Tools   Select Another Resource Report Type

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

An fMRI toolkit which contains a large selection of complex network measures in Matlab GUI. These measures are increasingly used to characterize structural and functional brain connectivity datasets.

Proper citation: Brainnetome fMRI toolkit (RRID:SCR_014092) 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/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.nitrc.org/projects/glmdenoise

A MATLAB toolbox for denoising task-based fMRI data. It derives noise regressors from voxels unrelated to the experimental paradigm and uses these regressors in a general linear model (GLM) analysis of the data. The technique only requires a design matrix indicating the experimental design and an fMRI dataset.

Proper citation: GLMdenoise: a fast, automated technique for denoising task-based fMRI data (RRID:SCR_014116) Copy   

•   Source: SciCrunch Registry

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

A software tool (with GUI) for investigating inter-regional functional connectivity in event-related fMRI data and allows the user to assess the modulation of functional connectivity by an experimental condition.

Proper citation: BetA-Series COrrelation (RRID:SCR_014086) Copy   

•   Source: SciCrunch Registry

http://www.uzh.ch/keyinst/loreta

Software package for functional imaging of human brain. Used to compute three dimensional distribution of electric neuronal activity from non-invasive measurements of scalp electric potential differences with high time resolution in millisecond range. Non-invasive intracranial time series are used for studying functional dynamic connectivity.. Current software version includes two new, improved variants of the original method: standardized (sLORETA) and exact (eLORETA). The new methods are characterized by exact localization when tested with point sources. Due to the fact that these methods are multivariate tomographies that are solutions to the inverse EEG problem, and that they are linear in nature, they will produce a low spatial resolution image for any distribution of activity. This property is not shared by naive one-at-a-time single dipole techniques.

Proper citation: Low Resolution Electromagnetic Tomography (RRID:SCR_007077) 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://www.nitrc.org/projects/sct

A comprehensive and open-source library of analysis tools for multi-parametric MRI of the spinal cord. The toolbox includes a template and several atlases, along with state-of-the-art methods to register any data to the template. It also includes useful scripts for data preprocessing: extraction of centerline, automatic segmentation, slice-wise motion correction, etc.

Proper citation: Spinal Cord Toolbox (RRID:SCR_014170) 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

http://sourceforge.net/projects/cudasphere/

A CUDA C based toolkit which provides a GPU based implementation of the spherical model forward solution for the 306 channel Elekta Neuromag MEG system and the EEG. The 1-Sphere forward solution for the MEG and the 4-Sphere forward solution for the EEG is implemented in CUDA C and an accelerated solution is obtained using the NVIDIA GPU when the solution is calculated for a large number of dipoles (on the order of 15000 and above) and sensor location. Speedup by a factor of 22 and 32 is obtained for the EEG and MEG solution respectively when compared to the fastest CPU implementation available in the public domain. The complete source code and pre-compiled binaries are also made available via an open source license (GPL Version 3). A CUDA enabled NVIDIA graphics card is required to use the software.

Proper citation: CUDA-SPHERE-FWD-MEEG (RRID:SCR_013225) Copy   

•   Source: SciCrunch Registry

http://www.calatk.org/

An open-source toolkit for cross-sectional and longitudinal atlas building. The CalaTK project develops innovative methods and tools for longitudinal atlases with a focus on neurodevelopment. The computational toolbox is developed with the objective to analyze the neural developmental patterns observed in human and non-human primate structural and diffusion tensor magnetic resonance (MR) images.

Proper citation: CalaTK (RRID:SCR_009547) Copy   

•   Source: SciCrunch Registry

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

An on-line tutorial on how to use MATLAB for Diffusion-Weighted MRI processing. The following subjects are covered in this tutorial: Generation of Synthetic Diffusion-Weighted MRI datasets, Diffusion Tensor (DTI) Estimation from DW-MRI, DTI Visualization as a field of ellipsoids, Higher-order Diffusion Tensor Estimation from DW-MRI, Computing of Tensor Orientation Distribution Functions (Tensor ODF), Computing of Fiber Orientations, Higher-order Diffusion Tensor Image Visualization as fields of spherical functions, Multi-fiber reconstruction etc. The tutorial contains numerous illustrations, figures and Matlab scripts embedded in the text. The reader/user can automatically generate Matlab script for a self-designed DW-MRI experiment by selecting which steps needs to be followed. The code that corresponds to the selected steps is then appropriately merged in the Matlab Script Generator, and the user can easily copy and paste the produced code directly to the Matlab command prompt.

