Association of widespread pain and peak alpha frequency cannot be isolated to a single network
Source: Pain reports
Authors: Jason Cherin, Natalie McLain, Andrew Schrepf, Larissa V Rodriguez, Jason J Kutch, Publication Date: Mon, 13 Apr 2026 06:00:00 -0400
Pain Rep. 2026 Apr 9;11(3):e1436. doi: 10.1097/PR9.0000000000001436. eCollection 2026 Jun.
ABSTRACT
INTRODUCTION: Peak alpha frequency (PAF), an electroencephalography measure, has gained interest due to its association to pain-related neural processes in both healthy and clinical populations. In healthy groups, PAF derived from the sensorimotor component has strongly correlated with pain sensitivity, but in patient populations, only channel-based methods such as the global average and regions of interest have been investigated. Therefore, the specific role of component-based PAF such as the sensorimotor component and how it compares with channel-based methods remains undefined in chronic pain.
OBJECTIVE: To evaluate the performance of multiple spatial variations of PAF in distinguishing between widespread and localized chronic pelvic pain.
METHODS: This secondary analysis included 38 individuals with urologic chronic pelvic pain syndrome and were categorized as having widespread (n = 24) or localized (n = 14) pain based on self-reported body maps. Resting-state electroencephalography data were collected, and 4 types of spatial variations were used: global average, region of interest, sensorimotor component, and group independent component analysis.
RESULTS: Both the global average and region of interest-based PAF had significantly lower average PAF among individuals in the widespread compared with localized pain groups. There was no significant difference for the sensorimotor component or group independent component analysis-component PAF. Channel-based methods had larger effect sizes compared with the component-based methods when explaining differences in PAF between pain groups.
CONCLUSION: These findings suggest that chronic widespread pain is associated with widely distributed changes in cortical function that cannot be captured in a single component, indicating that it affects multiple distinct neural networks.
PMID:41972210 | PMC:PMC13068460 | DOI:10.1097/PR9.0000000000001436
Pubmed ID: pubmed:41972210
Identifiers: pmid:41972210, pmc:PMC13068460, doi:10.1097/PR9.0000000000001436,