Studying APOE ɛ4 Allele Dose Effects with a Univariate Morphometry Biomarker

Article type: Research Article

Authors: Wang, Gang^{a; *} | Zhou, Wenju^b | Kong, Deping^c | Qu, Zongshuai^c | Ba, Maowen^d | Hao, Jinguang^c | Yao, Tao^c | Dong, Qunxi^{e; f} | Su, Yi^g | Reiman, Eric M.^g | Caselli, Richard J.^h | Chen, Kewei^g | Wang, Yalin^{f; *} | for the Alzheimer’s Disease Neuroimaging Initiative

Affiliations: [a] School of Ulsan Ship and Ocean College, Ludong University, Yantai, China | [b] School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China | [c] School of Information and Electrical Engineering, Ludong University, Yantai, China | [d] Department of Neurology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China | [e] Institute of Engineering Medicine, Beijing Institute of Technology, Beijing, China | [f] School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA | [g] Banner Alzheimer’s Institute, Phoenix, AZ, USA | [h] Department of Neurology, Mayo Clinic Arizona, Scottsdale, AZ, USA

Correspondence: [*] Correspondence to: Dr. Gang Wang, School of Ulsan Ship and Ocean College, Ludong University, Yantai, China. Tel.: +86 535 6653786; E-mail: gangwang1970@ldu.edu.cn and Dr. Yalin Wang, School of Computing and Augmented Intelligence, Arizona State University, P.O. Box 878809, Tempe, AZ 85287, USA. Tel.: +1 480 965 6871; Fax: +1 480 965 2751; E-mail: ylwang@asu.edu.

Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

Abstract: Background:A univariate neurodegeneration biomarker (UNB) based on MRI with strong statistical discrimination power would be highly desirable for studying hippocampal surface morphological changes associated with APOE ɛ4 genetic risk for AD in the cognitively unimpaired (CU) population. However, existing UNB work either fails to model large group variances or does not capture AD induced changes. Objective:We proposed a subspace decomposition method capable of exploiting a UNB to represent the hippocampal morphological changes related to the APOE ɛ4 dose effects among the longitudinal APOE ɛ4 homozygotes (HM, N = 30), heterozygotes (HT, N = 49) and non-carriers (NC, N = 61). Methods:Rank minimization mechanism combined with sparse constraint considering the local continuity of the hippocampal atrophy regions is used to extract group common structures. Based on the group common structures of amyloid-β (Aβ) positive AD patients and Aβ negative CU subjects, we identified the regions-of-interest (ROI), which reflect significant morphometry changes caused by the AD development. Then univariate morphometry index (UMI) is constructed from these ROIs. Results:The proposed UMI demonstrates a more substantial statistical discrimination power to distinguish the longitudinal groups with different APOE ɛ4 genotypes than the hippocampal volume measurements. And different APOE ɛ4 allele load affects the shrinkage rate of the hippocampus, i.e., HM genotype will cause the largest atrophy rate, followed by HT, and the smallest is NC. Conclusion:The UMIs may capture the APOE ɛ4 risk allele-induced brain morphometry abnormalities and reveal the dose effects of APOE ɛ4 on the hippocampal morphology in cognitively normal individuals.

Keywords: Effect size, magnetic resonance imaging, permutation t-test, radial distance, regions-of-interest, subspace decomposition

DOI: 10.3233/JAD-215149

Journal: Journal of Alzheimer's Disease, vol. 85, no. 3, pp. 1233-1250, 2022