Application of Artificial Intelligence in the Headache Field.

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. 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.

Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR Images.

Harnessing the power of deep neural networks in the medical imaging domain is challenging due to the difficulties in acquiring large annotated datasets, especially for rare diseases, which involve high costs, time, and effort for annotation. Unsupervised disease detection methods, such as anomaly detection, can significantly reduce human effort in these scenarios. While anomaly detection typically focuses on learning from images of healthy subjects only, real-world situations often present unannotated datasets with a mixture of healthy and diseased subjects. Recent studies have demonstrated that utilizing such unannotated images can improve unsupervised disease and anomaly detection. However, these methods do not utilize knowledge specific to registered neuroimages, resulting in a subpar performance in neurologic disease detection. To address this limitation, we propose Brainomaly, a GAN-based image-to-image translation method specifically designed for neurologic disease detection. Brainomaly not only offers tailored image-to-image translation suitable for neuroimages but also leverages unannotated mixed images to achieve superior neurologic disease detection. Additionally, we address the issue of model selection for inference without annotated samples by proposing a pseudo-AUC metric, further enhancing Brainomaly's detection performance. Extensive experiments and ablation studies demonstrate that Brainomaly outperforms existing state-of-the-art unsupervised disease and anomaly detection methods by significant margins in Alzheimer's disease detection using a publicly available dataset and headache detection using an institutional dataset. The code is available from https://github.com/mahfuzmohammad/Brainomaly.

Brain structural and functional abnormalities associated with acute post-traumatic headache: iron deposition and functional connectivity.

BACKGROUND: The purpose of this study was to interrogate brain iron accumulation in participants with acute post-traumatic headache (PTH) due to mild traumatic brain injury (mTBI), and to determine if functional connectivity is affected in areas with iron accumulation. We aimed to examine the correlations between iron accumulation and headache frequency, post-concussion symptom severity, number of mTBIs, and time since most recent TBI. METHODS: Sixty participants with acute PTH and 60 age-matched healthy controls (HC) underwent 3T magnetic resonance imaging including quantitative T2* maps and resting-state functional connectivity imaging. Between group T2* differences were determined using T-tests (p < 0.005, cluster size threshold of 90 voxels). For regions with T2* differences, two analyses were conducted. First, the correlations with clinical variables including headache frequency, number of lifetime mTBIs, time since most recent mTBI, and Sport Concussion Assessment Tool (SCAT) symptom severity scale scores were investigated using linear regression. Second, the functional connectivity of these regions with the rest of the brain was examined (significance of p < 0.05 with family wise error correction for multiple comparisons). RESULTS: The acute PTH group consisted of 60 participants (22 male, 38 female) with average age of 42 ± 14 years. The HC group consisted of 60 age-matched controls (17 male, 43 female, average age of 42 ± 13). PTH participants had lower T2* values compared to HC in the left posterior cingulate and the bilateral cuneus. Stronger functional connectivity was observed between bilateral cuneus and right cerebellar areas in PTH compared to HC. Within the PTH group, linear regression showed negative associations of T2* in the left posterior cingulate with SCAT symptom severity score (p = 0.05) and T2* in the left cuneus with headache frequency (p = 0.04). CONCLUSIONS: Iron accumulation in posterior cingulate and cuneus was observed in those with acute PTH relative to HC; stronger functional connectivity was detected between the bilateral cuneus and the right cerebellum. The correlations of decreased T2* (suggesting higher iron content) with headache frequency and post mTBI symptom severity suggest that the iron accumulation that results from mTBI might reflect the severity of underlying mTBI pathophysiology and associate with post-mTBI symptom severity including PTH.

Physical impairment during and between migraine attacks: A daily diary study of patients with chronic migraine.

OBJECTIVE: While a substantial body of research describes the disabling impacts of migraine attacks, less research has described the impacts of migraine on physical functioning between migraine attacks. The objective of this study is to describe physical impairment during and between migraine attacks as a dimension of burden experienced by people living with chronic migraine. METHODS: The physical impairment domain of the Migraine Physical Function Impact Diary was recorded in headache diaries from the Medication Overuse Treatment Strategy trial. Days with moderate to severe headache were used to approximate migraine attacks. Factor analysis and regression analysis were used to describe associations between migraine and physical impairment. RESULTS: 77,662 headache diary entries from 720 participants were analyzed, including 25,414 days with moderate to severe headache, 19,149 days with mild headache, and 33,099 days with no headache. Mean physical impairment score was 41.5 (SD = 26.1) on days with moderate to severe headache, 12.8 (SD = 15.0) on days with mild headache, and 5.2 (SD = 13.1) on days with no headache. Physical impairment on days with mild headache and days with no headache was significantly associated with days since last moderate to severe headache, physical impairment with last moderate to severe headache, mild headache (compared to no headache), depression, hypersensitivities and cranial autonomic symptoms. CONCLUSIONS: Physical impairment occurs on migraine and non-migraine days. Study participants with frequent headaches, symptoms of depression, hypersensitivities and cranial autonomic symptoms experience physical impairment at a higher rate on days with no headache and days with mild headache.Clinical Trial Registration: ClinicalTrials.gov (NCT02764320).

