Migraine Classification Research Articles

Research trends and hotspots in clinical trials of migraine in the past 20 years: bibliometric analysis

BackgroundMigraine is a widespread, recurrent primary headache disorder primarily characterized by severe pulsatile headache, typically on one or both sides. It is often accompanied by nausea, vomiting, and hypersensitivity to sound and light. Despite the availability of multiple drugs for migraine management, the condition often becomes chronic due to untimely or irrational drug use, significantly distressing patients and increasing the burden on families and society. Over the past two decades, numerous clinical studies on migraine have been published. This study aimed to provide a comprehensive summary of the current status and trends of migraine clinical trials through bibliometric analysis.MethodsWe used visual network tools such as CiteSpace and VOSviewer to perform a knowledge graph analysis of publications related to migraine clinical trials extracted from the WoSCC.ResultsThis study analyzed 1,129 articles published in 389 journals from 61 countries. The number of publications on migraine clinical trials has steadily increased from 2004 to 2023. The United States and Albert Einstein College of Medicine are the leading countries and institutions in this field, respectively. Richard B. Lipton is the most prolific author, making significant contributions to the research. The journal Headache has the highest number of publications and citations in this area. Keywords such as “efficacy,” “RCT,” “CGRP,” “prophylaxis,” “disability,” “depression,” “questionnaire,” and “real-world effectiveness” received significant attention.ConclusionThis study identified reliable research hotspots and provided directions for clinicians. The treatment of migraine continues to be challenging. Future trends may include continued growth in migraine classification, risk factor analysis, and comorbidity studies. Research on CGRP and epigenetics will advance the progress of precision medicine in the migraine field.

Flexible modeling of headache frequency fluctuations in migraine with hidden Markov models.

To explore hidden Markov models (HMMs) as an approach for defining clinically meaningful headache-frequency-based groups in migraine. Monthly headache frequency in patients with migraine is known to vary over time. This variation has not been completely characterized and is not well accounted for in the classification of individuals as having chronic or episodic migraine, a diagnosis with potentially significant impacts on the individual. This study investigated variation in reported headache frequency in a migraine population and proposed a model for classifying individuals by frequency while accounting for natural variation. The American Registry for Migraine Research (ARMR) was a longitudinal multisite study of United States adults with migraine. Study participants completed quarterly questionnaires and daily headache diaries. A series of HMMs were fit to monthly headache frequency data calculated from the diary data of ARMR. Changes in monthly headache frequency tended to be small, with 47% of transitions resulting in a change of 0 or 1 day. A substantial portion (24%) of months reflected daily headache with individuals ever reporting daily headache likely to consistently report daily headache. An HMM with four states with mean monthly headache frequency emissions of 3.52 (95% Prediction Interval [PI] 0-8), 10.10 (95% PI 4-17), 20.29 (95% PI 12-28), and constant 28 days/month had the best fit of the models tested. Of sequential month-to-month headache frequency transitions, 12% were across the 15-headache days chronic migraine cutoff. Under the HMM, 38.7% of those transitions involved a change in the HMM state, and the remaining 61.3% of the time, a change in chronic migraine classification was not accompanied by a change in the HMM state. A divide between the second and third states of this model aligns most strongly with the current episodic/chronic distinction, although there is a meaningful overlap between the states that supports the need for flexibility. An HMM has appealing properties for classifying individuals according to their headache frequency while accounting for natural variation in frequency. This empirically derived model may provide an informative classification approach that is more stable than the use of a single cutoff value.

Application of EEG in the Diagnosis and Classification of Migraine: A Scoping Review.

Migraine is a chronic debilitating disease affecting a significant number of people, more often women than men. The gold standard for diagnosis is the International Classification of Headache Disorders-3 (ICHD-3). Authors have identified multiple tight spots in the present method of diagnosis. An alternative method of diagnosis has always been coveted. Electroencephalogram (EEG) is one of the most researched of such alternatives. The visually evoked potential is the most studied; auditory evoked potentials and transcranial direct current stimulation are also being studied. Cortical hyperexcitability and habituation deficit to sensory stimuli are some of the consistent findings. Alpha oscillations are among the most frequently studied bands; spectral analysis of EEGwaves has often shown more reliable and consistent results than features read off the EEGdirectly. EEGmicrostate is a novel and promising method showing characteristic identifiable features that may help diagnose Migraine patients. An alternative to the ICHD-3 criterion for diagnosing Migraines would be instrumental in promptly diagnosing the disease. EEGis one of the most explored alternatives within which enumerable features can be used to identify Migraines, of which the most promising are EEGmicrostates.

