Todd J Schwedt’s research while affiliated with Mayo Clinic and other places

Magnetic resonance spectroscopic imaging provides a means to quantify brain metabolites including N-Acetylaspartate (NAA), choline (Cho), creatine (Cr), myoinositol (Ins), and glutamate and glutamine (Glx). The goal of this work was to investigate the metabolite differences between participants with migraine, participants with acute post-traumatic headache (PTH) with migraine-like phenotype, and healthy controls. For spectroscopy acquisitions, a 3D echo-planar spectroscopic imaging sequence was used with full brain coverage. 3D metabolite maps for NAA, Cho, Cr, Ins, and Glx were compared between participants with migraine (n = 12), participants with acute PTH (n = 10), and healthy controls (n = 11) using a full factorial design with significance defined as p < 0.05 with family-wise error corrections for multiple comparisons. The migraine group showed increased Cho and Ins in the right hippocampus and increased Ins in the bilateral parahippocampal regions, left inferior temporal, and bilateral fusiform areas relative to healthy controls. Relative to healthy controls, the acute PTH cohort had decreased NAA in the right precuneus, increased Glx in the right lingual and the right calcarine gyrus, and increased Ins in the left amygdala. Relative to individuals with migraine, those with acute PTH had higher Glx in the right calcarine gyrus, decreased Glx in the left insula, decreased Ins in the left fusiform gyrus, and increased NAA in the left frontal inferior area. The metabolite differences between migraine, PTH, and healthy controls observed in this study could provide insights into the mechanisms and consequences of migraine and PTH.

Background and Objective Limited data are available describing the frequency, severity, and consistency of prodromal symptoms followed by headache. This analysis of the PRODROME trial screening period characterized prodromal symptoms in people with migraine, including the most common symptoms and their severity, and the frequency and consistency with which prodromal symptoms were followed by headache. Methods PRODROME was a multicenter, randomized, double-blind, placebo-controlled, crossover trial conducted in the United States that enrolled adults with 2–8 migraine attacks per month who stated they could identify prodromal symptoms that were reliably followed by a headache. The trial included a 60-day screening period designed to test the predictive validity of “qualifying prodrome events” before the onset of headache. Participants used an eDiary to report qualifying prodrome events, defined as prodromal symptoms whereby the participant was confident a headache would follow within 1–6 hours. This analysis evaluated common prodromal symptoms and their severity, time from prodrome onset to headache onset, and the percentage of participants who identified prodromal symptoms that were followed by a headache ≥75% of the time over the 60-day screening period. Results A total of 920 participants entered eDiary data, with a mean of 5.2 qualifying prodrome events during the 60-day screening period. A total of 4,802 qualifying prodrome events were recorded. The most common prodromal symptoms identified were sensitivity to light (57.2%; 2,748/4,802), fatigue (50.1%; 2,408/4,802), neck pain (41.9%; 2,013/4,802), sensitivity to sound (33.9%; 1,630/4,802), either difficulty thinking or concentrating (30.0%; 1,442/4,802), and dizziness (27.8%; 1,333/4,802). Of all qualifying prodrome events reported, 81.5% (3,913/4,802) were followed by headache of any intensity within 1–6 hours. For each participant, a mean of 84.4% of their qualifying prodrome events were followed by a headache within 1–6 hours, with 76.9% of participants identifying qualifying prodrome events that were followed by headache within 1–6 hours ≥75% of the time. Discussion Participants were able to identify migraine attacks in which prodromal symptoms were reliably followed by a headache within 1–6 hours. These findings suggest the potential for initiating treatment during the prodrome to prevent headache. Trial Registration Information ClinicalTrials.gov NCT04492020. Submitted: July 27, 2020; First patient enrolled: August 21, 2020. clinicaltrials.gov/study/NCT04492020.

Background The term ‘precision medicine’ encompasses strategies to optimize diagnosis and outcome prediction and to tailor treatment for individual patients, in consideration of their unique characteristics. The greater availability of multifaceted datasets and strategies to model such data have made precision medicine increasingly possible in recent years. Precision medicine is especially needed in the migraine field since the response to migraine treatments is not universal amongst all individuals with migraine. Objective To provide a narrative review describing contributions to achieving precision medicine for migraine treatment. Methods A search of PubMed for English language articles of human participants published from 2005 to January 2024 was conducted to identify articles that reported research contributing to precision medicine for migraine treatment. The published literature was categorized and summarized according to the type of data that were included: clinical phenotypes, genomics, proteomics, physiologic measures, and brain imaging. Results Published studies have investigated characteristics associated with acute and preventive treatment responses, such as nonsteroidal anti-inflammatory drugs, triptans, onabotulinumtoxinA, and anti-calcitonin gene-related peptide monoclonal antibodies, in patients with episodic or chronic migraine. There is evidence that clinical, genetic, epigenetic, proteomic, physiologic, and brain imaging features might associate with migraine treatment outcomes, although inconsistencies for such findings clearly exist. Conclusions The published literature suggests that there are clinical and biological features which associate with, and might be useful for predicting, migraine treatment responses. To achieve precision medicine for migraine treatment, further research is needed that validates and expands on existing findings and tests the accuracy and value of migraine treatment prediction models in clinical settings.

