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

Authors

Suprateek Kundu, Emory University
Joshua Lukemire, Emory University
Yikai Wang, Emory University
Ying Guo, 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
Tamie Sather, University of California, San Diego
Gus Jiminez, University of California, San Diego
Leon Thal, University of California, San Diego
James Brewer, University of California, San Diego
Helen Vanderswag, University of California, San Diego
Adam Fleisher, University of California, San Diego
Melissa Davis, University of California, San Diego
Rosemary Morrison, University of California, San Diego
Ronald Petersen, Mayo Clinic
Clifford R. Jack, Mayo Clinic
Matthew Bernstein, Mayo Clinic
Bret Borowski, Mayo Clinic
Jeff Gunter, Mayo Clinic
Matt Senjem, Mayo Clinic
Prashanthi Vemuri, Mayo Clinic

Document Type

Article

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.

Publication Date

12-1-2019

Publication Title

Scientific Reports

E-ISSN

20452322

Volume

9

Issue

1

PubMed ID

31863067

Digital Object Identifier (DOI)

10.1038/s41598-019-55818-z

Recommended Citation

Kundu, Suprateek; Lukemire, Joshua; Wang, Yikai; Guo, Ying; Weiner, Michael W.; Schuff, Norbert; Rosen, Howard J.; Miller, Bruce L.; Neylan, Thomas; Hayes, Jacqueline; Finley, Shannon; Aisen, Paul; Khachaturian, Zaven; Thomas, Ronald G.; Donohue, Michael; Walter, Sarah; Gessert, Devon; Sather, Tamie; Jiminez, Gus; Thal, Leon; Brewer, James; Vanderswag, Helen; Fleisher, Adam; Davis, Melissa; Morrison, Rosemary; Petersen, Ronald; Jack, Clifford R.; Bernstein, Matthew; Borowski, Bret; Gunter, Jeff; Senjem, Matt; and Vemuri, Prashanthi, "A Novel Joint Brain Network Analysis Using Longitudinal Alzheimer’s Disease Data" (2019). Neurology. 726.
https://scholar.barrowneuro.org/neurology/726