Schwedt, Todd J.

Objectives The objective of this longitudinal study was to determine whether brain iron accumulation, measured using magnetic resonance imaging magnetic transverse relaxation rates (T2*), is associated with response to erenumab for the treatment of migraine. Methods Participants (n = 28) with migraine, diagnosed using international classification of headache disorders 3rd edition criteria, were eligible if they had six to 25 migraine days during a four-week headache diary run-in phase. Participants received two treatments with 140 mg erenumab, one immediately following the pre-treatment run-in phase and a second treatment four weeks later. T2* data were collected immediately following the pre-treatment phase, and at two weeks and eight weeks following the first erenumab treatment. Patients were classified as erenumab responders if their migraine-day frequency at five-to-eight weeks post-initial treatment was reduced by at least 50% compared to the pre-treatment run-in phase. A longitudinal Sandwich estimator approach was used to compare longitudinal group differences (responders vs non-responders) in T2* values, associated with iron accumulation. Group visit effects were calculated with a significance threshold of p = 0.005 and cluster forming threshold of 250 voxels. T2* values of 19 healthy controls were used for a reference. The average of each significant region was compared between groups and visits with Bonferroni corrections for multiple comparisons with significance defined as p < 0.05. Results Pre- and post-treatment longitudinal imaging data were available from 28 participants with migraine for a total of 79 quantitative T2* images. Average subject age was 42 ± 13 years (25 female, three male). Of the 28 subjects studied, 53.6% were erenumab responders. Comparing longitudinal T2* between erenumab responders vs non-responders yielded two comparisons which survived the significance threshold of p < 0.05 after correction for multiple comparisons: the difference at eight weeks between the erenumab-responders and non-responders in the periaqueductal gray (mean ± standard error; responders 43 ± 1 ms vs non-responders 32.5 ± 1 ms, p = 0.002) and the anterior cingulate cortex (mean ± standard error; responders 50 ± 1 ms vs non-responders 40 ± 1 ms, p = 0.01). Conclusions Erenumab response is associated with higher T2* in the periaqueductal gray and anterior cingulate cortex, regions that participate in pain processing and modulation. T2* differences between erenumab responders vs non-responders, a measure of brain iron accumulation, are seen at eight weeks post-treatment. Less iron accumulation in the periaqueductal gray and anterior cingulate cortex might play a role in the therapeutic mechanisms of migraine reduction associated with erenumab.

Abstract Objective To assess preferences among adults with migraine for differentiating attributes of injected or infused preventive treatment options and evaluate their importance in determining a treatment choice. Background Adults with migraine and health‐care providers consider many factors when making treatment decisions. Injected or infused preventive migraine treatment options differ in several attributes, including mode of administration and dosing frequency, which may be preferentially selected or avoided by patients. Understanding a patient's preference is important for clinicians as they advise on various treatment options. Methods A total of 604 US adults diagnosed with migraine participated in an online survey that captured information on demographics, migraine history, and treatment preferences. A discrete choice experiment (DCE) was used to evaluate participants’ preferences for specific attributes of injected/infused preventive migraine therapies. The DCE data were utilized to estimate attribute importance (expressed as a percentage) and identify subgroups that had different distributions of preferences. Results In the overall migraine population, mode of administration (28.8%), durability of effectiveness (27.0%), and speed of onset (25.5%) had the highest relative importance, whereas administration setting (9.9%) and dosing frequency (8.8%) had the lowest. Four distinct subgroups were identified: Group 1 ( n = 128) preferred self‐injection administration and durability of effectiveness; Group 2 ( n = 189) expressed aversion to cranial injections; Group 3 ( n = 158) prioritized rapid speed of onset; and Group 4 ( n = 129) favored health‐care provider administration and durability of effectiveness. Conclusions Speed of onset, durability of effectiveness, and mode of administration are key moderators of treatment preference among US adults with migraine. Certain segments of the migraine population prioritize specific treatment attributes over others, with intravenous infusion not considered a barrier in three of four identified segments. Clinicians can best help their patients find the right medication if they understand which medication attributes are most and least important to them.