Brain Structural and Functional Abnormalities Associated with Acute Post-Traumatic Headache: Iron Deposition and Functional Connectivity.

Background: The purpose of this study was to interrogate brain iron accumulation in participants with acute post-traumatic headache (PTH) due to mild traumatic brain injury (mTBI), and to determine if functional connectivity is affected in areas with iron accumulation. We aimed to examine the correlations between iron accumulation and headache frequency, post-concussion symptom severity, number of mTBIs and time since most recent TBI. Methods: Sixty participants with acute PTH and 60 age-matched healthy controls (HC) underwent 3T magnetic resonance imaging including quantitative T2* maps and resting-state functional connectivity imaging. Between group T2* differences were determined using T-tests (p < 0.005, cluster size threshold of 10 voxels). For regions with T2* differences, two analyses were conducted. First, the correlations with clinical variables including headache frequency, number of lifetime mTBIs, time since most recent mTBI, and Sport Concussion Assessment Tool (SCAT) symptom severity scale scores were investigated using linear regression. Second, the functional connectivity of these regions with the rest of the brain was examined (significance of p < 0.05 with family wise error correction for multiple comparisons). Results: The acute PTH group consisted of 60 participants (22 male, 38 female) with average age of 42 ± 14 years. The HC group consisted of 60 age-matched controls (17 male, 43 female, average age of 42 ± 13). PTH participants had lower T2* values compared to HC in the left posterior cingulate and the bilateral cuneus. Stronger functional connectivity was observed between bilateral cuneus and right cerebellar areas in PTH compared to HC. Within the PTH group, linear regression showed negative associations of T2* and SCAT symptom severity score in the left posterior cingulate (p = 0.05) and with headache frequency in the left cuneus (p = 0.04). Conclusions: Iron accumulation in posterior cingulate and cuneus was observed in those with acute PTH relative to HC; stronger functional connectivity was detected between the bilateral cuneus and the right cerebellum. The correlations of decreased T2* (suggesting higher iron content) with headache frequency and post mTBI symptom severity suggest that the iron accumulation that results from mTBI might reflect the severity of underlying mTBI pathophysiology and associate with post-mTBI symptom severity including PTH.

A large language model-based generative natural language processing framework fine-tuned on clinical notes accurately extracts headache frequency from electronic health records.

OBJECTIVE: To develop a natural language processing (NLP) algorithm that can accurately extract headache frequency from free-text clinical notes. BACKGROUND: Headache frequency, defined as the number of days with any headache in a month (or 4 weeks), remains a key parameter in the evaluation of treatment response to migraine preventive medications. However, due to the variations and inconsistencies in documentation by clinicians, significant challenges exist to accurately extract headache frequency from the electronic health record (EHR) by traditional NLP algorithms. METHODS: This was a retrospective cross-sectional study with patients identified from two tertiary headache referral centers, Mayo Clinic Arizona and Mayo Clinic Rochester. All neurology consultation notes written by 15 specialized clinicians (11 headache specialists and 4 nurse practitioners) between 2012 and 2022 were extracted and 1915 notes were used for model fine-tuning (90%) and testing (10%). We employed four different NLP frameworks: (1) ClinicalBERT (Bidirectional Encoder Representations from Transformers) regression model, (2) Generative Pre-Trained Transformer-2 (GPT-2) Question Answering (QA) model zero-shot, (3) GPT-2 QA model few-shot training fine-tuned on clinical notes, and (4) GPT-2 generative model few-shot training fine-tuned on clinical notes to generate the answer by considering the context of included text. RESULTS: The mean (standard deviation) headache frequency of our training and testing datasets were 13.4 (10.9) and 14.4 (11.2), respectively. The GPT-2 generative model was the best-performing model with an accuracy of 0.92 (0.91, 0.93, 95% confidence interval [CI]) and R2 score of 0.89 (0.87, 0.90, 95% CI), and all GPT-2-based models outperformed the ClinicalBERT model in terms of exact matching accuracy. Although the ClinicalBERT regression model had the lowest accuracy of 0.27 (0.26, 0.28), it demonstrated a high R2 score of 0.88 (0.85, 0.89), suggesting the ClinicalBERT model can reasonably predict the headache frequency within a range of ≤ ± 3 days, and the R2 score was higher than the GPT-2 QA zero-shot model or GPT-2 QA model few-shot training fine-tuned model. CONCLUSION: We developed a robust information extraction model based on a state-of-the-art large language model, a GPT-2 generative model that can extract headache frequency from EHR free-text clinical notes with high accuracy and R2 score. It overcame several challenges related to different ways clinicians document headache frequency that were not easily achieved by traditional NLP models. We also showed that GPT-2-based frameworks outperformed ClinicalBERT in terms of accuracy in extracting headache frequency from clinical notes. To facilitate research in the field, we released the GPT-2 generative model and inference code with open-source license of community use in GitHub. Additional fine-tuning of the algorithm might be required when applied to different health-care systems for various clinical use cases.

