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http://wiki.na-mic.org/Wiki/index.php/2010_Winter_Project_Week_Spine_Segmentation_Module_in_Slicer3
3D Slicer module for automated segmentation of the spine. This is an implementation of a novel model-based segmentation algorithm. This work was presented at the NA-MIC Week in Salt Lake City, Jan 2010.
Proper citation: SpineSegmentation module for 3DSlicer (RRID:SCR_002593) Copy
Software library for time-series analysis of data from neuroscience experiments. It contains a core of numerical algorithms for time-series analysis both in the time and spectral domains, a set of container objects to represent time-series, and auxiliary objects that expose a high level interface to the numerical machinery and make common analysis tasks easy to express with compact and semantically clear code.
Proper citation: Nitime (RRID:SCR_002504) Copy
Issue
Software package for analysis of brain imaging data sequences. Sequences can be a series of images from different cohorts, or time-series from same subject. Current release is designed for analysis of fMRI, PET, SPECT, EEG and MEG.
Proper citation: SPM (RRID:SCR_007037) Copy
https://neuroscienceblueprint.nih.gov/Resources-Tools/Blueprint-Resources-Tools-Library
THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 22, 2023. National initiative to advance biomedical research through data sharing and online collaboration that provides data sharing infrastructure, software tools, strategies and advisory services. Groups may choose whether to share data internally or with external audiences. Hardware and data remain under control of individual user groups.
Proper citation: Biomedical Informatics Research Network (RRID:SCR_005163) Copy
http://www.math.mcgill.ca/keith/surfstat
A Matlab toolbox for the statistical analysis of univariate and multivariate surface data using linear mixed effects models and random field theory.
Proper citation: SurfStat (RRID:SCR_007081) Copy
http://www.nitrc.org/projects/bnv/
Aa brain network visualization tool, which can help researchers to visualize structural and functional connectivity patterns from different levels in a quick, easy, and flexible way.
Proper citation: BrainNet Viewer (RRID:SCR_009446) Copy
A tool for automatic segmentation of 3D biological datasets, with emphasis on 3D electron microscopy. It works best for 3D blob shaped objects like mitochondria, lysosomes, etc. The project is written in Python and uses the pythonxy platform (which includes scipy and ITK image processing tools).
Proper citation: Cytoseg (RRID:SCR_009553) Copy
http://www.nitrc.org/projects/nihlungseg/
A segmentation tool for the segmentation of a lung from CT images. The sofware can be run in two modes: fully automatic and semi-automatic with manual seeding by the user. The software also allows the user to perform basic filtering operations and manual correction to the segmentation. The VTK-based rendering implementation, along with option to view in axial, coronal, and sagittal, provides the user with better visualization of the segmented lung.
Proper citation: NIH-CIDI Lung Segmentation Tool (RRID:SCR_014150) Copy
A C++ software framework to develop, simulate and run magnetic resonance sequences on different platforms.
Proper citation: Object-Oriented Development Interface for NMR (RRID:SCR_005974) Copy
A Monte Carlo (MC) solver for photon migration in 3D turbid media. Different from existing MC software designed for layered (such as MCML) or voxel-based media (such as MMC or tMCimg), MMC can represent a complex domain using a tetrahedral mesh. This not only greatly improves the accuracy of the solutions when modeling objects with smooth/complex boundaries, but also gives an efficient way to sample the problem domain to use less memory. The current version of MMC support multi-threaded programming and can give a almost proportional speed-up when using multiple CPU cores.
Proper citation: Mesh-based Monte Carlo (MMC) (RRID:SCR_006950) Copy
http://cmic.cs.ucl.ac.uk/mig/index.php?n=Tutorial.NODDImatlab
This MATLAB toolbox implements a data fitting routine for Neurite Orientation Dispersion and Density Imaging (NODDI). NODDI is a new diffusion MRI technique for imaging brain tissue microstructure. Compared to DTI, it has the advantage of providing measures of tissue microstructure that are much more direct and hence more specific. It achieves this by adopting the model-based strategy which relates the signals from diffusion MRI to geometric models of tissue microstructure. In contrast to typical model-based techniques, NODDI is much more clinically feasible and can be acquired on standard MR scanners with an imaging time comparable to DTI.
