Lashaki Reza Ahmadi, Raeisi Zahra, Sodagartojgi Abolfazl, Abedi Lomer Fatemeh, Aghdaei Elnaz, Najafzadeh Hossein
Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.
Department of Computer Science, University of Fairleigh Dickinson, Vancouver Campus, Vancouver, Canada.
Acta Neurol Belg. 2025 May 26. doi: 10.1007/s13760-025-02812-0.
This study investigated EEG microstate dynamics in trigeminal neuralgia (TN) patients to understand the central nervous system's contribution to this neuropathic pain condition. Despite TN's traditional classification as a peripheral neuropathy, altered brain network organization may play a critical role in pain chronification and treatment resistance, making EEG microstates a valuable tool for capturing these dynamic neural signatures.
We analyzed resting-state EEG recordings from 14 healthy individuals and 36 TN patients through a systematic analytical pipeline. After preprocessing with a fifth-order Butterworth band-pass filter (10-40 Hz), we employed k-means clustering to identify four distinct microstate configurations (4-7 states). From these configurations, we extracted temporal parameters (duration, occurrence, coverage, and mean global field power) and constructed transition probability matrices to characterize brain state dynamics. These features were then evaluated using ANOVA and utilized in machine learning classification models to assess their discriminative potential.
TN patients demonstrated distinct microstate abnormalities, including dramatically increased durations in specific microstates (5-6 times longer than controls) and consistently reduced global field power (0.03 vs. 0.35). Transition probability analyses revealed striking differences between groups: healthy subjects exhibited balanced bidirectional transitions (particularly B↔C at ~ 31-33%), whereas TN patients showed highly asymmetric patterns with strong directional flows (B→A: 33.5%, C→A: 35.2%, D→A: 34.4% in 4-state model). Most notably, state E functioned as a distinctive "sink" in TN patients, receiving significant transitions while exhibiting minimal outward flow (only 2.8-3.6% in 7-state model), suggesting trapped neural processing. Machine learning classification achieved exceptional discrimination between groups (91.9% accuracy with SVM), with optimal performance using four features in simpler 4-state models.
Our findings establish EEG microstate analysis as a promising neurophysiological framework for understanding TN pathophysiology, revealing objective biomarkers that reflect altered brain network dynamics rather than simply peripheral nerve dysfunction. These distinctive microstate patterns align with contemporary pain processing theories and offer potential applications in diagnosis, treatment monitoring, and development of novel therapeutic approaches targeting the central mechanisms of TN.
本研究调查三叉神经痛（TN）患者的脑电图微状态动力学，以了解中枢神经系统对这种神经性疼痛状况的作用。尽管TN传统上被归类为周围神经病变，但脑网络组织的改变可能在疼痛慢性化和治疗抵抗中起关键作用，这使得脑电图微状态成为捕捉这些动态神经特征的有价值工具。
我们通过一个系统的分析流程，分析了14名健康个体和36名TN患者的静息态脑电图记录。在用五阶巴特沃斯带通滤波器（10 - 40Hz）进行预处理后，我们采用k均值聚类来识别四种不同的微状态配置（4 - 7种状态）。从这些配置中，我们提取了时间参数（持续时间、出现次数、覆盖范围和平均全局场功率），并构建转移概率矩阵来表征脑状态动力学。然后使用方差分析评估这些特征，并将其用于机器学习分类模型以评估其判别潜力。
TN患者表现出明显的微状态异常，包括特定微状态的持续时间显著增加（比对照组长5 - 6倍）以及全局场功率持续降低（0.03对0.35）。转移概率分析显示两组之间存在显著差异：健康受试者表现出平衡的双向转移（特别是B↔C约为31 - 33%），而TN患者表现出高度不对称的模式，具有强烈的方向流（在4状态模型中，B→A：33.5%，C→A：35.2%，D→A：34.4%）。最值得注意的是，状态E在TN患者中起到了独特的“汇”的作用，接收大量转移，同时向外流动极少（在7状态模型中仅为2.8 - 3.6%），表明神经处理被困。机器学习分类在两组之间实现了出色的区分（支持向量机的准确率为91.9%），在更简单的4状态模型中使用四个特征时性能最佳。
我们的研究结果将脑电图微状态分析确立为理解TN病理生理学的一个有前景的神经生理学框架，揭示了反映脑网络动态改变而非仅仅是周围神经功能障碍的客观生物标志物。这些独特的微状态模式与当代疼痛处理理论一致，并在诊断、治疗监测以及针对TN中枢机制的新型治疗方法开发中具有潜在应用。
