Abnormal sensorimotor network in brain functional connectivity in herpes zoster and postherpetic neuralgia patients

Abstract

OBJECTIVE: This study aimed to investigate the changes in resting-state functional connectivity (rsFC) of the sensorimotor network(SMN) in patients with herpes zoster(HZ) and postherpetic neuralgia patients(PHN). Then, We applied machine learning to distinguish PHN/HZ patients from healthy controls(HC). METHODS: HZ (n=53), PHN (n=57), and HC (n=50) were included, and resting-state functional magnetic resonance imaging (rs-fMRI) was performed on them. Seed-based and ROI-to-ROI analyses were applied to evaluate connectivity inside and between the SMN and other voxels throughout the brain. After that, we used machine learning to separate patients with PHN/HZ from those with HC. RESULTS: Compared to HC, there was a substantial reduction in functional connectivity between the lateral SMN (R), lateral SMN (L), and superior SMN in PHN patients. There was a disruption of rsFC between SMN subregions and several brain regions (insula, parietal, occipital, and superior frontal gyrus) in PHN. These damaged FCs were linked positively with clinical data (such as mood scores, disease duration, and VAS scores). Furthermore, We discovered that the rsFC value of SMN could successfully classify PHN patients from other types of pain with an accuracy of 85.7% when applied to a machine-learning approach. CONCLUSION: Significant changes occurred in the rsFC of SMN in HZ and PHN. Suggesting that the role of SMN in HZ/PHN may help understand the pathophysiology and development of these diseases.