Chiang, Chia-Chun

White matter hyperintensities (WMHs) are common MRI findings and established markers of cerebral small-vessel disease associated with stroke, cognitive decline, and vascular risk factors. Several studies have suggested that individuals with migraine, particularly migraine with aura (MA), may have an increased burden of WMHs compared with individuals without migraine. Early population-based studies and meta-analyses reported a higher prevalence of WMHs in migraine, whereas more recent large neuroimaging cohorts have reported conflicting findings or no association. Differences in study design, migraine ascertainment, MRI acquisition parameters, WMH quantification methods, and adjustment for vascular risk factors complicate interpretation of the existing literature. An updated systematic review focusing on methodologically comparable population-based cohorts is therefore warranted. The primary objective of this systematic review and meta-analysis is to determine whether adults with migraine have a higher prevalence or burden of WMHs on MRI than non-migraine controls. Secondary objectives include evaluating associations according to migraine subtype (MA vs. migraine without aura and episodic vs. chronic migraine), sex, WMH location, age, and MRI acquisition or WMH quantification methods, as well as examining longitudinal WMH progression where available. We will include population-based cross-sectional or longitudinal cohort studies of adults undergoing brain MRI with sequences suitable for WMH detection. Migraine exposure may be defined using International Classification of Headache Disorders criteria, structured interviews, validated questionnaires, physician diagnosis, or ICD codes. Controls must originate from the same source population. Searches will be conducted in PubMed/MEDLINE, EMBASE, Web of Science, and Scopus. Study selection, data extraction, and risk-of-bias assessment will be performed independently by two reviewers using Covidence, and risk of bias will be evaluated using the ROBINS-E tool. Because WMH burden is reported using heterogeneous metrics across studies, the primary synthesis will use a precision-weighted direction-of-effect approach, in which each study contributes the direction of the association, weighted by statistical precision. Where sufficiently comparable outcomes are available, random-effects meta-analysis will be performed. Sensitivity analyses will explore the influence of diagnostic criteria, MRI field strength, and confounder adjustment. This review will provide an updated and methodologically rigorous synthesis of the evidence on the association between migraine and WMHs.

Aim To determine whether there is a difference in the time to complete a rapid number naming task during the interictal state compared to prodrome, migraine headache, postdrome phases of migraine and non-migraine headache, and characterize non-headache symptoms experienced during these phases. Methods This prospective longitudinal cohort study evaluated 30 adults with migraine with or without aura who completed diary entries and a timed rapid number naming task at least three times daily for up to four months from October 2020 to July 2022 at a tertiary headache center. Diary entries included self-report of current migraine phase (having a migraine headache, non-migraine headache, prodrome, postdrome, or interictal baseline) and detailed symptoms experienced. The outcome of interest for the timed rapid number naming task was the time to complete the task with zero errors. Results Compared to the interictal baseline, the time with zero errors for the rapid number naming task was significantly slower during the prodrome (median (Q1, Q3) 1.0 s slower (0.4, 2.9), p -value 0.010), migraine headache (median (Q1, Q3) 3.8 s slower (2.6, 7.5), p -value <0.001), and postdrome (median (Q1, Q3) 0.9 s slower (0.2, 2.1), p -value 0.011) phases of migraine, and non-migraine headache attacks (median (Q1, Q3) 1.2 s slower (0.1, 3.1), p -value 0.006). For those with migraine with aura (N = 15), the time with zero errors was significantly slower during all phases compared to interictal phase. For those with migraine without aura (N = 15), the time with zero errors was significantly slower only during the migraine headache, but not other phases compared to the interictal phase. The top 5 most reported non-headache symptoms during any phase of migraine and non-migraine headache attacks included fatigue, neck stiffness, light sensitivity, blurred vision/vision changes, and difficulty concentrating. Conclusions Among the overall participant cohort, the time with zero errors to complete a rapid number naming task was significantly slower during all ictal phases compared to interictal phase. This was also the case in the migraine with aura cohort but in the migraine without aura cohort there was only a significant difference during the migraine headache phase versus interictal phase. There is overlap in non-headache symptoms between the prodrome, migraine headache, postdrome, and non-migraine headache phases, which may make distinguishing between these phases difficult. Combining subjective data, such as symptom diaries, with objective measures, such as rapid number naming tasks, may allow for more refined assessment of migraine phases and distinguishing migraine from non-migraine headache attacks.

