Disentangling Accelerated Cognitive Decline from the Normal Aging Process and Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach

Article type: Research Article

Authors: Dai, Yulin^{a; 1} | Hsu, Yu-Chun^{b; 1} | Fernandes, Brisa S.^a | Zhang, Kai^b | Li, Xiaoyang^{a; c} | Enduru, Nitesh^{a; d} | Liu, Andi^{a; d} | Manuel, Astrid M.^a | Jiang, Xiaoqian^{b; *} | Zhao, Zhongming^{a; d; e; *} | for the Alzheimer’s Disease Neuroimaging Initiative²

Affiliations: [a] Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA | [b] Center for Secure Artificial Intelligence for Healthcare, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA | [c] Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA | [d] Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA | [e] Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA

Correspondence: [*] Correspondence to: Zhongming Zhao, PhD, Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St., Suite 600, Houston, TX 77030, USA. Tel.: +1 713 500 3631; E-mail: [email protected] and Xiaoqian Jiang, PhD, Center for Secure Artificial Intelligence for Healthcare, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St., Suite 600, Houston, TX 77030, USA. Tel.: +1 713 500 3930; E-mail: [email protected].

Note: [1] These authors contributed equally to this work.

Note: [2] 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:The progressive cognitive decline, an integral component of Alzheimer’s disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and AD between different chronological points. Objective:To disentangle the normal aging effect from the AD-related accelerated cognitive decline and unravel its genetic components using a neuroimaging-based deep learning approach. Methods:We developed a deep-learning framework based on a dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. We then conducted genome-wide association studies (GWAS) and post-GWAS analyses to reveal the genetic basis of AD-related accelerated cognitive decline. Results:We used our model to process data from 1,313 individuals, training it on 414 cognitively normal people and predicting cognitive assessment for all participants. In our analysis of accelerated cognitive decline GWAS, we identified two genome-wide significant loci: APOE locus (chromosome 19 p13.32) and rs144614292 (chromosome 11 p15.1). Variant rs144614292 (G > T) has not been reported in previous AD GWA studies. It is within the intronic region of NELL1, which is expressed in neurons and plays a role in controlling cell growth and differentiation. The cell-type-specific enrichment analysis and functional enrichment of GWAS signals highlighted the microglia and immune-response pathways. Conclusions:Our deep learning model effectively extracted relevant neuroimaging features and predicted individual cognitive decline. We reported a novel variant (rs144614292) within the NELL1 gene.

Keywords: Alzheimer’s disease, cognitive decline, deep learning, genome-wide association study, neuroimaging

DOI: 10.3233/JAD-231020

Journal: Journal of Alzheimer's Disease, vol. 97, no. 4, pp. 1807-1827, 2024