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.
Keywords: graph theory, data sharing, functional connectivity, structural connectivity, resting-state fMRI, diffusion-weighted MRI
Citation: Brown JA, Rudie JD, Bandrowski A, Van Horn JD and Bookheimer SY (2012) The UCLA multimodal connectivity database: a web-based platform for brain connectivity matrix sharing and analysis. Front. Neuroinform. 6:28. doi: 10.3389/fninf.2012.00028
Received: 19 June 2012; Accepted: 14 November 2012;
Published online: 28 November 2012.
Edited by:Marc-Oliver Gewaltig, Ecole Polytechnique Fédérale de Lausanne, Switzerland
Reviewed by:Mihail Bota, University of Southern California, USA
Copyright © 2012 Brown, Rudie, Bandrowski, Van Horn and Bookheimer. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.
*Correspondence: Jesse A. Brown, Semel Institute, University of California, Los Angeles, 760 Westwood Plaza, B8-169, Los Angeles, CA 90095, USA. e-mail: email@example.com
John D. Van Horn, Laboratory of Neuro Imaging, Department of Neurology, UCLA, 635 Charles Young Drive South, Suite 225, Los Angeles, CA 90095-7334, USA. firstname.lastname@example.org