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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   

•   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.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://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/gppi/

An automated toolbox for a generalized form of psychophysiological interactions for SPM and FSFAST. The automated toolbox can do the following: (a1) produce identical results to the current implementation in SPM (a2) use the current implementation of PPI in SPM but using the regional mean instead of the eigenvariate (a3) use a generalized form that allows a PPI for each task to be in the same model using either the regional mean of eigenvariate (b) create the model using the output of one of the (a) options and the first level design (c) estimate the model (/results directory) (d) compute the contrasts specified.

Proper citation: Generalized PPI Toolbox (RRID:SCR_009489) 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.nitrc.org/projects/dti-denoising/

A Matlab package which contains six denoising filters and a noise estimation method for 4D DWI. The package includes nonlocal means, local PCA and Oracle DCT methods. Based on image redundancy and/or sparsity, the proposed filters provide efficient denoising while preserving fine structures.

Proper citation: DTI denoising (RRID:SCR_014102) Copy   

•   Source: SciCrunch Registry

http://www.kitware.com

A software repository which provides open source software and technology for visualization, computer vision, medical imaging, data publishing, and quality software process solutions. Kitware also provides services such as creating customized applications for clients, porting their open-source tools to specialized computing platforms, and supporting their open-source software tools with documentation, professional consulting services, and software training.

Proper citation: Kitware (RRID:SCR_013989) 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.nitrc.org/projects/sfmproject/

Structure from motion algorithms repository. Common interface for various sfm algorithms.

Proper citation: SFMProject (RRID:SCR_014166) Copy   

•   Source: SciCrunch Registry

http://neuromorphometrics.com/?page_id=23

Collection of neuroanatomically labeled MRI brain scans, created by neuroanatomical experts. Regions of interest include the sub-cortical structures (thalamus, caudate, putamen, hippocampus, etc), along with ventricles, brain stem, cerebellum, and gray and white matter and sub-divided cortex into parcellation units that are defined by gyral and sulcal landmarks.

Proper citation: Manually Labeled MRI Brain Scan Database (RRID:SCR_009604) Copy   

•   Source: SciCrunch Registry

http://pingstudy.ucsd.edu/

A large multi-site pediatric MRI and genetics data resource to facilitate studies of the genomic landscape of the developing human brain. It includes information about the developing mental and emotional functions of the children to understand the genetic basis of individual differences in brain structure and connectivity, cognition, and personality. Investigators on the project are studying 1400 children between the ages of 3 and 20 years so that links between genetic variation and developing patterns of brain connectivity can be examined. Investigators interested in the effects of a particular gene will be able to search the database for any brain areas or connections between areas that differ as a function of variation in a particular gene, and also to determine if the genes appear to affect the course of brain development at some point during childhood. A data exploration tool has been created for mapping and analyzing MRI data sets collected for PING and related developmental studies. Approved investigators will be able to view raw image sets and derived 3D brain maps of MRI and DTI data, conduct hypothesis testing, and graph brain area measures as they change across the time course of development. PING Cores * Coordinating Core: Functions include project management, screening of participants and maintaining the database * Neuroimaging Core: applying a standardized high-resolution structural MRI protocol involving 3-D T1-weighted scans, a T2-weighted volume, and a set of diffusion-weighted scans with multiple b values and diffusion directions, scans to estimate MRI relaxation rates, and gradient echo EPI scans for resting state fMRI. Importantly, adaptive motion compensation, using ����??PROMO����??, a novel real-time motion correction algorithm will be used. Specific PING protocols for each scanner manufacturer: ** PING MRI Protocol - GE ** PING MRI Protocol - Philips ** PING MRI Protocol - Siemens * Assessment Core: Cognitive assessments for the PING project are conducted using the NIH Toolbox for Cognition. * Genomics Core: functions as a central repository for receipt of saliva samples collected for each study participant. Once received, samples are catalogued, maintained, and DNA is extracted using state-of-the-field laboratory techniques. Ultimately, genome-wide genotyping is performed on the extracted DNA using the Illumina Human660W-Quad BeadChip. PING involves 10 sites throughout the country including UCSD, University of Hawaii, Scripps Genomics, UCLA, UC Davis, Kennedy Krieger Institute/Johns Hopkins, Sacker Institute/Cornell University, University of Massachusetts, Massachusetts General Hospital/Harvard, and Yale. Families who may want to participate in the study, or others who want to know more about it, may email questions to ping (at) ucsd.edu.

