BIGFormer: A Graph Transformer With Local Structure Awareness for Diagnosis and Pathogenesis Identification of Alzheimer's Disease Using Imaging Genetic Data.

Abstract

Alzheimer's disease (AD) is a highly inheritable neurological disorder, and brain imaging genetics (BIG) has become a rapidly advancing field for comprehensive understanding its pathogenesis. However, most of the existing approaches underestimate the complexity of the interactions among factors that cause AD. To take full appreciate of these complexity interactions, we propose BIGFormer, a graph Transformer with local structural awareness, for AD diagnosis and identification of pathogenic mechanisms. Specifically, the factors interaction graph is constructed with lesion brain regions and risk genes as nodes, where the connection between nodes intuitively represents the interaction between nodes. After that, a perception with local structure awareness is built to extract local structure around nodes, which is then injected into node representation. Then, the global reliance inference component assembles the local structure into higher-order structure, and multi-level interaction structures are jointly aggregated into a classification projection head for disease state prediction. Experimental results show that BIGFormer demonstrated superiority in four classification tasks on the AD neuroimaging initiative dataset and proved to identify biomarkers closely intimately related to AD.