Wenxin, Su and Antonopoulos, Chris G and Valentini, Elia (2026) EEG Network Reorganisation Reveals Somato-Motor Transition from Segregation to Integration during Tonic Pain. Pain. DOI https://doi.org/10.1097/j.pain.0000000000003897
Wenxin, Su and Antonopoulos, Chris G and Valentini, Elia (2026) EEG Network Reorganisation Reveals Somato-Motor Transition from Segregation to Integration during Tonic Pain. Pain. DOI https://doi.org/10.1097/j.pain.0000000000003897
Wenxin, Su and Antonopoulos, Chris G and Valentini, Elia (2026) EEG Network Reorganisation Reveals Somato-Motor Transition from Segregation to Integration during Tonic Pain. Pain. DOI https://doi.org/10.1097/j.pain.0000000000003897
Abstract
The sustained nature of tonic pain makes it a useful experimental analogue for studying the prolonged neural processing involved in chronic pain. However, research is yet to identify its consistent and generalisable biomarkers. Here, we analysed electroencephalography data recorded in 36 volunteers during 5-minute sessions of noxious hot and innocuous warm water immersion using network-based statistics and graph theory-based analysis. Our results revealed a brain-wide reorganisation of functional connectivity during tonic pain, marked by a global shift from segregation to integration. This shift was characterised by a transition from intra- to internetwork communication, with the Somato-Motor (SomMot) network playing a pivotal role. During innocuous warmth, the SomMot network exhibited significantly higher functional specialisation for localised sensory processing. During noxious heat, however, it shifted to an integrative coordinator, a finding reinforced by a significant discrepancy in global clustering coefficient when intranetwork connections were excluded. We also found that psychological traits modulated global network inferences (GNIs) in distinct, clinically relevant ways: pain catastrophising was positively associated with network segregation and integration during pain, whereas anxiety was negatively associated with segregation and integration during innocuous warmth. Notably, a machine learning model using these GNIs achieved 86% accuracy in classifying noxious heat from innocuous warmth. Together, our findings elucidate the transformation from segregated processing to integrated network dynamics induced by tonic pain, characterised by a transition in the SomMot network functioning as an integrator. Critically, global network inferences may serve as valuable predictors of pain experiences, highlighting their translational potential in pain neuroscience.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Biomarker; Classification; EEG; Graph theory; Internetwork connectivity; Machine learning; Somato-motor network; Tonic pain |
| Subjects: | Z Bibliography. Library Science. Information Resources > ZR Rights Retention |
| Divisions: | Faculty of Science and Health Faculty of Science and Health > Mathematics, Statistics and Actuarial Science, School of Faculty of Science and Health > Psychology, Department of |
| SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
| Depositing User: | Unnamed user with email elements@essex.ac.uk |
| Date Deposited: | 22 Jan 2026 17:28 |
| Last Modified: | 22 Jan 2026 17:43 |
| URI: | http://repository.essex.ac.uk/id/eprint/42052 |
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Licence: Creative Commons: Attribution 4.0