Static and dynamic functional connectivity differences between migraine and persistent post-traumatic headache: A resting-state magnetic resonance imaging study

INTRODUCTION

Although migraine and persistent post-traumatic headache often share phenotypic characteristics, few studies have interrogated the pathophysiological differences underlying these headache types. While there is now some indication of differences in brain structure between migraine and persistent post-traumatic headache, differences in brain function have not been adequately investigated. The objective of this study was to compare static and dynamic functional connectivity patterns in migraine versus persistent post-traumatic headache using resting-state magnetic resonance imaging.

METHODS

This case-control study interrogated the static functional connectivity and dynamic functional connectivity patterns of 59 a priori selected regions of interest involved in pain processing. Pairwise connectivity (region of interest to region of interest) differences between migraine (n = 33) and persistent post-traumatic headache (n = 44) were determined and compared to healthy controls (n = 36) with ANOVA and subsequent t-tests. Pearson partial correlations were used to explore the relationship between headache burden (headache frequency; years lived with headache) and functional connectivity and between pain intensity at the time of imaging and functional connectivity for migraine and persistent post-traumatic headache groups, separately.

RESULTS

Significant differences in static functional connectivity between migraine and persistent post-traumatic headache were found for 17 region pairs that included the following regions of interest: Primary somatosensory, secondary somatosensory, posterior insula, hypothalamus, anterior cingulate, middle cingulate, temporal pole, supramarginal gyrus, superior parietal, middle occipital, lingual gyrus, pulvinar, precuneus, cuneus, somatomotor, ventromedial prefrontal cortex, and dorsolateral prefrontal cortex. Significant differences in dynamic functional connectivity between migraine and persistent post-traumatic headache were found for 10 region pairs that included the following regions of interest: Secondary somatosensory, hypothalamus, middle cingulate, temporal pole, supramarginal gyrus, superior parietal, lingual gyrus, somatomotor, precentral, posterior cingulate, middle frontal, fusiform gyrus, parieto-occiptal, and amygdala. Although there was overlap among the regions demonstrating static functional connectivity differences and those showing dynamic functional connectivity differences between persistent post-traumatic headache and migraine, there was no overlap in the region pair functional connections. After controlling for sex and age, there were significant correlations between years lived with headache with static functional connectivity of the right dorsolateral prefrontal cortex with the right ventromedial prefrontal cortex in the migraine group and with static functional connectivity of right primary somatosensory with left supramarginal gyrus in the persistent post-traumatic headache group. There were significant correlations between headache frequency with static functional connectivity of left secondary somatosensory with right cuneus in the migraine group and with static functional connectivity of left middle cingulate with right pulvinar and right posterior insula with left hypothalamus in the persistent post-traumatic headache group. Dynamic functional connectivity was significantly correlated with headache frequency, after controlling for sex and age, in the persistent post-traumatic headache group for one region pair (right middle cingulate with right supramarginal gyrus). Dynamic functional connectivity was correlated with pain intensity at the time of imaging for the migraine cohort for one region pair (right posterior cingulate with right amygdala).

CONCLUSIONS

Resting-state functional imaging revealed static functional connectivity and dynamic functional connectivity differences between migraine and persistent post-traumatic headache for regions involved in pain processing. These differences in functional connectivity might be indicative of distinctive pathophysiology associated with migraine versus persistent post-traumatic headache.

Chronic versus episodic migraine: The 15-day threshold does not adequately reflect substantial differences in disability across the full spectrum of headache frequency

OBJECTIVE

To evaluate whether the 15-day threshold of headache days per month adequately reflects substantial differences in disability across the full spectrum of migraine.

BACKGROUND

The monthly frequency of headache days defines migraine subtypes and has crucial implications for epidemiological and clinical research as well as access to care.

METHODS

The patients with migraine (N = 836) who participated in the American Registry for Migraine Research, which is a multicenter, longitudinal patient registry, between February 2016 and March 2020, were divided into four groups based on monthly headache frequency: Group 1 (0-7 headache days/month, n = 286), Group 2 (8-14 headache days/month, n = 180), Group 3 (15-23 headache days/month, n = 153), Group 4 (≥24 headache days/month, n = 217). Disability (MIDAS), Pain intensity (NRS), Work Productivity and Activity Impairment (WPAI), Pain Interference (PROMIS-PI), Patient Health Questionnaire-4 (PHQ-4), and General Anxiety Disorder-7 (GAD-7) scores were compared.

