Local Structural Indices Changes During Different Periods of Postherpetic Neuralgia: A Graphical Study in Structural Covariance Networks

J Pain Res. 2025 Mar 11:18:1175-1187. doi: 10.2147/JPR.S515047. eCollection 2025.

Abstract

Purpose: In this study, we aim to explore the changes in network graph theory indices of structural covariance networks (SCNs) in PHN patients with different disease durations.

Patients and methods: High-resolution T1 magnetic resonance images were collected from 109 subjects. We constructed SCNs based on cortical thickness data and analyzed the changes in global and regional network measures of PHN patients and herpes zoster (HZ) patients, and get hubs of each group.

Results: (1) PHN patients with a disease duration >6 months had reduced global efficiency (P=0.035) and increased characteristic shortest path length (P=0.028). (2) Nodal efficiency of the right pars opercularis was greater in both HZ and PHN patients with a disease duration of 1 to 3 months (P<0.001); in PHN patients with a disease duration > 6 months, the nodal degree of the left pars triangularis and nodal efficiency of the right middle temporal gyrus were greater (P<0.001). (3) The right supramarginal gyrus was the common hub of healthy controls (HCs) and HZ patients, the right pars opercularis was the common hub of HZ patients and PHN patients with a disease duration of 1 to 3 months, and the bilateral superior frontal gyrus was the common hub of HZ patients and PHN patients with a disease duration >6 months.

Conclusion: There have changes in SCN indices in PHN patients with different disease durations. PHN patients with a disease duration >6 months had increased SCN integration and diminished information transfer capability between nodes, which complemented the topological properties of previous PHN networks. Eglobal and Lp can be considered as potential imaging markers for future clinical restaging of PHN.

Keywords: cortical thickness; graph theory; herpes zoster; postherpetic neuralgia; small world; structural covariance network.