An imaging genetics network model for clinical score assessment in Alzheimer's disease

Imaging genomics has recently emerged as a prominent focus in Alzheimer's disease (AD) research, showing great potential in predicting and diagnosing. In this paper, we propose a dual-stream imaging genetics network (DS-IGN) approach to AD clinical score assessment. DS-IGN is composed of two branches: one processes longitudinal data (neuroimaging) and the other handles static data (gene information). The imaging branch leverages hypergraphs to capture high-order relationships, constructing hypergraphs for samples and image features and performing weighted fusion. The genetic branch introduces an attention mechanism to adaptively adjust the weights of different genetic loci, which is particularly effective when multiple genes interact. By integrating both imaging and genetic features, DS-IGN effectively predicts patients' clinical scores in advance, providing early warnings of cognitive decline and supporting timely interventions to slow disease progression.