RESULTS

Mean (standard deviation [SD]) age was 46 (13) years (87.9% [735/836] female). The proportion of patients in each group was as follows: Group 1 (34.2% [286/836]), Group 2 (21.5% [180/836]), Group 3 (18.3% [153/836]), and Group 4 (26.0% [217/836]). There were significant relationships with increasing disability, lost productive time, and pain interference in higher headache frequency categories. There were no significant differences between Group 2 and Group 3 for most measures (NRS, all WPAI scores, PROMIS-PI, GAD-7, and PHQ-4), although MIDAS scores differed (median [interquartile range (IQR)]; 38 [20-58] vs. 55 [30-90], p < 0.001). Patients in Group 1 had significantly lower MIDAS (median [IQR];16 [7-30], p < 0.001), WPAI-% total active impairment (mean (SD): Group 1 [30.9 (26.8)] vs. Group 2 [39.2 (24.5), p = 0.017], vs. Group 3 [45.9 (24.1), p < 0.001], vs. Group 4 [55.3 (23.0), p < 0.001], and PROMIS-PI-T score (Group 1 [60.3 (7.3)] vs. Group 2 [62.6 (6.4), p = 0.008], vs. Group 3 [64.6 (5.6), p < 0.001], vs. Group 4 [66.8 (5.9), p < 0.001]) compared to all other groups. Patients in Group 4 had significantly higher MIDAS (median (IQR): Group 4 [90 (52-138)] vs. Group 1 [16 (7-30), p < 0.001], vs. Group 2 [38 (20-58), p < 0.001], vs. Group 3 [55 (30-90), p < 0.001], WPAI-%Presenteeism (Group 4 [50.4 (24.4)] vs. Group 1 [28.8 (24.9), p < 0.001], vs. Group 2 [34.9 (22.3), p < 0.001], vs. Group 3 [40.9 (22.3), p = 0.048], WPAI-% total work productivity impairment (Group 4 [55.9 (26.1)] vs. Group 1 [32.1 (37.6), p < 0.001], vs. Group 2 [38.3 (24.0), p < 0.001], vs. Group 3 [44.6 (24.4), p = 0.019]), and WPAI-%Total activity impairment (Group 4 [55.3 (23.0)] vs. Group 1 [30.9 (26.8), p < 0.001], vs. Group 2 [39.2 (24.5), p < 0.001], vs. Group 3 [45.9 (24.1), p = 0.025]) scores compared with all other groups.

CONCLUSION

Our data suggest that the use of a 15 headache day/month threshold to distinguish episodic and chronic migraine does not capture the burden of illness nor reflect the treatment needs of patients. These results have important implications for future refinements in the classification of migraine.

Structural Co-Variance Patterns in Migraine: A Cross-Sectional Study Exploring the Role of the Hippocampus

OBJECTIVE

To interrogate hippocampal morphology and structural co-variance patterns in migraine patients and to investigate whether structural co-variance patterns relate to migraine disease characteristics.

BACKGROUND

Migraine is associated with structural alterations in widespread cortical and subcortical regions associated with the sensory, cognitive, and affective components of pain processing. Recent studies have shown that migraine patients have differences in hippocampal structure and function relative to healthy control subjects, but whether hippocampal structure relates to disease characteristics including frequency of attacks, years lived with migraine and symptoms of allodynia remains unknown. Furthermore, this study investigated hippocampal volume co-variance patterns in migraineurs, an indirect measure of brain network connectivity. Here, we explore differences in hippocampal volume and structural co-variance patterns in migraine patients relative to healthy controls and examine whether these hippocampal measures relate to migraine disease burden.

METHODS

This study included 61 migraine patients and 57 healthy control subjects (healthy controls: median age = 34.0, IQR = 19.0; migraine patients: median age = 35.0, IQR = 17.5; P = .65). Regional brain volumes were automatically calculated using FreeSurfer version 5.3. Symptoms of allodynia were determined using the Allodynia Symptom Checklist 12 (ASC-12). Structural co-variance patterns were interrogated using pairwise correlations and group differences in correlation strength were estimated using Euclidian distance. A stepwise regression was used to investigate the relationship between structural co-variance patterns with migraine burden.

RESULTS

Migraine patients had less left hippocampal volume (healthy controls: left hippocampal volume = 4276.8 mm3 , SD = 425.3 mm3 , migraine patients: left hippocampal volume = 4089.5 mm3 , SD = 453.9 mm3 , P = .02) and less total (right plus left) hippocampal volume (healthy controls: total hippocampal volume= 8690.8 mm3 , SD = 855.1 mm3 ; migraine patients: total hippocampal volume = 8341.8 mm3 , SD = 917.9 mm3 ; P = .03) compared to healthy controls. Migraineurs had stronger structural covariance between the hippocampi and cortico-limbic regions in the frontal lobe (inferior opercular gyrus), temporal lobe (planum temporale, amygdala), parietal lobe (angular gyrus, precuneus), and the cerebellar white matter. Results of a stepwise regression showed that hippocampal volumes and the interactions between hippocampal volumes with the volumes of other cortico-limbic regions associate with migraine-related allodynia but not with headache frequency or years lived with migraine.

