BackgroundMany migraine sufferers report difficulties in managing their attacks, leading to missed workdays, impaired cognitive function and reduced participation in social activities. This challenge underscores the importance of identifying reliable indicators of an impending attack. The present study aimed to develop and evaluate machine learning models for predicting migraine attacks based on continuous, non-invasive monitoring of autonomic nervous system activity.MethodsData from a wrist-worn biosensor device the Empatica EmbracePlus (Empatica Inc., Boston, MA, USA) capturing electrodermal activity, heart rate, skin temperature and metabolic equivalent of task were analysed using machine learning models to detect prodromal autonomic nervous system alterations and predict impending migraine headaches. Personalised models were developed using participant-specific model selection, whereas generalised transferability was assessed using a leave-one-participant-out approach.ResultsData from 27 participants were analysed, with an average monitoring period of 30.8 ± 12.9 days, during which 96 migraine attacks were recorded, corresponding to 3.6 ± 1.0 episodes per individual. Personalised prediction models, each reflecting a participant-specific best-selected configuration, achieved good overall predictive performance, with mean values of 0.82 ± 0.10 for accuracy, 0.75 ± 0.16 for precision, 0.72 ± 0.12 for recall and 0.73 ± 0.12 for the F1-score. The mean area under the receiver operating characteristic curve was 0.84 ± 0.10. Gaussian Naive Bayes was most frequently selected, accounting for 44% of valid participant-specific configurations. Generalised models provided above-chance performance in only one-third of participants.ConclusionsThis study supports the feasibility of migraine attack prediction using continuous physiological data from a wrist-worn biosensor analysed with personalised machine learning approaches.

BackgroundExploring the pathogenesis of chronic migraine (CM) remains a formidable challenge. While 7T MRI enables precise detection of microstructural alterations in subcortical nuclei and subregions in CM, comprehensive multimodal imaging studies are few. We therefore conducted a 7T multimodal imaging study to characterize subcortical nuclei and limbic subregions within the hypothalamus, thalamus, hippocampus, and amygdala alterations associated with migraine chronification and to determine the impact of medication-overuse headache (MOH) on CM brain changes.MethodsWe performed multimodal 7T MRI in 62 healthy controls (HCs), 62 episodic migraine (EM) patients, and 62 CM patients (31 with MOH). MRI measures included volumes, functional indicators (fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo)), quantitative susceptibility mapping (QSM), and diffusion tensor imaging analysis along the perivascular space, choroid plexus volume, and basal ganglia perivascular space (PVS) metrics for glymphatic function. Group differences and associations with clinical measures were assessed.ResultsAfter false discovery rate (FDR) correction, fALFF in the accumbens area (L) and mean individual PVS volume differed significantly among the three groups (q < 0.05). Both EM and CM showed lower left accumbens fALFF and greater PVS volume than HCs, whereas no significant difference was detected between EM and CM. No group effects survived FDR correction for regional volume, ReHo, QSM, or other PVS metrics. No significant differences were found between CM patients with and without MOH. Left accumbens fALFF was negatively correlated with the headache duration, whereas PVS volume was positively correlated with headache duration.ConclusionsMigraine was associated with reduced spontaneous activity in the left accumbens area and increased basal ganglia PVS volume. The absence of detectable differences between EM and CM suggests that these abnormalities may represent shared migraine-related features rather than changes that increase linearly with chronification. The findings implicate altered pain-reward processing and perivascular morphology, while indicating that the transition to CM may depend on changes that static regional measures do not capture.