A Large Language Model-Based Generative Natural Language Processing Framework Finetuned on Clinical Notes Accurately Extracts Headache Frequency from Electronic Health Records.

Background: Headache frequency, defined as the number of days with any headache in a month (or four weeks), remains a key parameter in the evaluation of treatment response to migraine preventive medications. However, due to the variations and inconsistencies in documentation by clinicians, significant challenges exist to accurately extract headache frequency from the electronic health record (EHR) by traditional natural language processing (NLP) algorithms. Methods: This was a retrospective cross-sectional study with human subjects identified from three tertiary headache referral centers- Mayo Clinic Arizona, Florida, and Rochester. All neurology consultation notes written by more than 10 headache specialists between 2012 to 2022 were extracted and 1915 notes were used for model fine-tuning (90%) and testing (10%). We employed four different NLP frameworks: (1) ClinicalBERT (Bidirectional Encoder Representations from Transformers) regression model (2) Generative Pre-Trained Transformer-2 (GPT-2) Question Answering (QA) Model zero-shot (3) GPT-2 QA model few-shot training fine-tuned on Mayo Clinic notes; and (4) GPT-2 generative model few-shot training fine-tuned on Mayo Clinic notes to generate the answer by considering the context of included text. Results: The GPT-2 generative model was the best-performing model with an accuracy of 0.92[0.91 - 0.93] and R2 score of 0.89[0.87, 0.9], and all GPT2-based models outperformed the ClinicalBERT model in terms of the exact matching accuracy. Although the ClinicalBERT regression model had the lowest accuracy 0.27[0.26 - 0.28], it demonstrated a high R2 score 0.88[0.85, 0.89], suggesting the ClinicalBERT model can reasonably predict the headache frequency within a range of ≤ ± 3 days, and the R2 score was higher than the GPT-2 QA zero-shot model or GPT-2 QA model few-shot training fine-tuned model. Conclusion: We developed a robust model based on a state-of-the-art large language model (LLM)- a GPT-2 generative model that can extract headache frequency from EHR free-text clinical notes with high accuracy and R2 score. It overcame several challenges related to different ways clinicians document headache frequency that were not easily achieved by traditional NLP models. We also showed that GPT2-based frameworks outperformed ClinicalBERT in terms of accuracy in extracting headache frequency from clinical notes. To facilitate research in the field, we released the GPT-2 generative model and inference code with open-source license of community use in GitHub.

Photophobia Contributes to Migraine-Associated Disability and Reduced Work Productivity: Results From the American Registry for Migraine Research (ARMR).

