manner under strict protocols and includes clinical (demographic, motor and non-motor, cognitive and neurobehavioral), imaging (raw and processed MRI, SPECT and DAT), and blood chemistry and hematology subject assessments and biospecimen inventories (serum, plasma, whole blood, CSF, DNA, RNA and urine). All data are de-identified to protect patient privacy. PPMI will be carried out over five years at 21 clinical sites in the United States and Europe and requires the participation of 400 Parkinson's patients and 200 control participants. The PPMI database provides researchers with access to correlated clinical and imaging data, along with annotated biospecimens, all available within an open access system that encourages data sharing (http://www.ppmi-info.org/access-data-specimens/). The website hosts an Ongoing Analysis section to keep the scientific community apprised of analyses being completed, in hopes of stimulating collaborations between researchers who are using PPMI data and specimens.

Proper citation: Parkinson's Progression Markers Initiative (RRID:SCR_006431) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/camino-trackvis/

Software package that allows interoperability between CAMINO and TRACKVIS. CAMINO is a leading software package in DTI processing. The package is from University of College London. TRACKVIS is a tract visualizing utility with capability of visualizing up to and over a million white matter tracts seamlessly. The package is from Massachusetts General Hospital. With increasing efforts on brain connectivity analyses it becomes important to have tools that can allow increased interoperability among different tractography tools. The tools in this package allow conversion of tracts from one format to another in a very effective way with ability to handle over a million tracts.

Proper citation: CAMINO-TRACKVIS (RRID:SCR_009450) Copy   

•   Source: SciCrunch Registry

http://www.montefiore.ulg.ac.be/~phillips/FASST.html

An EEG toolbox developed to help users with 3 specific types of data and problems: simulatenous EEG-fMRI recording, continuous EEG scoring (e.g. sleep) and handling (visualisation, cutting, power spectrum, etc.) multi-channel recording of spontaneous EEG. The toolbox is written in Matlab and is specifically compatible with the BrainAmp family of EEG recorders (from BrainProducts GmbH) Three other data formats are now also supported: the edf "European Data Format", exported raw-EGI data (from Electrical Geodesics, Inc.) and the BCI2000 format.The results are directly compatible with SPM8 and are saved with SPM8 EEG data format.

Proper citation: fMRI Artefact rejection and Sleep Scoring Toolbox (RRID:SCR_009620) 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://www.jeiglesias.com

An automatic whole-brain extraction tool for T1-weighted MRI data (commonly known as skull stripping). Whole-brain segmentation is often the first component in neuroimage pipelines and therefore, its robustness is critical for the overall performance of the system. Many methods have been proposed in the literature, but they often: * work well on certain datasets but fail on others. * require case-specific parameter tuning ROBEX aims for robust skull-stripping across datasets with no parameter settings. It fits a triangular mesh, constrained by a shape model, to the probabilistic output of a supervised brain boundary classifier. Because the shape model cannot perfectly accommodate unseen cases, a small free deformation is subsequently allowed. The deformation is optimized using graph cuts.

Proper citation: ROBEX (RRID:SCR_002534) Copy   

•   Source: SciCrunch Registry

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

A stand-alone MATLAB-based toolkit for the rapid and flexible exploration of Region of Interest (ROI) response waveforms and other signals from across large fMRI datasets. An alpha-release is currently available for use with an example dataset and tutorial.

Proper citation: REX (RRID:SCR_002532) Copy   

•   Source: SciCrunch Registry

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

A GUI-enabled EEGLAB plugin for modeling and visualizing dynamical interactions between electrophysiological signals (EEG, ECoG, MEG, etc), preferably after transforming signals into the source domain. The toolbox consists of four modules: (1) Data Preprocessing, (2) Model Fitting and Connectivity Estimation, (3) Statistical Analysis, (4) Visualization, with a fifth Group Analysis module in development. Module 2 currently includes several adaptive multivariate autoregressive modeling (AMVAR) algorithms, including segmentation AMVAR and Kalman filtering. This subsequently allows the user to validate the model and estimate (in the time-frequency domain) a wide range of multivariate Granger-causal and coherence measures published to date. Module 3 includes routines for parametric and non-parametric significance testing. Module 4 contains routines for interactive visualization of dynamical interactions across time, frequency and anatomical source location.

Proper citation: Source Information Flow Toolbox (RRID:SCR_002561) Copy   

•   Source: SciCrunch Registry

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

Segmentation tool that uses image analysis and machine learning techniques (Support Vector Machines). Image intensities from multiple MR acquisition protocols, after coregistration, are used to form a voxel-wise attribute vector which is used to perform the segmentation. Computer algorithms have started to complement expert-readings of MRI as they may improve throughput and consistency, in addition to providing more accurate quantitative measures of lesion type and volume. Computerized segmentation methods can also offer more precise measurements of longitudinal change of a lesion with disease progression or treatment response.

Proper citation: Brain lesion segmentation tool using SVM (RRID:SCR_002583) Copy   

•   Source: SciCrunch Registry

http://tools.robjellis.net/

Simple, menu-driven software toolbox for SPM 5/8 for exploratory data analysis for functional or structural images (.img / .nii) provides the user with several options: # a histogram of all non-zero voxel values in a brain image; # a scatter plot, Q-Q plot, or Bland-Altman plots comparing two images; # a surface plot of all voxel values at a particular axial slice; # easy Region of Interst (ROI)-based extraction of voxel values. Note: the toolbox calls various SPM 5/8 functions. The Q-Q plot function requires the MATLAB stats toolbox.

Proper citation: vis: SPM Visualized Statistics toolbox (RRID:SCR_002619) Copy   

•   Source: SciCrunch Registry

https://github.com/incf-nidash/XCEDE

Data management software that provides an extensive metadata hierarchy for describing and documenting research and clinical studies. The schema organizes information into five general hierarchical levels: a complete project, studies within a project, subjects involved in the studies, visits for each of the subjects, the full description of the subject's participation during each visit.

Proper citation: XCEDE Schema (RRID:SCR_002571) Copy   

•   Source: SciCrunch Registry

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.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/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

https://github.com/UCSFBiomagneticImagingLab/nutmeg

Software MEG/EEG analysis toolbox for reconstructing neural activation and overlaying it onto structural MR images. Toolbox runs under MATLAB in conjunction with SPM2 and can be used with Linux/UNIX, Mac OS X, and Windows platforms.

Proper citation: NUTMEG (RRID:SCR_002748) Copy   

•   Source: SciCrunch Registry

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

Workflow environment bringing together heterogenous, online biological image resources, a user's image data and biological atlases in a concise, unified and intuitive workspace. The MBAT viewer displays multiple images on a single virtual canvas allowing easy side-by-side comparisons and image compositing. MBAT is written in Java so it is platform independent and is highly extensible through it's plugin architecture. MBAT integrates three distinct workspaces for online search, image alignment (registration) and image display: * Search Workspace: able to submit a query to multiple databases simultaneously and online literature searches. * Registration Workspace: performs 2D landmark based registration. * Viewer Workspace: displays & composites images and image volumes using high performance graphics hardware. * Atlas Viewer: allows navigation and interrogation of volumetric atlases. * Hierarchy Editor: create logical groupings of atlas labels.

Proper citation: Mouse BIRN Atlasing Toolkit (RRID:SCR_002814) Copy   

•   Source: SciCrunch Registry