CONCLUSION

Migraineurs have less hippocampal volume and stronger hippocampal-cortico-limbic connectivity compared to healthy controls. Hippocampal volumes and measures of hippocampal volume connectivity with other cortico-limbic network regions associate with symptoms of allodynia.

The American Registry for Migraine Research: Research Methods and Baseline Data for an Initial Patient Cohort

BACKGROUND

The American Registry for Migraine Research (ARMR) is a multicenter, prospective, longitudinal patient registry, biorepository, and neuroimaging repository that collects clinical data, electronic health record (EHR) data, blood samples, and brain imaging data from individuals with migraine or other headache types. In this manuscript, we outline ARMR research methods and report baseline data describing an initial cohort of ARMR participants.

METHODS

Adults with any International Classification of Headache Disorders (ICHD) diagnosis were prospectively enrolled from one of the 8 participating headache specialty centers. At baseline, ARMR participants complete web-based questionnaires, clinicians enter the participant's ICHD diagnoses, an optional blood specimen is collected, and neuroimaging data are uploaded to the ARMR neuroimaging repository. Participants maintain the ARMR daily headache diary longitudinally and follow-up questionnaires are completed by participants every 3 months. EHR data are integrated into the ARMR database from a subset of ARMR sites. Herein, we describe the ARMR methodology and report the summary data from ARMR participants who had, from February 2016 to May 2019, completed at least 1 baseline questionnaire from which data are reported in this manuscript. Descriptive statistics are used to provide an overview of patient's sociodemographics, headache diagnoses, headache characteristics, most bothersome symptoms other than headache, headache-related disability, comorbidities, and treatments.

RESULTS

Data were available from 996 ARMR participants, enrolled from Mayo Clinic Arizona, Dartmouth-Hitchcock Medical Center, University of Utah, University of Colorado, Thomas Jefferson University, University of Texas Health Science Center at Houston, Georgetown University Medical Center, and DENT Neurological Institute. Among ARMR participants, 86.7% (n = 864) were female and the mean age at the time of enrollment was 48.6 years (±13.9; range 18-84). The most common provider-reported diagnosis was chronic migraine (n = 622), followed by migraine without aura (n = 327), migraine with aura (n = 196), and medication overuse headache (n = 65). Average headache frequency was 19.1 ± 9.2 days per month (n = 751), with 68% reporting at least 15 headache days per month. Sensitivity to light was the most frequent (n = 222) most bothersome symptom overall, other than headache, but when present, cognitive dysfunction was most frequently (n = 157) the most bothersome symptom other than headache. Average migraine disability assessment (MIDAS) score was 52 ± 49 (n = 760), (very severe headache-related disability); however, 17% of the ARMR population had MIDAS scores suggesting "no" or "mild" disability. The most common non-headache health issues were allergies (n = 364), back pain (n = 296), neck pain (n = 296), depression (n = 292), and anxiety (n = 278). Nearly 85% (n = 695) of patients were using preventive medications and 24.7% were using non-medication preventive therapy (eg, vitamins and neuromodulation). The most common preventive medication classes were neurotoxins, anticonvulsants, antidepressants, vitamins/supplements, and anticalcitonin gene-related peptide ligand or receptor-targeted monoclonal antibodies. Nearly 90% (n = 734) of ARMR participants was taking medications to treat migraine attacks, with the most common classes being triptans, non-steroidal anti-inflammatory drugs, antiemetics, acetaminophen, and combination analgesics.

CONCLUSIONS

ARMR is a source of real-world patient data, biospecimens, and brain neuroimaging data that provides comprehensive insight into patients with migraine and other headache types being seen in headache specialty clinics in the United States. ARMR data will allow for longitudinal and advanced analytics that are expected to lead to a better characterization of patient heterogeneity, healthcare resource utilization, identification of endophenotypes, factors that predict treatment outcomes and clinical course, and ultimately advance the field toward precision headache medicine.

Sub-classifying patients with mild traumatic brain injury: A clustering approach based on baseline clinical characteristics and 90-day and 180-day outcomes

Background

The current classification of traumatic brain injury (TBI) into “mild”, “moderate”, or “severe” does not adequately account for the patient heterogeneity that still exists within each of these categories. The objective of this study was to identify “sub-groups” of mild TBI (mTBI) patients based on data available at the time of the initial post-TBI patient evaluation and to determine if the sub-grouping correlates with patient outcomes at 90 and 180 days post-TBI.

Methods

Data from patients in the TRACK-TBI Pilot dataset who had a Glasgow Coma Scale (GCS) score of 13 to 15 at arrival to the Emergency Department and a closed head injury were included. Considering 53 clinical variables that are typically available during the initial evaluation of the patient with mild TBI, sparse heirarchial clustering with cluster quality assessment was used to identify the optimal number of patient sub-groups. Patient sub-groups were then compared for ten outcomes measured at 90 or 180 days post-TBI.