BACKGROUND: Photosensitivity, often called "photophobia" in the migraine literature, is a common and bothersome symptom for most people during their migraine attacks. This study aimed to investigate the association of photophobia severity with work productivity, activity impairment, and migraine-associated disability using data from a large cohort of patients with migraine who were enrolled into the American Registry for Migraine Research (ARMR). METHODS: This study used Photosensitivity Assessment Questionnaire (PAQ) scores to investigate the relationship between photophobia severity with work productivity and activity impairment (using the Work Productivity and Activity Impairment [WPAI] questionnaire) and migraine-related disability (using the Migraine Disability Assessment [MIDAS]) among those with migraine. Summary statistics are presented as means and standard deviations for variables that were normally distributed and as medians and interquartile ranges for variables that were not normally distributed. Multiple linear regression models were developed to measure the relationships between photophobia scores with work productivity and activity impairment and migraine-associated disability, controlling for age, sex, headache frequency, headache intensity, anxiety (using the generalized anxiety disorder [GAD-7]), and depression (using the Patient Health Questionnaire [PHQ-2]). RESULTS: One thousand eighty-four participants were included. Average age was 46.1 (SD 13.8) years, 87.2% (n = 945) were female, average headache frequency during the previous 90 days was 44.3 (SD 29.9), average headache intensity was 5.9 (SD 1.7), median PHQ-2 score was 1 (IQR 0-2), and median GAD-7 was 5 (IQR 2-8). Mean PAQ score was 0.47 (SD 0.32), and median MIDAS score was 38 (IQR 15.0-80.0). Among the 584 employed participants, 47.4% (n = 277) reported missing work in the past week because of migraine, mean overall work impairment was 42.8% (SD 26.7), mean activity impairment was 42.5% (SD 26.2), mean presenteeism score was 38.4% (SD 24.4), and median absenteeism was 0 (IQR 0-14.5). After controlling for age, sex, headache frequency, average headache intensity, PHQ-2 score, and GAD-7 score, there was a statistically significant association between photophobia scores with: a) MIDAS scores (F[7,1028] = 127.42, P < 0.001, R2 = 0.461, n = 1,036); b) overall work impairment (F[7,570] = 29.23, P < 0.001, R2 = 0.255, n = 578); c) activity impairment (F[7,570] = 27.42, P < 0.001, R2 = 0.243, n = 578); d) presenteeism (F[7,570] = 29.17, P < 0.001, R2 = 0.255, n = 578); and e) absenteeism for the zero-inflated (P = 0.003) and negative binomial (P = 0.045) model components (P < 0.001, n = 578). CONCLUSIONS: In those with migraine, severe photophobia is associated with reduced work productivity and higher presenteeism, absenteeism, activity impairment, and migraine-related disability.

Questionnaire and structural imaging data accurately predict headache improvement in patients with acute post-traumatic headache attributed to mild traumatic brain injury.

BACKGROUND: Our prior work demonstrated that questionnaires assessing psychosocial symptoms have utility for predicting improvement in patients with acute post-traumatic headache following mild traumatic brain injury. In this cohort study, we aimed to determine whether prediction accuracy can be refined by adding structural magnetic resonance imaging (MRI) brain measures to the model. METHODS: Adults with acute post-traumatic headache (enrolled 0-59 days post-mild traumatic brain injury) underwent T1-weighted brain MRI and completed three questionnaires (Sports Concussion Assessment Tool, Pain Catastrophizing Scale, and the Trait Anxiety Inventory Scale). Individuals with post-traumatic headache completed an electronic headache diary allowing for determination of headache improvement at three- and at six-month follow-up. Questionnaire and MRI measures were used to train prediction models of headache improvement and headache trajectory. RESULTS: Forty-three patients with post-traumatic headache (mean age = 43.0, SD = 12.4; 27 females/16 males) and 61 healthy controls were enrolled (mean age = 39.1, SD = 12.8; 39 females/22 males). The best model achieved cross-validation Area Under the Curve of 0.801 and 0.805 for predicting headache improvement at three and at six months. The top contributing MRI features for the prediction included curvature and thickness of superior, middle, and inferior temporal, fusiform, inferior parietal, and lateral occipital regions. Patients with post-traumatic headache who did not improve by three months had less thickness and higher curvature measures and notably greater baseline differences in brain structure vs. healthy controls (thickness: p < 0.001, curvature: p = 0.012) than those who had headache improvement. CONCLUSIONS: A model including clinical questionnaire data and measures of brain structure accurately predicted headache improvement in patients with post-traumatic headache and achieved improvement compared to a model developed using questionnaire data alone.

Headache classification and automatic biomarker extraction from structural MRIs using deep learning.

Data-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.

Longitudinal differences in iron deposition in periaqueductal gray matter and anterior cingulate cortex are associated with response to erenumab in migraine.

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.

Thalamic subfield iron accumulation after acute mild traumatic brain injury as a marker of future post-traumatic headache intensity.

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.

Developing multivariable models for predicting headache improvement in patients with acute post-traumatic headache attributed to mild traumatic brain injury: A preliminary study.

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 R = 0.64 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.

Longitudinal changes in functional connectivity and pain-induced brain activations in patients with migraine: a functional MRI study pre- and post- treatment with Erenumab.