BackgroundMigraine is frequently comorbid with depression and anxiety, conditions that amplify disease burden and may impair preventive treatment response. Calcitonin gene-related peptide (CGRP)-targeted therapies have demonstrated robust efficacy in reducing migraine frequency, with emerging evidence suggesting concurrent improvements in psychiatric symptom burden. However, findings derive from heterogeneous study designs and outcome instruments, limiting conclusions regarding consistency and magnitude of these effects.MethodsWe conducted a systematic review and meta-analysis of studies reporting changes in depressive and anxiety symptoms in adults with migraine receiving CGRP-targeted preventive therapies. PubMed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), and PsycINFO were searched from inception through July 2026. Eligible studies included randomised controlled trials, post hoc analyses, and real-world observational studies reporting validated self-report or clinician-rated symptom measures at baseline and follow-up. Risk of bias was assessed using the Jadad Scale for randomised trials and the Newcastle-Ottawa Scale for observational studies. Standardised mean differences with 95% confidence intervals were calculated using random-effects models.ResultsTwelve studies (n = 3603; 63% female) published between 2020 and 2026 met eligibility criteria, of which nine contributed to quantitative synthesis. Pooled analyses demonstrated consistent reductions in depressive symptoms across instruments at 3 months: Hospital Anxiety and Depression Scale - Depression subscale -1.35 points (95% CI, -1.63 to -1.07), Hamilton Depression Rating Scale -3.73 points (95% CI, -5.76 to -1.71), and Beck Depression Inventory -3.70 points (95% CI, -4.92 to -2.48). Anxiety outcomes improved similarly: Hospital Anxiety and Depression Scale - Anxiety subscale -1.50 points (95% CI, -2.45 to -0.55), Hamilton Anxiety Rating Scale -1.64 points (95% CI, -3.21 to -0.07), and Beck Anxiety Inventory -4.36 points (95% CI, -5.24 to -3.48). In the single placebo-controlled subgroup analysis, fremanezumab was numerically superior to placebo for depressive outcomes, though this difference did not reach statistical significance.ConclusionsCGRP-targeted therapies were consistently associated with reductions in depressive and anxiety symptom scores across multiple validated instruments, with improvements appearing to correlate with effective migraine control, although current study designs cannot differentiate between direct and indirect effects on psychiatric symptoms. Placebo-controlled evidence remains insufficient to draw firm conclusions, and adequately powered randomized controlled trials with psychiatric outcomes as prespecified endpoints are warranted before the nature and magnitude of these effects can be definitively characterized.

Headache disorders remain one of the most disabling and prevalent neurological diseases worldwide. Headache medicine and care have evolved tremendously over the past few years. In order to foster adequate education in headache medicine in medical school curricula and clinical residency programs, the Education Committee of the International Headache Society (IHS) updated the core curriculum in headache medicine for establishing core competencies in general neurologists and headache specialists. This document describes the background and methodical approach of the IHS in developing and updating the current core curriculum in headache medicine and presents the output of the endeavor. The process has been guided by international expert consultation, evidence-based content development, and alignment with evolving scientific and clinical advances. The 2026 updated Core Curriculum outlines a tiered approach for general neurologists and headache specialists, which includes advances in pathophysiology, diagnosis, therapeutics, multidisciplinary management, and internationally transferable training standards. This revised core curriculum will guide headache education worldwide and enable higher-quality patient care across a range of healthcare settings.

AimTo determine whether a two-variable scorecard combining monthly acute medication days and the six-item Migraine Treatment Optimization Questionnaire can identify high multidimensional migraine burden, defined operationally as a four-domain composite (monthly migraine days, acute medication days, pain intensity, and disability) score ≥50 (range 0-100), with sufficient discrimination and clinical utility for bedside triage.MethodsWe performed a cross-sectional analysis of baseline data from a multicenter, prospective, real-world migraine registry in China (enrolment January 2024 - December 2025). Adults with migraine and complete data for the four-domain composite and the six-item Migraine Treatment Optimization Questionnaire were included (total sample, 1161; derivation cohort, 344; external validation cohort, 817). A two-predictor logistic regression model was developed at the coordinating center and validated at the remaining centers. Bootstrap-adjusted discrimination and calibration were assessed. A categorical version of the model was converted into an integer scorecard (0-11 points) with decision-curve-derived triage zones.ResultsThe area under the receiver operating characteristic curve was 0.899 (95% confidence interval 0.863-0.935) in derivation and 0.866 (0.837-0.895) in external validation. Calibration in the validation cohort showed systematic overestimation (expected-to-observed ratio 1.85); after intercept recalibration, the Brier score decreased from 0.14 to 0.11. Decision curve analysis showed net benefit across threshold probabilities 0.10-0.50. In validation, the Green zone (score ≤2) included 313 participants (38.3%) with a negative predictive value of 0.98; the Yellow zone (score 3-4) included 180 (22.0%); and the Red zone (score ≥5) included 324 (39.7%) with a positive predictive value of 0.45. Monthly acute medication days accounted for most of the discriminative performance; the treatment-optimization component provided additional stratification, particularly in the intermediate medication-use range (4-14 days/month).ConclusionThe Migraine Analgesic Usage Index offers a concise, externally validated triage aid for identifying high multidimensional migraine burden as defined by the four-domain composite. It may help prioritize comprehensive assessment but does not replace clinical evaluation. Prospective studies should test whether index-guided triage improves decision quality and patient outcomes.Trial registrationChinese Clinical Trial Registry, https://www.chictr.org.cn/, ChiCTR2400083678.

