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SciCrunch Registry is a curated repository of scientific resources, with a focus on biomedical resources, including tools, databases, and core facilities - visit SciCrunch to register your resource.
http://www.brainvoyager.com/products/brainvoyagerqx.html
Commercial neuroimaging software package for multi-modal data analysis and management. It has been programmed in C++ with efficient statistical, numerical, and image processing routines. It supports parallelized basic math routines on all platforms and uses modern multi-core, multi-processor hardware for demanding computational routines.
Proper citation: BrainVoyager (RRID:SCR_013057) Copy
http://www.nitrc.org/projects/fiber-sig/
Used to analyze the fibers produced by ukf tractography
Proper citation: Fiber Tracking Tool (RRID:SCR_009474) Copy
http://www.nitrc.org/projects/diseasestate/
These are the scripts used for the analyses reported in: Craddock RC, Holtzheimer PE, 3rd, Hu XP, Mayberg HS. (2009): Disease state prediction from resting state functional connectivity. Magn Reson Med 62(6):1619-28. Specifically included are scripts for performing t-test filter, reliability filter, recursive feature elimination, and reliability recursive feature elimination feature selection methods. These make use of wrappers that perform .632 bootstrap and k-fold cross validation strategies. The scripts are written in matlab and require the Bioinformatics toolbox. If you do not have the bioinformatics toolbox, the scripts can be easily modified to run with other matlab SVM toolboxes (i.e., libsvm, svmlight, shogun, etc.).
Proper citation: Disease State Prediction (RRID:SCR_009467) Copy
http://www.nmr.mgh.harvard.edu/DOT/resources/homer2/home.htm
Software matlab scripts used for analyzing fNIRS data to obtain estimates and maps of brain activation. Graphical user interface (GUI) for visualization and analysis of functional near-infrared spectroscopy (fNIRS) data.
Proper citation: Homer2 (RRID:SCR_009586) Copy
Atlas that contains new anatomical, DTI, HARDI templates and probabilistic gray matter labels of the adult human brain in ICBM-152 space. Artifact-free MRI data from 72 human subjects was used in the development of the atlas. All diffusion MRI data collection was conducted using Turboprop, and spatial normalization was accomplished in a population-based fashion. A description of the contents of the atlas can be found in the Downloads link. NOTE: The files of the older IIT2 DTI Brain Template are still available. However, the new DTI template of the IIT Human Brain Atlas (v.3) is of superior quality and allows more accurate registration across subjects.
Proper citation: IIT Human Brain Atlas (RRID:SCR_009587) Copy
A toolbox for the Matlab environment designed to study functional and effective brain connectivity from neurophysiological data such as multivariate EEG and/or MEG records. It includes also visualization tools and statistical methods to address the problem of multiple comparisons. This toolbox may be very helpful to all the researchers working in the emerging field of brain connectivity analysis.
Proper citation: HERMES (RRID:SCR_009584) Copy
http://www.nitrc.org/projects/dti_tract_stat/
This is a command line tool which allows the user to study the behavior of water diffusion (using DTI data) along the length of the white matter fiber-tracts. Various tract-oriented scalar diffusion measures obtained from DTI brain images, are treated as a continuous function of white matter fibers'' arc-length. To analyze the trend along a given fiber tract, a command line tool performs kernel regression on this data. The idea is to try out different noise models and maximum likelihood estimates within kernel windows (along the tract), such that they best represent the data and are robust to noise and Partial Volume effect. The package contains several command line based modules and an GUI based tool called DTIAtlasFiberAnalyzer to access most functions. The features available in the tool currently, its use and input / output formats and other relevant details are provided in the first draft of the documentation. (http://www.na-mic.org/Wiki/index.php/Projects:dtistatisticsfibers).
Proper citation: DTI Fiber Tract Statistics (RRID:SCR_009460) Copy
http://www.nitrc.org/projects/inia19/
Primate brain atlas created from over 100 structural MR scans of 19 rhesus macaque animals. The atlas currently comprises high-resolution T1-weighted average MR images with and without skull stripping, tissue probability maps, and a detailed parcellation map based on the NeuroMaps atlas.
Proper citation: INIA19 Primate Brain Atlas (RRID:SCR_009498) Copy
http://www.nitrc.org/projects/graphtools/
A set of MATLAB scripts for analysis of networks derived from neuroimaging data. Some of these scripts are entirely original, while some are adapted (or just copied) from the Brain Connectivity Toolbox (https://sites.google.com/a/brain-connectivity-toolbox.net/bct) The source code is available via git: git clone ssh://user
Proper citation: Graphtools (RRID:SCR_009490) Copy
http://www.nitrc.org/projects/r-spit/
Group ICA (Independent Component Analysis) was used to generate spatial templates for 12 common resting-state networks in 62 typically-developing children, ages 9-15. They have made these available for those that will find them useful for masking and spatial template matching procedures. Basic demographic data on the sample is provided along with the protocol used to generate the templates.