BACKGROUND: Migraine involves central and peripheral nervous system mechanisms. Erenumab, an anti-calcitonin gene-related peptide (CGRP) receptor monoclonal antibody with little central nervous system penetrance, is effective for migraine prevention. The objective of this study was to determine if response to erenumab is associated with alterations in brain functional connectivity and pain-induced brain activations. METHODS: Adults with 6-25 migraine days per month during a 4-week headache diary run-in phase underwent pre-treatment brain functional MRI (fMRI) that included resting-state functional connectivity and BOLD measurements in response to moderately painful heat stimulation to the forearm. This was followed by two treatments with 140 mg erenumab, at baseline and 4 weeks later. Post-treatment fMRI was performed 2 weeks and 8 weeks following the first erenumab treatment. A longitudinal Sandwich estimator analysis was used to identify pre- to post-treatment changes in resting-state functional connectivity and brain activations in response to thermal pain. fMRI findings were compared between erenumab treatment-responders vs. erenumab non-responders. RESULTS: Pre- and post-treatment longitudinal imaging data were available from 32 participants. Average age was 40.3 (+/- 13) years and 29 were female. Pre-treatment average migraine day frequency was 13.8 (+/- 4.7) / 28 days and average headache day frequency was 15.8 (+/- 4.4) / 28 days. Eighteen of 32 (56%) were erenumab responders. Compared to erenumab non-responders, erenumab responders had post-treatment differences in 1) network functional connectivity amongst pain-processing regions, including higher global efficiency, clustering coefficient, node degree, regional efficiency, and modularity, 2) region-to-region functional connectivity between several regions including temporal pole, supramarginal gyrus, and hypothalamus, and 3) pain-induced activations in the middle cingulate, posterior cingulate, and periaqueductal gray matter. CONCLUSIONS: Reductions in migraine day frequency accompanying erenumab treatment are associated with changes in resting state functional connectivity and central processing of extracranial painful stimuli that differ from erenumab non-responders. TRIAL REGISTRATION: clinicaltrials.gov (NCT03773562).

Migraine and Posttraumatic Headache: Similarities and Differences in Brain Network Connectivity.

Posttraumatic 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.

The impact of pre-morbid headaches on headache features and long-term health outcomes following traumatic brain injury: Insights from the American Registry for Migraine Research.

OBJECTIVE: To investigate the impact of having headaches prior to traumatic brain injury (TBI) on headache features and long-term patient health outcomes. BACKGROUND AND METHODS: This was an exploratory analysis of patients with TBI who were enrolled in the American Registry for Migraine Research (ARMR), a multicenter, prospective, longitudinal patient registry composed of patients with International Classification of Headache Disorders, 3rd edition (ICHD-3)-defined headache diagnoses. The ARMR study enrolled 2,707 patients between February 1, 2016 and May 6, 2020, 565 of whom qualified for this analysis. Those with headaches prior to their TBI were compared to those without headaches prior to their TBI for ICHD-3 diagnoses, headache frequency and intensity, headache-related disability (Migraine Disability Assessment score), symptoms of anxiety (General Anxiety Disorder [GAD-7]), depression (two items from Patient Health Questionnaire-9), post-traumatic stress disorder (PTSD), cutaneous allodynia (12-item Allodynia Symptom Checklist [ASC-12]), cognitive dysfunction (Migraine Attacks Subjective Cognitive Impairments Scale [Mig-SCog]), pain interference (Patient-Reported Outcomes Measurement Information System-Pain Interference), and work productivity (Work Productivity and Activity Impairment). RESULTS: Among 565 participants with TBI, 350 had headaches prior to their TBI. Those with pre-TBI headaches were less likely to receive a diagnosis of post-traumatic headache (PTH; 14/350 [4.0%] vs. 21/215 [9.8%], p = 0.006), even though 25.7% reported new or worsening headaches within 7 days of their TBI. Those with pre-TBI headaches had higher ASC-12 scores (2.4 ± 3.5 vs. 1.8 ± 3.4, p = 0.030), Mig-SCog scores (9.3 ± 4.7 vs. 8 ± 4.9, p = 0.004), and GAD-7 scores (6.9 ± 5.1 vs. 6.2 ± 5.4, p = 0.039), and were more likely to have a migraine diagnosis (335/350 [95.7%] vs. 192/215 [89.3%], p = 0.003). CONCLUSIONS: Those with headaches prior to TBI are less likely to receive a diagnosis of PTH. They have more severe symptoms of cutaneous allodynia, cognitive impairment, and generalized anxiety. This analysis suggests that pre-TBI headaches might impact post-TBI headache diagnoses and associated features.

