Locally similar multi-hop fusion GNNs with data augmentation for early Alzheimer’s detection

Abstract

Alzheimer’s disease (AD) is an irreversible brain disease that has an enormous impact on individuals and society. However, existing AD diagnostic models based on the spatiotemporal correlation of resting-state functional magnetic resonance imaging (rs-fMRI) are unable to focus on temporal correlation information between long-distance time points. In addition, graph neural networks (GNNs) based on imaging information and phenotypic information suffer from excessive smoothing or information loss. To address these issues, we propose a local similarity multi-hop fusion graph neural network (LSMHF-GNN) for the early classification of AD. The main work includes three aspects: 1) the dynamic brain functional connectivity network (dBFC) is constructed using the sliding window method with data enhancement to address the problem of imperfect use of information regarding the long-term brain function damage caused by AD. 2) the LSMHF-GNN is constructed by combining neuroimaging and non-imaging information to alleviate the problem of imperfect use of information and the problem of excessive smoothing or message passing failure that is prone to occur with heterogeneous graph message delivery. 3) We discovered key brain regions that are closely associated with early AD and found abnormal connectivity of lesioned brain regions at various stages of AD deterioration. The results of model validation in the alzheimer’s disease neuroimaging initiative (ADNI) database showed that the LSMHF-GNN achieved competitive results in the diagnosis of early AD and identified abnormal connectivity consistent with clinical diagnosis.