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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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://cis.jhu.edu/software

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   

•   Source: SciCrunch Registry

http://www.loni.usc.edu/Software/moreinfo.php?package=BGE

A JAVA application designed to create taxonomies or hierarchies in order to classify and organize information.

Proper citation: BrainGraph Editor (RRID:SCR_009536) Copy   

•   Source: SciCrunch Registry

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

Software tools optimized for performing univariate and multivariate imaging genetics analyses while providing practical correction strategies for multiple testing. The goal of this project is to merge two important research directions in modern science, genetics and neuroimaging. This entails combining modern statistical genetic methods and quantitative phenotyping performed with high dimensional neuroimaging modalities. So far, however, standard imaging tools are unable to deal with large-scale genetics data, and standard genetics tools, in turn, are unable to accommodate large size and binary format of the image data. Their focus is to create imaging genetics tools for classical genetic and epigenetic epidemiological analyses such as heritability, pleiotropy, quantitative trait loci (QTL) and genome-wide association (GWAS), gene expression, and methylation analyses optimized for traits derived from structural and functional brain imaging data

Proper citation: Solar Eclipse Imaging Genetics tools (RRID:SCR_009645) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/dcm2nii/

A tool for converting images from the complicated formats used by scanner manufacturers (DICOM, PAR/REC) to the NIfTI format used by various scientific tools. dcm2nii works for all modalities (CT, MRI, PET, SPECT) and sequence types.

Proper citation: dcm2nii (RRID:SCR_014099) Copy   

•   Source: SciCrunch Registry

http://iso2mesh.sourceforge.net/

A Matlab / Octave-based mesh generation toolbox designed for easy creation of high quality surface and tetrahedral meshes from 3D volumetric images. It contains a rich set of mesh processing scripts/programs, functioning independently or interfacing with external free meshing utilities. Iso2mesh toolbox can operate directly on 3D binary, segmented or gray-scale images, such as those from MRI or CT scans, making it particularly suitable for multi-modality medical imaging data analysis or multi-physics modeling.

Proper citation: iso2mesh (RRID:SCR_013202) Copy   

•   Source: SciCrunch Registry

http://www.openbioinformatics.org/gengen/

A suite of free software tools to facilitate the analysis of high-throughput genomics data sets. The package is currently a work-in-progress and infrequently updated.

Proper citation: GenGen (RRID:SCR_013447) Copy   

•   Source: SciCrunch Registry

http://www.multifactordimensionalityreduction.org/

Software application that is a data mining strategy for detecting and characterizing nonlinear interactions among discrete attributes (e.g. SNPs, smoking, gender, etc.) that are predictive of a discrete outcome (e.g. case-control status). The MDR software combines attribute selection, attribute construction and classification with cross-validation to provide a powerful approach to modeling interactions. (entry from Genetic Analysis Software)

Proper citation: MDR (RRID:SCR_013427) Copy   

•   Source: SciCrunch Registry

http://www.cns.atr.jp/dni/en/downloads/brain-decoder-toolbox/

Software that performs ?decoding? of brain activity, by learning the difference between brain activity patterns among conditions and then classifying the brain activity based on the learning results. BDTB is a set of Matlab functions. BDTB is OS-independent.

Proper citation: Brain Decoder Toolbox (RRID:SCR_013150) Copy   

•   Source: SciCrunch Registry

http://www.openbioinformatics.org/annovar/

An efficient software tool to utilize update-to-date information to functionally annotate genetic variants detected from diverse genomes (including human genome hg18, hg19, as well as mouse, worm, fly, yeast and many others). Given a list of variants with chromosome, start position, end position, reference nucleotide and observed nucleotides, ANNOVAR can perform: 1. gene-based annotation. 2. region-based annotation. 3. filter-based annotation. 4. other functionalities. (entry from Genetic Analysis Software)

Proper citation: ANNOVAR (RRID:SCR_012821) Copy   

•   Source: SciCrunch Registry

http://brainbox.pasteur.fr/

Web application which allows users to visualise and collaboratively segment and annotate any brain MRI dataset available online via URL. A list of brains are available for use on the main site. Segmentations are automatically saved and can be downloaded as Nifti files or triangular meshes. Users can point BrainBox to their own Nifti data, or try data catalogues created by the community.

Proper citation: BrainBox (RRID:SCR_014750) Copy   

•   Source: SciCrunch Registry

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

Biomedical technology resource center specializing in novel approaches and tools for neuroimaging. It develops novel strategies to investigate brain structure and function in their full multidimensional complexity. There is a rapidly growing need for brain models comprehensive enough to represent brain structure and function as they change across time in large populations, in different disease states, across imaging modalities, across age and sex, and even across species. International networks of collaborators are provided with a diverse array of tools to create, analyze, visualize, and interact with models of the brain. A major focus of these collaborations is to develop four-dimensional brain models that track and analyze complex patterns of dynamically changing brain structure in development and disease, expanding investigations of brain structure-function relations to four dimensions.

Proper citation: Laboratory of Neuro Imaging (RRID:SCR_001922) Copy   

•   Source: SciCrunch Registry

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

This resouce will centralize development of tools for interaction with medical imaging data in immersive virtual environments (based on the Vizard platform).

Proper citation: Medical Image Processing and Visualization in Virtual Environments (RRID:SCR_001751) Copy   

•   Source: SciCrunch Registry

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

Based on JIST and MIPAV, this pipeline combines structural magnetic resonance data with diffusion tensor imaging to estimate a connectome, which is a comprehensive description of the wiring diagram of the brain.

Proper citation: MR Connectome Automated Pipeline (RRID:SCR_002252) Copy   

•   Source: SciCrunch Registry

http://www.incf.org/

Independent international facilitator catalyzing and coordinating global development of neuroinformatics aiming to advance data reuse and reproducibility in global brain research. Integrates and analyzes diverse data across scales, techniques, and species to understand brain function and positively impact the health and well being of society.

Proper citation: International Neuroinformatics Coordinating Facility (RRID:SCR_002282) Copy   

•   Source: SciCrunch Registry

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

An EEGLAB plugin which adaptively estimates and removes sinusoidal artifacts from independent component analysis (ICA) components or scalp channels using a frequency-domain (multi-taper) regression technique with a Thompson F-statistic for identifying significant sinusoidal artifacts. This approach has been advocated by Partha Mitra and Hemant Bokil (Observed Brain Dynamics, Chapter 7.3.4., 2007) and CleanLine utilizes modified routines from the Mitra Lab's Chronux Toolbox (www.chronux.org). Sinusoidal noise can be a prominent artifact in recorded electrophysiological data. This can stem from AC power line fluctuations (e.g. 50/60 Hz line noise + harmonics), power suppliers (e.g. in medical equipment), fluorescent lights, etc. Notch filtering is generally undesirable due to creation of band-holes, and significant distortion of frequencies around the notch frequency (as well as phase distortion at other frequencies and Gibbs rippling in the time-domain).

Proper citation: CleanLine (RRID:SCR_002233) Copy   

•   Source: SciCrunch Registry