Dynamic brainstem and somatosensory cortical excitability during migraine cycles

Hsiao, Fu Jung; Chen, Wei-Ta; Pan, Li; Liu, Hung‐Yu; Chen, Shih‐Pin; Lai, Kuan‐Lin; Wang, Shuu Jiun

doi:10.1186/s10194-022-01392-1

Cited by 18 publications

(14 citation statements)

References 53 publications

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“…The preictally decreased LICI after habitual sleep (Figure 4) may be in coherence with the theory that migraine patients have increased thresholds for inhibitory homeostatic responses in the preictal phase (36), which may be related to hypoexcitability measured at rest (12,14). This model may also be in line with observations of preictal hyperresponsiveness in previous studies (10,14). indicating an involvement of slow GABA-B receptor mediated inhibition.…”

Section: Discussionsupporting

confidence: 92%

“…The preictal phase just prior to the onset of migraine headache encompass connectivity changes involving dopaminergic centres (8) and the hypothalamus (9). The preictal phase may also be characterised by increased somatosensory excitability in the brainstem and cortex, possibly caused by decreased intracortical inhibition (10). Preictal patterns in electroencephalography (EEG) may be indicative of underlying thalamocortical dysrhythmia (11), able to cause increased cortical synchronicity (12,13), which has been interpreted as reduced cortical preactivation (14).…”

Section: Introductionmentioning

confidence: 99%

“…Preictal patterns in electroencephalography (EEG) may be indicative of underlying thalamocortical dysrhythmia (11), able to cause increased cortical synchronicity (12,13), which has been interpreted as reduced cortical preactivation (14). In the ictal phase, the increased somatosensory excitability (10,14), connectivity changes (9,15), and the reduced cortical preactivation before the attack (11,14) may flip or normalise towards interictal levels. Less is known about the postictal phase (16), but one group have found slight alterations of postictal brainstem functionality (17).…”

Section: Introductionmentioning

confidence: 99%

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Insufficient sleep may alter cortical excitability near the migraine attack: A blinded TMS crossover study

et al. 2023

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Background Migraine is a brain disorder with a multifaceted and unexplained association to sleep. Brain excitability likely changes periodically throughout the migraine cycle. In this study we examine the effect of insufficient sleep on neuronal excitability during the course of the migraine cycle. Methods We examined 54 migraine patients after two nights of eight-hour habitual sleep and two nights of four-hour restricted sleep in a randomised, blinded crossover study. We performed transcranial magnetic stimulation and measured cortical silent period, short- and long-interval intracortical inhibition, intracortical facilitation and short-latency afferent inhibition. We analysed how responses changed before and after attacks with linear mixed models. Results Short- interval intracortical inhibition was more reduced after sleep restriction compared to habitual sleep the shorter the time that had elapsed since the attack ( p = 0.041), and specifically in the postictal phase ( p = 0.013). Long-interval intracortical inhibition was more increased after sleep restriction with time closer before the attack ( p = 0.006), and specifically in the preictal phase ( p = 0.034). Short-latency afferent inhibition was more decreased after sleep restriction with time closer to the start of the attack ( p = 0.026). Conclusion Insufficient sleep in the period leading up to a migraine attack may cause dysfunction in cortical GABAergic inhibition. The results also suggest that migraine patients may have increased need for sufficient sleep during a migraine attack to maintain normal neurological function after the attack.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Insufficient sleep may alter cortical excitability near the migraine attack: A blinded TMS crossover study

et al. 2023

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“…Second, the presence of background or interval headache, but not ictal attack, was allowed for patients with CM. Although our study noted the dynamic brain excitability within the migraine cycle in patients with EM [ 56 ], the longitudinal day-to-day dynamics of FC between brain networks in patients with CM remain unresolved, which leads to an unsettled question of how the classification model performs for the factor of headache status. Third, we demonstrated the reliability and generalizability of using MEG-based electrophysiological features.…”

