EIGNN: An Explainable Imaging-Genetic Neural Network for Robust Alzheimer's Disease Risk Prediction

Accurate risk prediction and early diagnosis are crucial for the early intervention of Alzheimer's disease (AD). Current prediction models usually have limited power in capturing the complex interplays between the heterogeneous inputs or lack biological explainability required for clinical adoption and new diagnosis biomarkers discovery. Inspired by pioneering works on biologically informed network and multi-modal learning, we presented an Explainable Imaging-Genetic Neural Network (EIGNN), integrating genetic and neuroimaging data to generate accurate, robust, and explainable AD risk prediction. The EIGNN model features a biologically-informed architecture, incorporating a multi-GWAS SNP selection strategy, enhanced explainable neural network design, and a modal attention mechanism. Genetic variants were hierarchically mapped to their target genes and biological pathways, and further integrated with neuroimaging features. We demonstrated that the EIGNN model outperformed existing methods and exhibited improved robustness, explainability, and reproducibility. Finally, by applying a novel biologically informed multi-modal feature interaction map, we prioritized a set of AD risk genes and biological pathways, and explored the intricate interactions between the risk genes and brain regions implicated in AD risk.