Abstract
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.
Highlights
Successful neuroimaging data sharing efforts have taken a variety of organizational approaches, including top-down centralized strategies and bottom-up grassroots efforts
The raw NKI_fcmri_avg_GSR network was 100% connected with an average edge weight of 0.017 ± 0.199 (Table 1)
The average Euclidean distance between ROIs was exactly the same as the global signal regression (GSR) network, given that every ROI is connected in the raw connectivity matrix (CM)
Summary
Successful neuroimaging data sharing efforts have taken a variety of organizational approaches, including top-down centralized strategies and bottom-up grassroots efforts Centralized projects such as the Alzheimer’s Disease Neuroimaging Initiative (ADNI; http://www.adni-info.org) begin by defining a targeted subject population, the type of imaging data to be included, and a set of criteria to ensure the quality and similarity of the data collection across multiple sites and scanners. Once data has been collected, it can be stored in a database where users can search and download desired data This allows researchers to freely access the data, enabling them to apply their own preprocessing and run custom analyses. These sites typically collect image files in a specific format such as NiFTI or DICOM along with relevant meta-information about the data acquisition, the individual receiving the scan, and the study design
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