Interpretable GAN for Alzheimer’s Disease Progression Identification Based on Gene Expression and sMRI

Significant progress has been made in the study of Alzheimer’s disease (AD) and Mild Cognitive Impairment(MCI) progression using multimodal approaches. Recent studies have identified peripheral blood gene expression data as valuable biomarkers for distinguishing AD and MCI progression subtypes. However, these studies either rely entirely on prior data for gene selection or are purely data-driven. These strategies are not conducive to discovering potentially important genes or may lead to results unrelated to brain neural function pathways. This study adopts a data-driven approach based on prior knowledge, using genes mapped onto morphologically different brain regions as features for selection. These features are then input into our Generative Adversarial Network framework to obtain attention masks for cortical morphological indicators, which are weighted and used in training the structural MRI feature extraction main network. Our method ensures that the selected genes are correlated with brain regions and group differences, and through post-hoc interpretability analysis, we identify potential biomarkers in both genes and brain regions across two modalities.