Improving Automatic Migraine Classification Performance with Naive Bayes

Improving Automatic Migraine Classification Performance with Naive Bayes

Migraine or any headaches and white matter hyperintensities and their progression in women and men

BackgroundCross-sectional and longitudinal studies have been conducted to investigate the association between migraine and any headache and white matter hyperintensities (WMH). However, studies are inconsistent regarding the strength of the association and its clinical significance. The aim of our study was to investigate the association between headache and its subtypes (migraine with aura (MigA+), migraine without aura (MigA-), non-migraine headache (nonMigHA)) and WMH and its course in the population-based 1000BRAINS study using state-of-the-art imaging techniques and migraine classification according to modified international classification of headache disorders.MethodsData from 1062 participants (45% women, 60.9 ± 13.0 years) with ever or never headache (neverHA) and complete quantitative (WMH volume) and qualitative (Fazekas classification) WMH data at first imaging and after 3.7 ± 0.7 years (393 participants) were analyzed. The sex-specific association between headache and its subtypes and WMH volume and its change was evaluated by linear regression, between headache and its subtypes and Fazekas score high vs. low (2–3 vs. 0–1) by log-binomial regression, adjusted for confounders.ResultsThe lifetime prevalence of headache was 77.5% (10.5% MigA+, 26.9% MigA-, 40.1% nonMigHA). The median WMH volume was 4005 (IQR: 2454–6880) mm3 in women and 4812 (2842–8445) mm3 in men. Women with any headaches (all headache types combined) had a 1.23 [1.04; 1.45]-fold higher WMH volume than women who reported never having had a headache. There was no indication of higher Fazekas grading or more WMH progression in women with migraine or any headaches. Men with migraine or any headaches did not have more WMH or WMH progression compared to men without migraine or men who never had headache.ConclusionsOur study demonstrated no increased occurrence or progression of WMH in participants with mgiraine. But, our results provide some evidence of greater WMH volume in women with headache of any type including migraine. The underlying pathomechanisms and the reasons why this was not shown in men are unclear and require further research.

Estimation of migraine prevalence considering active and inactive states across different age groups

BackgroundMigraine is a very common headache disorder on the population level, characterized by symptomatic attacks (activity). For many people with migraine, the migraine symptoms intermittently or permanently cease during their lifetime (inactive migraine). The current diagnostic classification of migraine considers two states: active migraine (having migraine symptoms within the last year) and not having active migraine (including both individuals with inactive migraine and those who never had migraine). Defining a state of inactive migraine that has gone into remission may better capture the trajectories of migraine across the lifespan and contribute to a better understanding of its biological processes. We aimed to quantify the prevalence of never, active, and inactive migraine separately, using modern prevalence and incidence estimation methodology to better describe the complexity of migraine trajectories at the population level.MethodsUsing a multistate modeling approach, data from the Global Burden of Disease (GBD) study, and results from a population-based study, we estimated the transition rates by which individuals moved between migraine disease states and estimated prevalences of never, active and inactive migraine. We used data from the GBD project and a hypothetical cohort of 100,000 people with a starting age of 30 and 30 years of follow-up, both in Germany and globally, stratified by sex.ResultsIn Germany, the estimated rate of transition from active to inactive migraine (remission rate) increased after the age of 22.5 in women and 27.5 in men. The pattern for men in Germany was similar to the one observed on the global level. The prevalence of inactive migraine among women reaches 25.7% in Germany and 16.5% globally at age 60. For men, the inactive migraine prevalence estimates at the same age were 10.4% in Germany and 7.1% globally.ConclusionsConsidering an inactive migraine state explicitly reflects a different epidemiological picture of migraine across the lifecourse. We have demonstrated that many women of older ages may be in an inactive migraine state. Many pressing research questions can only be answered if population-based cohort studies collect information not only on active migraine but also on inactive migraine states.