Proper citation: Pediatric Imaging Neurocognition and Genetics (RRID:SCR_008953) Copy   

•   Source: SciCrunch Registry

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

An effort to provide a set of quasi-probabilistic atlases for established functional ROIs in the human neuroimaging literature. Many atlases exist for various anatomical parcellation schemes, such as the Brodmann areas, the structural atlases, tissue segmentation atlases, etc. To date, however, there is no atlas for so-called functional ROIs. Such fROIs are typically associated with an anatomical label of some kind (e.g. the _fusiform_ face area), but these labels are only approximate and can be misleading inasmuch as fROIs are not constrained by anatomical landmarks, whether cytoarchitectonic or based on sulcal and gyral landmarks. The goal of this project is to provide quasi-probabilistic atlases for fROIs that are based on published coordinates in the neuroimaging literature. This is an open-ended enterprise and the atlas can grow as needed. Members of the neuroscience and neuroimaging community interested in contributing to the project are encouraged to do so.

Proper citation: Functional ROI Atlas (RRID:SCR_009481) Copy   

•   Source: SciCrunch Registry

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

This atlas takes advantage of ultra-high resolution 7T MRI to provide unprecedented levels of detail on structures of the basal ganglia in-vivo. The atlas includes probability maps of the Subthalamic Nucleus (STh) using T2*-imaging. For now it has been created on 13 young healthy participants with a mean age of 24.38 (range: 22-28, SD: 2.36). We recently also created atlas STh probability maps from 8 middle-aged participants with a mean age of 50.67 (range: 40-59, SD: 6.63), and 9 elderly participants with a mean age of 72.33 (range: 67-77, SD: 2.87). You can find more details about the creation of these maps in the following papers: Young: http://www.ncbi.nlm.nih.gov/pubmed/22227131 Middle-aged & Elderly: http://www.ncbi.nlm.nih.gov/pubmed/23486960 Participating institutions are the Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, and the Cognitive Science Center Amsterdam, University of Amsterdam, the Netherlands.

Proper citation: Atlasing of the basal ganglia (RRID:SCR_009431) Copy   

•   Source: SciCrunch Registry

http://www.birncommunity.org/current-users/morphometry-birn/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 4th,2023. Calibration data set of spoiled gradient-recalled echo magnetic resonance imaging data from five healthy volunteers (four males and one female) scanned twice at four sites having 1.5T systems from different vendors (Siemens, GE, Marconi Medical Systems) pooled by the Morphometry Testbed's (MBIRN). Some subjects were also scanned a single time at another site. One subject was only scanned twice at three sites (subject 73213384) and once at another site. For each subject, four Fast Low-Angle Shot (FLASH) scans with flip angles of 3, 5, 20, and 30 degrees were obtained in a single scan session, from which tissue proton density and T1 maps can be derived. These data were acquired to investigate various metrics of within-site and across-site reproducibility. The images have been defaced so that no facial features can be reconstructed from these data. The Morphometry Testbed (MBIRN) of the Biomedical Informatics Research Network (BIRN) focused on pooling and analyzing of neuroimaging data acquired at multiple sites. Specific applications include potential relationships between anatomical differences and specific memory dysfunctions, such as Alzheimer's disease. With the completion of the initial BIRN testbed phase, each of the original BIRN testbeds have now been retired in order to focus on new users in other biomedical domains.

Proper citation: Morphometry BIRN (RRID:SCR_000155) Copy   

•   Source: SciCrunch Registry

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

Behavioral and imaging data from about 120 participants aged 18-89. Data were collected as part of a grant to use high-resolution imaging and advanced behavioral tasks to understand how aging affects the hippocampus and how this is related to age-related cognitive decline. The full dataset includes traditional neuropsycholgical measures, hippocampal-specific behavioral measures, whole-brain DTI, high-resolution DTI of the medial temporal lobes, and structural MRI including segmentation of grey/white/CSF, of cortical regions and of hippocampal subfields.

Proper citation: Stark Cross-Sectional Aging (RRID:SCR_014171) Copy   

•   Source: SciCrunch Registry