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Objective <p>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.</p> Methods <p>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&#xa0;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.</p> Results <p>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.</p> Conclusion <p>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.</p>

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EEG microstate analysis in trigeminal neuralgia: identifying potential biomarkers for enhanced diagnostic accuracy

  • 期刊论文

  • 发表日期：

    2025年5月26日

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  • Reza Ahmadi Lashaki,
  • Zahra Raeisi,
  • Abolfazl Sodagartojgi,
  • Fatemeh Abedi Lomer,
  • Elnaz Aghdaei,
  • Hossein Najafzadeh

摘要

Objective

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.

Methods

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.

Results

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.

Conclusion

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.