Classification of Migraine Using Static Functional Connectivity Strength and Dynamic Functional Connectome Patterns: A Resting-State fMRI Study

Migraine is a common, chronic dysfunctional disease with recurrent headaches. Its etiology and pathogenesis have not been fully understood and there is a lack of objective diagnostic criteria and biomarkers. Meanwhile, resting-state functional magnetic resonance imaging (RS-fMRI) is increasingly being used in migraine research to classify and diagnose brain disorders. However, the RS-fMRI data is characterized by a large amount of data information and the difficulty of extracting high-dimensional features, which brings great challenges to relevant studies. In this paper, we proposed an automatic recognition framework based on static functional connectivity (sFC) strength features and dynamic functional connectome pattern (DFCP) features of migraine sufferers and normal control subjects, in which we firstly extracted sFC strength and DFCP features and then selected the optimal features using the recursive feature elimination based on the support vector machine (SVM-RFE) algorithm and, finally, trained and tested a classifier with the support vector machine (SVM) algorithm. In addition, we compared the classification performance of only using sFC strength features and DFCP features, respectively. The results showed that the DFCP features significantly outperformed sFC strength features in performance, which indicated that DFCP features had a significant advantage over sFC strength features in classification. In addition, the combination of sFC strength and DFCP features had the optimal performance, which demonstrated that the combination of both features could make full use of their advantage. The experimental results suggested the method had good performance in differentiating migraineurs and our proposed classification framework might be applicable for other mental disorders.

Research on migraine time-series features classification based on small-sample functional magnetic resonance imaging data

The extraction of neuroimaging features of migraine patients and the design of identification models are of great significance for the auxiliary diagnosis of related diseases. Compared with the commonly used image features, this study directly uses time-series signals to characterize the functional state of the brain in migraine patients and healthy controls, which can effectively utilize the temporal information and reduce the computational effort of classification model training. Firstly, Group Independent Component Analysis and Dictionary Learning were used to segment different brain areas for small-sample groups and then the regional average time-series signals were extracted. Next, the extracted time series were divided equally into multiple subseries to expand the model input sample. Finally, the time series were modeled using a bi-directional long-short term memory network to learn the pre-and-post temporal information within each time series to characterize the periodic brain state changes to improve the diagnostic accuracy of migraine. The results showed that the classification accuracy of migraine patients and healthy controls was 96.94%, the area under the curve was 0.98, and the computation time was relatively shorter. The experiments indicate that the method in this paper has strong applicability, and the combination of time-series feature extraction and bi-directional long-short term memory network model can be better used for the classification and diagnosis of migraine. This work provides a new idea for the lightweight diagnostic model based on small-sample neuroimaging data, and contributes to the exploration of the neural discrimination mechanism of related diseases.

The ambiguity and limit of caffeine in migraine: a literature review

Introduction Migraine is a chronic disease that affects about 15% of world's population. It is the most disabling disease among men and women under 50 years. Studies indicate that the caffeine, which is the most consumed stimulating in the world and present on the daily diet, can be related to the migraine in an ambiguous way. ObjectiveConsidering the epidemiologic and economic point of view, the disease phisiopatology and the accessibility to caffeine, this study aims to understand if there would be a safe minimum dosage of caffeine for patients with migraine. MethodsThe words "migraine" and "caffeine" were searched in July 2022 at PUBMED and BVS with predetermined filters and selection of publication in qualis journals A1 and A2. 13 articles were found for this revision. ResultsCaffeine's effects on the nervous system and its relation with headaches have been target of several studies, mainly for producing symptoms similars to the prodromic phase of migraine e for the existence of the Caffeine Abstinence Syndrome, known as a diagnostic entity by the International Migraine Classification. The sensory hypersensitivity (migraine's characteristic) does not appear at the abstinence syndrome. The lack of information about pre-existing headache at studies shows a gap in the parameter ''caffeine withdrawal as a trigger per se''. It is possible that premonitory symptoms such as yawns, low energy and sleepiness can lead to caffeine intake, bringing forth the wrong idea that it has initiated the migraine.Caffeine, however, induces urinary magnesium loss, reducing its reabsorption. As magnesium affects neuromuscular conduction, nerve transmission and is beneficial in chronic pain conditions and migraines, caffeine, by decreasing magnesium level, can induce headache. Additionally, caffeine contains thiaminases, enzymes that degrade thiamine, which may have protective potential in migraine. (To see the complete abstract, please, check out the PDF).