Abstract Objectives/Background Post‐traumatic headache (PTH) is a common symptom after mild traumatic brain injury (mTBI). Although there have been several studies that have used clinical features of PTH to attempt to predict headache recovery, currently no accurate methods exist for predicting individuals’ improvement from acute PTH. This study investigated the utility of clinical questionnaires for predicting (i) headache improvement at 3 and 6 months, and (ii) headache trajectories over the first 3 months. Methods We conducted a clinic‐based observational longitudinal study of patients with acute PTH who completed a battery of clinical questionnaires within 0–59 days post‐mTBI. The battery included headache history, symptom evaluation, cognitive tests, psychological tests, and scales assessing photosensitivity, hyperacusis, insomnia, cutaneous allodynia, and substance use. Each participant completed a web‐based headache diary, which was used to determine headache improvement. Results Thirty‐seven participants with acute PTH (mean age = 42.7, standard deviation [SD] = 12.0; 25 females/12 males) completed questionnaires at an average of 21.7 (SD = 13.1) days post‐mTBI. The classification of headache improvement or non‐improvement at 3 and 6 months achieved cross‐validation area under the curve (AUC) of 0.72 (95% confidence interval [CI] 0.55 to 0.89) and 0.84 (95% CI 0.66 to 1.00). Sub‐models trained using only the top five features still achieved 0.72 (95% CI 0.55 to 0.90) and 0.77 (95% CI 0.52 to 1.00) AUC. The top five contributing features were from three questionnaires: Pain Catastrophizing Scale total score and helplessness sub‐domain score; Sports Concussion Assessment Tool Symptom Evaluation total score and number of symptoms; and the State‐Trait Anxiety Inventory score. The functional regression model achieved for modeling headache trajectory over the first 3 months. Conclusion Questionnaires completed following mTBI have good utility for predicting headache improvement at 3 and 6 months in the future as well as the evolving headache trajectory. Reducing the battery to only three questionnaires, which assess post‐concussive symptom load and biopsychosocialecologic factors, was helpful to determine a reasonable prediction accuracy for headache improvement.

Abstract Objective To explore alterations in thalamic subfield volume and iron accumulation in individuals with post‐traumatic headache (PTH) relative to healthy controls. Background The thalamus plays a pivotal role in the pathomechanism of pain and headache, yet the role of the thalamus in PTH attributed to mild traumatic brain injury (mTBI) remains unclear. Methods A total of 107 participants underwent multimodal T1‐weighted and T2* brain magnetic resonance imaging. Using a clinic‐based observational study, thalamic subfield volume and thalamic iron accumulation were explored in 52 individuals with acute PTH (mean age = 41.3; standard deviation [SD] = 13.5), imaged on average 24 days post mTBI, and compared to 55 healthy controls (mean age = 38.3; SD = 11.7) without history of mTBI or migraine. Symptoms of mTBI and headache characteristics were assessed at baseline (0–59 days post mTBI) ( n = 52) and 3 months later ( n = 46) using the Symptom Evaluation of the Sports Concussion Assessment Tool (SCAT‐5) and a detailed headache history questionnaire. Results Relative to controls, individuals with acute PTH had significantly less volume in the lateral geniculate nucleus (LGN) (mean volume: PTH = 254.1, SD = 43.4 vs. controls = 278.2, SD = 39.8; p = 0.003) as well as more iron deposition in the left LGN (PTH: T2* signal = 38.6, SD = 6.5 vs. controls: T2* signal = 45.3, SD = 2.3; p = 0.048). Correlations in individuals with PTH revealed a positive relationship between left LGN T2* iron deposition and SCAT‐5 symptom severity score at baseline ( r = −0.29, p = 0.019) and maximum headache intensity at the 3‐month follow‐up ( r = −0.47, p = 0.002). Conclusion Relative to healthy controls, individuals with acute PTH had less volume and higher iron deposition in the left LGN. Higher iron deposition in the left LGN might reflect mTBI severity and poor headache recovery.