Results

Amongst the 485 patients with mTBI, optimal clustering was based on the inclusion of 12 clinical variables that divided the patients into 5 mild TBI sub-groups. Clinical variables driving the sub-clustering included: gender, employment status, marital status, TBI due to falling, brain CT scan result, systolic blood pressure, diastolic blood pressure, administration of IV fluids in the Emergency Department, alcohol use, tobacco use, history of neurologic disease, and history of psychiatric disease. These 5 mild TBI sub-groups differed in their 90 day and 180 day outcomes within several domains including global outcomes, persistence of TBI-related symptoms, and neuropsychological impairment.

Conclusions

Sub-groups of patients with mTBI can be identified according to clinical variables that are relatively easy to obtain at the time of initial patient evaluation. A patient’s sub-group assignment is associated with multidimensional patient outcomes at 90 and 180 days. These findings support the notion that there are clinically meaningful subgroups of patients amongst those currently classified as having mTBI.

Effect of Migraine on Pregnancy Planning: Insights From the American Registry for Migraine Research

OBJECTIVE

To evaluate the effect of migraine on women's pregnancy plans.

PATIENTS AND METHODS

Participants were enrolled in the American Registry for Migraine Research, an observational study that recruits patients from headache specialty clinics across the United States. Data for this analysis were collected via patient-completed questionnaires completed from February 1, 2016, through September 23, 2019. Participants were adult women with migraine who answered the American Registry for Migraine Research family planning questions.

RESULTS

Of 607 women, 19.9% (n=121) avoided pregnancy because of migraine. Compared with women who did not avoid pregnancy, those who did were younger (37.5±9.2 years vs 47.2±13.3 years; P<.001), had fewer children (0.8±1.1 vs 1.5±1.5; P<.001), and were more likely to have chronic migraine (n=99 [81.8%] vs n=341 [70.2%]; P=.012) and menstrually associated migraine (n=5 [4.1%] vs n=5[1.0%]; P=.031). Women who avoided pregnancy believed that their migraine would be worse during pregnancy (n=87[72.5%]), disability caused by migraine would make pregnancy difficult (n=82[68.3%]), the migraine medications they take would negatively affect their child's development (n=92[76.0%]), and migraine would cause the baby to have abnormalities at birth (n=17[14.0%]).

CONCLUSION

Migraine effects pregnancy plans of many women, especially of those who are younger and have menstrual migraine and chronic migraine. Women who avoid pregnancy because of migraine believe that migraine will worsen during pregnancy, make their pregnancy difficult, and have negative effects on their child. Study results highlight the importance of educating women with migraine about the relationships between migraine and pregnancy so that informed family planning decisions can be made.

Distinguishing persistent post-traumatic headache from migraine: Classification based on clinical symptoms and brain structural MRI data

BACKGROUND

Persistent post-traumatic headache most commonly has symptoms that overlap those of migraine. In some cases, it can be clinically difficult to differentiate persistent post-traumatic headache with a migraine phenotype from migraine. The objective of this study was to develop a classification model based on questionnaire data and structural neuroimaging data that distinguishes individuals with migraine from those with persistent post-traumatic headache.

METHODS

Questionnaires assessing headache characteristics, sensory hypersensitivities, cognitive functioning, and mood, as well as T1-weighted magnetic resonance imaging and diffusion tensor data from 34 patients with migraine and 48 patients with persistent post-traumatic headache attributed to mild traumatic brain injury were included for analysis. The majority of patients with persistent post-traumatic headache had a migraine/probable migraine phenotype (77%). A machine-learning leave-one-out cross-validation algorithm determined the average accuracy for distinguishing individual migraine patients from individual patients with persistent post-traumatic headache.

RESULTS

Based on questionnaire data alone, the average classification accuracy for determining whether an individual person had migraine or persistent post-traumatic headache was 71.9%. Adding imaging data features to the model improved the classification accuracy to 78%, including an average accuracy of 97.1% for identifying individual migraine patients and an average accuracy of 64.6% for identifying individual patients with persistent post-traumatic headache. The most important clinical features that contributed to the classification accuracy included questions related to anxiety and decision making. Cortical brain features and fibertract data from the following regions or tracts most contributed to the classification accuracy: Bilateral superior temporal, inferior parietal and posterior cingulate; right lateral occipital, uncinate, and superior longitudinal fasciculus. A post-hoc analysis showed that compared to incorrectly classified persistent post-traumatic headache patients, those who were correctly classified as having persistent post-traumatic headache had more severe physical, autonomic, anxiety and depression symptoms, were more likely to have post-traumatic stress disorder, and were more likely to have had mild traumatic brain injury attributed to blasts.