Shift in diagnostic classification of migraine after initiation of preventive treatment with eptinezumab: post hoc analysis of the PROMISE studies

BackgroundMonthly headache frequency directly correlates with personal/societal burden and impacts severity and preventive treatment decisions. This post hoc analysis identified shifts from higher to lower frequency headache categories over 6 months in patients with migraine participating in the PROMISE clinical trials receiving two eptinezumab doses.MethodsHeadache frequency at baseline and over study months 1–6 was categorized into 4 groups: chronic migraine (CM; ≥ 15 monthly headache days [MHDs]), high-frequency episodic migraine (HFEM; 10–14 MHDs), low-frequency episodic migraine (LFEM; 4–9 MHDs), and ≤ 3 MHDs. Outcomes included the percentage of patients within each MHD category, the percentage of patients improving by ≥ 1 MHD category, and the number of months with reduction of ≥ 1 MHD category. Data from patients who received approved eptinezumab doses (100 mg or 300 mg) or placebo were included.ResultsMean headache frequency at baseline in PROMISE-1 was 10 MHDs; most patients were classified as having HFEM (48.6%) or LFEM (43.9%). At Month 1, 62/221 (28.1%), 75/222 (33.8%), and 45/222 (20.3%) patients who received eptinezumab 100 mg, 300 mg, and placebo had ≤ 3 MHDs, with 97/221 (43.9%), 108/222 (48.6%), and 84/222 (37.8%), respectively, falling below the diagnostic EM threshold at Month 6. More than one-third (79/221 [35.7%], 83/222 [37.4%], and 68/222 [30.6%] of patients in the eptinezumab 100 mg, 300 mg, and placebo groups, respectively), had 6 months of reduction of ≥ 1 frequency category. At baseline in PROMISE-2, mean headache frequency was 20.5 MHDs. All patients (100%) in the eptinezumab 100 mg and placebo groups had CM, as did 99.4% of patients receiving eptinezumab 300 mg. At Month 1, 209/356 (58.7%), 216/350 (61.7%), and 167/366 (45.6%) patients treated with eptinezumab 100 mg, 300 mg, and placebo had ≤ 14 MHDs, with 240/356 (67.4%), 249/350 (71.1%), and 221/366 (60.4%), respectively, falling below CM threshold at Month 6. Additionally, 153/356 (43.0%), 169/350 (48.3%), and 116/366 (31.7%) patients in the eptinezumab 100 mg, 300 mg, and placebo groups, respectively, had 6 months of reduction of ≥ 1 frequency category.ConclusionIn the PROMISE studies, episodic and chronic migraine patients treated with eptinezumab were more likely to reduce their headache frequency versus placebo, which directly and in a sustained way improved their diagnostic category classification.Trial registrationClinicalTrials.gov Identifier: NCT02559895, NCT02974153.

Migraine classification by machine learning with functional near-infrared spectroscopy during the mental arithmetic task

Migraine is a common and complex neurovascular disorder. Clinically, the diagnosis of migraine mainly relies on scales, but the degree of pain is too subjective to be a reliable indicator. It is even more difficult to diagnose the medication-overuse headache, which can only be evaluated by whether the symptom is improved after the medication adjustment. Therefore, an objective migraine classification system to assist doctors in making a more accurate diagnosis is needed. In this research, 13 healthy subjects (HC), 9 chronic migraine subjects (CM), and 12 medication-overuse headache subjects (MOH) were measured by functional near-infrared spectroscopy (fNIRS) to observe the change of the hemoglobin in the prefrontal cortex (PFC) during the mental arithmetic task (MAT). Our model shows the sensitivity and specificity of CM are 100% and 75%, and that of MOH is 75% and 100%.The results of the classification of the three groups prove that fNIRS combines with machine learning is feasible for the migraine classification.