AbstractData-driven machine-learning methods on neuroimaging (e.g. MRI) are of great interest for the investigation and classification of neurological diseases. However, traditional machine learning requires domain knowledge to delineate the brain regions first, followed by feature extraction from the regions. Compared with this semi-automated approach, recently developed deep learning methods have advantages since they do not require such prior knowledge; instead, deep learning methods can automatically find features that differentiate MRIs from different cohorts. In the present study, we developed a deep learning-based classification pipeline distinguishing brain MRIs of individuals with one of three types of headaches [migraine (n = 95), acute post-traumatic headache (n = 48) and persistent post-traumatic headache (n = 49)] from those of healthy controls (n = 532) and identified the brain regions that most contributed to each classification task. Our pipeline included: (i) data preprocessing; (ii) binary classification of healthy controls versus headache type using a 3D ResNet-18; and (iii) biomarker extraction from the trained 3D ResNet-18. During the classification at the second step of our pipeline, we resolved two common issues in deep learning methods, limited training data and imbalanced samples from different categories, by incorporating a large public data set and resampling among the headache cohorts. Our method achieved the following classification accuracies when tested on independent test sets: (i) migraine versus healthy controls—75% accuracy, 66.7% sensitivity and 83.3% specificity; (2) acute post-traumatic headache versus healthy controls—75% accuracy, 66.7% sensitivity and 83.3% specificity; and (3) persistent post-traumatic headache versus healthy controls—91.7% accuracy, 100% sensitivity and 83.3% specificity. The most significant biomarkers identified by the classifier for migraine were caudate, caudal anterior cingulate, superior frontal, thalamus and ventral diencephalon. For acute post-traumatic headache, lateral occipital, cuneus, lingual, pericalcarine and superior parietal regions were identified as most significant biomarkers. Finally, for persistent post-traumatic headache, the most significant biomarkers were cerebellum, middle temporal, inferior temporal, inferior parietal and superior parietal. In conclusion, our study shows that the deep learning methods can automatically detect aberrations in the brain regions associated with different headache types. It does not require any human knowledge as input which significantly reduces human effort. It uncovers the great potential of deep learning methods for classification and automatic extraction of brain imaging–based biomarkers for these headache types.

Abstract Objective To compare the artificial intelligence‐enabled electrocardiogram (AI‐ECG) atrial fibrillation (AF) prediction model output in patients with migraine with aura (MwA) and migraine without aura (MwoA). Background MwA is associated with an approximately twofold risk of ischemic stroke. Longitudinal cohort studies showed that patients with MwA have a higher incidence of developing AF compared to those with MwoA. The Mayo Clinic Cardiology team developed an AI‐ECG algorithm that calculates the probability of concurrent paroxysmal or impending AF in ECGs with normal sinus rhythm (NSR). Methods Adult patients with an MwA or MwoA diagnosis and at least one NSR ECG within the past 20 years at Mayo Clinic were identified. Patients with an ECG‐confirmed diagnosis of AF were excluded. For each patient, the ECG with the highest AF prediction model output was used as the index ECG. Comparisons between MwA and MwoA were conducted in the overall group (including men and women of all ages), women only, and men only in each age range (18 to <35, 35 to <55, 55 to <75, ≥75 years), and adjusted for age, sex, and six common vascular comorbidities that increase risk for AF. Results The final analysis of our cross‐sectional study included 40,002 patients (17,840 with MwA, 22,162 with MwoA). The mean (SD) age at the index ECG was 48.2 (16.0) years for MwA and 45.9 (15.0) years for MwoA ( p < 0.001). The AF prediction model output was significantly higher in the MwA group compared to MwoA (mean [SD] 7.3% [15.0%] vs. 5.6% [12.4%], mean difference [95% CI] 1.7% [1.5%, 2.0%], p < 0.001). After adjusting for vascular comorbidities, the difference between MwA and MwoA remained significant in the overall group (least square means of difference [95% CI] 0.7% [0.4%, 0.9%], p < 0.001), 18 to <35 (0.4% [0.1%, 0.7%], p = 0.022), and 35 to <55 (0.5% [0.2%, 0.8%], p < 0.001), women of all ages (0.6% [0.3%, 0.8%], p < 0.001), men of all ages (1.0% [0.4%, 1.6%], p = 0.002), women 35 to <55 (0.6% [0.3%, 0.9%], p < 0.001), and men 18 to <35 (1.2% [0.3%, 2.1%], p = 0.008). Conclusions Utilizing a novel AI‐ECG algorithm on a large group of patients, we demonstrated that patients with MwA have a significantly higher AF prediction model output, implying a higher probability of concurrent paroxysmal or impending AF, compared to MwoA in both women and men. Our results suggest that MwA is an independent risk factor for AF, especially in patients <55 years old, and that AF‐mediated cardioembolism may play a role in the migraine–stroke association for some patients.