BackgroundAvailable instruments to quantify migraine-related disability, such as the Headache Impact Test (HIT-6) and Migraine Disability Assessment Test (MIDAS), have certain limitations (e.g., recall bias, overlooking interictal burden), and do not always facilitate patient-doctor communication. We developed a migraine scoring system, namely the European Migraine and Headache Alliance-Migraine Scoring System (EMHA-MSS) with the aim of improving patient communication with healthcare professionals.MethodsCross-sectional, descriptive, quantitative study conducted between June 2024 and March 2025. First, we used exhaustive diary registries from 319 Spanish-speaking patients for a pilot study (of whom n = 280 had complete HIT-6/MIDAS). Second, the EMHA-MSS was administered to a validation cohort of Spanish patients (N = 504). Third, we undertook cross-language analyses using a separate English-language dataset from English-speaking patients (N = 51). The pilot benchmarking analyses used an exploratory three-item version of the scale with different scoring, whereas the final psychometric analyses used the four-item EMHA-MSS.ResultsThe EMHA-MSS demonstrated unidimensional structure with excellent Confirmatory Factor Analysis fit (Comparative Fit Index = 1.000, Tucker-Lewis Index = 1.000, Root Mean Square Error of Approximation = 0.000, Standardized Root Mean Residual = 0.003). Acute treatment reliability was identified as the main factor driving reclassification between severity categories when comparing EMHA-MSS and HIT-6 or MIDAS (comparisons used an exploratory 3-item EMHA-MSS with different scoring in the pilot cohort). Our model provided an excellent fit, supported the expected factorial structure, and demonstrated that factor scores could be reliably calculated. The EMHA-MSS showed good to excellent test-retest reliability. Cross-language validity was demonstrated for the English version using configural and metric invariance, as well as partial scalar invariance.ConclusionThe EMHA-MSS highlighted the importance of treatment reliability, pain and attack intensity, and frequency in classifying patients with migraine and evaluating the severity of the disease. This novel scoring system could facilitate tailored management and monitoring of migraine over time, potentially improving alignment with patients' perceptions and enhancing the understanding between them and healthcare providers.

BackgroundVisual snow syndrome (VSS) is characterized by persistent visual disturbances and is considered to involve altered sensory integration and salience processing. Neuroimaging studies suggest aberrant attention and salience network integration, yet the real-time interaction between internal bodily signals and external sensory processing remains unclear.MethodsWe conducted a case-control electroencephalogram study in 18 VSS patients and 16 matched controls performing a visual oddball task with standard and deviant stimuli. Heartbeat-evoked potentials (HEP) were extracted as an index of cortical interoceptive processing and visual mismatch negativity (MMN) as a marker of exteroceptive prediction error. Trial-wise analyses and source localization assessed interoceptive-exteroceptive interactions.ResultsVSS participants exhibited altered HEP amplitudes relative to controls, together with reduced condition-dependent differentiation between standard and deviant trials. The relationship between HEP and MMN amplitudes was significantly moderated by group and associated with symptom intensity in VSS. Source analysis localized these effects to a left frontotemporal network.ConclusionsVSS is associated with altered interoceptive-exteroceptive interactions during visual perception, which may contribute to persistent visual symptoms.

AimTo evaluate the efficacy and safety of a fully self-administered, app-based biofeedback intervention for the prevention of episodic migraineMethodsThis open-label, randomized, waitlist-controlled multicenter trial included adults with episodic migraine assigned to 12 weeks of daily biofeedback or a waitlist control. The primary outcome was change in monthly migraine days (MMDs) from baseline to weeks 9-12. Secondary outcomes included migraine intensity, acute medication use, and migraine-specific quality of life (MSQ v2.1). Statistical analyses were performed according to the intention-to-treat principle by a statistician masked to treatment assignment.ResultsOf 1113 individuals screened, 279 participants (mean [SD] age, 41 [10] years; 92% women) were randomized to the intervention (n = 140) or waitlist control (n = 139). Baseline MMDs were 4.8 (2.4) and 4.6 (2.3) in the intervention and control groups, respectively. During weeks 9-12, the estimated reduction in MMDs was 0.9 days (95% CI, -1.4 to -0.5) in the intervention group and 0.0 days (95% CI, -0.5 to 0.4) in the control group (between-group difference, 0.9 days; 95% CI, 0.3 to 1.5; P = 0.002). Participants receiving biofeedback also showed greater improvements in MSQ v2.1 total scores (mean difference, 5.3 points; 95% CI, 2.1 to 8.6; P = 0.001). Nine device-related adverse events occurred, all with mild severity.ConclusionApp-based self-administered biofeedback was associated with a statistically significant reduction in migraine frequency and improved quality of life, with a favorable safety profile.

