The journal of headache and pain · 2026
Temporal neural dynamics patterns in episodic and chronic migraine: a magnetoencephalography study.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Lin Cunxin, Qiu Dong, Liao Pan, Liu Geyu, Liang Jie, Xiong Zhonghua, Li Xiaoshuang, Duan Chenyang, Wang Zhe, Guo Zhi, Zhang Yongxiang, Ge Zhaoli, Liu Xin, Li Yuanxiang, Gao Tianshuang, Zhang Mantian, Gao Jia-Hong, Lyu Bingjiang, Wang Yonggang
Аннотация
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.
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.