Subtyping Autism Spectrum Disorder Using Multimodal Multilayer Hypergraphs

The heterogeneity has been recognized as a large obstacle to the treatment of autism spectrum disorder (ASD). Recent studies have identified several subgroups of ASD that exhibited heterogeneous alterations in brain. However, most of them primarily depicted the pairwise similarity between individuals, relying solely on a single imaging modality. This leads to an underestimation of the complexity in inter-individual relationships and the rich information provided by multimodal images. To capture the high-order relationships among individuals, we utilized multi-task method to construct multilayer hypergraph based on brain structure and function. We then developed a novel co-optimized community detection algorithm, which jointly optimizes the modular structure across hypergraph layer, with the aim of categorizing subtypes of ASD by fusing multimodal information. By applying the proposed method on the Autism Brain Imaging Data Exchange repository data (n = 287/303, ASD/typical development [TD]), we identified two ASD subtypes with distinct alteration patterns in both brain structure and function. Distinct clinical manifestations in social and communication were observed between the two subtypes. Furthermore, subtyping significantly enhanced the diagnostic accuracy of ASD by over 10%. In addition, our method exhibited superior clustering performance that outperformed traditional community detection algorithms on graphs. Taken together, our study demonstrated the effectiveness of subtyping ASD through a multimodal multilayer hypergraph, highlighting its potential in elucidating the heterogeneity of autism and improving clinical diagnosis.