Proper citation: resting-state pediatric imaging template (RRID:SCR_009647) Copy
http://www.nitrc.org/projects/factory4t1ndti/
Tools to make easier on using spm, pipedream, dti-tk, and other softwares to analyze t1 or dti images.
Proper citation: factory t1 dti (RRID:SCR_009523) Copy
http://www.nitrc.org/projects/gretna/
A graph theoretical network analysis toolbox which allows researchers to perform comprehensive analysis on the topology of brain connectome by integrating the most of network measures studied in current neuroscience field.
Proper citation: GRETNA (RRID:SCR_009487) Copy
http://www.nitrc.org/projects/gcva_pca/
A platform for any Principal Component Analysis (PCA)-based analysis on functional neuroimaging data (PET and fMRI). Includes: * Ordinal Trend Canonical Variance Analysis for parametric designs (C. Habeck et al. A New Approach to Spatial Covariance Modeling of Functional Brain Imaging Data: Ordinal Trend Analysis. Neural Computation 2005; 17: 1602-1645) * Partial Least Squares for any design matrix * Subprofile Scaling Model for cross-sectional designs (JR. Moeller, Strother SC. A regional covariance approach to the analysis of functional patterns in positron emission tomographic data.J Cereb Blood Flow Metab. 1991 Mar;11(2):A121-35.)
Proper citation: Generalized Covariance Analysis (RRID:SCR_009488) Copy
http://www.nitrc.org/projects/gamma_suite/
GAMMA suite is an open-source cross-platform data mining software package designed to analyze neuroimaging data. A neuroimaging study often focuses on biomarker detection and classification. We designed and implemented a Bayesian, multivariate, nonparametric suite of algorithms for analyzing neuroimaging data. The GAMMA suite can be used for brain morphometric analysis, lesion-deficit analysis, and functional MR data analysis.
Proper citation: GAMMA (RRID:SCR_009484) Copy
http://www.nitrc.org/projects/autoseg/
A novel C++ based application developped at UNC-Chapel Hill that performs automatic brain tissue classification and structural segmentation. AutoSeg is designed for use with human and non-human primate pediatric, adolescent and adult data. AutoSeg uses a BatchMake pipeline script that includes the main steps of the framework entailing N4 bias field correction, rigid registration to a common coordinate image, tissue segmentation, skull-stripping, intensity rescaling, atlas-based registration, subcortical segmentation and lobar parcellation, regional cortical thickness and intensity statistics. AutoSeg allows efficient batch processing and grid computing to process large datasets and provides quality control visualizations via Slicer3 MRML scenes.
Proper citation: AutoSeg (RRID:SCR_009438) Copy
http://www.nitrc.org/projects/bxh_xcede_tools/
A collection of data processing and image analysis tools for data in BXH or XCEDE format. This includes data format encapsulation/conversion, event-related analysis, QA tools, and more. These tools form the basis of the fBIRN QA procedures and are also distributed as part of the fBIRN Data Upload Scripts.
Proper citation: BXH/XCEDE Tools (RRID:SCR_009439) Copy
http://dsi-studio.labsolver.org
A software for diffusion MR images analysis. The provided functions include reconstruction (DTI, QBI, DSI, and GQI), deterministic fiber tracking, and 3D visualization. It has a window-based interface and operates on Microsoft Windows system.
Proper citation: DSI Studio (RRID:SCR_009557) Copy
http://www.nitrc.org/projects/cmrep/
A set of deformable modeling algorithms for shape analysis and structure-specific normalization. Applications of cm-reps include structure-specific fMRI analysis, DTI analysis, and structural brain mor
Proper citation: cmrep (RRID:SCR_009434) Copy
http://sourceforge.net/projects/erppcatoolkit/
This Matlab toolkit is a general purpose tool for editing, visualizing, and analyzing EEG data (both Event Related Potential - ERP and spectral) whose most recent version has been downloaded over 1000 times. Its three chief highlights are: 1) an optimized automatic artifact correction function that includes ICA correction for eye blinks and saccades. 2) Extensive support for easily conducting PCA and ICA through all stages of the procedure, including inspection of reconstituted waveforms and batch ANOVAs. 3) Implementation of robust ANOVAs, including McCarthy-Wood vector test. It has a graphical user interface for point and click usage and comes with an extensive illustrated tutorial. A description of the toolkit was published in Dien (2010) in Journal of Neuroscience Methods. It relies on both internal functions as well as borrowed functions from both EEGlab and FieldTrip.
Proper citation: ERP PCA Toolkit (RRID:SCR_013105) Copy
http://sourceforge.net/projects/gsa-snp/
A tool for the gene-set (or pathway) analysis of a genome-wide association study result. It accepts a genome-wide list of SNPs and their association P-values. It summarizes the SNP P-values into nearby genes. The gene-by-gene summary results are then further summarized by gene-sets such as Gene Ontology, KEGG pathways, or user-created gene-sets. Various standardization and statistical tests can be performed and the resulting gene-sets that pass a significance level after multiple-testing correction are reported. The tool is written in Java and is available as a standalone version.
Proper citation: GSA-SNP (RRID:SCR_013109) Copy
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