Abstract Objective To investigate the ability of artificial intelligence‐enabled electrocardiogram (AI‐ECG) atrial fibrillation (AF) prediction model output and delta age, defined as AI‐estimated age minus chronological age, to predict adverse vascular outcomes in patients with migraine. Background Migraine, especially migraine with aura, is associated with an increased risk of cardiovascular events. Using normal sinus rhythm ECG, our institution has developed AI‐ECG models that predict the probability of AF and estimate patients' age, and from that, delta age (estimated age minus actual age) has been shown to reflect cardiovascular health and physiologic aging. Methods We conducted a retrospective cohort study that included adult patients with migraine who had at least one digital, standard 12‐lead ECG from 2000 to 2020 at any Mayo Clinic site. The first normal sinus rhythm ECG was defined as the index ECG, and those with adverse vascular events or AF prior to, or within 3 days after, index ECG were excluded. AI‐ECG AF prediction model output and delta age were obtained from the index ECG. The primary outcome was a composite endpoint of adverse vascular events that included acute myocardial infarction, acute ischemic stroke, venous thromboembolism, and death. New‐onset AF was also recorded as a secondary outcome. Results A total of 29,928 patients were included. The mean age was 44.3 ± 14.5 years, and the median follow‐up time was 54 months. During the study period, 4662 (15.6%) patients had adverse vascular events, and 1384 (4.6%) patients developed new‐onset AF. A higher AF prediction model output was associated with a higher risk of adverse vascular outcomes (adjusted hazard ratio [aHR]:1.15 [95% confidence interval: 1.12 to 1.18] for every 10% increase in prediction model output), as was a larger delta age (aHR:1.16 [1.12 to 1.21] for every 10‐year increase in delta age). Moreover, AI‐ECG AF prediction model outputs of ≥1% (aHR: 1.45 [1.36 to 1.54]) and delta age ≥−1 years (aHR: 1.09 [1.02 to 1.16]) were found to be the optimal cutoffs to dichotomize patients into high‐risk and low‐risk groups for adverse vascular events. Higher AI‐ECG AF prediction model output at baseline also predicted a higher likelihood of having new‐onset AF during the study period (aHR: 1.31 [1.26 to 1.37]), and the development of new‐onset AF was associated with a higher risk of adverse vascular events (aHR: 2.43 [2.17 to 2.73]). Conclusions For patients with migraine, a higher AI‐ECG AF prediction model output and delta age are predictive of adverse vascular outcomes. A higher AF prediction model output is also predictive of future AF development. Furthermore, we identified the cutoffs for both AI‐ECG models that can distinguish patients between high‐ and low‐risk groups. Our results showed that AI‐ECG models could inform clinical practice and identify patients with migraine at risk for future adverse vascular events.

Background Many guidelines list migraine with aura (MwA) as a contraindication to estrogen-containing combined hormonal contraceptives (CHCs) due to vascular risks. However, current evidence is based on small sample studies with potential influence by confounding factors. Additionally, few studies have examined the vascular risk associated with modern CHCs with lower dose estrogen, particularly in relation to aura status. The present study aims to investigate the vascular risk of modern CHCs in women with migraine with and without aura. Methods We used a de-identified electronic medical record database with 120 million patients across multiple health systems in the United States of America. We included female patients aged 18–45 years who received a migraine diagnosis code, had at least three office visits within three years, and were prescribed at least one migraine-specific medication within 6 months following the first outpatient visit. All data after 2010 were included. Patients with prior cardiovascular events were excluded. Our composite endpoint consisted of acute ischemic stroke, acute myocardial infarction, deep vein thrombosis/pulmonary embolism and intravenous thrombolytic administration. We stratified our analysis according to CHC exposure and aura status and compared the incidence of the endpoint with high-dimensional propensity score-matching between CHC users and non-users in (i) the overall cohort, (ii) MwA and (iii) migraine without aura (MwoA); between MwA and MwoA in (iv) patients prescribed CHCs; and in (v) those without CHC prescriptions. Results We included 5535 patients who received CHC prescriptions and 21,520 who did not. 114 (2.06%) of CHC users and 547 (2.54%) of CHC non-users had at least one vascular event. With propensity score-matched comparison, the composite endpoint did not significantly differ between CHC and non-CHC in the overall migraine group, those with MwA and the MwoA group. In those prescribed CHC, MwA and MwoA did not differ in all the outcomes. For CHC non-users, MwA was associated with a higher incidence of acute ischemic stroke (hazard ratio = 2.45; 95% confidence interval = 1.58–3.78; p < 0.001; n = 6201 in each group) and the composite endpoint (hazard ratio = 1.34; 95% confidence interval = 1.08–1.67; p = 0.008). Conclusions Our real-world study showed that exposure to modern CHC was not associated with a significant increase in vascular risk in women aged 18–45 years with migraine, MwA or MwoA who have no prior cardiovascular events. However, in those who never received CHC, MwA was associated with higher vascular risks compared to MwoA. While limitations exist using large scale electronic medical record databases for analysis, our results suggest that carefully designed prospective studies should be conducted to reassess the vascular risk associated with CHC use in women with migraine, especially MwA.

Background Migraine is a highly prevalent neurological disorder with many treatment options, both pharmacological and non-pharmacological. Artificial intelligence (AI) has great potential to optimize treatment selection strategies for individual patients. This review provides an overview of AI models and the techniques used to predict migraine treatment outcomes. Methods We conducted a literature search in PubMed and examined studies that reported employing AI models to predict migraine preventive and acute treatment outcomes. We also explored incorporating AI/machine learning to enhance personalized migraine treatment strategies, including forecasting migraine attacks. Additionally, we summarized future research directions, including incorporating multimodality data, using AI frameworks for the discovery of novel treatment targets, and advancing the field with innovative AI techniques such as digital twins, conversational AI and virtual AI agents. Results Studies have employed ML and deep learning on a combination of clinical features and imaging data to predict acute or preventive migraine treatment outcomes with reported success. Continued model optimization, validation, and prospective assessment of the clinical utility of deploying ML models in real-world settings are crucial. Conclusions While AI has demonstrated success in predicting migraine treatment responses, future research incorporating novel AI techniques and diverse data sources could pave the way to advance personalized migraine treatment.

