Estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer’s disease

Motivation: Understanding Alzheimer’s disease (AD) requires decoding the complex interplay of risk factors, particularly how age-related structural connectivity changes affect AD onset and progression. Goal(s): Using a state-of-the-art deep learning method, we aim to identify key brain connections, predict age and assess AD risk factors with structural brain connectomes and behavioral data from mouse models with humanized APOE genotypes. Approach: Our Feature Attention Graph Neural Network (FAGNN) integrates multivariate data types, focusing on aging-related brain connections with a quadrant attention module. Results: FAGNN surpassed other models in age prediction and identified critical neural pathways, like striatum-cingulum connection, offering insights into age-related brain connectivity changes. Impact: We used AI and FAGNN to advance Alzheimer’s disease research, predicting risk factors such as age and identifying crucial neural connections pertinent to the risk factors, potentially paving the way for early detection and targeted interventions in aging-related cognitive decline.