Proper citation: NODDI Matlab Toolbox (RRID:SCR_006826) Copy
http://www.pstnet.com/eprime.cfm
A suite of applications to fulfill all of your computerized experiment needs. Used by more than 15,000 professionals in the research community, E-Prime provides a truly easy-to-use environment for computerized experiment design, data collection, and analysis. E-Prime provides millisecond precision timing to ensure the accuracy of your data. E-Prime's flexibility to create simple to complex experiments is ideal for both novice and advanced users. The E-Prime suite of applications includes: * E-Studio ? Drag and drop graphical interface for experiment design * E-Basic ? Underlying scripting language of E-Prime * E-Run ? Once the experiment is generated with a single click, E-Run affords you the millisecond precision of stimulus presentation, synchronizations, and data collection. * E-Merge ? Merges your single session data files for group analysis * E-DataAid ? Data management utility * E-Recovery ? Recovers data files
Proper citation: E-Prime (RRID:SCR_009567) Copy
https://github.com/hjmjohnson/DTIPrep
DTIPrep performs a Study-specific Protocol based automatic pipeline for DWI/DTI quality control and preparation. This is both a GUI and command line tool. The configurable pipeline includes image/diffusion information check, padding/Cropping of data, slice-wise, interlace-wise and gradient-wise intensity and motion check, head motion and Eddy current artifact correction, and DTI computing.
Proper citation: DWI/DTI Quality Control Tool: DTIPrep (RRID:SCR_009562) Copy
http://genome.sph.umich.edu/wiki/Mach2dat:_Association_with_MACH_output
Software that performs logistic regression, using imputed SNP dosage data and adjusting for covariates.
Proper citation: Mach2dat (RRID:SCR_009599) Copy
http://www.nitrc.org/projects/fvlight/
Light version of the existing tool Fiber Viewer. It includes every clustering methods of Fiber Viewer such as : Lenght, Gravity, Hausdorff, and Mean methods but also a Normalized Cut algorithm. As in the full version you can also display a plane on the fiber. This tool works faster than the full version due to simplified visualizations.
Proper citation: FiberViewerLight (RRID:SCR_009476) Copy
http://www.unc.edu/~yunmli/MaCH-Admix/
A genotype imputation software that is an extension to MaCH for faster and more flexible imputaiton, especially in admixed populations. It has incorporated a novel piecewise reference selection method to create reference panels tailored for target individual(s). This reference selection method generates better imputation quality in shorter running time. MaCH-Admix also separates model parameter estimation from imputation. The separation allows users to perform imputation with standard reference panels + pre-calibrated parameters in a data independent fashion. Alternatively, if one works with study-specific reference panels, or isolated target population, one has the option to simultaneously estimate these model parameters while performing imputation. MaCH-Admix has included many other useful options and supports VCF input files. All existing MaCH documentation applies to MaCH-Admix.
Proper citation: MaCH-Admix (RRID:SCR_009598) Copy
http://www.nitrc.org/projects/finslerbacktr/
Software provided as a sub-project in the Finsler-tractography module: http://www.nitrc.org/projects/finslertract
Proper citation: Fiber-tracking based on Finsler distance (RRID:SCR_009475) Copy
http://www.nitrc.org/projects/fdrw/
Simple and efficient, this application performs the Weighted False Discovery Rate procedure of Benjamini and Hochberg (1997) to correct for multiple testing. The good think is that you can test virtually any number of p-values (even millions) obtained with any test-statistics for any data set. The bonus is that you can assign a-priori weights to give a better chance to those variables that you deem important. In practice, this procedure is powerful only with a relatively small number of p-values.
Proper citation: False Discovery Rate Weighted (RRID:SCR_009473) Copy
Software application which aims to assign metric distances on the space of anatomical images in Computational Anatomy thereby allowing for the direct comparison and quantization of morphometric changes in shapes. As part of these efforts the Center for Imaging Science at Johns Hopkins University developed techniques to not only compare images, but also to visualize the changes and differences. For additional information please refer to: Faisal Beg, Michael Miller, Alain Trouve, and Laurent Younes. Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms. International Journal of Computer Vision, Volume 61, Issue 2; February 2005. M.I. Miller and A. Trouve and L. Younes, On the Metrics and Euler-Lagrange Equations of Computational Anatomy, Annual Review of biomedical Engineering, 4:375-405, 2002. Software developed with support from National Institutes of Health NCRR grant P41 RR15241.
Proper citation: LDDMM (RRID:SCR_009590) Copy
https://www.nitrc.org/projects/gmac_2012/
Open-source software toolbox implemented multivariate spectral Granger Causality Analysis for studying brain connectivity using fMRI data. Available features are: fMRI data importing, network nodes definition, time series preprocessing, multivariate autoregressive modeling, spectral Granger causality indexes estimation, statistical significance assessment using surrogate data, network analysis and visualization of connectivity results. All functions are integrated into a graphical user interface developed in Matlab environment. Dependencies: Matlab, BIOSIG, SPM, MarsBar.
Proper citation: GMAC: A Matlab toolbox for spectral Granger causality analysis of fMRI data (RRID:SCR_009581) Copy
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