Proper citation: MATLAB Tutorial on Diffusion Tensor MRI (RRID:SCR_009507) Copy   

•   Source: SciCrunch Registry

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

A multi-day event hosted by the Organization for Human Brain Mapping which features collaborative and open neuroscience projects in data analysis and methods development. Locations change annually.

Proper citation: HBM Hackathon (RRID:SCR_014119) Copy   

•   Source: SciCrunch Registry

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

An international symposium held initially to assess the new technology and innovation in the various established fields of genetics and imaging, and to facilitate the transdisciplinary fusion needed to optimize the development of the emerging field of Imaging Genetics. This annual conference features presentations from investigators world-wide and places emphasis on facilitating in-depth discussions among the participants and presenters.

Proper citation: International Imaging Genetics Conference (RRID:SCR_014125) Copy   

•   Source: SciCrunch Registry

http://bio3d.colorado.edu/imod

A free, cross-platform set of image processing, modeling and display programs used for tomographic reconstruction and for 3D reconstruction of EM serial sections and optical sections. The package contains tools for assembling and aligning data within multiple types and sizes of image stacks, viewing 3-D data from any orientation, and modeling and display of the image files. IMOD 4.1.8 Is Now Available for Linux, Windows, and Mac OS X

Proper citation: IMOD (RRID:SCR_003297) Copy   

•   Source: SciCrunch Registry

http://www.humanconnectomeproject.org/

A multi-center project comprising two distinct consortia (Mass. Gen. Hosp. and USC; and Wash. U. and the U. of Minn.) seeking to map white matter fiber pathways in the human brain using leading edge neuroimaging methods, genomics, architectonics, mathematical approaches, informatics, and interactive visualization. The mapping of the complete structural and functional neural connections in vivo within and across individuals provides unparalleled compilation of neural data, an interface to graphically navigate this data and the opportunity to achieve conclusions about the living human brain. The HCP is being developed to employ advanced neuroimaging methods, and to construct an extensive informatics infrastructure to link these data and connectivity models to detailed phenomic and genomic data, building upon existing multidisciplinary and collaborative efforts currently underway. Working with other HCP partners based at Washington University in St. Louis they will provide rich data, essential imaging protocols, and sophisticated connectivity analysis tools for the neuroscience community. This project is working to achieve the following: 1) develop sophisticated tools to process high-angular diffusion (HARDI) and diffusion spectrum imaging (DSI) from normal individuals to provide the foundation for the detailed mapping of the human connectome; 2) optimize advanced high-field imaging technologies and neurocognitive tests to map the human connectome; 3) collect connectomic, behavioral, and genotype data using optimized methods in a representative sample of normal subjects; 4) design and deploy a robust, web-based informatics infrastructure, 5) develop and disseminate data acquisition and analysis, educational, and training outreach materials.

