A lesion-derived brain network of somatic symptoms for transdiagnostic individual application

Somatic symptoms is an umbrella term that describes distressing somatic complaints occurring across a wide spectrum of diseases. However, their underlying neural mechanisms remain poorly understood. Elucidating their mechanisms could therefore benefit patients with diverse conditions.

Using a recently validated lesion network mapping method and a large-scale healthy connectome database (n = 652, recruited in Anhui Province, China), we identified a somatic network from brain lesions causing somatic symptoms. The lesion-derived network was validated using independent multimodal neuroimaging signatures of somatic symptoms and interoceptive processing. It was further characterized by transcriptomic, neurochemical, and cognitive meta-analytic mapping. We further assessed the therapeutic potential of the somatic network by quantifying its spatial convergence with empirically validated neuromodulation targets. Finally, to determine whether the group-level network can predict personalized symptom severity across diagnoses, we built a normative model of gray matter volume using Gaussian process regression based on 1,342 healthy controls from Anhui. This model was then used to generate personalized atrophy maps in 399 local somatic patients with anxiety, depression, schizophrenia and bipolar disorder. We then assessed whether individual atrophy volume within the somatic network was associated with somatic symptom severity.

We found that 21 heterogeneous lesions associated somatic symptoms occurred in many different brain locations but were characterized by a common brain network, with the hub region of the right insula and putamen. The somatic network demonstrated strong spatial alignment with multimodal somatic imaging abnormalities from 66 independent studies and interoceptive processing circuits from 69 task-fMRI studies. 11 effective brain stimulation targets were colocalization with the somatic network. In individual transdiagnostic patients, greater atrophy volume within the somatic network was significantly correlated with somatic symptom severity (r = 0.182, p = 0.004), but not with anxiety or depression symptoms.

These convergent findings establish a unified somatic network framework, advancing both mechanistic understanding and precision medicine applications.