Jaltare Ketan Prafull, Torta Diana M
Health Psychology, Faculty of Psychology and Educational Sciences, KU Leuven, Belgium.
Pain. 2025 Feb 18;166(9):e185-e199. doi: 10.1097/j.pain.0000000000003546.
Pain perception is a dynamic and time-varying phenomenon. The high temporal resolution of electroencephalography (EEG) can be leveraged to gain insight into its cortical dynamics. Electroencephalography microstate analysis is a novel technique that parses multichannel EEG signals into a limited number of quasi-stable topographies (microstates) that have a meaningful temporal structure and have been linked to the activity of resting state networks. In recent years, several studies have investigated alterations in EEG microstate parameters associated with acute and chronic pain states, with mixed results. In the present study, we used high-frequency stimulation (HFS), in healthy human volunteers, to induce mechanical hypersensitivity (a perceptual correlate of central sensitization) and investigated (1) changes in microstate parameters before vs after the induction of mechanical hypersensitivity and (2) whether microstate parameters before HFS were linked to the development of mechanical hypersensitivity. Results showed that the duration of microstate E, typically related to the activity of the salience/default mode network, was consistently decreased post-HFS. The global explained variance of microstates A (Auditory network) and E and coverage of microstate A were positively associated with mechanical hypersensitivity. Conversely, the transition probabilities from microstates B (Visual network) to A and the bidirectional transition probabilities between B and C (saliency and default mode networks) were negatively associated with mechanical hypersensitivity. We discuss these findings in the context of the functional significance of EEG microstates. Our results highlight the potential utility of microstate analysis in understanding pain processing and its potential link to changes in the nociceptive system.
疼痛感知是一种动态且随时间变化的现象。脑电图(EEG)的高时间分辨率可用于深入了解其皮层动力学。脑电图微状态分析是一种新技术,它将多通道EEG信号解析为有限数量的准稳定地形图(微状态),这些微状态具有有意义的时间结构,并与静息态网络的活动相关联。近年来,多项研究调查了与急性和慢性疼痛状态相关的EEG微状态参数的变化,结果不一。在本研究中,我们在健康人类志愿者中使用高频刺激(HFS)来诱发机械性超敏反应(中枢敏化的一种感知相关因素),并研究了(1)机械性超敏反应诱发前后微状态参数的变化,以及(2)HFS前的微状态参数是否与机械性超敏反应的发展有关。结果表明,通常与突显/默认模式网络活动相关的微状态E的持续时间在HFS后持续减少。微状态A(听觉网络)和E的全局解释方差以及微状态A的覆盖率与机械性超敏反应呈正相关。相反,从微状态B(视觉网络)到A的转换概率以及B和C(突显和默认模式网络)之间的双向转换概率与机械性超敏反应呈负相关。我们在EEG微状态的功能意义背景下讨论了这些发现。我们的结果突出了微状态分析在理解疼痛处理及其与伤害性系统变化的潜在联系方面的潜在效用。