Proper citation: MGH-USC Human Connectome Project (RRID:SCR_003490) Copy   

•   Source: SciCrunch Registry

http://www.pediatricmri.nih.gov/

Data sets of clinical / behavioral and image data are available for download by qualified researchers from a seven year, multi-site, longitudinal study using magnetic resonance technologies to study brain maturation in healthy, typically-developing infants, children, and adolescents and to correlate brain development with cognitive and behavioral development. The information obtained in this study is expected to provide essential data for understanding the course of normal brain development as a basis for understanding atypical brain development associated with a variety of developmental, neurological, and neuropsychiatric disorders affecting children and adults. This study enrolled over 500 children, ranging from infancy to young adulthood. The goal was to study each participant at least three times over the course of the project at one of six Pediatric Centers across the United States. Brain MR and clinical/behavioral data have been compiled and analyzed at a Data Coordinating Center and Clinical Coordinating Center. Additionally, MR spectroscopy and DTI data are being analyzed. The study was organized around two objectives corresponding to two age ranges at the time of enrollment, each with its own protocols. * Objective 1 enrolled children ages 4 years, 6 months through 18 years (total N = 433). This sample was recruited across the six Pediatric Study Centers using community based sampling to reflect the demographics of the United States in terms of income, race, and ethnicity. The subjects were studied with both imaging and clinical/behavioral measures at two year intervals for three time points. * Objective 2 enrolled newborns, infants, toddlers, and preschoolers from birth through 4 years, 5 months, who were studied three or more times at two Pediatric Study Centers at intervals ranging from three months for the youngest subjects to one year as the children approach the Objective 1 age range. Both imaging and clinical/behavioral measures were collected at each time point. Participant recruitment used community based sampling that included hospital venues (e.g., maternity wards and nurseries, satellite physician offices, and well-child clinics), community organizations (e.g., day-care centers, schools, and churches), and siblings of children participating in other research at the Pediatric Study Centers. At timepoint 1, of those enrolled, 114 children had T1 scans that passed quality control checks. Staged data release plan: The first data release included structural MR images and clinical/behavioral data from the first assessments, Visit 1, for Objective 1. A second data release included structural MRI and clinical/behavioral data from the second visit for Objective 1. A third data release included structural MRI data for both Objective 1 and 2 and all time points, as well as preliminary spectroscopy data. A fourth data release added cortical thickness, gyrification and cortical surface data. Yet to be released are longitudinally registered anatomic MRI data and diffusion tensor data. A collaborative effort among the participating centers and NIH resulted in age-appropriate MR protocols and clinical/behavioral batteries of instruments. A summary of this protocol is available as a Protocol release document. Details of the project, such as study design, rationale, recruitment, instrument battery, MRI acquisition details, and quality controls can be found in the study protocol. Also available are the MRI procedure manual and Clinical/Behavioral procedure manuals for Objective 1 and Objective 2.

Proper citation: NIH MRI Study of Normal Brain Development (RRID:SCR_003394) Copy   

•   Source: SciCrunch Registry

http://signalml.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 5, 2023.An XML-based language designed for metadescription of formats, used for digital storage of biomedical time series. Using SignalML, information on the structure of binary data files can be simply and efficiently coded. Once written, this information can be used by any software, which - owing to this metadescription - can read data files in the original format. This eliminates the need for conversions and duplication of data. signalml.org provides the following resources for interchange of relevant information and ideas: * SignalML wiki * Newsgroup / mailing list The main current software project is Svarog - a SignalML-compliant signal viewer, annotator, analyzer and (future) recorder. Svarog is written in Java and is currently best fitted for display of EEG and MEG signals. Also open platform for implementing advanced signal processing methods in user-friendly environment, at the moment interfacs for Java code, standalone executables and Matlab code via Matlab Builder for Java.

Proper citation: signalml.org (RRID:SCR_003383) Copy   

•   Source: SciCrunch Registry

http://fcon_1000.projects.nitrc.org/indi/CoRR/html/

Consortium that has aggregated resting state fMRI (R-fMRI) and diffusion imaging data from laboratories around the world, creating an open science resource for the imaging community, that facilitates the assessment of test-retest reliability and reproducibility for functional and structural connectomics. Given that this was a retrospective data collection, they have focused on basic phenotypic measures that are relatively standard in the neuroimaging field, as well as fundamental for analyses and sample characterization. Their phenotypic key is organized to reflect three classifications of variables: 1) core (i.e., minimal variables required to characterize any dataset), 2) preferred (i.e., variables that were strongly suggested for inclusion due to their relative import and/or likelihood of being collected by most sites), and 3) optional (variables that are data-set specific or only shared by a few sites). CoRR includes 33 datasets consisting of: * 1629 Subjects * 3357 Anatomical Scans * 5093 Resting Functional Scans * 1302 Diffusion Scans * 300 CBF and ASL Scans

Proper citation: Consortium for Reliability and Reproducibility (RRID:SCR_003774) Copy   

•   Source: SciCrunch Registry

http://neurovault.org/

Data repository where researchers can publicly store and share unthresholded statistical brain activation maps produced by MRI and PET studies.

Proper citation: NeuroVault (RRID:SCR_003806) Copy   

•   Source: SciCrunch Registry