Characteristics of migraine in patients with headache disorders: A clinic-based study from Central American and Caribbean countries.

ObjectiveThe objective of the study was to measure the proportion of patients who consulted for headache and of those who had a final diagnosis of migraine. We also assessed the proportion and characteristics of patients with migraine and the impact of migraine on the daily activities and the professional and social lives of patients visiting private/public medical centers in Guatemala, Costa Rica, Panama, and the Dominican Republic.BackgroundUnderdiagnosis of migraine is high in Central America and Caribbean urban communities. However, there is limited knowledge on characteristics of headache disorders for the appropriate classification of migraine, which is a prerequisite for targeted treatment. Hence, there is a need to improve migraine awareness among patients and medical professionals in this region.MethodsCentral America and Caribbean countries epidemioLogy study of Migraine (CALM) was a non‐interventional, cross‐sectional, multinational study in adults aged 18–65 years with a history of or current headache. The primary outcome was the proportion of patients with migraine visiting medical centers due to headache disorders. Using a specially designed migraine survey questionnaire, patients self‐reported migraine characteristics, duration, and severity of attacks and impact on work and social life.ResultsOf the 313 enrolled patients, 308 (98.4%) completed the study. Approximately 75.3% (232/308) of patients with headache visiting medical centers had migraine, with episodic migraine being the most common (193 [83.2%]). Overall, 34/308 (11.0%) patients had a new diagnosis of migraine. Among patients with migraine, 66 (28.4%) had a history of migraine for ≥20 years and 59 (25.4%) experienced severe pain. Overall, 52.2% (121/232) of patients reported that migraine affected their professional life and 78.4% (182/232) reported an impact on social life.ConclusionThe CALM study establishes that a high proportion of patients with migraine had a long duration and high severity of migraine attacks, leading to a direct impact on work/social life as well as on costs incurred by patients in these countries.

Evaluation of the genetic variant - 889 C > T Of Il-1α in migraine - partial analysis

IntroductionThe pathophysiology of migraine integrates inflammatory and genetic aspects, with interleukin-1α being a component of this picture. This pro-inflammatory cytokine, responsible for inducing pyrogenic, hematological, and metabolic phenomena, is produced by macrophages and monocytes. Genetic variants, which can be found in the regulatory region of the gene for this substance, have clinical implications in different systems.ObjectiveTo evaluate the frequency of the -889C>T genetic variant of IL-1α and its association with clinical variables related to migraine. MethodsProspective case-control study composed of migraine patients and healthy controls aged between 18 and 60 years of age. Project approved by the Research Ethics Committee of PUCPR (No. 3,029,972). Demographic, clinical data on migraine classification and characteristics were collected using a structured form and validated questionnaires on anxiety (STAIY2), depression (BDI) and migraine-related disability (MIDAS). Genetic evaluation was performed with blood or saliva samples that were subjected to polymerase chain reaction (PCR), followed by electrophoresis in 1.5% agarose gel. Categorical data were analyzed by chi-square test or Fisher's exact test and continuous data by t-test or Mann-Whitney test. ResultsA total of 156 participants, 73 migraineurs and 83 controls, were evaluated. The -889C>T variant of IL-1α was not associated with increased susceptibility to migraine when evaluated in allelic, codominant, dominant, or recessive models. The C allele, the lowest producer of the cytokine, was associated with a higher frequency of osmophobia in patients with migraine (65.5% vs. 48.2%; p=0.038). ConclusionNo association was identified between the -889C>T variant of IL-1α and susceptibility to migraine. Its effect on osmophobia should be further investigated. However, the present work is a partial analysis whose main limitation is the small sample size.

