Tools   Select Another Resource Report Type

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

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://fcon_1000.projects.nitrc.org/indi/retro/cobre.html

Data set of raw anatomical and functional MR data from 72 patients with Schizophrenia and 75 healthy controls (ages ranging from 18 to 65 in each group). All subjects were screened and excluded if they had: history of neurological disorder, history of mental retardation, history of severe head trauma with more than 5 minutes loss of consciousness, history of substance abuse or dependence within the last 12 months. Diagnostic information was collected using the Structured Clinical Interview used for DSM Disorders (SCID). A multi-echo MPRAGE (MEMPR) sequence was used with the following parameters: TR/TE/TI = 2530/(1.64, 3.5, 5.36, 7.22, 9.08)/900 ms, flip angle = 7��, FOV = 256x256 mm, Slab thickness = 176 mm, Matrix = 256x256x176, Voxel size =1x1x1 mm, Number of echos = 5, Pixel bandwidth =650 Hz, Total scan time = 6 min. With 5 echoes, the TR, TI and time to encode partitions for the MEMPR are similar to that of a conventional MPRAGE, resulting in similar GM/WM/CSF contrast. Rest data was collected with single-shot full k-space echo-planar imaging (EPI) with ramp sampling correction using the intercomissural line (AC-PC) as a reference (TR: 2 s, TE: 29 ms, matrix size: 64x64, 32 slices, voxel size: 3x3x4 mm3). Slice Acquisition Order: Rest scan - collected in the Axial plane - series ascending - multi slice mode - interleaved MPRAGE - collected in the Sag plane - series interleaved - multi slice mode - single shot The following data are released for every participant: * Resting fMRI * Anatomical MRI * Phenotypic data for every participant including: gender, age, handedness and diagnostic information.

Proper citation: COBRE (RRID:SCR_010482) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/colin3t7t

High-field extension of the Colin27 single-subject atlas with additional high-resolution, quantitative, averaged scans at both 3T and 7T.

Proper citation: Colin 3T/7T High-resolution Atlas (RRID:SCR_000160) Copy   

•   Source: SciCrunch Registry

http://www.dian-info.org/default.htm

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. An international research partnership of leading scientists determined to understand a rare form of Alzheimers disease that is caused by a gene mutation and to establish a research database and tissue repository to support research on Alzheimers disease by other investigators around the world. One goal of DIAN is to study possible brain changes that occur before Alzheimers disease is expressed in people who carry an Alzheimers disease mutation. Other family members without a mutation will serve as a comparison group. People in families in which a mutation has been identified will be tracked in order to detect physical or mental changes that might distinguish people who inherited the mutation from those who did not. DIAN currently involves eleven outstanding research institutions in the United States, United Kingdom, and Australia. John C. Morris, M.D., Friedman Distinguished Professor of Neurology at Washington University School of Medicine in St. Louis, is the principal investigator of the project., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: DIAN - Dominantly Inherited Alzheimer Network (RRID:SCR_000812) Copy   

•   Source: SciCrunch Registry

http://datacite.labs.orcid-eu.org

Service (Beta) that allows users to search the DataCite Metadata Store, and add their research outputs including datasets, software, and others to their ORCID profile. This should increase the visibility of these research data, and will make it easier to use these data citations in applications that connect to the ORCID Registry. In addition, the service is also providing formatted citations in several popular citation styles, supports COinS, links to related resources, and displays the attached Creative Commons license where this information is available. The DataCite Metadata Store of course also contains many text documents from academic publishers and services such as figshare or PeerJ Preprints, and these works can also be claimed. This tool is a collaborative effort by ORCID, CrossRef and DataCite.

Proper citation: ODIN (RRID:SCR_001386) Copy   

•   Source: SciCrunch Registry

http://ibvd.virtualbrain.org/

A database of brain neuroanatomic volumetric observations spanning various species, diagnoses, and structures for both individual and group results. A major thrust effort is to enable electronic access to the results that exist in the published literature. Currently, there is quite limited electronic or searchable methods for the data observations that are contained in publications. This effort will facilitate the dissemination of volumetric observations by making a more complete corpus of volumetric observations findable to the neuroscience researcher. This also enhances the ability to perform comparative and integrative studies, as well as metaanalysis. Extensions that permit pre-published, non-published and other representation are planned, again to facilitate comparative analyses. Design strategy: The principle organizing data structure is the "publication". Publications report on "groups" of subjects. These groups have "demographic" information as well as "volume" information for the group as a whole. Groups are comprised of "individuals", which also have demographic and volume information for each of the individuals. The finest-grained data structure is the "individual volume record" which contains a volume observation, the units for the observation, and a pointer to the demographic record for individual upon which the observation is derived. A collection of individual volumes can be grouped into a "group volume" observation; the group can be demographically characterized by the distribution of individual demographic observations for the members of the group.

