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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/dicomuploadgui/

A Java tool that takes an unorganized collection of DICOM scans, sorts and categorizes them according to user-customizable rules, gathers metadata about the scans, and saves out this information to help facilitate data uploads. Batch pr

Proper citation: DICOM UploadGUI (RRID:SCR_009458) Copy   

•   Source: SciCrunch Registry

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

This package is a set of three slicer modules which together are able to compute a DWI Atlas given a set of DWI''s. The modules included are: # A Groupwise Registration module -> compute''s a deformation field for each DWI, using, for example, FA maps as input # Warp DWI module -> used to warp each DWI using the deformation from (1) # DWI Averager -> used to average the set of warped DWI''s producing the final DWI Atlas

Proper citation: Create DWI Atlas (RRID:SCR_009455) Copy   

•   Source: SciCrunch Registry

http://fmripower.org

An easy to use matlab-based graphical user interface that calculates power for future studies based on older analyses or pilot data.

Proper citation: FMRIpower (RRID:SCR_009576) 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.ant-neuro.com/products/eeprobe

A complete software package for the study of event-related brain activity with high-resolution EEG/MEG. This package has been designed to suit the high standards of neuroscience research. The software has been developed originally at the Max Planck Institute for Cognitive Neuroscience in Leipzig, Germany, and is available for other institutions through ANT Neuro B.V., The Netherlands, enhanced with the EEProbe Databrowser. ERP investigations, both in psychophysiology research and clinical applications require a multitude of processing steps. Analysis of large data sets is made efficient through advanced scripting possibilities. All different aspects of data handling are efficiently available in the EEProbe Databrowser. Alternatively, external data can be imported from a multitude of formats. Processing in EEProbe makes use of open file formats (see LIBEEP) and is designed to integrate with ASA for advanced source analysis. EEProbe is available for Linux and Mac OS X.

Proper citation: EEProbe (RRID:SCR_009570) Copy   

•   Source: SciCrunch Registry

http://www.pstnet.com/software.cfm?ID=101

Software designed to optimize E-Prime experiments for fMRI research. EEfMRI allows you to synchronize the start of your experiment with the first scanner trigger pulse along with several valuable features to enhance the control you have over your experiment. Implementing EEfMRI into your current experiments is achieved by simply dragging and dropping the correct EEfMRI package calls into the E-Prime experiment in the appropriate places. EEfMRI is designed to integrate with other PST hardware and software to increase usability for researchers while maintaining the millisecond accuracy of E-Prime.

Proper citation: E-Prime Extensions for fMRI (RRID:SCR_009568) Copy   

•   Source: SciCrunch Registry

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

A Matlab toolbox that allows computation of task-related functional connectivity between multiple pairs of regions. Task-related functional connectivity is computed using the correlational psychophysiological interaction (cPPI) methodology described in Fornito et al. (2012) PNAS, 109: 12788-12793. The toolbox assumes that first-level design matrices have been specified and estimated using SPM5 or later. It takes as input these design matrices as well as user-extracted regional time courses and returns a matrix of pair-wise, task-related functional connectivity for each participant. The method is scalable to large networks comprising hundreds of regions and is well-suited to graph theoretic analyses and functional connectomics. One modifiable script, cPPI_master.m, can be used to run the analysis for an entire sample of participants.

Proper citation: cPPI Toolbox for fMRI (RRID:SCR_009518) Copy   

•   Source: SciCrunch Registry

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

This module implements the Finsler tractography method with HARDI data described by J. Melonakos et al. From a set of seeding and target points, the paths are estimated as the shortest path taking into account a local, directional dependent cost. The output provided is the connectivity map from each voxel in the volume to the seeding points, plus a vector volume with the directions tangent to the fiber bundles at each point. If the Backtracing module within is built, these directions can be traced back to actually compute the fiber bundles (VTK required). The software can be built as either a stand-alone or a CLI plugin for 3D Slicer.

Proper citation: Finsler tractography module for Slicer (RRID:SCR_009477) Copy   

•   Source: SciCrunch Registry

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

A reference for modifications, extensions, and utilities for the FMRIB Software Library (FSL).

Proper citation: FSL extensions (RRID:SCR_009472) Copy   

•   Source: SciCrunch Registry

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

Software application for removing patient-identifying information from medical image files. Removing this information is often necessary for enabling investigators to share image files in a HIPAA compliant manner.

Proper citation: LONI De-identification Debablet (RRID:SCR_009593) 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://www.sci.utah.edu/cibc/software/231-biomesh3d.html

A free, easy to use program for generating quality meshes for use in biological simulations. It is currently integrated with SCIRun and uses the SCIRun system to visualize the intermediate results. The BioMesh3D program uses a particle system to distribute nodes on the separating surfaces that separate the different materials and then uses the TetGen software package to generate a full tetrahedral mesh.

