Somatosensory Gating Responses Are Associated with Prognosis in Patients with Migraine

Characteristic oscillatory brain networks for predicting patients with chronic migraine

To determine specific resting-state network patterns underlying alterations in chronic migraine, we employed oscillatory connectivity and machine learning techniques to distinguish patients with chronic migraine from healthy controls and patients with other pain disorders. This cross-sectional study included 350 participants (70 healthy controls, 100 patients with chronic migraine, 40 patients with chronic migraine with comorbid fibromyalgia, 35 patients with fibromyalgia, 30 patients with chronic tension-type headache, and 75 patients with episodic migraine). We collected resting-state magnetoencephalographic data for analysis. Source-based oscillatory connectivity within each network, including the pain-related network, default mode network, sensorimotor network, visual network, and insula to default mode network, was examined to determine intrinsic connectivity across a frequency range of 1-40 Hz. Features were extracted to establish and validate classification models constructed using machine learning algorithms. The findings indicated that oscillatory connectivity revealed brain network abnormalities in patients with chronic migraine compared with healthy controls, and that oscillatory connectivity exhibited distinct patterns between various pain disorders. After the incorporation of network features, the best classification model demonstrated excellent performance in distinguishing patients with chronic migraine from healthy controls, achieving high accuracy on both training and testing datasets (accuracy > 92.6% and area under the curve > 0.93). Moreover, in validation tests, classification models exhibited high accuracy in discriminating patients with chronic migraine from all other groups of patients (accuracy > 75.7% and area under the curve > 0.8). In conclusion, oscillatory synchrony within the pain-related network and default mode network corresponded to altered neurophysiological processes in patients with chronic migraine. Thus, these networks can serve as pivotal signatures in the model for identifying patients with chronic migraine, providing reliable and generalisable results. This approach may facilitate the objective and individualised diagnosis of migraine.

Levcromakalim provokes an acute rapid-onset migraine-like phenotype without inducing cortical spreading depolarization

BACKGROUND

Migraine headache attacks and accompanying sensory augmentation can be induced by several agents including levcromakalim (LVC), that is also capable of provoking aura-like symptoms in migraineurs. We investigated whether single LVC injection causes acute migraine-like phenotype in rats and induces/modulates cortical spreading depolarization (CSD), a rodent model of migraine aura.

METHODS

Wistar rats were administered LVC (1 mg/kg, i.p.) and compared to control (CTRL, vehicle, i.p.) and nitroglycerin (NTG, 10 mg/kg, i.p.) groups. Von Frey filaments were used to examine the periorbital and hind paw mechanical allodynia. Dark-light box (DLB), elevated plus maze (EPM), and open field arena (OFA) were used to evaluate light sensitivity and anxiety-related behaviors. The effects of LVC on CSD parameters, somatosensory evoked potentials, and baseline dural EEG (electroencephalography) were investigated. Possible CSD-induced c-fos expression was studied with Western Blot. Blood-brain barrier integrity in cortex was examined with Evans blue assay.

RESULTS

LVC and NTG administration robustly reduced periorbital mechanical thresholds in rats and induced anxiety-like behaviors and photophobia within 30 and 120 min, respectively. LVC induced migraine-like phenotype recovered in 2 h while NTG group did not fully recover before 4 h. Both LVC and NTG did not provoke DC (direct current) shift, EEG alterations or cortical c-fos expression characteristic to CSD. LVC did not induce de novo CSD and affect KCl (potassium chloride)-induced CSD parameters except for an increase in propagation failure. However, NTG significantly increased both CSD susceptibility and propagation failure. Somatosensory evoked potential (SSEP) configurations were not altered in both LVC and NTG groups, but SSEP latencies were prolonged after CSD. Acute LVC or NTG injection did not increase cortical BBB permeability.

CONCLUSIONS

Single LVC administration induced the fastest manifestation and recovery of acute migraine-like phenotype which was not mediated by CSD waves in the cerebral cortex. We suppose LVC triggered rapid-onset migraine-like symptoms are probably related to functional alterations in the trigeminal nociceptive system and K⁺ channel opening properties of LVC. Understanding the neurobiological mechanisms of this nociceptive window, may provide a novel target in migraine treatment.

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

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.

Headache-related circuits and high frequencies evaluated by EEG, MRI, PET as potential biomarkers to differentiate chronic and episodic migraine: Evidence from a systematic review

Background

The diagnosis of migraine is mainly clinical and self-reported, which makes additional examinations unnecessary in most cases. Migraine can be subtyped into chronic (CM) and episodic (EM). Despite the very high prevalence of migraine, there are no evidence-based guidelines for differentiating between these subtypes other than the number of days of migraine headache per month. Thus, we consider it timely to perform a systematic review to search for physiological evidence from functional activity (as opposed to anatomical structure) for the differentiation between CM and EM, as well as potential functional biomarkers. For this purpose, Web of Science (WoS), Scopus, and PubMed databases were screened.

Findings

Among the 24 studies included in this review, most of them (22) reported statistically significant differences between the groups of CM and EM. This finding is consistent regardless of brain activity acquisition modality, ictal stage, and recording condition for a wide variety of analyses. That speaks for a supramodal and domain-general differences between CM and EM that goes beyond a differentiation based on the days of migraine per month. Together, the reviewed studies demonstrates that electro- and magneto-physiological brain activity (M/EEG), as well as neurovascular and metabolic recordings from functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), show characteristic patterns that allow to differentiate between CM and EM groups.

Conclusions

Although a clear brain activity-based biomarker has not yet been identified to distinguish these subtypes of migraine, research is approaching headache specialists to a migraine diagnosis based not only on symptoms and signs reported by patients. Future studies based on M/EEG should pay special attention to the brain activity in medium and fast frequency bands, mainly the beta band. On the other hand, fMRI and PET studies should focus on neural circuits and regions related to pain and emotional processing.

Resting-state occipital alpha power is associated with treatment outcome in patients with chronic migraine

ABSTRACT

Preventive treatment is crucial for patients with chronic migraine (CM). This study explored the association between resting-state cortical oscillations and 3-month treatment outcome in patients with CM. Treatment-naïve patients with CM were recruited with their demographic data, psychosocial data, and headache profiles as well as the healthy controls (HCs). Resting-state cortical activities were recorded using an electroencephalogram and analysed using source-based and electrode-based spectral power method. The regions of interest were the bilateral primary somatosensory (S1) and visual (V1) cortices. After 3-month treatment with flunarizine, patients with CM were categorized into responders and nonresponders. Demographic, clinical, and electroencephalogram data from 72 patients with CM and 50 HCs were analysed. Elevated anxiety, depression, and stress were observed in patients with CM. Theta power in bilateral S1 and alpha and gamma powers in the right S1 increased in patients with CM. Nonresponders (n = 34) exhibited larger alpha powers in bilateral V1 than those in responders (n = 38). Alpha powers also exhibited significant correlations with changes of monthly headache days. Notably, in responders and nonresponders, occipital alpha powers did not differ at baseline and in the third month. In conclusion, patients with CM who were not responsive to preventive treatment were associated with augmented resting-state occipital alpha activity. Moreover, changes in migraine attack frequency were associated with baseline occipital alpha power. However, the prognostic feature of visual alpha oscillation seems to be inherent because it is not altered by flunarizine treatment. These findings may be useful for developing personalised migraine treatment plans.