Polygenic Hazard Score Associated Multimodal Brain Networks Along the Alzheimer's Disease Continuum

• Type of Material: Article
• Authors: Kaicheng Li, Emory University; Zening Fu, Emory University; Shile Qi, Nanjing University of Aeronautics & Astronautics; Xiao Luo, Zhejiang University; Qingze Zeng, Zhejiang University; Xiaopei Xu, Zhejiang University; Peiyu Huang, Zhejiang University; Minming Zhang, Zhejiang University; Vince Calhoun, Emory University
• Language: English
• Date: 2021-09-03
• Publisher: FRONTIERS MEDIA SA
• Publication Version: Final Published Version
• Copyright Statement: © 2021 Li, Fu, Qi, Luo, Zeng, Xu, Huang, Zhang and Calhoun.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3389/fnagi.2021.725246
• Title of Journal or Parent Work: FRONTIERS IN AGING NEUROSCIENCE
• Volume: 13
• Start Page: 725246
• End Page: 725246
• Grant/Funding Information: work was funded by the National Natural Science Foundation of China (Grant Nos. 81901707 and 82001766) and the National Key Research and Development Program of China (Grant No. 2016YFC1306600) as well as the National Institutes of Health grant RF1AG063153.
• Keywords: DEFAULT MODE; Geriatrics & Gerontology; FUSION; Life Sciences & Biomedicine; cognitive decline; APOLIPOPROTEIN-E; LOCI; COMPONENTS; visuospatial network; executive control network; polygenic hazard score; FUNCTIONAL CONNECTIVITY; Alzheimer's disease; default mode network; VARIANTS; GWAS; Neurosciences; Neurosciences & Neurology; supervised multimodal fusion; NEUROIMAGING MEASURES; DEFICITS; Science & Technology
• Research Categories: Computer Science; Health Sciences, Radiology

Abstract

Background: Late-onset Alzheimer’s disease (AD) is a polygenic neurodegenerative disease. Identifying the neuroimaging phenotypes behind the genetic predisposition of AD is critical to the understanding of AD pathogenesis. Two major questions which previous studies have led to are: (1) should the general “polygenic hazard score” (PHS) be a good choice to identify the individual genetic risk for AD; and (2) should researchers also include inter-modality relationships in the analyses considering these may provide complementary information about the AD etiology. Methods: We collected 88 healthy controls, 77 patients with mild cognitive impairment (MCI), and 22 AD patients to simulate the AD continuum included from the ADNI database. PHS-guided multimodal fusion was used to investigate the impact of PHS on multimodal brain networks in AD-continuum by maximizing both inter-modality association and reference-modality correlation. Fractional amplitude of low frequency fluctuations, gray matter (GM) volume, and amyloid standard uptake value ratios were included as neuroimaging features. Eventually, the changes in neuroimaging features along AD continuum were investigated, and relationships between cognitive performance and identified PHS associated multimodal components were established. Results: We found that PHS was associated with multimodal brain networks, which showed different functional and structural impairments under increased amyloid deposits. Notably, along with AD progression, functional impairment occurred before GM atrophy, amyloid deposition started from the MCI stage and progressively increased throughout the disease continuum. Conclusion: PHS is associated with multi-facets of brain impairments along the AD continuum, including cognitive dysfunction, pathological deposition, which might underpin the AD pathogenesis.