A Novel Joint Brain Network Analysis Using Longitudinal Alzheimer’s Disease Data

• Type of Material: Article
• Authors: Suprateek Kundu, Emory University; Joshua Lukemire, Emory University; Yikai Wang, Emory University; Ying Guo, Emory University; James Lah, Emory University; Janet Cellar, Emory University; Allan Levey, Emory University; Michael W. Weiner, University of California, San Francisco; Norbert Schuff, University of California, San Francisco; Howard J. Rosen, University of California, San Francisco; Bruce L. Miller, University of California, San Francisco; Thomas Neylan, University of California, San Francisco; Jacqueline Hayes, University of California, San Francisco; Shannon Finley, University of California, San Francisco; Paul Aisen, University of California, San Diego; Zaven Khachaturian, University of California, San Diego; Ronald G. Thomas, University of California, San Diego; Michael Donohue, University of California, San Diego; Sarah Walter, University of California, San Diego; Devon Gessert, University of California, San Diego
• Language: English
• Date: 2019-12-01
• Publisher: Nature Research (part of Springer Nature): Fully open access journals
• Publication Version: Final Published Version
• Copyright Statement: © 2019, The Author(s).
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/s41598-019-55818-z
• Title of Journal or Parent Work: Scientific Reports
• ISSN: 2045-2322
• Volume: 9
• Issue: 1
• Start Page: 19589
• End Page: 19589
• Research Categories: Biology, Neuroscience; Health Sciences, Medicine and Surgery; Biology, Biostatistics

Abstract

There is well-documented evidence of brain network differences between individuals with Alzheimer’s disease (AD) and healthy controls (HC). To date, imaging studies investigating brain networks in these populations have typically been cross-sectional, and the reproducibility of such findings is somewhat unclear. In a novel study, we use the longitudinal ADNI data on the whole brain to jointly compute the brain network at baseline and one-year using a state of the art approach that pools information across both time points to yield distinct visit-specific networks for the AD and HC cohorts, resulting in more accurate inferences. We perform a multiscale comparison of the AD and HC networks in terms of global network metrics as well as at the more granular level of resting state networks defined under a whole brain parcellation. Our analysis illustrates a decrease in small-worldedness in the AD group at both the time points and also identifies more local network features and hub nodes that are disrupted due to the progression of AD. We also obtain high reproducibility of the HC network across visits. On the other hand, a separate estimation of the networks at each visit using standard graphical approaches reveals fewer meaningful differences and lower reproducibility.