Multiview hyperedge-aware hypergraph embedding learning for multisite, multiatlas fMRI based functional connectivity network analysis

• Published in: Medical image analysis Vol. 94; p. 103144
• Main Authors: Wang, Wei, Xiao, Li, Qu, Gang, Calhoun, Vince D., Wang, Yu-Ping, Sun, Xiaoyan
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.05.2024
• Subjects: Autism; Functional connectivity; Graph convolution networks; Graph embedding; Hypergraph; Functional connectivity; Graph convolution networks; Hypergraph; Autism; Graph embedding
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2024.103144

Recently, functional magnetic resonance imaging (fMRI) based functional connectivity network (FCN) analysis via graph convolutional networks (GCNs) has shown promise for automated diagnosis of brain diseases by regarding the FCNs as irregular graph-structured data. However, multiview information and site influences of the FCNs in a multisite, multiatlas fMRI scenario have been understudied. In this paper, we propose a Class-consistency and Site-independence Multiview Hyperedge-Aware HyperGraph Embedding Learning (CcSi-MHAHGEL) framework to integrate FCNs constructed on multiple brain atlases in a multisite fMRI study. Specifically, for each subject, we first model brain network as a hypergraph for every brain atlas to characterize high-order relations among multiple vertexes, and then introduce a multiview hyperedge-aware hypergraph convolutional network (HGCN) to extract a multiatlas-based FCN embedding where hyperedge weights are adaptively learned rather than employing the fixed weights precalculated in traditional HGCNs. In addition, we formulate two modules to jointly learn the multiatlas-based FCN embeddings by considering the between-subject associations across classes and sites, respectively, i.e., a class-consistency module to encourage both compactness within every class and separation between classes for promoting discrimination in the embedding space, and a site-independence module to minimize the site dependence of the embeddings for mitigating undesired site influences due to differences in scanning platforms and/or protocols at multiple sites. Finally, the multiatlas-based FCN embeddings are fed into a few fully connected layers followed by the soft-max classifier for diagnosis decision. Extensive experiments on the ABIDE demonstrate the effectiveness of our method for autism spectrum disorder (ASD) identification. Furthermore, our method is interpretable by revealing ASD-relevant brain regions that are biologically significant. •We introduce a multiview hyperedge-aware hypergraph convolutional network (HGCN) based functional connectivity network (FCN) embedding learning approach to integrate FCNs constructed on multiple brain atlases.•Class-consistency and site-independence modules are formulated to account for the between-subject association of intra- and inter-classes and the between-site heterogeneity in the embedding space, respectively, which can promote the learning of multiatlas-based FCN embeddings discriminative across classes and sites.•The extensive experiments on the multisite, multiatlas fMRI from the ABIDE demonstrate that the proposed class-consistency and site-independence multiview hyperedge-aware hypergraph embedding learning (CcSi-MHAHGEL) outperforms several other methods for autism spectrum disorder (ASD) identification.