Predicting brain age and associated structural networks in mouse models with humanized APOE alleles using integrative and interpretable graph neural networks

Hae Sol Moon; Ali Mahzarnia; Jacques Stout; Robert J. Anderson; Zay Yar Han; Cristian T. Badea; Alexandra Badea

doi:10.1117/12.3005695

3 April 2024 Predicting brain age and associated structural networks in mouse models with humanized APOE alleles using integrative and interpretable graph neural networks

Hae Sol Moon, Ali Mahzarnia, Jacques Stout, Robert J. Anderson, Zay Yar Han, Cristian T. Badea, Alexandra Badea

Author Affiliations +

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 1292715 (2024) https://doi.org/10.1117/12.3005695
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

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.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Hae Sol Moon, Ali Mahzarnia, Jacques Stout, Robert J. Anderson, Zay Yar Han, Cristian T. Badea, and Alexandra Badea "Predicting brain age and associated structural networks in mouse models with humanized APOE alleles using integrative and interpretable graph neural networks", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 1292715 (3 April 2024); https://doi.org/10.1117/12.3005695

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