Background/HypothesisRates of tobacco usage are higher in individuals with cluster headache. Tobacco usage exposes the user to a large variety of toxic compounds, detrimental to health, including heavy metals. We hypothesise that exposure to heavy metals through tobacco usage will drive epigenetic alterations in individuals with cluster headache.To elucidate the extent of heavy metal exposure and global epigenetic alterations arising from tobacco products in relation to cluster headache we performed a case-control study, using DNA methylation, gene and protein expression analysis to evaluate markers for smoking, epigenetic modifications and heavy metal exposure.MethodsRNA was purified from collected blood samples from 16 cluster headache participants and 16 control participants, converted to cDNA, and RNA expression quantified via RT-qPCR. This was done by investigating genes relevant for nicotine exposure; nicotinic acetylcholine receptors (nAChRs), heavy metal exposure; metallothionein (MT) family, markers of liver and kidney damage (ALT, Alb and NGAL), epigenetic enzymes for DNA methylation (DNMTs) and demethylation (TETs), normalized to the housekeeping gene TBP. MT3 protein expression was quantified using an ELISA kit in cerebrospinal fluid from 13 cluster headache participants and 13 control participants. The methylation status of CpG-rich regions of long-interspersed nucleotide element-1 (LINE-1), in bisulfite converted DNA of 20 smoking individuals with cluster headache, 33 non-smoking individuals with cluster headache and 26 control participants, was quantified by pyrosequencing.ResultsAn increased RNA expression of nicotinic acetylcholine receptor subunits CHRNA6 and CHRNA7 was found in the cluster headache group compared to controls. Additionally, there were an increased RNA expression of MT1H and increased protein levels of MT3 along with a decreased RNA expression of DNMT1. A positive correlation was found between the RNA expression of CHRNA7 and MT1H and a negative correlation was found between CHRNA6 and DNMT1.ConclusionIndirect evidence indicates heavy metal exposure in relation to cluster headache as well as a tendency for alterations in global DNA methylation. This supports the hypothesis that heavy metal exposure from smoking contributes towards the pathophysiology of cluster headache.

Background/HypothesisDifferentiating primary from secondary headaches remains challenging due to overlapping symptoms and the subjective nature of pain assessment. Machine learning (ML) combined with explainable artificial intelligence (XAI) offers a promising approach for improving diagnostic accuracy while maintaining clinical interpretability. The study aimed to develop an interpretable ML model to differentiate primary from secondary headaches and to identify the relative diagnostic contributions of individual clinical features.MethodsThis cross-sectional study analyzed 1219 patients using routine clinical history variables, including 21 triggers, 15 associated symptoms, temporal patterns, pain intensity (visual analog scale [VAS]), and demographic factors. Data were obtained from the Global Migraine and Pain Society (GMPS) registry and the epikriz.com/headache platform, restricted to patients managed by specialist headache neurologists at two headache clinics in Türkiye. The binary classification outcome was clinically assigned headache label: primary versus secondary headache disorder (primary headache designated as the positive class). An eXtreme Gradient Boosting (XGBoost) classifier was developed with Random Over-Sampling Examples to address class imbalance, which was applied exclusively to the training partition. Model interpretability was achieved using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME).ResultsThe model demonstrated excellent discrimination (AUC = 0.905), with sensitivity of 90.8%, specificity of 87.5%, precision of 99.7%, and an F1-score of 0.950. Associated symptom burden was the strongest predictor of primary headache classification (mean absolute SHAP = 1.075), driven principally by nausea, phonophobia, and photophobia. Trigger burden ranked second (mean absolute SHAP = 0.790), with stress, hunger, and odors most prevalent. Two laterality features independently discriminated headache type: strictly unilateral side-shifting (mean absolute SHAP = 0.545) and unilateral-to-bilateral transition (mean absolute SHAP = 0.481), both favoring primary classification. Higher diurnal pattern scores were associated with secondary headache disorders (mean absolute SHAP = 0.257). VAS contributed minimally (mean absolute SHAP = 0.101), suggesting that the syndromic profile outperforms pain severity as a diagnostic discriminator in this cohort.Conclusion/InterpretationIn this cohort, an ML model incorporating trigger profiles, associated symptoms, and temporal headache characteristics demonstrated good discrimination between clinically assigned primary and secondary headache labels. Associated symptom burden and trigger burden were more influential in model classification than pain intensity. This finding aligns with clinical practice, where headache diagnosis relies on symptom patterns and triggers rather than pain intensity alone. XAI frameworks may help make such classification models more clinically interpretable, although further external validation is required.