Abstract Objective The purpose of this study was to characterize cutaneous heat and light‐induced pain thresholds in people with post‐traumatic headache (PTH) compared with healthy controls (HCs). Background Photophobia and allodynia are common in PTH, and there is emerging evidence to support multimodal sensory dysfunction. Methods In this age‐ and sex‐matched cohort study, individuals with PTH ( n = 20) and HCs ( n = 20), aged 18–65 years, were recruited from an institutional database of research volunteers, from the concussion clinic, and via the use of approved flyers posted on the Mayo Clinic Campus in Scottsdale, Arizona. Participants were assessed using the Allodynia Symptom Checklist (ASC‐12), Photosensitivity Assessment Questionnaire (PAQ), State Trait Anxiety Inventory (STAI), and Beck Depression Inventory (BDI). Quantitative sensory testing quantified heat pain thresholds. A light stimulation device quantified light‐induced pain thresholds. Subsequently, heat pain thresholds were obtained immediately, 10, and 40 min after a bright light stressor. Results The mean photophobia symptom severity score, based on the PAQ, was higher in participants with PTH compared with HCs, mean 0.62 (SD = 0.25) versus mean 0.24 (SD = 0.24), p < 0.001. Light‐induced pain thresholds were lower in participants with PTH (median = 90.5 lux and quartiles = 17.8 to 378.5) compared with HCs (median = 863.5 lux and quartiles = 519.9 to 4906.5) and were independent from BDI and STAI ( p < 0.001). Allodynia scores did not differ between participants with PTH and HCs after adjusting for BDI and STAI scores. Baseline forehead heat pain thresholds were not different, participants with PTH mean 41.9°C (SD = 0.89) versus HCs mean 44.3°C (SD = 0.89), p = 0.061; however, forearm heat pain thresholds were lower in participants with PTH compared with HCs, mean 40.8°C (SD = 0.80) versus mean 44.4°C (SD = 0.80), p = 0.002. The forehead heat pain threshold change from baseline post bright light stressor in participants with PTH versus HCs was different immediately (mean −1.2 (SD = 0.53), p = 0.025), 10 min (mean −1.8 (SD = 0.74), p = 0.015), and 40 min (mean −1.8 (SD = 0.88), p = 0.047). The forearm heat pain threshold change immediately post bright light stressor in participants with PTH versus HCs was different, mean −1.9°C (SD = 0.58), p = 0.001, however, not different at 10 and 40 min. Conclusions Photophobia is higher and light‐induced pain thresholds are lower in participants with PTH. Exposure to a light stressor reduced heat pain thresholds in participants with PTH immediately post bright light stressor, but not in HCs. This study provides evidence for multimodal sensory dysfunction in people with PTH.