Background Slower speaking rates and higher pause rates are found in individuals with migraine or post‐traumatic headache during headache compared to when headache‐free. We aimed to determine whether headache intensity influences the speaking rate and pause rate of participants with migraine or acute post‐traumatic headache (aPTH) following mild traumatic brain injury (mTBI). Methods Using a speech elicitation tool, participants with migraine, aPTH, and healthy controls (HC) submitted speech samples over a period of 3 months. Speaking and pause rates were calculated when participants were headache‐free and when they had mild or moderate headache. In this observational study, speaking and pause rates in participants with migraine and aPTH were compared to HC, controlling for age, sex, and days since mTBI (participants with aPTH only). Results A total of 2902 longitudinal speech samples from 13 individuals with migraine (mean age = 33.5, SD = 6.6; 12 females/1 male), 43 individuals with aPTH (mean age = 44.4, SD = 13.5; 28 females/15 males), and 56 HC (mean age = 40.8, SD = 13.0; 36 females/20 males) were collected. There was no difference in speaking rate between HC and the combined headache cohort of participants (migraine and aPTH) when they had headache freedom or a mild headache. When participants had moderate intensity headache, their speaking rate was significantly slower compared to that of HC and compared to their speaking rate during mild headache intensity or headache freedom. For the combined headache cohort of participants, pause rates were significantly higher when they had headache freedom or had a headache of mild or moderate intensity relative to HC. Compared to participants' pause rate during headache freedom, their pause rate was significantly higher during mild and moderate headache intensity. Participants with aPTH had significantly slower speaking rates compared to participants with migraine during headache freedom, mild headache intensity, and moderate headache intensity. Participants with aPTH had significantly higher pause rates compared to participants with migraine when experiencing moderate headache intensity. Discussion For both aPTH and migraine, more severe headache pain was associated with higher pause rates and slower speaking rates, suggesting that speaking rate and pause rate could serve as objective biomarkers for headache‐related pain. Slower speaking rate in participants with aPTH could reflect additional consequences of TBI‐related effects on motor control and speech production.

Objective To develop machine learning models using patient and migraine features that can predict treatment responses to commonly used migraine preventive medications. Background Currently, there is no accurate way to predict response to migraine preventive medications, and the standard trial‐and‐error approach is inefficient. Methods In this cohort study, we analyzed data from the Mayo Clinic Headache database prospectively collected from 2001 to December 2023. Adult patients with migraine completed questionnaires during their initial headache consultation to record detailed clinical features and then at each follow‐up to track preventive medication changes and monthly headache days. We included patients treated with at least one of the following migraine preventive medications: topiramate, beta‐blockers (propranolol, metoprolol, atenolol, nadolol, timolol), tricyclic antidepressants (amitriptyline, nortriptyline), verapamil, gabapentin, onabotulinumtoxinA, and calcitonin gene‐related peptide (CGRP) monoclonal antibodies (mAbs) (erenumab, fremanezumab, galcanezumab, eptinezumab). We pre‐trained a deep neural network, “TabNet,” using 145 variables, then employed TabNet‐embedded data to construct prediction models for each medication to predict binary outcomes (responder vs. non‐responder). A treatment responder was defined as having at least a 30% reduction in monthly headache days from baseline. All model performances were evaluated, and metrics were reported in the held‐out test set (train 85%, test 15%). SHapley Additive exPlanations (SHAP) were conducted to determine variable importance. Results Our final analysis included 4260 patients. The responder rate for each medication ranged from 28.7% to 34.9%, and the mean time to treatment outcome for each medication ranged from 151.3 to 209.5 days. The CGRP mAb prediction model achieved a high area under the receiver operating characteristics curve (AUC) of 0.825 (95% confidence interval [CI] 0.726, 0.920) and an accuracy of 0.80 (95% CI 0.70, 0.88). The AUCs of prediction models for beta‐blockers, tricyclic antidepressants, topiramate, verapamil, gabapentin, and onabotulinumtoxinA were: 0.664 (95% CI 0.579, 0.745), 0.611 (95% CI 0.562, 0.682), 0.605 (95% CI 0.520, 0.688), 0.673 (95% CI 0.569, 0.724), 0.628 (0.533, 0.661), and 0.581 (95% CI 0.550, 0.632), respectively. Baseline monthly headache days, age, body mass index (BMI), duration of migraine attacks, responses to previous medication trials, cranial autonomic symptoms, family history of headache, and migraine attack triggers were among the most important variables across all models. A variable could have different contributions; for example, lower BMI predicts responsiveness to CGRP mAbs and beta‐blockers, while higher BMI predicts responsiveness to onabotulinumtoxinA, topiramate, and gabapentin. Conclusion We developed an accurate prediction model for CGRP mAbs treatment response, leveraging detailed migraine features gathered from a headache questionnaire before starting treatment. Employing the same methods, the model performances for other medications were less impressive, though similar to the machine learning models reported in the literature for other diseases. This may be due to CGRP mAbs being migraine‐specific. Incorporating medical comorbidities, genomic, and imaging factors might enhance the model performance. We demonstrated that migraine characteristics are important in predicting treatment responses and identified the most crucial predictors for each of the seven types of preventive medications. Our results suggest that precision migraine treatment is feasible.

