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

A MATLAB Toolbox for generating realistic head models from available data (MRI and/or electrode locations), for computing numerical solutions for the forward problem of electromagnetic source imaging and for single dipole source localization. The NFT includes tools for segmenting scalp, skull, cerebrospinal fluid (CSF) and brain tissues from T1-weighted magnetic resonance (MR) images. The Boundary Element Method (BEM) and Finite Element Method (FEM) are used for the numerical solution of the forward problem. When a subject MR image is not available a template head model can be warped to measured electrode locations to obtain an individualized head model. Toolbox functions may be called either from a graphic user interface compatible with EEGLAB or from the MATLAB command line.

Proper citation: NFT (RRID:SCR_002450) 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.pstnet.com/hardware.cfm?ID=90

MRI Digital Projection System that uses Digital Light Processing (DLP) technology providing microsecond pixel rise times, outstanding contrast with all-digital fiber optic control that allows you to project crystal clear, sharp images. Includes: * High resolution (1024x768) DLP Projector with RF filtered enclosure, custom lens assembly, digital video (DVI) over fiber, high flow fans, internal thermal sensor * Control room device to perform DVI to Fiber conversion, remotely power down the projector, and allow use of projector remote control from control room * 30 meter fiber optic cable that runs between the projector and projector control station * Heavy duty, magnet compatible, projector stand (assembly required) * Heavy duty, magnet compatible mirror stand with mirror (assembly required) * High resolution, lenticular pitch rear projection screen for high quality image reproduction * Optional VGA to DVI converter (native DVI video cards on Windows or Macintosh recommended)

Proper citation: MRI Digital Projection System (RRID:SCR_002486) 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://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://brainproducts.com/productdetails.php?id=17

Software to manage the daily work of analyzing various neurophysiological data. Features include a history tree, automated analysis, various data format readers, and more.

Proper citation: BrainVision Analyzer (RRID:SCR_002356) 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://rtimage.sourceforge.net/

Software application to visualize, segment, and quantify three-dimensional images. Multiple datasets may be loaded, displayed, fused, processed, and quantitatively analyzed simultaneously. Data may be imported from any DICOM-compatible three dimensional imaging modality. Regions-of-interest may be defined using a number of manual, semi-automatic, and automated tools to segment three-dimensional pixel volumes. They may also be imported from and exported to DICOM structure sets. This software has been applied to preclinical and clinical computed tomography (CT), positron emission tomography (PET), single photon emission computed tomography (SPECT), magnetic resonance imaging (MRI), and optical imaging data.

Proper citation: RT Image (RRID:SCR_002535) 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://www.nitrc.org/projects/rdti/

The package dti provides methods for structural adaptive smoothing of diffusion weighted data in the context of the diffusion tensor model. Through its edge preserving properties they reduce data noise without compromizing significant structures.

Proper citation: R-package for adaptive DWI analysis (RRID:SCR_002528) 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://www.nitrc.org/projects/unlmeans/

A fast and robust software implementation of the popular Nonlocal Means for MRI-Rician denoising. It works by computing the non-local weights based on distances in a features space comprising the local mean value and gradients of the image. It can reach an acceleration factor of 20x over the original implementation, with an improved performance for medium-low SNR images. They use a bias correction step for Rician noise based on the well-known Conventional Approach. This software can be compiled either as a Slicer module or a stand-alone: http://www.nitrc.org/snapshots.php?group_id=518

Proper citation: Fast Nonlocal Means for MRI denoising (RRID:SCR_002586) 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.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