DISCUSSION

A classification model that included a combination of questionnaire data and structural imaging parameters classified individual patients as having migraine versus persistent post-traumatic headache with good accuracy. The most important clinical measures that contributed to the classification accuracy included questions on mood. Regional brain structures and fibertracts that play roles in pain processing and pain integration were important brain features that contributed to the classification accuracy. The lower classification accuracy for patients with persistent post-traumatic headache compared to migraine may be related to greater heterogeneity of patients in the persistent post-traumatic headache cohort regarding their traumatic brain injury mechanisms, and physical, emotional, and cognitive symptoms.

Impact of Depression and Anxiety Symptoms on Patient-Reported Outcomes in Patients With Migraine: Results From the American Registry for Migraine Research (ARMR)

BACKGROUND AND OBJECTIVES

The association between migraine, depression, and anxiety has been established, but the impact of these psychiatric comorbidities on functional impairment in people with migraine has been under-investigated. The purpose of this cross-sectional observational study was to investigate the relationship between anxiety and depression symptoms on migraine-related disability, pain interference, work interference, and career success in a cohort of patients with migraine.

METHODS

This analysis included 567 migraine patients who had been enrolled into the American Registry for Migraine Research (ARMR) between February 2016 and June 2019. Patients completed the Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-2 (PHQ-2) to measure symptoms of anxiety and depression, respectively. Patients completed the Migraine Disability Assessment Scale (MIDAS), Pain Interference (PROMIS pain short) questionnaire, and Work Productivity and Activity Interference (WPAI) questionnaire to measure levels of functional impairment at work and in daily activities. In addition, patients answered questions designed for ARMR regarding education and career interference. Models were created to describe the relationship between severity of psychiatric symptoms (anxiety and depression), and each outcome of interest (WPAI, MIDAS, pain interference, reporting that migraine had interfered with career success). Each model was controlled for age, sex, headache frequency, years with migraine, and average headache intensity.

RESULTS

Among the 567 patients with migraine, mean (SD) age was 47.1 (13.7), 87.3% were female, and average headache frequency was 19.1 (9.3) days/month. PHQ-2 scores were positively associated with scores on MIDAS (b = 0.06, SE = 0.01, P ≤ .001), pain interference (b = 1.4, SE = 0.2, P < .001), and WPAI including absenteeism (b = 0.16, SE = 0.04, P = .007), presenteeism (b = 2.7, SE = 1.1, P = .012), overall work productivity impairment (b = 3.7, SE = 1.2, P = .001), and activity impairment (b = 3.0, SE = 1.2, P = .009). PHQ-2 scores were also associated with reporting that migraine interfered with career success (b = 0.34, SE = 0.08, P ≤ .001). GAD-7 scores were not associated with MIDAS, pain interference, WPAI, or reduced career success.

CONCLUSIONS

Severity of depression symptoms in patients with migraine is associated with migraine-related disability, work interference, pain interference, and reduced career success. Patients with more severe symptoms of depression are more likely to have greater functional impairment. A management approach that addresses depression in those with migraine may lead to improvements in patient functioning.

Impact of abuse on migraine-related sensory hypersensitivity symptoms: Results from the American Registry for Migraine Research

BACKGROUND AND OBJECTIVES

Prior studies have established an association between a history of abuse and the development of migraine. This cross-sectional observational study explored the relationship between self-reported abuse history with migraine-related sensory hypersensitivity symptoms.

METHODS

In total, 588 adult patients with migraine from the American Registry for Migraine Research completed questionnaires: Generalized Anxiety Disorder-7, Patient Health Questionnaire-2 for depression, Photosensitivity Assessment Questionnaire, Hyperacusis Questionnaire, and Allodynia Symptom Checklist. Using four binary screening questions, patients were asked to self-report if they believed they had suffered emotional, physical, or sexual abuse in their lifetime. Differences in questionnaire scores between groups with and without a history of abuse were determined. Regression models adjusted for age, sex, and basic headache features analyzed the relationship between abuse history and sensory hypersensitivity symptoms. Moderation analysis explored the role of headache frequency in this relationship. Mediation analysis assessed the indirect (Mediated) effect (IE) of abuse on sensory hypersensitivity through depression or anxiety. Additional models analyzed relationships between sensory hypersensitivity symptoms and abuse subtypes or the number of abuse subtypes.