Proper citation: BioMesh3D (RRID:SCR_009534) Copy   

•   Source: SciCrunch Registry

http://besa.de

Software for source analysis and dipole localization in EEG and MEG research. BESA Research has been developed on the basis of 20 years experience in human brain research by Michael Scherg, University of Heidelberg, and Patrick Berg, University of Konstanz. BESA Research is a highly versatile and user-friendly Windows program with optimized tools and scripts to preprocess raw or averaged data for source analysis. All important aspects of source analysis are displayed in one window for immediate selection of a wide range of tools. BESA Research provides a variety of source analysis algorithms, a standardized realistic head model (FEM), and allows for fast and easy hypothesis testing and integration with MRI and fMRI.

Proper citation: BESA (RRID:SCR_009530) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/hitachi2nirs/

A Matlab script to convert the raw .csv Hitachi ETG4000 output file into a .nirs file for use with Homer2. The script also requires a .pos file. This is the output of the polhemus 3D digitiser that they use to record where the optodes are located spatially. I realize that not everyone uses a 3D digitiser so I have included three example .pos files - one for each of the possible optode arrangements of the Hitachi system (either two 3x3 arrays, one 3x5 array or one 4x4 array). If you use a different arrangement or have more probes than them, feel free to get in touch and they may be able to advise on how to create a model .pos file. There are two versions of the conversion script: 1. single - this will read in ONE .csv file and ONE .pos file and create ONE .nirs file 2. multi - this will read in a user-specified number of .csv files and ONE .pos file. It will then create one .nirs file for each .csv file that was read in and deposit it in the same directory as that .csv file.

Proper citation: Hitachi2nirs (RRID:SCR_009494) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/gig-ica/

Software toolbox for group-information guided Independent Component Analysis (ICA). In GIG-ICA, group information captured by standard Independent Component Analysis (ICA) on the group level is used as guidance to compute individual subject specific Independent Components (ICs) using a multi-objective optimization strategy. For computing subject specific ICs, GIG-ICA is applicable to subjects that are involved or not involved in the computation of the group information. Besides the group ICs, group information captured from other imaging modalities and meta analysis could be used as the guidance in GIG-ICA too.

Proper citation: Group Information Guided ICA (RRID:SCR_009491) Copy   

•   Source: SciCrunch Registry

http://sites.google.com/site/mrilateralventricle/

A fully automated algorithm which works within SPM8 to segment the lateral ventricles from structural MRI images. The algorithm has been validated in infants, adults and patients with Alzheimer's disease (ICC>0.95). ALVIN is insensitive to different scanner sequences (ICC>0.99, 8 different sequences 1.5T and 3T) and sensitive to changes in ventricular volume. Processing time is approx 10mins per subject.

Proper citation: ALVIN (RRID:SCR_009527) Copy   

•   Source: SciCrunch Registry

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

Constrained Principal Component Analysis (CPCA) combines regression analysis and principal component analysis into a unified framework. This method derives images of functional neural networks from singular-value decomposition of BOLD signal time series, and allows derivation of images when the analyzed BOLD signal is constrained to the scans occurring in peristimulus time, using all other scans as baseline. CPCA provides allows (1) determination of multiple functional networks involved in a task, (2) estimation of the pattern of BOLD changes associated with each functional network over peristimulus time points, (3) quantification of the degree of interaction between these multiple functional networks, and (4) a statistical test of the degree to which experimental manipulations affect each functional network. fMRI CPCA provides all results in matlab.mat file format, as well as writing images in analyze format for all components, rotated and unrotated.

Proper citation: fMRI-CPCA (RRID:SCR_009520) Copy   

•   Source: SciCrunch Registry

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

An end-to-end application allowing Group-wise Automatic Mesh-Based analysis of cortIcal Thickness as well as other surface area measurements. This cross-platform tool can be run within 3D Slicer as an external module, or directly as a command line.

Proper citation: GAMBIT (RRID:SCR_009483) Copy   

•   Source: SciCrunch Registry

http://caid.cs.uga.edu/?name=software

A software toolbox to predict 358 DICCCOL landmarks (Dense Individualized and Common Connectivity-based Cortical landmarks (http://dicccol.cs.uga.edu) ) on a new brain given b0, brain surface data and DTI derived fiber data (vtk format). Each DICCCOL landmark is defined by group-wise consistent white-matter fiber connection patterns derived from diffusion tensor imaging (DTI) data. DICCCOL aims to provide large-scale cortical landmarks with finer granularity, better functional homogeneity, more accurate functional localization, and automatically-established cross-subjects correspondence.

Proper citation: DICCCOL predictor (RRID:SCR_009554) Copy   

•   Source: SciCrunch Registry