| URL: | http://umcd.humanconnectomeproject.org |
| Full name: | USC Multimodal Connectivity Database |
| Description: | The USC Multimodal Connectivity Database (UMCD) is a web-based repository and analysis site for connectivity matrices that have been derived from de-identified neuroimaging data. UMCD is an interactive web-based platform for brain connectivity matrix sharing and analysis. |
| Year founded: | 2012 |
| Last update: | |
| Version: | |
| Accessibility: |
Accessible
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| Country/Region: | United States |
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| University/Institution: | University of California San Francisco |
| Address: | Memory and Aging Center, UCSF Department of Neurology, 675 Nelson Rising Lane, San Francisco, CA 94143 |
| City: | San Francisco |
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| Country/Region: | United States |
| Contact name (PI/Team): | Jesse A. Brown |
| Contact email (PI/Helpdesk): | jesse.brown@ucsf.edu |
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Connected brains and minds--The UMCD repository for brain connectivity matrices. [PMID: 26311606]
We describe the USC Multimodal Connectivity Database (http://umcd.humanconnectomeproject.org), an interactive web-based platform for brain connectivity matrix sharing and analysis. The site enables users to download connectivity matrices shared by other users, upload matrices from their own published studies, or select a specific matrix and perform a real-time graph theory-based analysis and visualization of network properties. The data shared on the site span a broad spectrum of functional and structural brain connectivity information from humans across the entire age range (fetal to age 89), representing an array of different neuropsychiatric and neurodegenerative disease populations (autism spectrum disorder, ADHD, and APOE-4 carriers). An analysis combining 7 different datasets shared on the site illustrates the diversity of the data and the potential for yielding deeper insight by assessing new connectivity matrices with respect to population-wide network properties represented in the UMCD. |
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The UCLA multimodal connectivity database: a web-based platform for brain connectivity matrix sharing and analysis. [PMID: 23226127]
Brain connectomics research has rapidly expanded using functional MRI (fMRI) and diffusion-weighted MRI (dwMRI). A common product of these varied analyses is a connectivity matrix (CM). A CM stores the connection strength between any two regions ("nodes") in a brain network. This format is useful for several reasons: (1) it is highly distilled, with minimal data size and complexity, (2) graph theory can be applied to characterize the network's topology, and (3) it retains sufficient information to capture individual differences such as age, gender, intelligence quotient (IQ), or disease state. Here we introduce the UCLA Multimodal Connectivity Database (http://umcd.humanconnectomeproject.org), an openly available website for brain network analysis and data sharing. The site is a repository for researchers to publicly share CMs derived from their data. The site also allows users to select any CM shared by another user, compute graph theoretical metrics on the site, visualize a report of results, or download the raw CM. To date, users have contributed over 2000 individual CMs, spanning different imaging modalities (fMRI, dwMRI) and disorders (Alzheimer's, autism, Attention Deficit Hyperactive Disorder). To demonstrate the site's functionality, whole brain functional and structural connectivity matrices are derived from 60 subjects' (ages 26-45) resting state fMRI (rs-fMRI) and dwMRI data and uploaded to the site. The site is utilized to derive graph theory global and regional measures for the rs-fMRI and dwMRI networks. Global and nodal graph theoretical measures between functional and structural networks exhibit low correspondence. This example demonstrates how this tool can enhance the comparability of brain networks from different imaging modalities and studies. The existence of this connectivity-based repository should foster broader data sharing and enable larger-scale meta-analyses comparing networks across imaging modality, age group, and disease state. |