Disrupted Functional Connectivity of the Amygdala Predicts the Efficacy of Non-steroidal Anti-inflammatory Drugs in Migraineurs Without Aura

Machine learning (ML) has been largely applied for predicting migraine classification. However, the prediction of efficacy of non-steroidal anti-inflammatory drugs (NSAIDs) in migraine is still in the early stages. This study aims to evaluate whether the combination of machine learning and amygdala-related functional features could help predict the efficacy of NSAIDs in patients with migraine without aura (MwoA). A total of 70 MwoA patients were enrolled for the study, including patients with an effective response to NSAIDs (M-eNSAIDs, n = 35) and MwoA patients with ineffective response to NSAIDs (M-ieNSAIDs, n = 35). Furthermore, 33 healthy controls (HCs) were matched for age, sex, and education level. The study participants were subjected to resting-state functional magnetic resonance imaging (fMRI) scanning. Disrupted functional connectivity (FC) patterns from amygdala-based FC analysis and clinical characteristics were considered features that could promote classification through multivariable logistic regression (MLR) and support vector machine (SVM) for predicting the efficacy of NSAIDs. Further, receiver operating characteristic (ROC) curves were drawn to evaluate the predictive ability of the models. The M-eNSAIDs group exhibited enhanced FC with ipsilateral calcarine sulcus (CAL), superior parietal gyrus (SPG), paracentral lobule (PCL), and contralateral superior frontal gyrus (SFG) in the left amygdala. However, the M-eNSAIDs group showed decreased FC with ipsilateral caudate nucleus (CAU), compared to the M-ieNSAIDs group. Moreover, the M-eNSAIDs group showed higher FC with left pre-central gyrus (PreCG) and post-central gyrus (PoCG) compared to HCs. In contrast, the M-ieNSAIDs group showed lower FC with the left anterior cingulate cortex (ACC) and right SFG. Furthermore, the MwoA patients showed increased FC with the left middle frontal gyrus (MFG) in the right amygdala compared to HCs. The disrupted left amygdala-related FC patterns exhibited significant correlations with migraine characteristics in the M-ieNSAIDs group. The MLR and SVM models discriminated clinical efficacy of NSAIDs with an area under the curve (AUC) of 0.891 and 0.896, sensitivity of 0.971 and 0.833, and specificity of 0.629 and 0.875, respectively. These findings suggest that the efficacy of NSAIDs in migraine could be predicted using ML algorithm. Furthermore, this study highlights the role of amygdala-related neural function in revealing underlying migraine-related neuroimaging mechanisms.

Vestibular migraine: Diagnostic criteria1.

This paper presents diagnostic criteria for vestibular migraine, jointly formulated by the Committee for Classification of Vestibular Disorders of the Bárány Society and the Migraine Classification Subcommittee of the International Headache Society (IHS). It contains a literature update while the original criteria from 2012 were left unchanged. The classification defines vestibular migraine and probable vestibular migraine. Vestibular migraine was included in the appendix of the third edition of the International Classification of Headache Disorders (ICHD-3, 2013 and 2018) as a first step for new entities, in accordance with the usual IHS procedures. Probable vestibular migraine may be included in a later version of the ICHD, when further evidence has accumulated. The diagnosis of vestibular migraine is based on recurrent vestibular symptoms, a history of migraine, a temporal association between vestibular symptoms and migraine symptoms and exclusion of other causes of vestibular symptoms. Symptoms that qualify for a diagnosis of vestibular migraine include various types of vertigo as well as head motion-induced dizziness with nausea. Symptoms must be of moderate or severe intensity. Duration of acute episodes is limited to a window of between 5 minutes and 72 hours.

The use of the monoclonal antibody Erenumab in patients with chronic migraine in real clinical experience

Patients with chronic migraine are characterized by frequent severe headache attacks, polypharmacy (painkillers), insufficient effect of preventive therapy, and a decrease in the quality of life. The aim of a prospective open-label study was the evaluation of the efficacy and tolerance of Erenumab (Irinex) in the treatment of patients with chronic migraine in real clinical practice.Material and methods. 48 patients with chronic migraine were clinically and neurologically examined (35.5 [19; 56]; diagnosis was established according to the criteria of ICHD classification and diagnosis of migraine. Medical monitoring period was 3 months. Clinical and neurological testing and subcutaneous administration of Erenumab (Irinex) 70 mg were performed once a month during the whole period.Results. Safety, good tolerance and an obvious significant clinical effect were noted when using Erenumab (Irinex) 70 mg. It led to a significant improvement in clinical score and the course of chronic migraine, the effectiveness of rapid relief of symptoms and significant reduction in the need for painkillers in most patients. The best results were found after 3 months of therapy.

