Background & Aims
The aim of this study was to investigate the changes in resting-state functional connectivity (rsFC) in the sensorimotor network (SMN) in patients with herpes zoster (HZ) and postherpetic neuralgia (PHN). Then, we applied machine learning to distinguish PHN/HZ patients from healthy controls (HCs).
Methods
HZ (n=53), PHN (n=57), and HCs (n=50) were included, and resting-state functional magnetic resonance imaging (rs-fMRI) was performed. 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 HCs.
Results
Compared to those of HCs, there were substantial reductions 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 patients. These damaged FCs were positively linked with clinical data (such as mood scores, disease duration, and VAS scores). Furthermore, we discovered that the rsFC value of the SMN could be used to successfully distinguish PHN patients from patients with other types of pain, with an accuracy of 85.7% when this parameter was applied to a machine learning approach.
Conclusions
Significant changes in the rsFC of the SMN occurred in patients with HZ and PHN. The findings of this study suggested that the role of SMN in HZ/PHN may aid in elucidating the pathophysiology and development of these diseases.
References
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Presenting Author
Xiaofeng Jiang
Poster Authors
xiaofeng Jiang
postgraduate
the First Affiliated Hospital, Jiangxi Medical College
Lead Author
Topics
- Pain Imaging