Alzheimer's disease (AD), a prevalent neurodegenerative disorder, is influenced by an intricate mix of risk factors including age, genetics, and environmental variables. In our study, we employed mouse models with human APOE alleles and nitric oxide synthase 2, along with environmental factors like diet, to simulate controlled genetic risk and immune response of AD. We utilized a Feature Attention Graph Neural Network (FAGNN), integrating brain structural connectomes, genetic traits, environmental factors, and behavioral data, to estimate brain age. Our method demonstrated improved accuracy in age prediction over other methods and highlighted age-associated brain connections. The most impactful connections included the cingulum, striatum, corpus callosum, and hippocampus. We further investigated these findings through fractional anisotropy in different age groups of mice which underlined the significance of white matter degradation in the aging process. Our research underscores the effectiveness of integrative graph neural networks in predicting brain age and delineating important neural connectivity in brain aging.
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