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Abstract Background Post-traumatic headache (PTH) is a common acute and persistent symptom following mild traumatic brain injury (mTBI). Symptoms of cutaneous allodynia and presence of nociceptive sensitization might be associated with acute PTH and its persistence. The objectives of this study were to compare allodynia symptoms and cutaneous heat pain thresholds amongst males and females with acute PTH to healthy controls (HC) and determine if pain thresholds and allodynia symptoms are associated with PTH outcomes. Methods This prospective longitudinal study enrolled 139 adults with acute PTH attributed to mTBI as defined by the International Classification of Headache Disorders and 95 HC. All PTH participants completed a baseline research visit near PTH onset and a follow-up visit three to four months later. All PTH participants and a subset of HC completed the Allodynia Symptom Checklist (ASC-12) at each research visit. A different subset of the participants underwent quantitative sensory testing (QST) during baseline, 4-week, and 16-week research visits to quantify cutaneous heat pain thresholds at the forehead and forearms. Data from daily headache diaries were used to determine longitudinal PTH improvement versus non-improvement at three months. ASC-12 score and pain threshold comparisons were made between PTH and HC groups, PTH improved versus non-improved cohorts, and between PTH males and females. Results Participants with PTH had an average age of 42.6 years and 64.0% were female. HC had an average age of 40.0 years and 65.3% were female. At the first visit, PTH participant ASC-12 scores averaged 3.6 versus 0.1 amongst HC, p < 0.001. 44.8% of PTH participants had headache improvement at 3 months. ASC-12 scores were higher in the PTH non-improved versus improved group at baseline (4.0 versus 2.4, p = 0.038) and 3-month follow-up (3.4 versus 1.9, p = 0.012). ASC-12 scores were higher in females than males at baseline (4.7 versus 1.6, p < 0.001) and 3-months (3.9 versus 1.2, p < 0.001). Cutaneous heat pain thresholds at the forehead and forearm did not differ between any group. Conclusions PTH attributed to mTBI is associated with symptoms of cutaneous allodynia. Greater allodynia symptoms are present in females with PTH compared to males and may be associated with PTH non-improvement.

AbstractBackgroundPrevious publications have described the presence and severity of insomnia amongst those with persistent post‐traumatic headache (PPTH), but there has been a paucity of studies investigating insomnia during the acute phase of post‐traumatic headache (APTH). The primary study objective was to investigate insomnia symptoms and their severity in individuals with APTH due to mild traumatic brain injury (mTBI), and the secondary objective was to assess how psychological factors, sensory hypersensitivities, headache burden, and cognitive performance might associate with insomnia.MethodsIn this clinic‐based, cross‐sectional study, 82 individuals with APTH due to mTBI and 82 healthy controls (HC) were included. Participants completed the Insomnia Severity Index (ISI), Trail Making Test (TMT A and B), 12‐item Allodynia Symptom Checklist (ASC‐12), State–Trait Anxiety Inventory, Photosensitivity Assessment Questionnaire (PAQ), Beck Depression Inventory (BDI), Hyperacusis Questionnaire (HQ), Rey Auditory Verbal Learning Test (RAVLT), and a detailed headache characteristics questionnaire within 59 days of their mTBI to assess insomnia severity, psychological features, sensory hypersensitivities, headache symptoms, and cognitive performance. The questionnaire and test results were compared between APTH and HC groups. Within the APTH group, ISI scores were correlated with the other assessments to determine underlying clinical relationships.ResultsParticipants with APTH, evaluated an average of 27.2 days after their mTBI, had significantly higher ISI scores (median [interquartile range, IQR] score 12 [6–17]) compared to HC (0 [1.3–6], p < 0.001). Twenty‐four (29%) individuals with APTH had ISI scores categorized as moderate clinical insomnia compared to two (2%) in the HC group. Additionally, seven (9%) in the APTH group and none of the HC had ISI scores indicative of severe clinical insomnia. Those with APTH significantly differed from the HC on the BDI (median [IQR] score APTH: 9 [5–16.8] vs. HC: 2 [0–4]; p < 0.001), state anxiety (median [IQR] score APTH: 36.5 [26–45] vs. HC: 23 [20–27.5]; p < 0.001), trait anxiety (median [IQR] score APTH: 38 [27.3–46.8] vs. HC: 26 [23–30]; p < 0.001), photosensitivity (median [IQR] PAQ score APTH: 2 [1–4] vs. HC: 0 [0–1]; p < 0.001), allodynia (median [IQR] ASC‐12 score APTH: 2 [0–5.8] vs. HC: 0 [0–0], p < 0.001), hyperacusis (median [IQR] HQ score APTH: 15.5 [7–23] vs. HC: 4 [2.3–7], p < 0.001), and RAVLT‐delayed recall (median [IQR] score APTH: −0.8 [−1.5 to 0] vs. HC: −0.1 [−0.9 to 0.7], p = 0.019). For participants with APTH, the ISI was most significantly associated with the BDI (standardized regression coefficient (RC) [95% confidence interval, CI] 0.68 [0.52–0.84], p < 0.001), followed by state anxiety (RC [95% CI] 0.60 [0.42–0.78], p < 0.001), trait anxiety (RC [95% CI] 0.56 [0.37–0.74], p < 0.001), headache severity (RC [95% CI] 0.40 [0.19–0.60], p < 0.001), HQ (RC [95% CI] 0.36 [0.16–0.57], p = 0.001), and PAQ (RC [95% CI] 0.35 [0.15–0.56], p = 0.001).ConclusionsIndividuals with APTH have more symptoms of insomnia compared to HC with 38% of individuals with APTH experiencing moderate or severe insomnia symptoms. Insomnia symptoms may be associated with more severe depression, anxiety, photophobia, hyperacusis, headache severity, and cognitive impairment. To address the overall well‐being of patients with APTH, clinicians should screen for and appropriately manage insomnia along with other symptoms following mTBI.

Abstract Objectives/Background Post‐traumatic headache (PTH) is a common symptom following mild traumatic brain injury (mTBI). Currently, there is no identified way to accurately predict if, when, and at what pace a person will have PTH improvement. In our prior studies, we focused on predicting headache improvement at 3 months post‐mTBI. However, that approach may overlook individual differences in how headaches evolve over time. This study aimed to identify individual subgroups based on their headache trajectories and to develop machine‐learning (ML) models for early prediction of headache evolution. Methods Participants with acute PTH completed a daily electronic headache diary over 3 months, recording their headache‐related symptoms. Tensor decomposition was utilized to extract latent factors underlying the time‐varying symptoms. We applied clustering techniques on the latent factors to identify patient subgroups with varying headache improvement trajectory. Next, we developed an ML method to classify each individual into a headache trajectory subgroup as early as possible within the 3‐month interval. Results Seventy‐three individuals with acute PTH (mean age = 44.8 years, SD = 14.0; 50 females/23 males) were enrolled between 0 and 59 days post‐mTBI. Data from 54 individuals were used as the training cohort for model training, and 19 individuals were used as the test cohort for model evaluation. Tensor decomposition extracted two latent factors: one factor representing the overall state of PTH severity and disability and the other representing the improvement state of these symptoms over 3 months. Clustering identified four patient subgroups with distinct headache evolution trajectories: (1) severe symptoms without improvement, (2) severe symptoms with mild improvement, (3) milder symptoms with substantial improvement, and (4) mildest symptoms with mild improvement. The proposed ML model achieved 0.80 cross‐validation accuracy in classifying individuals with PTH into subgroups for the training cohort and 0.84 accuracy for the test cohort. Notably, the model required only the first 2 weeks of headache data to accurately identify the subgroup with the mildest headaches, 3 additional weeks to identify the subgroup with the most severe headaches and no improvement in 3 months, and 2 additional weeks to distinguish the remaining subgroups. Conclusion This study identified subgroups of individuals with acute PTH with distinct headache improvement trajectories. The proposed ML method accurately classified individuals into these subgroups using the minimally necessary early headache data for each person, including detecting the subgroup with the mildest headaches at 2 weeks. This approach could offer an estimated forecast of headache burden over time and could assist clinicians with determining treatment needs and eligibility for PTH clinical trials.

