Headache · 2025
Exploring the alterations in microstate dynamics during the migraine cycle and detecting pre-ictal phases.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Xie Siyuan, Huo Yunyun, Geng Xinyi, Hu Li, Ao Ran, Su Hui, Li Desheng, Hu Miaomiao, Liu Yusha, Chen Xiaomei, Liu Jinghuan, Li Qianqian, Lin Jiayin, Yu Shengyuan, Dong Zhao
Аннотация
BACKGROUND: Microstate analysis captures brief but critical fluctuations in brain activity, making it a powerful tool for exploring the cyclic nature of migraine. In this study, we aimed to investigate microstate features during different migraine phases and develop a classification model to identify the pre-ictal phase.
METHODS: From May 2023 to June 2024, we conducted a cross-sectional study with consecutive recruitment, collecting resting-state electroencephalography data from 174 individuals with migraine without aura and 50 healthy controls, followed by classification of migraine phases. Microstate features, Lempel-Ziv complexity, and sample entropy were compared across five groups. A model was developed to identify the pre-ictal phase and validated on a test set.
RESULTS: Microstate features, particularly for microstates A and B, exhibited dynamic changes across the migraine cycle. The duration of microstate A was significantly longer in the inter-ictal phase than in the pre-ictal phase, whereas microstate B showed prolonged duration in the pre-ictal phase compared to healthy controls and the post-ictal phase. Microstate A displayed reduced coverage in the pre-ictal phase, whereas microstate B had increased occurrence and coverage during the pre-ictal and ictal phases. Transition probabilities also varied significantly: the pre-ictal phase showed elevated transitions from microstates A, C, and D to B, and the post-ictal phase showed reduced transitions from C and D to A. A classification model based on these microstate features achieved an area under the receiver operating characteristic curve (AUROC) of 0.85 (0.73-0.95), an area under the precision-recall curve (AUPRC) of 0.83 (0.66-0.95), and an F1 score of 0.78 (0.62-0.90) in the training set; and an AUROC of 0.84 (0.69-0.97), an AUPRC of 0.86 (0.67-0.98), and an F1 score of 0.81 (0.65-0.93) in the test set, indicating robust performance in identifying the pre-ictal phase.
CONCLUSION: Through the observation of cyclic alterations in the microstates of patients with migraine, we identified a reduction in microstate A and an enhancement in microstate B during the pre-ictal phase. These changes may indicate a heightened sensitivity to auditory stimuli and increased activity in the visual cortex, providing new insights into migraine pathophysiology. Our model effectively identified the pre-ictal phase, offering a promising approach for early intervention in migraine attacks.
METHODS: From May 2023 to June 2024, we conducted a cross-sectional study with consecutive recruitment, collecting resting-state electroencephalography data from 174 individuals with migraine without aura and 50 healthy controls, followed by classification of migraine phases. Microstate features, Lempel-Ziv complexity, and sample entropy were compared across five groups. A model was developed to identify the pre-ictal phase and validated on a test set.
RESULTS: Microstate features, particularly for microstates A and B, exhibited dynamic changes across the migraine cycle. The duration of microstate A was significantly longer in the inter-ictal phase than in the pre-ictal phase, whereas microstate B showed prolonged duration in the pre-ictal phase compared to healthy controls and the post-ictal phase. Microstate A displayed reduced coverage in the pre-ictal phase, whereas microstate B had increased occurrence and coverage during the pre-ictal and ictal phases. Transition probabilities also varied significantly: the pre-ictal phase showed elevated transitions from microstates A, C, and D to B, and the post-ictal phase showed reduced transitions from C and D to A. A classification model based on these microstate features achieved an area under the receiver operating characteristic curve (AUROC) of 0.85 (0.73-0.95), an area under the precision-recall curve (AUPRC) of 0.83 (0.66-0.95), and an F1 score of 0.78 (0.62-0.90) in the training set; and an AUROC of 0.84 (0.69-0.97), an AUPRC of 0.86 (0.67-0.98), and an F1 score of 0.81 (0.65-0.93) in the test set, indicating robust performance in identifying the pre-ictal phase.
CONCLUSION: Through the observation of cyclic alterations in the microstates of patients with migraine, we identified a reduction in microstate A and an enhancement in microstate B during the pre-ictal phase. These changes may indicate a heightened sensitivity to auditory stimuli and increased activity in the visual cortex, providing new insights into migraine pathophysiology. Our model effectively identified the pre-ictal phase, offering a promising approach for early intervention in migraine attacks.