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Network reorganisation reveals somato-motor transition from segregation to integration during tonic pain

补品(生理学) 心理学 聚类系数 神经科学 有害刺激 慢性疼痛 焦虑 感觉系统 网络动力学 脑电图 物理医学与康复 人工神经网络 聚类分析 过渡(遗传学) 认知心理学 警惕(心理学) 伤害 神经网络 生物神经网络 复杂网络 网络结构 发展心理学
作者
Wenxin X. Su,Chris G. Antonopoulos,Elia Valentini
出处
期刊:Pain [Lippincott Williams & Wilkins]
标识
DOI:10.1097/j.pain.0000000000003897
摘要

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.
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