Background Headache is a common symptom following mild traumatic brain injury (mTBI). Post-traumatic headache (PTH), a secondary headache disorder that develops after mTBI, often persists for months or years. To identify potential recovery mechanisms and prognostic biomarkers, the present study investigated whether longitudinal changes in pain-induced brain activation differ between healthy controls (HC) and PTH participants showing headache improvement and those without improvement. Methods Thirty-three participants who met International Classification of Headache Disorders, 3rd edition, criteria for acute PTH within 59 days post-mTBI and 33 HC participants were included with no significant differences in demographics. All participants underwent functional magnetic resonance imaging scans at baseline, four weeks, and 16 weeks post-enrollment using a thermal stimulation paradigm with noxious and non-painful heat stimuli. ‘Painful vs. Non-Painful Heat’ contrasts were generated using SPM12. PTH improvement was assessed at three months post-enrollment via electronic headache diaries. Two-sample t -tests compared the brain activation between HC and PTH at baseline. Linear mixed-effects models examined longitudinal changes for HC, PTH improvement and non-improvement groups across visits. Generalized linear models compared these groups within visits. Results Baseline analysis revealed several regions with significantly higher activation in acute PTH compared to HC, including bilateral postcentral gyrus, right superior temporal gyrus, right middle temporal gyrus, left inferior parietal gyrus, right superior parietal gyrus, left ventral striatum, left olfactory cortex, left gyrus rectus, and left middle occipital gyrus. Over time, the PTH improvement group demonstrated progressive normalization across all identified brain regions, whereas the non-improvement group showed only partial normalization in left ventral striatum, left olfactory cortex, and left gyrus rectus. Sustained elevated activation in specific regions distinguished PTH participants without headache improvement from those with headache improvement, suggesting potential biomarkers for persistent PTH. Conclusions Our findings demonstrate significantly altered pain-induced brain activations in participants with acute PTH compared to HC. Longitudinal analysis revealed distinct recovery trajectories: progressive normalization in the improvement group versus persistent alterations in the non-improvement group. These neuroimaging patterns may serve as biomarkers for identifying individuals at risk for persistent PTH, with implications for early intervention and personalized treatment approaches.

Background: Prior studies indicate a relationship between migraine and MACE. Here we assess whether brain white matter hyperintensities (WMH), which are a common imaging finding in patient with migraine, contribute to the risk of MACE above demographics and common risk factors for MACE. Methods: 60,454 patients, ages 18-89, with a ICD-9 or ICD-10 migraine diagnosis code in 2010 or later were identified from the Mayo Clinic electronic health record. Only patients who were seen for migraine in the Neurology Department were included. Patients with a migraine diagnosis who did not have MACE were included only if they had at least two visits at Mayo Clinic during five years. Only patients with sex and race information were included. The final cohort included 577 migraine patients with and 598 migraine patients without MACE. Presence of WMH was determined from radiology notes. Individuals without a brain MRI were assumed not to have WMH. A logistic regression model that included sex, race, known lifetime MACE risk factors (atrial fibrillation, diabetes, hypertension, hyperlipidemia, tobacco use) and WMH as independent variables to predict MACE outcome was fit. Results: Significant factors that increased the risk of MACE in individuals with migraine included being Black or African American (adjusted OR: 2.9, 95% CI: 1.24-6.82, p = 0.014), presence of atrial fibrillation (adj. OR: 1.63, 95% CI: 1.23-2.17, p < 0.001), diabetes (adj. OR: 1.34, 95% CI: 1.02-1.75, p = 0.036), hypertension (adj. OR: 1.9, 95% CI: 1.39-2.6, p < 0.001), tobacco use (adj. OR: 1.66, 95% CI: 1.29-2.14, p < 0.001), and the presence of WMH (adj. OR: 1.43, 95% CI: 1.1-1.87, p = 0.008). Hyperlipidemia showed a marginal association (adj. OR: 1.34, 95% CI: 0.99-1.81, p = 0.061), while other variables such as sex and other racial/ethnic groups did not significantly alter the risk of MACE outcome. Discussion: Results indicate that African American race and presence of WMH in addition to common comorbidities independently increase the risk of MACE outcome.

