ArticlePNAS nexus2025
An imaging genetics network model for clinical score assessment in Alzheimer's disease.
Article in PNAS nexus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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5 authors.
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Abstract
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.
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