RESULTS

Of 588 participants, 222 (38%) reported a history of abuse. Patients with a history of abuse reported statistically significantly greater average headache frequency (7.6 vs. 4.7 days, p = 0.030). Patients with a history of abuse also reported higher average or median questionnaire scores: anxiety (7.6 vs. 4.7, p < 0.001, d = 0.56), depression (1.7 vs. 1.3, p = 0.009, d = 0.24), photophobia (0.54 vs. 0.44, p < 0.001, d = 0.32), hyperacusis (19.6 vs. 14.9, p < 0.001, d = 0.49), ictal allodynia (6.0 vs. 3.0, p < 0.001, d = 0.46), and interictal allodynia (1.0 vs. 0.0, p < 0.001, d = 0.30). After controlling for patient age, sex and years lived with headache, abuse maintained a significant association with every sensory hypersensitivity measure. Headache frequency significantly moderated the relationship between a history of abuse with increased ictal allodynia (p = 0.036). Anxiety significantly mediated the relationships between abuse with photophobia (IE = 0.03, 95% CI = 0.01-0.04), hyperacusis (IE = 1.51, 95% CI = 0.91-2.24), ictal allodynia (IE = 0.02, 95% CI = 0.01-0.04), and interictal allodynia (IE = 0.02, 95% CI = 0.01-0.06). Depression significantly mediated the relationship between abuse with photophobia (IE = 0.02, 95% CI = 0.01-0.03) and with hyperacusis (IE = 0.45, 95% CI = 0.11-0.88). The association between the individual subtypes of abuse and the number of subtypes of abuse with sensory hypersensitivity symptoms varied.

CONCLUSION

A history of abuse is associated with greater migraine-related sensory hypersensitivity symptoms. To reduce the impact of abuse on migraine symptoms, future studies should explore mechanistic connections between abuse and migraine-associated symptoms.

Clinical correlates of insomnia in patients with persistent post-traumatic headache compared with migraine

BACKGROUND

Close associations between insomnia with other clinical factors have been identified in migraine, but there have been few studies investigating associations between insomnia and clinical factors in patients with persistent post-traumatic headache (PPTH). The study objective was to contrast the severity of insomnia symptoms in PPTH, migraine, and healthy controls, and to identify factors associated with insomnia in patients with PPTH vs. migraine.

METHODS

In this cross-sectional cohort study, 57 individuals with PPTH attributed to mild traumatic brain injury, 39 with migraine, and 39 healthy controls were included. Participants completed a detailed headache characteristics questionnaire, the Migraine Disability Assessment Scale (MIDAS), Insomnia Severity Index (ISI), Hyperacusis Questionnaire (HQ), Allodynia Symptom Checklist, Photosensitivity Assessment Questionnaire, Beck Depression Inventory (BDI), State-Trait Anxiety Inventory, Post-Traumatic Stress Disorder (PTSD) checklist, Ray Auditory Verbal Learning Test, and the Trail Making Test A and B to assess headache characteristics, disability, insomnia symptoms, sensory hypersensitivities, and neuropsychological factors. Fisher's test and one-way ANOVA or Tukey's Honest Significant Difference were used to assess group differences of categorical and continuous data. Stepwise linear regression analyses were conducted to identify clinical variables associated with insomnia symptoms.

RESULTS

Those with PPTH had significantly higher ISI scores (16.7 ± 6.6) compared to migraine patients (11.3 ± 6.4) and healthy controls (4.1 ± 4.8) (p <  0.001). For those with PPTH, insomnia severity was most strongly correlated with the BDI (Spearman's rho (ρ) = 0.634, p <  0.01), followed by Trait Anxiety (ρ = 0.522, p <  0.01), PTSD (ρ = 0.505, p <  0.01), HQ (ρ = 0.469, p <  0.01), State Anxiety (ρ = 0.437, p <  0.01), and MIDAS scores (ρ = 0.364, p <  0.01). According to linear regression models, BDI, headache intensity, and hyperacusis scores were significantly positively associated with insomnia severity in those with PPTH, while only delayed memory recall was negatively associated with insomnia severity in those with migraine.

CONCLUSIONS

Insomnia symptoms were more severe in those with PPTH compared to migraine and healthy control cohorts. Depression, headache intensity, and hyperacusis were associated with insomnia in individuals with PPTH. Future studies should determine the bidirectional impact of treating insomnia and its associated symptoms.

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

Abstract

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).

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.

A Cross-Study Analysis for Reproducible Sub-classification of Traumatic Brain Injury

Objective: To identify reproducible sub-classes of traumatic brain injury (TBI) that correlate with patient outcomes.

Methods: Two TBI datasets from the Federal Interagency Traumatic Brain Injury Research (FITBIR) Informatics System were utilized, Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot and Citicoline Brain Injury Treatment Trial (COBRIT). Patients included in these analyses had closed head injuries with Glasgow Comas Scale (GCS) scores of 13–15 at arrival at the Emergency Department (ED). Sparse hiearchical clustering was applied to identify TBI sub-classes within each dataset. The reproducibility of the sub-classes was evaluated by investigating similarities in clinical variable profiles and patient outcomes in each sub-class between the two datasets, as well as by using a statistical metric called in-group proportion (IGP).

Results: Seven TBI sub-classes were identified in the first dataset. There were between-class differences in patient outcomes at 90 days (Glasgow Outcome Scale Extended (GOSE): p < 0.001) and 180 days (Trail Making Test (TMT): p = 0.03). Four of seven sub-classes were reproducible in the second dataset with very high IGPs (94, 100, 99, 97%). Seven TBI sub-classes were also identified in the second dataset. There were significant between-class differences in patient outcomes at 180 days (GOSE: p = 0.024; Brief Symptom Inventory (BSI) p = 0.007; TMT: p < 0.001). Three of seven sub-classes were reproducible in the second dataset with very high IGPs (100% for all).