Introduction: Prior data suggest that migraine increases the risk of stroke. However, there is a lack of understanding whether specific migraine features like white matter hyperintensities (WMH) commonly found on brain magnetic resonance (MR) imaging in migraine patients and migraine medications might contribute to Major Adverse Cardiovascular Event (MACE) outcomes including ischemic and hemorrhagic stroke. We assess MACE risk estimation model stratified by event types using Mayo Clinic Electronic Health Record (EHR) data. Methods: Case and control sets, based on MACE occurrence among migraine patients at Mayo Clinic Neurology Department, were propensity matched on age, sex, and MACE risk-factors (atrial fibrillation, diabetes, hyperlipidemia, hypertension, tobacco use) to capture migraine-specific predictors ( Figure 1) . Migraine medications were categorized into 9 classes; Triptans, Ditans, Gepants, Ergots, calcitonin gen-related peptide targeting medications (CGRPs), Beta-blockers, Topiramate, Tricyclic antidepressants, serotonin-norepinephrine reuptake inhibitors (SNRIs). Demographics (age and sex), migraine characteristics (chronic migraine, migraine with aura), and presence of WMH on MR imaging were curated. To model non-linear relationship between predictors, two comparative Random Forest (RF) models were evaluated for MACE prediction; i) Model-1: using demographics, migraine characteristics and WMH, ii) Model-2: medications, demographics, migraine characteristics and WMH ( Table 1) . Results: Model-2 outperformed Model-1 indicating combined interaction between medications, WMH, and migraine characteristics for estimating MACE risk. Model-2 achieved AUC of 0.68 for overall MACE, but showed higher performance for ischemic stroke (AUC 0.76). We visualized the effects of input features on ischemic stroke risk for four individual patients using SHAP force plots ( Figure 2 ). The model often considered use of betablockers, chronic migraine, and presence of WMH as factors for increasing ischemic stroke risk. Discussion: Our experiments show that medications, migraine features, WMH and demographics are more effective estimators for ischemic risk stroke risk compared to overall MACE risk, indicating stronger predictive correlation between ischemic stroke and migraine related clinical features. Interestingly, the model inferred the clinical practice pattern of not prescribing Triptans (TRIPTAN=0) to the patients with high risk of stroke from the data itself.

Introduction: We assess the risk of stroke in individuals with and without a migraine diagnosis, adjusting for demographic variables and stroke risk factors using participant information from the All of Us database. Method: Diagnoses of migraine, stroke and comorbidities were identified using ICD9 and ICD10 codes. A multivariable logistic regression analysis was performed to assess the association between migraine and risk of stroke adjusting for comorbidities including hypertension, atrial fibrillation, hyperlipidemia, diabetes, tobacco use, depression and demographics (age, sex at birth, race and ethnicity). To compare the prevalence of stroke between individuals with and without migraine, odds ratios using a 95% confidence interval (CI) were calculated. Results: Within the All of Us database, 31,444 individuals received a migraine diagnosis (female=25,374/81%, male=5,391/17%, other=679/2%; mean (std) age=54.9 (15.6)) and 379,283 did not have a migraine diagnosis (female=222,104/59%, male=149,182/39%, other=7,997/2%; mean (SD) age=55.9 (17.2)), see Figure 1 for detailed demographics. The migraine cohort had a greater proportion of women (81% vs 59%, p<0.001), a higher proportion of white individuals (61% vs. 55%, p<0.001) fewer African American individuals (15% vs. 19%, p<0.001) and a lower proportion of individuals who were 61 or older (39% vs 45%, p<0.001) compared to the non-migraine cohort. Compared to the non-migraine cohort, individuals with migraine had significantly higher rates of comorbidities, including depression (13% vs. 4%, p<0.001), diabetes (16% vs. 7%), tobacco use (23% vs. 9%, p<0.001), hyperlipidemia (48% vs. 22%, p<0.001), hypertension (57% vs. 27%, p<0.001), and atrial fibrillation (7% vs. 4%, p<0.001). A multivariate regression model adjusted for differences between cohort demographics and comorbidities found that individuals with migraine had 2.14 higher odds of overall stroke compared to the non-migraine cohort (OR 2.14, 95% CI [ 2.04, 2.24]. Discussion: After adjusting for cohort imbalances in demographics and comorbidities, individuals with migraine had a 2.14 times higher risk of stroke compared to those without a migraine diagnosis. Our analysis, using data from the All of Us database, confirms previous findings and suggests that while comorbidities are more prevalent in those with migraine, they do not fully account for the increased stroke risk.