Section: Discussionmentioning

confidence: 99%

Resting-state magnetoencephalographic oscillatory connectivity to identify patients with chronic migraine using machine learning

et al. 2022

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To identify and validate the neural signatures of resting-state oscillatory connectivity for chronic migraine (CM), we used machine learning techniques to classify patients with CM from healthy controls (HC) and patients with other pain disorders. The cross-sectional study obtained resting-state magnetoencephalographic data from 240 participants (70 HC, 100 CM, 35 episodic migraine [EM], and 35 fibromyalgia [FM]). Source-based oscillatory connectivity of relevant cortical regions was calculated to determine intrinsic connectivity at 1–40 Hz. A classification model that employed a support vector machine was developed using the magnetoencephalographic data to assess the reliability and generalizability of CM identification. In the findings, the discriminative features that differentiate CM from HC were principally observed from the functional interactions between salience, sensorimotor, and part of the default mode networks. The classification model with these features exhibited excellent performance in distinguishing patients with CM from HC (accuracy ≥ 86.8%, area under the curve (AUC) ≥ 0.9) and from those with EM (accuracy: 94.5%, AUC: 0.96). The model also achieved high performance (accuracy: 89.1%, AUC: 0.91) in classifying CM from other pain disorders (FM in this study). These resting-state magnetoencephalographic electrophysiological features yield oscillatory connectivity to identify patients with CM from those with a different type of migraine and pain disorder, with adequate reliability and generalizability.

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“…Regarding pain processing, the pain generator or mediator in the peripheral trigeminal system, brainstem and midbrain (7,8), and cortical areas and networks are involved in the dysfunctional modulatory mechanisms of sensory perception. Accordingly, abnormal quantitative sensory tests (9), altered pain processing (4)(5)(6)(10)(11)(12), and reduced habituation (4,6,13) have been observed among patients with migraine. However, whether modulating cortical oscillations for painful (PF) and nonpainful (NP) sensory processing represents a potential brain signature that can be used to discern patients with CM from those without migraine remains debatable.…”

Section: Introductionmentioning

confidence: 99%

Identification of patients with chronic migraine by using sensory-evoked oscillations from the electroencephalogram classifier

et al. 2023

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Background To examine whether the modulating evoked cortical oscillations could be brain signatures among patients with chronic migraine, we investigated cortical modulation using an electroencephalogram with machine learning techniques. Methods We directly record evoked electroencephalogram activity during nonpainful, painful, and repetitive painful electrical stimulation tasks. Cortical modulation for experimental pain and habituation processing was analyzed and used to differentiate patients with chronic migraine from healthy controls using a validated machine-learning model. Results This study included 80 participants: 40 healthy controls and 40 patients with chronic migraine. Evoked somatosensory oscillations were dominant in the alpha band. Longer latency (nonpainful and repetitive painful) and augmented power (nonpainful and repetitive painful) were present among patients with chronic migraine. However, for painful tasks, alpha increases were observed among healthy controls. The oscillatory activity ratios between repetitive painful and painful tasks represented the frequency modulation and power habituation among healthy controls, respectively, but not among patients with chronic migraine. The classification models with oscillatory features exhibited high performance in differentiating patients with chronic migraine from healthy controls. Conclusion Altered oscillatory characteristics of sensory processing and cortical modulation reflected the neuropathology of patients with chronic migraine. These characteristics can be reliably used to identify patients with chronic migraine using a machine-learning approach.

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Dynamic brainstem and somatosensory cortical excitability during migraine cycles

Cited by 18 publications

References 53 publications

Insufficient sleep may alter cortical excitability near the migraine attack: A blinded TMS crossover study

Insufficient sleep may alter cortical excitability near the migraine attack: A blinded TMS crossover study

Resting-state magnetoencephalographic oscillatory connectivity to identify patients with chronic migraine using machine learning

Identification of patients with chronic migraine by using sensory-evoked oscillations from the electroencephalogram classifier

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