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Background <p>Chronic migraine (CM) severely affects patients’ work and daily life, imposing a significant economic burden. However, the underlying neural mechanisms of migraine chronification remain unclear. This study aimed to characterize temporal neural dynamics patterns of migraine via magnetoencephalography (MEG) combined with dynamic network mode (DyNeMo), providing further neuroimaging evidence for migraine chronification.</p> Methods <p>This cross-sectional study recruited patients with episodic migraine (EM), CM, and healthy controls (HC). MEG data were acquired during resting and somatosensory stimulation states. The DyNeMo model was applied to source-reconstructed MEG data to quantify temporal neural dynamics metrics, including mean lifetime, mean interval, switching rate and fractional occupancy. Permutation-based analysis of covariance (ANCOVA) with Bonferroni correction and Spearman correlation with false discovery rate (FDR) correction were applied.</p> Results <p>Six distinct brain modes were identified: visual network (VN), anterior and posterior default mode networks (aDMN/pDMN), right and left sensorimotor networks (rSMN/lSMN), and auditory network (AN). During resting state, EM showed prolonged mean lifetime and decreased switching rate of the VN vs. HC; CM showed increased switching rate of the VN vs. EM; CM showed prolonged mean lifetime and decreased switching rate of the AN vs. HC. During somatosensory stimulation state, both EM and CM showed prolonged mean lifetime and decreased switching rate of the AN vs. HC. In CM patients, longer duration of disease was correlated with shorter mean lifetime and higher switching rate of the AN during somatosensory stimulation state.</p> Conclusions <p>This study identifies distinct alterations in the temporal dynamics of VN and AN in EM and CM. Abnormalities in AN were observed during both resting and somatosensory stimulation states, while disease duration in CM was associated with altered temporal metrics of the AN. These cross-sectional findings support the involvement of altered sensory and cross-modal network processing in migraine and warrant longitudinal validation.</p>

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Temporal neural dynamics patterns in episodic and chronic migraine: a magnetoencephalography study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月27日

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  • Cunxin Lin,
  • Dong Qiu,
  • Pan Liao,
  • Geyu Liu,
  • Jie Liang,
  • Zhonghua Xiong,
  • Xiaoshuang Li,
  • Chenyang Duan,
  • Zhe Wang,
  • Zhi Guo,
  • Yongxiang Zhang,
  • Zhaoli Ge,
  • Xin Liu,
  • Yuanxiang Li,
  • Tianshuang Gao,
  • Mantian Zhang,
  • Jia-Hong Gao,
  • Bingjiang Lyu,
  • Yonggang Wang

摘要

Background

Chronic migraine (CM) severely affects patients’ work and daily life, imposing a significant economic burden. However, the underlying neural mechanisms of migraine chronification remain unclear. This study aimed to characterize temporal neural dynamics patterns of migraine via magnetoencephalography (MEG) combined with dynamic network mode (DyNeMo), providing further neuroimaging evidence for migraine chronification.

Methods

This cross-sectional study recruited patients with episodic migraine (EM), CM, and healthy controls (HC). MEG data were acquired during resting and somatosensory stimulation states. The DyNeMo model was applied to source-reconstructed MEG data to quantify temporal neural dynamics metrics, including mean lifetime, mean interval, switching rate and fractional occupancy. Permutation-based analysis of covariance (ANCOVA) with Bonferroni correction and Spearman correlation with false discovery rate (FDR) correction were applied.

Results

Six distinct brain modes were identified: visual network (VN), anterior and posterior default mode networks (aDMN/pDMN), right and left sensorimotor networks (rSMN/lSMN), and auditory network (AN). During resting state, EM showed prolonged mean lifetime and decreased switching rate of the VN vs. HC; CM showed increased switching rate of the VN vs. EM; CM showed prolonged mean lifetime and decreased switching rate of the AN vs. HC. During somatosensory stimulation state, both EM and CM showed prolonged mean lifetime and decreased switching rate of the AN vs. HC. In CM patients, longer duration of disease was correlated with shorter mean lifetime and higher switching rate of the AN during somatosensory stimulation state.

Conclusions

This study identifies distinct alterations in the temporal dynamics of VN and AN in EM and CM. Abnormalities in AN were observed during both resting and somatosensory stimulation states, while disease duration in CM was associated with altered temporal metrics of the AN. These cross-sectional findings support the involvement of altered sensory and cross-modal network processing in migraine and warrant longitudinal validation.