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

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. 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. 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. 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.

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Publication Details

Journal
The Journal of Headache and Pain
Published
2026-07-27
DOI
https://doi.org/10.1186/s10194-026-02466-0
Primary Topic
Migraine and Headache Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Bingjiang Lyu, X X Li, Tianshuang Gao, Cunxin Lin et al.
The Journal of Headache and Pain
Migraine and Headache Studies
article

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

Bingjiang Lyu, X X Li, Tianshuang Gao, Cunxin Lin, Zhaoli Ge, Geyu Liu, Chenyang Duan, Pan Lp, Zi Wang, Y Zhang, Liu Xm, Mantian Zhang, J Gao, Jie Liang, Yuanxiang Li, Zhi Guo, Zhonghua Xiong, Dong Qiu, Yonggang Wang
article en

Abstract

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. 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. 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. 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.

The Journal of Headache and Pain
Capital Medical University (CN), Dalian Medical University (CN), Peking University (CN), Beijing Tian Tan Hospital (CN), Second Affiliated Hospital of Chongqing Medical University (CN), Chinese Institute for Brain Research (CN), First Affiliated Hospital of Dalian Medical University (CN), First Affiliated Hospital of Zhengzhou University (CN), Shenzhen Second People's Hospital (CN), Beihang University (CN), Chongqing Medical University (CN)
National Natural Science Foundation of China
Decent work and economic growth
Openalex Percentile: Top 8%
Migraine and Headache Studies
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