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.
Biological Psychiatry
|July 29, 2026
Summary
Researchers identified three distinct subtypes of Somatic Symptom Disorder (SSD) using electroencephalography (EEG) connectomics. This approach may improve treatment strategies for patients with SSD.
Area of Science:
- Neuroscience
- Psychiatry
- Computational Biology
Background:
- Somatic Symptom Disorder (SSD) presents significant clinical heterogeneity, impacting treatment effectiveness, with over 40% of patients not responding to standard interventions.
- Existing treatments for SSD often fail due to the disorder's complex and varied nature.
Purpose of the Study:
- To develop a framework integrating multi-frequency electroencephalography (EEG) connectomics and contrastive learning for identifying distinct SSD subtypes.
- To investigate the neurobiological underpinnings of SSD heterogeneity and enable personalized treatment approaches.
Main Methods:
- A contrastive variational autoencoder with Gaussian mixture modeling (CVAE-GM) was developed using resting-state EEG connectomics from 1,419 SSD patients.
- Subtypes were correlated with clinical symptom dimensions and validated in an independent cohort of 530 patients.
Main Results:
- Three robust subtypes were identified, characterized by dominant connectivity in somatomotor (SMN), central executive (CEN), and limbic (LN) networks.
- Distinct associations were found between subtypes and clinical dimensions: SMN with symptom burden, CEN with negative event reactivity, and LN with emotional symptoms.
- The insula served as a convergent hub, with subtype differentiation linked to its coupling with specific brain regions; external validation achieved 85% accuracy.
Conclusions:
- An EEG-based connectomic framework can effectively investigate neurobiological heterogeneity in SSD.
- Insula-centered network features are promising for future mechanistic stratification and targeted interventions in SSD.
