Subtyping insomnia disorder with a population graph attention autoencoder: revealing two distinct biotypes - Zhang, Heng; Deng, Hanbin; Zhai, Yiran; Zhang, Jiang; Zhao, Zixi; Gong, Liang (Frontiers in Neuroscience, 2026)
Insomnia disorder (ID) is neurobiologically heterogeneous and often eludes characterization by traditional group-level neuroimaging. Subtyping based on neuroimaging and clinical data offers a promising strategy for identifying biologically and clinically meaningful ID subgroups. To address this need, we developed a Gray Matter Population Graph Attention Autoencoder (GM-PGAAE) to subtype insomnia disorder in a cohort comprising 140 patients diagnosed with ID and 57 matched healthy controls. Each subject was represented as a node defined by atlas-based gray matter (GM) volumes. Population edges combined imaging-derived intersubject correlations with clinical similarity via a Hadamard product, generating an adjacency matrix that jointly encodes structural and phenotypic relationships. A Graph Attention Autoencoder learned low-dimensional embeddings that adaptively weighted informative intersubject connections, and clustering these embeddings identified distinct subtypes. Regional and network-level differences were further assessed using Voxel-Based Morphometry (VBM) and individualized differential structural covariance networks (IDSCNs). Through this framework, two ID subtypes were identified. Compared with Subtype 2, Subtype 1 showed higher symptom severity and greater GM reductions–particularly in the cerebellar vermis, thalamus, middle occipital cortex, fusiform gyrus, and paracentral lobule–alongside negative associations between GM volume and clinical scores. IDSCNs further revealed reduced thalamocortical and subcortical Z-scores in Subtype 1, indicating subtype-specific network alterations. Overall, GM-PGAAE integrates structural MRI and clinical measures to derive individualized embeddings and delineate biologically distinct ID subtypes.