We here present evidence-based guidelines for the pharmacological treatment of migraine. These guidelines, created by the Italian Society for the Study of Headache and the International Headache Society, aim to offer clear, actionable recommendations to healthcare professionals. They incorporate evidence-based recommendations from randomized controlled trials and expert-based opinions. The guidelines follow the GRADE approach for assessing the quality of evidence. The guideline development involved a systematic review of literature across multiple databases, adherence to Cochrane review methods, and a structured framework for data extraction and interpretation. Although the guidelines provide a robust foundation for migraine treatment, they also highlight gaps in current research, such as the paucity of head-to-head drug comparisons and the need for long-term outcome studies. These guidelines serve as a resource to standardize migraine treatment and promote high-quality care across different healthcare settings.

We here present evidence-based guidelines for the pharmacological treatment of migraine. These guidelines, created by the Italian Society for the Study of Headache and the International Headache Society, aim to offer clear, actionable recommendations to healthcare professionals. They incorporate evidence-based recommendations from randomized controlled trials and expert-based opinions. The guidelines follow the Grading of Recommendations, Assessment, Development and Evaluation approach for assessing the quality of evidence. The guideline development involved a systematic review of literature across multiple databases, adherence to Cochrane review methods, and a structured framework for data extraction and interpretation. Although the guidelines provide a robust foundation for migraine treatment, they also highlight gaps in current research, such as the paucity of head-to-head drug comparisons and the need for long-term outcome studies. These guidelines serve as a resource to standardize migraine treatment and promote high-quality care across different healthcare settings.

Abstract Objective The objective was to determine if migraine is independently associated with a greater odds of spontaneous coronary artery dissection (SCAD) in a case–control study. Background There is growing evidence that migraine is associated with SCAD, a non‐atherosclerotic, non‐traumatic cause of myocardial infarction. However, few studies have examined the independent association between migraine and SCAD. Methods A total of 1295 SCAD cases and 1291 (1:1) age, sex, and race/ethnicity matched controls presenting to the Boston, Massachusetts area, using the International Classification of Diseases ninth revision and 10th revision codes from our Research Patient Data Registry, were identified between the years 1990 and 2022. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were calculated. For mediation, total effects, natural direct, and indirect effects were estimated using causal inference. Receiver operating characteristic (ROC) curves and area under the curve (AUC) was used to determine predictive power. Statistical significance was defined as p < 0.05. Results Adjusting for age, sex, and race/ethnicity, the data demonstrated an almost two‐fold increased odds of SCAD in patients with migraine (aOR 1.96, 95% CI 1.48–2.6; p < 0.001). There was no effect of aura phenotype on this association. Fibromuscular dysplasia (FMD) was found only in patients with SCAD ( n = 133). After accounting for vascular risk factors, there was no longer an independent association between migraine and SCAD (aOR 0.90, 95% CI 0.63–1.29; p = 0.568). Using a model that adjusted for only demographic variables produced a ROC curve with the lowest predictive power (AUC 0.55) for SCAD, while the vascular risk factor‐adjusted model had a higher predictive power (AUC 0.83). Hypertension appeared to mediate some of the effect of migraine on SCAD (proportion of effect mediated 0.70). FMD was highly associated with an increased risk of SCAD. There remained no independent association between SCAD and migraine when FMD was included in the model. Conclusions The migraine–SCAD association can be partially attributed to the connection between migraine and vascular comorbidities including hypertension and FMD.

Abstract Objective The goal is to provide an overview of artificial intelligence (AI) and machine learning (ML) methodology and appraisal tailored to clinicians and researchers in the headache field to facilitate interdisciplinary communications and research. Background The application of AI to the study of headache and other healthcare challenges is growing rapidly. It is critical that these findings be accurately interpreted by headache specialists, but this can be difficult for non‐AI specialists. Methods This paper is a narrative review of the fundamentals required to understand ML/AI headache research. Using guidance from key leaders in the field of headache medicine and AI, important references were reviewed and cited to provide a comprehensive overview of the terminology, methodology, applications, pitfalls, and bias of AI. Results We review how AI models are created, common model types, methods for evaluation, and examples of their application to headache medicine. We also highlight potential pitfalls relevant when consuming AI research, and discuss ethical issues of bias, privacy and abuse generated by AI. Additionally, we highlight recent related research from across headache‐related applications. Conclusion Many promising current and future applications of ML and AI exist in the field of headache medicine. Understanding the fundamentals of AI will allow readers to understand and critically appraise AI‐related research findings in their proper context. This paper will increase the reader's comfort in consuming AI/ML‐based research and will prepare them to think critically about related research developments.