Proper citation: Internet Brain Volume Database (RRID:SCR_002060) Copy   

•   Source: SciCrunch Registry

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

A set of tools for deriving region of interest (ROI) atlases by whole brain clustering of task or resting state data. This resource also contains several atlases derived by parcellating publicly available resting state fMRI datasets. The initial release will include python scripts and ROI atlases developed to perform the analyses described in Craddock et. al., A whole brain fMRI atlas generated via spatially constrained spectral clustering, which is currently in revision in Human Brain Mapping. The scripts provide all of the tools necessary to derive an ROI atlases using spatially constrained Ncut spectral clustering. The scripts require python, numpy and scipy to run. Source code and parcellations now available! Go to http://ccraddock.github.io/cluster_roi/ for more information.

Proper citation: Spatially Constrained Parcellation (RRID:SCR_002198) Copy   

•   Source: SciCrunch Registry

http://mindboggle.info/data.html

Complete set of free, publicly accessible, downloadable atlases, templates, and individual manually labeled brain image data, the largest collection of publicly available, manually labeled human brains in the world! http://journal.frontiersin.org/article/10.3389/fnins.2012.00171/full

Proper citation: Mindboggle-101 atlases (RRID:SCR_002439) Copy   

•   Source: SciCrunch Registry

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

Symmetric atlas of the primate brain created using 18 cases of rhesus macaques aged 16-34 months. It includes the T1-weighted image (with and without skull), and also tissue segmentation probability maps (white matter, gray matter, CSF, rest), subcortical structures segmentation (amygdala, caudate, hippocampus, pallidus, putamen), and a lobar parcellation map. You can find more details about the creation of this atlas in the following paper : M. Styner, R. Knickmeyer, S. Joshi, C. Coe, S. J. Short, and J. Gilmore. Automatic brain segmentation in rhesus monkeys. In Proc SPIE Vol 6512, Medical Imaging, 2007, pp. 65122 L1-8

Proper citation: UNC Primate Brain Atlas (RRID:SCR_002570) Copy   

•   Source: SciCrunch Registry

http://www.med.unc.edu/bric/ideagroup/free-softwares/unc-infant-0-1-2-atlases

3 atlases dedicated for neonates, 1-year-olds, and 2-year-olds. Each atlas comprises a set of 3D images made up of the intensity model, tissue probability maps, and anatomical parcellation map. These atlases are constructed with the help of state-of-the-art infant MR segmentation and groupwise registration methods, on a set of longitudinal images acquired from 95 normal infants (56 males and 39 females) at neonate, 1-year-old, and 2-year-old.

Proper citation: UNC Infant 0-1-2 Atlases (RRID:SCR_002569) Copy   

•   Source: SciCrunch Registry

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

A 3D stereoscopic (anaglyph method) full brain functional connectivity atlas created using a parcellation atlas published by Craddock et al. (2012). Using 3D Slicer 3.6.3 and the two hundred Region of Interest (ROI) version of the Craddock atlas, 200 grayscale surface models were created using a z-stat threshold > 2.3, and each surface model was processed with a surface decimation algorithm, smoothed with the Taubin algorithm and without surface normals. For improved visualization of the functional connectivity networks and their relative anatomical position, the surface model of five subcortical anatomical structures (corpus callosum, bilateral caudate, pallidum, putamen, thalamus, amygdala and hippocampus) were included in SAIBN. These surfaces were created with 3D Slicer using the segmentation computed with Freesurfer v. 5.1. The viewer should use red-cyan glasses to see the 3D stereoscopic effect using 3D Slicer (version 3.6.3, http://www.slicer.org/pages/Special:SlicerDownloads).

Proper citation: Stereoscopic Atlas of Intrinsic Brain Networks (RRID:SCR_002568) Copy   

•   Source: SciCrunch Registry

http://epilepsy.uni-freiburg.de/database

A comprehensive database for human surface and intracranial EEG data that is suitable for a broad range of applications e.g. of time series analyses of brain activity. Currently, the EU database contains annotated EEG datasets from more than 200 patients with epilepsy, 50 of them with intracranial recordings with up to 122 channels. Each dataset provides EEG data for a continuous recording time of at least 96 hours (4 days) at a sample rate of up to 2500 Hz. Clinical patient information and MR imaging data supplement the EEG data. The total duration of EEG recordings included execeeds 30000 hours. The database is composed of different modalities: Binary files with EEG recording / MR imaging data and Relational database for supplementary meta data.

Proper citation: EPILEPSIE database (RRID:SCR_003179) Copy   

•   Source: SciCrunch Registry