Identifying vulnerable brain networks associated with Alzheimer’s disease risk

• Published in: Cerebral cortex (New York, N.Y. 1991) Vol. 33; no. 9; pp. 5307 - 5322
• Main Authors: Mahzarnia, Ali, Stout, Jacques A, Anderson, Robert J, Moon, Hae Sol, Yar Han, Zay, Beck, Kate, Browndyke, Jeffrey N, Dunson, David B, Johnson, Kim G, O’Brien, Richard J, Badea, Alexandra
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 25.04.2023
• Subjects: Aging; Alzheimer Disease - pathology; Brain - pathology; Genotype; Humans; Magnetic Resonance Imaging; Original; diffusion MRI; connectome; Alzheimer’s disease; brain; predictive modeling
• ISSN: 1047-3211; 1460-2199
• DOI: 10.1093/cercor/bhac419

Abstract The selective vulnerability of brain networks in individuals at risk for Alzheimer’s disease (AD) may help differentiate pathological from normal aging at asymptomatic stages, allowing the implementation of more effective interventions. We used a sample of 72 people across the age span, enriched for the APOE4 genotype to reveal vulnerable networks associated with a composite AD risk factor including age, genotype, and sex. Sparse canonical correlation analysis (CCA) revealed a high weight associated with genotype, and subgraphs involving the cuneus, temporal, cingulate cortices, and cerebellum. Adding cognitive metrics to the risk factor revealed the highest cumulative degree of connectivity for the pericalcarine cortex, insula, banks of the superior sulcus, and the cerebellum. To enable scaling up our approach, we extended tensor network principal component analysis, introducing CCA components. We developed sparse regression predictive models with errors of 17% for genotype, 24% for family risk factor for AD, and 5 years for age. Age prediction in groups including cognitively impaired subjects revealed regions not found using only normal subjects, i.e. middle and transverse temporal, paracentral and superior banks of temporal sulcus, as well as the amygdala and parahippocampal gyrus. These modeling approaches represent stepping stones towards single subject prediction.