The diagnosis and treatment of pediatric vertigo diseases: interpretation of the Bárány Society's consensus document

The diagnostic criteria for "Vestibular Migraine of Childhood", "Probable Vestibular Migraine of Childhood" and "Recurrent Vertigo of Childhood" is put forth by the Committee for the Classification of Vestibular Disorders of the Bárány Society and the Migraine Classification subgroup of the International Headache Society at the beginning of 2021. It provides directions for the diagnosis and treatment of these syndromes to international community of pediatricians, otolaryngologists, neurologists, neurosurgeons, neuro-otologists physiotherapists, neurophysiologists, and audiologists. The paper concludes the current situation and research progress of diagnosis and treatment of children vertigo diseases.

Chronic versus episodic migraine: The 15-day threshold does not adequately reflect substantial differences in disability across the full spectrum of headache frequency.

To evaluate whether the 15-day threshold of headache days per month adequately reflects substantial differences in disability across the full spectrum of migraine. The monthly frequency of headache days defines migraine subtypes and has crucial implications for epidemiological and clinical research as well as access to care. The patients with migraine (N=836) who participated in the American Registry for Migraine Research, which is a multicenter, longitudinal patient registry, between February 2016 and March 2020, were divided into four groups based on monthly headache frequency: Group 1 (0-7headache days/month, n=286), Group 2 (8-14headache days/month, n=180), Group 3 (15-23headache days/month, n=153), Group 4 (≥24headache days/month, n=217). Disability (MIDAS), Pain intensity (NRS), Work Productivity and Activity Impairment (WPAI), Pain Interference (PROMIS-PI), Patient Health Questionnaire-4 (PHQ-4), and General Anxiety Disorder-7 (GAD-7) scores were compared. Mean (standard deviation [SD]) age was 46 (13) years (87.9% [735/836] female). The proportion of patients in each group was as follows: Group 1 (34.2% [286/836]), Group 2 (21.5% [180/836]), Group 3 (18.3% [153/836]), and Group 4 (26.0% [217/836]). There were significant relationships with increasing disability, lost productive time, and pain interference in higher headache frequency categories. There were no significant differences between Group 2 and Group 3 for most measures (NRS, all WPAI scores, PROMIS-PI, GAD-7, and PHQ-4), although MIDAS scores differed (median [interquartile range (IQR)]; 38 [20-58] vs. 55 [30-90], p<0.001). Patients in Group 1had significantly lower MIDAS (median [IQR];16 [7-30], p<0.001), WPAI-% total active impairment (mean (SD): Group 1 [30.9 (26.8)] vs. Group 2 [39.2 (24.5), p=0.017], vs. Group 3 [45.9 (24.1), p<0.001], vs. Group 4 [55.3 (23.0), p<0.001], and PROMIS-PI-T score (Group 1 [60.3 (7.3)] vs. Group 2 [62.6 (6.4), p=0.008], vs. Group 3 [64.6 (5.6), p<0.001], vs. Group 4 [66.8 (5.9), p<0.001]) compared to all other groups. Patients in Group 4had significantly higher MIDAS (median (IQR): Group 4 [90 (52-138)] vs. Group 1 [16 (7-30), p<0.001], vs. Group 2 [38 (20-58), p<0.001], vs. Group 3 [55 (30-90), p<0.001], WPAI-%Presenteeism (Group 4 [50.4 (24.4)] vs. Group 1 [28.8 (24.9), p<0.001], vs. Group 2 [34.9 (22.3), p<0.001], vs. Group 3 [40.9 (22.3), p=0.048], WPAI-% total work productivity impairment (Group 4 [55.9 (26.1)] vs. Group 1 [32.1 (37.6), p<0.001], vs. Group 2 [38.3 (24.0), p<0.001], vs. Group 3 [44.6 (24.4), p=0.019]), and WPAI-%Total activity impairment (Group 4 [55.3 (23.0)] vs. Group 1 [30.9 (26.8), p<0.001], vs. Group 2 [39.2 (24.5), p<0.001], vs. Group 3 [45.9 (24.1), p=0.025]) scores compared with all other groups. Our data suggest that the use of a 15headache day/month threshold to distinguish episodic and chronic migraine does not capture the burden of illness nor reflect the treatment needs of patients. These results have important implications for future refinements in the classification of migraine.

