Graduate School of Health Science, Kio University. 4-2-2 Umaminaka, Kitakatsuragigun, Nara, Japan.
Neurorehabilitation Research Center, Kio University. 4-2-2 Umaminaka, Kitakatsuragigun, Nara, Japan.
Clin EEG Neurosci. 2024 Jan;55(1):121-129. doi: 10.1177/15500594231204174. Epub 2023 Oct 16.
: Severe pain and other symptoms in complex regional pain syndrome (CRPS), such as allodynia and hyperalgesia, are associated with abnormal resting-state brain network activity. No studies to date have examined resting-state brain networks in CRPS patients using electroencephalography (EEG), which can clarify the temporal dynamics of brain networks. : We conducted microstate analysis using resting-state EEG signals to prospectively reveal direct correlations with pain intensity in CRPS patients (n = 17). Five microstate topographies were fitted back to individual CRPS patients' EEG data, and temporal microstate measures were subsequently calculated. : Our results revealed five distinct microstates, termed microstates A to E, from resting EEG data in patients with CRPS. Microstates C, D and E were significantly correlated with pain intensity before pain treatment. Particularly, microstates D and E were significantly improved together with pain alleviation after pain treatment. As microstates D and E in the present study have previously been related to attentional networks and the default mode network, improvement in these networks might be related to pain relief in CRPS patients. : The functional alterations of these brain networks affected the pain intensity of CRPS patients. Therefore, EEG microstate analyses may be used to identify surrogate markers for pain intensity.
: 在复杂性区域疼痛综合征 (CRPS) 中,严重的疼痛和其他症状(如感觉过敏和痛觉过敏)与异常的静息状态大脑网络活动有关。迄今为止,尚无研究使用脑电图 (EEG) 检查 CRPS 患者的静息状态大脑网络,而 EEG 可以阐明大脑网络的时间动态。: 我们使用静息态 EEG 信号进行微状态分析,前瞻性地揭示与 CRPS 患者疼痛强度的直接相关性(n=17)。将五种微状态拓扑拟合回个体 CRPS 患者的 EEG 数据,随后计算时间微状态测量值。: 我们的结果显示,从 CRPS 患者的静息 EEG 数据中,发现了五个不同的微状态,分别命名为微状态 A 到 E。微状态 C、D 和 E 在接受疼痛治疗前与疼痛强度显著相关。特别是,微状态 D 和 E 在疼痛治疗后与疼痛缓解一起显著改善。由于本研究中的微状态 D 和 E 先前与注意力网络和默认模式网络有关,因此这些网络的改善可能与 CRPS 患者的疼痛缓解有关。: 这些大脑网络的功能改变影响了 CRPS 患者的疼痛强度。因此,EEG 微状态分析可能用于识别疼痛强度的替代标志物。