Conclusions: Reproducible TBI sub-classes were identified across two independent datasets, suggesting that these sub-classes exist in a general population. Differences in patient outcomes according to sub-class assignment suggest that this sub-classification could be used to guide post-TBI prognosis.

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.

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.

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.

Differentiating Between Migraine and Post-traumatic Headache Using a Machine Learning Classifier

OBJECTIVE

The objective was to develop classification models differentiating persistent PTH (PPTH) and migraine using clinical data and MRI-based measures of brain structure and functional connectivity.

BACKGROUND

PTH and migraine commonly have similar phenotypes. Furthermore, migraine is a risk factor for developing PTH, sometimes making it difficult to differentiate PTH from exacerbation of migraine symptoms.

DESIGN/METHODS

Thirty-four individuals with migraine without history of TBI and 48 individuals with mild TBI attributed to PPTH but without history of migraine or prior frequent tension type headache were included. Subjects completed questionnaires assessing headache characteristics, mood, sensory hypersensitivities and cognitive function and underwent MRI imaging during the same day. Clinical features and structural brain measures from T1-weighted imaging, diffusion tensor imaging and functional resting-state measures were included as potential variables. A classifier using ridge logistic regression of principal components (PC) was fit. Since PCs can hinder identification of significant variables in a model, a second regression model was fit directly to the data. In the non-PC based model, input variables were selected based on lowest t-test or chi-square p-value by modality. Average accuracy was calculated using leave-one-out cross validation. The importance of variables to the classifier were examined.

RESULTS

The PC-based classifier achieved an average classification accuracy of 85%. The non-PC based classifier achieved an average classification accuracy of 74.4%. Both classifiers were more accurate at classifying migraine subjects than PPTH. The PC-based model incorrectly classified 9/48 (18.8%) PPTH subjects compared to 3/34 (8.8%) migraine patients, whereas the non-PC classifier incorrectly classed 16/48 (33.3%) vs 5/34 (14.7%) of migraine subjects. Important variables in the non-PC model included static and dynamic functional connectivity values, several questions from the Beck Depression Inventory, and worsening symptoms and headaches with mental activity.

CONCLUSIONS

Multivariate models including clinical characteristics, functional connectivity, and brain structural data accurately classify and differentiate PPTH vs migraine.

Headache Characteristics and Psychological Factors Associated with Functional Impairment in Individuals with Persistent Posttraumatic Headache

OBJECTIVE

Persistent posttraumatic headache (PPTH), one of the most common symptoms following mild traumatic brain injury, is often associated with substantial functional disability. The objective of this study was to assess the contribution of demographics, headache characteristics, and psychological symptoms to disability associated with PPTH.

METHODS

Participants completed the State-Trait Anxiety Inventory (STAI), the Beck Depression Inventory (BDI), the Pain Catastrophizing Scale (PCS), and the Migraine Disability Assessment (MIDAS) questionnaire. Two linear regression models were formulated to interrogate the relationships between 1) demographics and headache characteristics with the MIDAS questionnaire and 2) demographics, headache characteristics, and psychological symptoms with the MIDAS questionnaire. A two-way stepwise regression using the Akaike information criterion was performed to find a parsimonious model describing the relationships between demographics, headache characteristics, and psychological measures with the MIDAS questionnaire.

RESULTS

Participants included 58 patients with PPTH and 39 healthy controls (HCs). The median MIDAS score among those with PPTH was 48.0 (first quartile [1Q] = 20.0, third quartile [3Q] = 92.0), indicative of severe disability. Compared with the HCs, those with PPTH had higher scores on the BDI, STAI, and PCS. Older age predicted lower MIDAS scores (age: B=-0.11, P<0.01), whereas higher headache frequency, greater headache intensity, and higher trait anxiety scores predicted higher MIDAS scores in individuals with PPTH (headache frequency: B=0.07, P<0.001; headache intensity: B=0.51, P=0.04; trait anxiety score: B=1.11, P=0.01).

CONCLUSIONS

Individuals with PPTH had substantial psychological symptoms and headache-related disability. Disability was partially explained by age, headache frequency and intensity, and trait anxiety. Holistic management of patients with PPTH to address headaches and psychological symptoms might reduce headache-associated disability.

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.

A history of abuse is associated with more severe migraine- and pain-related disability: Results from the American Registry for Migraine Research

BACKGROUND

Prior studies have established an association between a history of abuse and more severe migraine presentation.

OBJECTIVES

This cross-sectional, observational study of a clinic-based migraine population used validated measures to elucidate migraine-specific and migraine-related burdens among patients with a history of abuse.

