Multi-Frequency EEG Connectomics Uncovers Insula-Network Subtypes in Somatic Symptom Disorder
Shuzhi Zhao1, Chongyuan Lian2, Xue Shi2
1CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Background:
Somatic symptom disorder (SSD) exhibits substantial clinical heterogeneity that limits treatment efficacy, with over 40% of patients failing to respond to standard interventions. Here, we developed a framework that integrates multi-frequency electroencephalography (EEG) connectomics with contrastive learning to identify distinct subtypes of SSD.
Methods:
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).
Results:
We identified three robust subtypes, characterized by dominant connectivity in somatomotor, central executive, and limbic networks. Cross-validated canonical correlation analysis revealed distinct associations between subtype-related neural features and Neuro-11 clinical dimensions: the SMN-dominant subtype was associated with greater somatic symptom burden (cross-validated rcv = 0.42, fold-wise SD = 0.021, permutation p < 0.001), the CEN-dominant subtype with lower negative event reactivity (rcv = -0.38, SD = 0.017, p < 0.001), and the LN-dominant subtype with greater emotional symptoms (rcv = 0.36, SD = 0.014, p = 0.002). Notably, the insula emerged as a convergent hub across subtypes, whereas subtype differentiation was characterized by preferential insula coupling with the anterior cingulate cortex, dorsolateral prefrontal cortex, and thalamus, respectively. Independent validation in an external cohort confirmed subtype reproducibility with superior classification performance (accuracy=0.85, AUC=0.87).
Conclusions:
These findings support an EEG-based connectomic framework for investigating neurobiological heterogeneity in SSD and highlight insula-centered network features as promising candidates for future mechanistic stratification studies.
