Chong, Catherine D.

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 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.

AbstractPosttraumatic headache (PTH) is the most common symptom following mild traumatic brain injury (mTBI) (also known as concussion). Migraine and PTH have similar phenotypes, and a migraine-like phenotype is common in PTH. The similarities between both headache types are intriguing and challenge a better understanding of the pathophysiological commonalities involved in migraine and PTH due to mTBI. Here, we review the PTH resting-state functional connectivity literature and compare it to migraine to assess overlap and differences in brain network function between both headache types. Migraine and PTH due to mTBI have overlapping and disease-specific widespread alterations of static and dynamic functional networks involved in pain processing as well as dysfunctional network connections between frontal regions and areas of pain modulation and pain inhibition. Although the PTH functional network literature is still limited, there is some evidence that dysregulation of the top-down pain control system underlies both migraine and PTH. However, disease-specific differences in the functional circuitry are observed as well, which may reflect unique differences in brain architecture and pathophysiology underlying both headache disorders.

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.

Objectives Although iron accumulation in pain-processing brain regions has been associated with repeated migraine attacks, brain structural changes associated with post-traumatic headache have yet to be elucidated. To determine whether iron accumulation is associated with acute post-traumatic headache, magnetic resonance transverse relaxation rates (T2*) associated with iron accumulation were investigated between individuals with acute post-traumatic headache attributed to mild traumatic brain injury and healthy controls. Methods Twenty individuals with acute post-traumatic headache and 20 age-matched healthy controls underwent 3T brain magnetic resonance imaging including quantitative T2* maps. T2* differences between individuals with post-traumatic headache versus healthy controls were compared using age-matched paired t-tests. Associations of T2* values with headache frequency and number of mild traumatic brain injuries were investigated using multiple linear regression in individuals with post-traumatic headache. Significance was determined using uncorrected p-value and cluster size threshold. Results Individuals with post-traumatic headache had lower T2* values compared to healthy controls in cortical (bilateral frontal, bilateral anterior and posterior cingulate, right postcentral, bilateral temporal, right supramarginal, right rolandic, left insula, left occipital, right parahippocampal), subcortical (left putamen, bilateral hippocampal) and brainstem regions (pons). Within post-traumatic headache subjects, multiple linear regression showed a negative association between T2* in the right inferior parietal/supramarginal regions and number of mild traumatic brain injuries and a negative association between T2* in bilateral cingulate, bilateral precuneus, bilateral supplementary motor areas, bilateral insula, right middle temporal and right lingual areas and headache frequency. Conclusions Acute post-traumatic headache is associated with iron accumulation in multiple brain regions. Correlations with headache frequency and number of lifetime mild traumatic brain injuries suggest that iron accumulation is part of the pathophysiology or a marker of mild traumatic brain injury and post-traumatic headache.

Abstract BackgroundThe presence of white matter hyperintensities (WMH) in migraine is well-documented, but the location of brain WMH in patients with migraine are insufficiently researched. Although recent semi-automatic software packages have been developed for calculating WMH, visual inspection remains the gold standard for measuring WMH. The goal of this study was to assess WMH in patients with migraine using a modified version of the Scheltens visual rating scale, a semiquantitative scale for categorizing WMH in the following brain regions: periventricular, lobar (frontal, temporal, parietal, occipital), basal ganglia, and infratentorial regions.Methods263 patients with migraine (31 male/232 female; mean age: 48.0) who were enrolled in the American Registry for Migraine Research from Mayo Clinic with a diagnosis of episodic (n = 46; 17.5%) or chronic migraine (n = 217; 82.5%) and who had brain magnetic resonance imaging were included in this study. Those with imaging evidence for gross anatomical abnormalities other than WMH were excluded. WMH were identified on axial T2 and FLAIR sequences by a board certified neuroradiologist. WMH were characterized via manual inspection and categorized according to the scale’s criteria.Results95 patients (36.1 %: mean age: 41.8) had no WMH on axial T2 and FLAIR imaging and 168 patients (63.9%, mean age: 51.4) had WMH. Of those with WMH, 94.1% (n = 158) had lobar hyperintensities (frontal: 148/158, 93.7%; parietal: 57/158, 36.1%; temporal: 35/158, 22.1%; occipital: 9/158, 5.7%), 13/168, 7.7% had basal ganglia WMH, 49/168, 29.1% had periventricular WMH, and 17/168, 10.1% had infratentorial WMH. 101/168 patients (60.1%) had bilateral WMH and 67/168 (39.9%) had unilateral WMH (34 right hemisphere /33 left hemisphere). 30.0% of patients with WMH did not have WMH reported in their clinical radiology reports.DiscussionNearly 2/3 of patients with migraine had WMH. They were most common in the lobar regions, specifically in the frontal lobe. The categorization of WMH in migraine using the modified Scheltens visual rating scale could help in future studies to clarify the relationship between WMH and headache features and might be a useful method for developing classifiers that differentiate between migraine-specific WMH and other causes of WMH.

Abstract Background/Objective: Changes in speech can be detected objectively before and during migraine attacks. The goal of this study was to interrogate whether speech changes can be detected in subjects with post-traumatic headache (PTH) attributed to mild traumatic brain injury (mTBI) and whether there are within-subject changes in speech during headaches compared to the headache-free state. Methods: Using a series of speech elicitation tasks uploaded via a mobile application, PTH subjects and healthy controls (HC) provided speech samples once every three days, over a period of 12 weeks. The following speech parameters were assessed: vowel space area, vowel articulation precision, consonant articulation precision, average pitch, pitch variance, speaking rate and pause rate. Speech samples of subjects with PTH were compared to HC. To assess speech changes associated with PTH, speech samples of subjects during headache were compared to speech samples when subjects were headache-free. All analyses were conducted using a mixed-effect model design.Results: Longitudinal speech samples were collected from nineteen subjects with PTH (mean age=42.5, SD=13.7) who were an average of 14 days (SD=32.2) from their mTBI at the time of enrollment and thirty-one HC (mean age=38.7, SD=12.5). Regardless of headache presence or absence, PTH subjects had longer pause rates and reductions in vowel and consonant articulation precision relative to HC. On days when speech was collected during a headache, there were longer pause rates, slower sentence speaking rates and less precise consonant articulation compared to the speech production of HC. During headache, PTH subjects had slower speaking rates and altered vowel articulation compared to when they were headache-free. Conclusions: Compared to HC, subjects with acute PTH demonstrate altered speech as measured by objective features of speech production. For individuals with PTH, speech was altered during headache vs. when they were headache-free, suggesting that speech alterations were related to PTH and not solely due to the underlying mTBI.