Characteristic patterns of inter- and intra-hemispheric metabolic connectivity in patients with stable and progressive mild cognitive impairment and Alzheimer’s disease

Authors

Sheng Yao Huang, Chang Gung University
Jung Lung Hsu, Chang Gung Memorial Hospital
Kun Ju Lin, Chang Gung University
Ho Ling Liu, University of Texas MD Anderson Cancer Center
Shiaw Pying Wey, Chang Gung University
Ing Tsung Hsiao, Chang Gung University
Michael Weiner, University of California, San Francisco
Paul Aisen, University of California, San Diego
Ronald Petersen, Mayo Clinic
Clifford R. Jack, Mayo Clinic
William Jagust, University of California, Berkeley
John Q. Trojanowki, University of Pennsylvania
Arthur W. Toga, Keck School of Medicine of USC
Laurel Beckett, University of California, Davis
Robert C. Green, Brigham and Women's Hospital
Andrew J. Saykin, Indiana University Bloomington
John Morris, Washington University in St. Louis
Leslie M. Shaw, University of Pennsylvania
Enchi Liu, Janssen Pharmaceuticals, Inc.
Tom Montine, University of Washington
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
Danielle Harvey, University of California, Davis
Matthew Bernstein, Mayo Clinic
Nick Fox, University of London
Paul Thompson, Keck School of Medicine of USC
Norbert Schuff, University of California, San Francisco
Charles DeCarli, University of California, Davis

Document Type

Article

Publication Date

12-1-2018

Journal

Scientific Reports

Volume

8

Issue

1

URL with Digital Object Identifier

10.1038/s41598-018-31794-8

Abstract

The change in hypometabolism affects the regional links in the brain network. Here, to understand the underlying brain metabolic network deficits during the early stage and disease evolution of AD (Alzheimer disease), we applied correlation analysis to identify the metabolic connectivity patterns using 18F-FDG PET data for NC (normal control), sMCI (stable MCI), pMCI (progressive MCI) and AD, and explore the inter- and intra-hemispheric connectivity between anatomically-defined brain regions. Regions extracted from 90 anatomical structures were used to construct the matrix for measuring the inter- and intra-hemispheric connectivity. The brain connectivity patterns from the metabolic network show a decreasing trend of inter- and intra-hemispheric connections for NC, sMCI, pMCI and AD. Connection of temporal to the frontal or occipital regions is a characteristic pattern for conversion of NC to MCI, and the density of links in the parietal-occipital network is a differential pattern between sMCI and pMCI. The reduction pattern of inter and intra-hemispheric brain connectivity in the metabolic network depends on the disease stages, and is with a decreasing trend with respect to disease severity. Both frontal-occipital and parietal-occipital connectivity patterns in the metabolic network using 18F-FDG PET are the key feature for differentiating disease groups in AD.