METHODS

Patients with migraine (n = 866) from the American Registry for Migraine Research self-reported if they had a history of emotional, physical, and/or sexual abuse and completed questionnaires assessing migraine-related burden: Migraine Disability Assessment, Subjective Cognitive Impairment Scale for Migraine Attacks, Work Productivity and Activity Impairment, Patient-Reported Outcomes Measurement Information System Pain Interference, Patient Health Questionnaire-2, and Generalized Anxiety Disorder-7. Migraine-related burden in patients with versus without a history of abuse was compared. Subsequently, a mediation analysis evaluated the impact of depression and anxiety symptoms in the relationship between abuse history and migraine burden.

RESULTS

A history of abuse was reported by 36.5% (n = 316/866) of participants. After controlling for patient age, sex, years lived with headache, and headache frequency, a history of abuse was significantly associated with more severe migraine-related disability. The combined burden of depression and anxiety symptoms mediated the relationship.

CONCLUSION

A history of abuse is associated with greater migraine-related disability. Future studies should determine if identification and management of the psychological and physical sequelae of abuse reduce migraine burden.

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, R 2 = 0.461, n = 1,036); b) overall work impairment (F[7,570] = 29.23, P < 0.001, R 2 = 0.255, n = 578); c) activity impairment (F[7,570] = 27.42, P < 0.001, R 2 = 0.243, n = 578); d) presenteeism (F[7,570] = 29.17, P < 0.001, R 2 = 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.

Advancing toward precision migraine treatment: Predicting responses to preventive medications with machine learning models based on patient and migraine features

OBJECTIVE

To develop machine learning models using patient and migraine features that can predict treatment responses to commonly used migraine preventive medications.

BACKGROUND

Currently, there is no accurate way to predict response to migraine preventive medications, and the standard trial-and-error approach is inefficient.

METHODS

In this cohort study, we analyzed data from the Mayo Clinic Headache database prospectively collected from 2001 to December 2023. Adult patients with migraine completed questionnaires during their initial headache consultation to record detailed clinical features and then at each follow-up to track preventive medication changes and monthly headache days. We included patients treated with at least one of the following migraine preventive medications: topiramate, beta-blockers (propranolol, metoprolol, atenolol, nadolol, timolol), tricyclic antidepressants (amitriptyline, nortriptyline), verapamil, gabapentin, onabotulinumtoxinA, and calcitonin gene-related peptide (CGRP) monoclonal antibodies (mAbs) (erenumab, fremanezumab, galcanezumab, eptinezumab). We pre-trained a deep neural network, "TabNet," using 145 variables, then employed TabNet-embedded data to construct prediction models for each medication to predict binary outcomes (responder vs. non-responder). A treatment responder was defined as having at least a 30% reduction in monthly headache days from baseline. All model performances were evaluated, and metrics were reported in the held-out test set (train 85%, test 15%). SHapley Additive exPlanations (SHAP) were conducted to determine variable importance.

RESULTS

Our final analysis included 4260 patients. The responder rate for each medication ranged from 28.7% to 34.9%, and the mean time to treatment outcome for each medication ranged from 151.3 to 209.5 days. The CGRP mAb prediction model achieved a high area under the receiver operating characteristics curve (AUC) of 0.825 (95% confidence interval [CI] 0.726, 0.920) and an accuracy of 0.80 (95% CI 0.70, 0.88). The AUCs of prediction models for beta-blockers, tricyclic antidepressants, topiramate, verapamil, gabapentin, and onabotulinumtoxinA were: 0.664 (95% CI 0.579, 0.745), 0.611 (95% CI 0.562, 0.682), 0.605 (95% CI 0.520, 0.688), 0.673 (95% CI 0.569, 0.724), 0.628 (0.533, 0.661), and 0.581 (95% CI 0.550, 0.632), respectively. Baseline monthly headache days, age, body mass index (BMI), duration of migraine attacks, responses to previous medication trials, cranial autonomic symptoms, family history of headache, and migraine attack triggers were among the most important variables across all models. A variable could have different contributions; for example, lower BMI predicts responsiveness to CGRP mAbs and beta-blockers, while higher BMI predicts responsiveness to onabotulinumtoxinA, topiramate, and gabapentin.

CONCLUSION

We developed an accurate prediction model for CGRP mAbs treatment response, leveraging detailed migraine features gathered from a headache questionnaire before starting treatment. Employing the same methods, the model performances for other medications were less impressive, though similar to the machine learning models reported in the literature for other diseases. This may be due to CGRP mAbs being migraine-specific. Incorporating medical comorbidities, genomic, and imaging factors might enhance the model performance. We demonstrated that migraine characteristics are important in predicting treatment responses and identified the most crucial predictors for each of the seven types of preventive medications. Our results suggest that precision migraine treatment is feasible.