Multimodal Classification Framework Based on Hypergraph Latent Relation for End-Stage Renal Disease Associated with Mild Cognitive Impairment

Combined arterial spin labeling (ASL) and functional magnetic resonance imaging (fMRI) can reveal more comprehensive properties of the spatiotemporal quantitative brain networks. Imaging markers end-stage renal disease associated with mild cognitive impairment (ESRDaMCI) will be sought from these properties. The current multimodal classification methods often neglect to collect high-order relationships regions remove noise feature matrix. A framework is proposed address this issue using hypergraph latent relation (HLR). network structural information constructed by fMRI data. matrix obtained through graph theory (GT). cerebral blood flow (CBF) ASL selected as second modal Then, adaptive similarity learning between matrices. Latent (LRAS) introduced multi-task construct a selection method based on (LRMFS). experimental results show that best accuracy (ACC) reaches 88.67%, at least 2.84% better than state-of-the-art methods. preserves valuable reduces among matrixes. It provides an essential reference value for ESRDaMCI recognition.