Biological psychiatry · 2026
Multifrequency Electroencephalography Connectomics Uncovers Insula-Network Subtypes in Somatic Symptom Disorder.
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Zhao Shuzhi, Lian Chongyuan, Shi Xue, Dang Ge, Pei Zi'an, Lan Xiaoyong, Liu Hanjun, Hung Hiu Ching, Yao Dezhong, Wang Lan, Jiang Xin, Guo Yi, Yan Nan
Аннотация
BACKGROUND: Somatic symptom disorder (SSD) exhibits substantial clinical heterogeneity that limits treatment efficacy, with more than 40% of patients failing to respond to standard interventions. Here, we developed a framework that integrates multifrequency electroencephalography (EEG) connectomics with contrastive learning to identify distinct subtypes of SSD.
METHODS: A contrastive variational autoencoder with Gaussian mixture modeling was developed using resting-state EEG connectomics from a discovery cohort of 1419 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 3 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 sensorimotor network-dominant subtype was associated with greater somatic symptom burden (cross-validated rcv = 0.42, foldwise SD = 0.021, permutation p < .001), the central executive network-dominant subtype was associated with lower negative event reactivity (rcv = -0.38, SD = 0.017, p < .001), and the limbic network-dominant subtype was associated with greater emotional symptoms (rcv = 0.36, SD = 0.014, p = .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, area under the curve = 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.
METHODS: A contrastive variational autoencoder with Gaussian mixture modeling was developed using resting-state EEG connectomics from a discovery cohort of 1419 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 3 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 sensorimotor network-dominant subtype was associated with greater somatic symptom burden (cross-validated rcv = 0.42, foldwise SD = 0.021, permutation p < .001), the central executive network-dominant subtype was associated with lower negative event reactivity (rcv = -0.38, SD = 0.017, p < .001), and the limbic network-dominant subtype was associated with greater emotional symptoms (rcv = 0.36, SD = 0.014, p = .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, area under the curve = 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.