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

Authors

Yulin Dai
Yu-Chun Hsu
Brisa S Fernandes
Kai Zhang
Xiaoyang Li
Nitesh Enduru
Andi Liu
Astrid M Manuel
Xiaoqian Jiang
Zhongming Zhao
Alzheimer’s Disease Neuroimaging Initiative

Publication Date

1-1-2024

Journal

Journal of Alzheimer's Disease

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

Humans, Alzheimer Disease, Deep Learning, Genome-Wide Association Study, Neuroimaging, Magnetic Resonance Imaging, Brain, Cognitive Dysfunction

DOI

10.3233/JAD-231020

PMID

38306043

PMCID

PMC11649026

PubMedCentral® Posted Date

12-16-2024

PubMedCentral® Full Text Version

Author MSS

Published Open-Access

yes