Do we need to reconsider the classification of vestibular migraine?

ABSTRACT Introduction: Vestibular migraine (VM) is a complex disease characterized by recurrent episodes of migraine associated with vertigo attacks that are observed in 1–3% of the general population. Given its high prevalence and the impact on the health system, it is important to characterize these patients, in order to offer an accurate diagnosis and a proper treatment. As the diagnosis of VM is based on clinical features, the study of potential biomarkers has gained more interest in the last years, to improve the precision in the diagnosis of this disease. The aim of this review is to summarize the main tests available for the diagnosis of VM, including the accuracy of biomarkers for the diagnosis of VM. Areas covered: This review summarizes the main information on VM, including all diagnosis records published in the field in the last 10 years, and focusing on candidate biomarkers for the diagnosis of VM patients. Expert opinion: There is a limited knowledge in the pathophysiology of VM. The search of biomarkers for diagnosis of VM is needed to improve the precision in the diagnosis promoting clinical and translational research. The potential reclassification of VM will depend upon the discovery and validation of these biomarkers.

Migraine Triggers: An Overview of the Pharmacology, Biochemistry, Atmospherics, and Their Effects on Neural Networks.

We define a migraine trigger to be an endogenous agent or agency such as the menses or an exogenous agent or agency such as red wine or a drop in barometric pressure, and their ability to reduce the threshold of a migraine attack in those predisposed to migraine. This definition excludes agents with idiosyncratic mechanisms that may trigger a migrainous (migraine-like) headache in non-migraineurs such as benign cough headaches or headaches due to altitude-sickness. We also assume as axiomatic that migraine has as its basis the activation of the trigeminovascular pathway (TVP) and the key role of serotonin and the calcitonin gene-related peptide (CGRP). The network activation of the visual/auditory association cortices and the rostrodorsal pons (locus ceruleus and raphe nucleus) are also accepted as key features of activation of the TVP. In addition, we outline the role of the superior salivatory nucleus-sphenopalatine ganglion-greater superficial petrosal nerve (SSN-SPG-GSPN) arc in migraine activation.We also explore how olfactory afferents intermingle with trigeminal nerve collaterals in the glomeruli of the olfactory bulb thus allowing volatile molecules to activate the TVP and induce a migraine. The classification of migraine triggers is complex, as there is a wide panorama of inciting agents, including atmospheric conditions, a wide-ranging variety of foods and beverages, endogenous hormonal influences, synthetic alkaloids and dyes, and volatile molecules (odorants). We will explore the high-frequency migraine-provoking agents in each category. There are exciting and intriguing hypotheses regarding the role of atmospheric chemistry when the barometric pressure drops; the role of hot, dry desert winds and lightning discharges in the generation of cations and the turnover of serotonin in the nervous system. We will explore the effects of a drop in barometric pressure on the vestibular nuclei and the modulation of sympathetically mediated pain. The role of volatile odorants and their activation of the transient receptor potential ankyrin-1 (TRPA-1) receptor will be outlined. We will streamline the highly complex role of estrogen fluctuation in the precipitation of migraine headaches, its pharmacodynamic effects, and the role of the sexually dimorphic nucleus of the preoptic area (SDN-POA) of the hypothalamus. We will also adumbrate the protean effects of alcohol and its congeners and the role of stress and sleep disturbances in the allostatic load model of salience network-pain perception.