T2* reduction in patients with acute post-traumatic headache.

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.

The value of brain MRI functional connectivity data in a machine learning classifier for distinguishing migraine from persistent post-traumatic headache.

Background: Post-traumatic headache (PTH) and migraine often have similar phenotypes. The objective of this exploratory study was to develop classification models to differentiate persistent PTH (PPTH) from migraine using clinical data and magnetic resonance imaging (MRI) measures of brain structure and functional connectivity (fc). Methods: Thirty-four individuals with migraine and 48 individuals with PPTH attributed to mild TBI were included. All individuals completed questionnaires assessing headache characteristics, mood, sensory hypersensitivities, and cognitive function and underwent brain structural and functional imaging during the same study visit. Clinical features, structural and functional resting-state measures were included as potential variables. Classifiers using ridge logistic regression of principal components were fit on the data. Average accuracy was calculated using leave-one-out cross-validation. Models were fit with and without fc data. The importance of specific variables to the classifier were examined. Results: With internal variable selection and principal components creation the average accuracy was 72% with fc data and 63.4% without fc data. This classifier with fc data identified individuals with PPTH and individuals with migraine with equal accuracy. Conclusion: Multivariate models based on clinical characteristics, fc, and brain structural data accurately classify and differentiate PPTH vs. migraine suggesting differences in the neuromechanism and clinical features underlying both headache disorders.

Altered speech patterns in subjects with post-traumatic headache due to mild traumatic brain injury.

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 3 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 yet more precise 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 production may have been more effortful resulting in slower speaking rates and more precise vowel articulation during headache vs. when they were headache-free, suggesting that speech alterations were related to PTH and not solely due to the underlying mTBI.

Mild traumatic brain injury affects the features of migraine.

BACKGROUND: Headache is one of the most common symptoms after concussion, and mild traumatic brain injury (mTBI) is a risk factor for chronic migraine (CM). However, there remains a paucity of data regarding the impact of mTBI on migraine-related symptoms and clinical course. METHODS: Of 2161 migraine patients who participated in the American Registry for Migraine Research between February 2016 and March 2020, 1098 completed questions assessing history of TBI (50.8%). Forty-four patients reported a history of moderate to severe TBI, 413 patients reported a history of mTBI. Patients' demographics, headache symptoms and triggers, history of physical abuse, allodynia symptoms (ASC-12), migraine disability (MIDAS), depression (PHQ-2), and anxiety (GAD-7) were compared between migraine groups with (n = 413) and without (n = 641) a history of mTBI. Either the chi-square-test or Fisher's exact test, as appropriate, was used for the analyses of categorical variables. The Mann-Whitney test was used for the analyses of continuous variables. Logistic regression models were used to compare variables of interest while adjusting for age, gender, and CM. RESULTS: A significantly higher proportion of patients with mTBI had CM (74.3% [307/413] vs. 65.8% [422/641], P = 0.004), had never been married or were divorced (36.6% [147/402] vs. 29.4% [187/636], P = 0.007), self-reported a history of physical abuse (24.3% [84/345] vs. 14.3% [70/491], P <  0.001), had mild to severe anxiety (50.5% [205/406] vs. 41.0% [258/630], P = 0.003), had headache-related vertigo (23.0% [95/413] vs. 15.9% [102/640], P = 0.009), and difficulty finding words (43.0% [174/405] vs. 32.9% [208/633], P <  0.001) in more than half their attacks, and headaches triggered by lack of sleep (39.4% [155/393] vs. 32.6% [198/607], P = 0.018) and reading (6.6% [26/393] vs. 3.0% [18/607], P = 0.016), compared to patients without mTBI. Patients with mTBI had significantly greater ASC-12 scores (median [interquartile range]; 5 [1-9] vs. 4 [1-7], P < 0.001), MIDAS scores (42 [18-85] vs. 34.5 [15-72], P = 0.034), and PHQ-2 scores (1 [0-2] vs. 1 [0-2], P = 0.012). CONCLUSION: Patients with a history of mTBI are more likely to have a self-reported a history of physical abuse, vertigo, and allodynia during headache attacks, headaches triggered by lack of sleep and reading, greater headache burden and headache disability, and symptoms of anxiety and depression. This study suggests that a history of mTBI is associated with the phenotype, burden, clinical course, and associated comorbid diseases in patients with migraine, and highlights the importance of inquiring about a lifetime history of mTBI in patients being evaluated for migraine.