Objective To identify and disseminate research priorities for the headache field that should be areas of research focus during the next 10 years. Background Establishing research priorities helps focus and synergize the work of headache investigators, allowing them to reach the most important research goals more efficiently and completely. Methods The Headache Research Priorities organizing and executive committees and working group chairs led a multistakeholder and international group of experts to develop headache research priorities. The research priorities were developed and reviewed by clinicians, scientists, people with headache, representatives from headache organizations, health‐care industry representatives, and the public. Priorities were revised and finalized after receiving feedback from members of the research priorities working groups and after a public comment period. Results Twenty‐five research priorities across eight categories were identified: human models, animal models, pathophysiology, diagnosis and management, treatment, inequities and disparities, research workforce development, and quality of life. The priorities address research models and methods, development and optimization of outcome measures and endpoints, pain and non‐pain symptoms of primary and secondary headaches, investigations into mechanisms underlying headache attacks and chronification of headache disorders, treatment optimization, research workforce recruitment, development, expansion, and support, and inequities and disparities in the headache field. The priorities are focused enough that they help to guide headache research and broad enough that they are widely applicable to multiple headache types and various research methods. Conclusions These research priorities serve as guidance for headache investigators when planning their research studies and as benchmarks by which the headache field can measure its progress over time. These priorities will need updating as research goals are met and new priorities arise.

Background In an effort to improve migraine management around the world, the International Headache Society (IHS) has here developed a list of practical recommendations for the acute pharmacological treatment of migraine. The recommendations are categorized into optimal and essential, in order to provide treatment options for all possible settings, including those with limited access to migraine medications. Methods An IHS steering committee developed a list of clinical questions based on practical issues in the management of migraine. A selected group of international senior and junior headache experts developed the recommendations, following expert consensus and the review of available national and international headache guidelines and guidance documents. Following the initial search, a bibliography of twenty-one national and international guidelines was created and reviewed by the working group. Results A total of seventeen questions addressing different aspects of acute migraine treatment have been outlined. For each of them we provide an optimal recommendation, to be used whenever possible, and an essential recommendation to be used when the optimal level cannot be attained. Conclusion Adoption of these international recommendations will improve the quality of acute migraine treatment around the world, even where pharmacological options remain limited.

Background Prior studies have established an association between a history of abuse and more severe migraine presentation. Objectives This cross‐sectional, observational study of a clinic‐based migraine population used validated measures to elucidate migraine‐specific and migraine‐related burdens among patients with a history of abuse. Methods Patients with migraine ( n = 866) from the American Registry for Migraine Research self‐reported if they had a history of emotional, physical, and/or sexual abuse and completed questionnaires assessing migraine‐related burden: Migraine Disability Assessment, Subjective Cognitive Impairment Scale for Migraine Attacks, Work Productivity and Activity Impairment, Patient‐Reported Outcomes Measurement Information System Pain Interference, Patient Health Questionnaire‐2, and Generalized Anxiety Disorder‐7. Migraine‐related burden in patients with versus without a history of abuse was compared. Subsequently, a mediation analysis evaluated the impact of depression and anxiety symptoms in the relationship between abuse history and migraine burden. Results A history of abuse was reported by 36.5% ( n = 316/866) of participants. After controlling for patient age, sex, years lived with headache, and headache frequency, a history of abuse was significantly associated with more severe migraine‐related disability. The combined burden of depression and anxiety symptoms mediated the relationship. Conclusion A history of abuse is associated with greater migraine‐related disability. Future studies should determine if identification and management of the psychological and physical sequelae of abuse reduce migraine burden.

Purpose of Review Headache disorders are highly prevalent worldwide. Rapidly advancing capabilities in artificial intelligence (AI) have expanded headache-related research with the potential to solve unmet needs in the headache field. We provide an overview of AI in headache research in this article. Recent Findings We briefly introduce machine learning models and commonly used evaluation metrics. We then review studies that have utilized AI in the field to advance diagnostic accuracy and classification, predict treatment responses, gather insights from various data sources, and forecast migraine attacks. Furthermore, given the emergence of ChatGPT, a type of large language model (LLM), and the popularity it has gained, we also discuss how LLMs could be used to advance the field. Finally, we discuss the potential pitfalls, bias, and future directions of employing AI in headache medicine. Summary Many recent studies on headache medicine incorporated machine learning, generative AI and LLMs. A comprehensive understanding of potential pitfalls and biases is crucial to using these novel techniques with minimum harm. When used appropriately, AI has the potential to revolutionize headache medicine.