Multi-Frequency EEG Connectomics Uncovers Insula-Network Subtypes in Somatic Symptom Disorder

Substantial clinical heterogeneity in Somatic Symptom Disorder (SSD) limits treatment efficacy. Here， this study propose a data-driven framework using neurophysiology information to identify distinct patient subtypes. A contrastive variational autoencoder with Gaussian mixture modeling (CVAE-GM) was developed using resting-state EEG connectomics from a discovery cohort of 1,419 patients with SSD. The derived subtypes were clinically correlated with symptom dimensions and validated for reproducibility in an independent external cohort (n=530). We identified three robust and reproducible subtypes, characterized by dominant connectivity in somatomotor, central executive, or limbic networks with insula area. Each neurophysiological subtype was significantly associated with distinct clinical profiles (somatic symptoms, adverse cognitive, and negative emotions). The subtyping model demonstrated high reproducibility in the validation cohort (accuracy = 0.85; AUC = 0.87). This EEG-based framework provides a validated, neurobiological basis for stratifying SSD patients. It transcends symptom-based nosology, enabling a precision medicine approach for developing targeted interventions.