Chong, Catherine D.

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.

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.

Objectives Often, persistent post-traumatic headache and migraine are phenotypically similar. However, the similarities and differences in the neuropathological underpinnings of persistent post-traumatic headache and migraine require further understanding. We used diffusion tensor imaging (DTI) and a novel method for detecting subtle changes in fibertract integrity by measuring node-by-node parameters along each tract to compare fibertract profiles between those with migraine and those with persistent post-traumatic headache, and compared both cohorts to a group of controls. Methods Eighteen fibertracts were reconstructed for 131 subjects, including 49 patients with persistent post-traumatic headache attributed to mild traumatic brain injury, 41 with migraine, and 41 controls. Node-by-node diffusion parameters of mean diffusivity and radial diffusivity were calculated along each tract. Mean diffusivity and radial diffusivity measurements were averaged along quartiles of each tract for statistical interpretation and group comparison. Using a post-hoc analysis, correlations between tract quartile measurements and headache frequency were calculated. Results There were significant differences between migraine and persistent post-traumatic headache cohorts for quartile measurements of mean diffusivity or radial diffusivity in the bilateral anterior thalamic radiations, cingulum (angular bundles and cingulate gyri), inferior longitudinal fasciculi, and uncinate fasciculi, the left corticospinal tract, and the right superior longitudinal fasciculi-parietal portion. For migraine patients, there was a significant positive correlation between headache frequency and forceps major mean diffusivity, whereas for persistent post-traumatic headache there was a positive correlation between headache frequency and cingulum angular bundle mean diffusivity and radial diffusivity. Conclusions Quartile measurements of radial diffusivity and mean diffusivity indicate unique differences in fibertract profiles between those with migraine vs. persistent post-traumatic headache. Although for both migraine and persistent post-traumatic headache there was a positive relationship between fibertract alterations and headache frequency, there were disease-specific differences between headache frequency and fibertract injury patterns. These findings might suggest potential differences in the neuropathological mechanisms underlying migraine and persistent post-traumatic headache.

Background Most persistent posttraumatic headaches (PPTH) have a phenotype that meets diagnostic criteria for migraine or probable migraine. Although symptoms of autonomic dysfunction have been well described among those with migraine, the presence and relative severity of such symptoms among those with PPTH have yet to be reported. Objective The objective of this study was to assess and compare symptoms of autonomic dysfunction among those with PPTH attributed to mild traumatic brain injury (mTBI) vs migraine vs healthy controls using Composite Autonomic Symptom Score 31 (COMPASS‐31) questionnaire scores. Methods Individuals with PPTH (n = 56) (87.5% of whom had a migraine/probable migraine phenotype), migraine (n = 30), and healthy controls (n = 36) were prospectively assessed in this cross‐sectional cohort study using the COMPASS‐31 questionnaire. Total COMPASS‐31 scores and individual domain scores (bladder, gastrointestinal, orthostatic intolerance, pupillomotor, secretomotor, vasomotor) were compared between subject groups. Results COMPASS‐31 mean total weighted score was 37.22 ± 15.44 in the PPTH group, 27.15 ± 14.37 in the migraine group, and 11.67 ± 8.98 for healthy controls. COMPASS‐31 mean weighted total scores were significantly higher in those with PPTH vs migraine ( P = .014), for PPTH vs healthy controls ( P = .001), and for migraine vs healthy controls ( P = .001). Those with PPTH had numerically higher scores for all COMPASS‐31 domains compared to those with migraine, and the domain scores were significantly higher for orthostatic intolerance (PPTH = 4.80 ± 2.47 vs migraine = 3.33 ± 2.31, P = .027) and bladder (PPTH = 1.14 ± 1.45 vs migraine = 0.47 ± 0.73, P = .020). Among individuals with PPTH, post hoc correlations indicated a positive association between number of total lifetime TBIs with total weighted COMPASS‐31 scores (rho = 0.32, P = .020), between years lived with headache and vasomotor domain subscores (rho = 0.27; P = .044), and between headache frequency with vasomotor domain subscores (rho = 0.27; P = .041). Conclusions Symptoms of autonomic dysfunction were greatest among those with PPTH compared to migraine and healthy controls. Among individuals with PPTH, number of lifetime TBIs was associated with greater symptoms of autonomic dysfunction, while greater headache burden was associated with higher vasomotor domain autonomic dysfunction subscores, potentially indicating that PPTH patients with higher disease burden have an increased risk for having autonomic dysfunction. Symptoms of autonomic dysfunction should be ascertained during the clinical management of patients with PPTH and might be a characteristic that helps differentiate PPTH from migraine.

Even when concussions are associated with prolonged physical and cognitive sequelae, concussions are typically “invisible” on diagnostic brain imaging, indicating that the neuropathology associated with concussion lies under the detection threshold of routine imaging. However, data from brain structural and functional research imaging studies using diffusion tensor imaging, resting‐state functional magnetic resonance imaging, and brain perfusion imaging indicate that these imaging sequences have a role in identifying concussion‐related neuropathology. These advanced imaging techniques provide insights into concussion neuropathology and might be useful for differentiating concussed patients from healthy controls. In this review article, we provide an overview of research findings from brain structural and functional imaging studies of concussion, and discuss the accuracy of classification models developed via machine‐learning algorithms for identifying individual patients with concussion based on imaging data.

Resting-state functional magnetic resonance imaging (rs-fMRI) is used to interrogate the functional connectivity and network organization amongst brain regions. Functional connectivity is determined by measuring the extent of synchronization in the spontaneous fluctuations of blood oxygenation level dependent (BOLD) signal. Here, we review current rs-fMRI studies in headache disorders including migraine, trigeminal autonomic cephalalgias, and medication overuse headache. We discuss (1) brain network alterations that are shared amongst the different headache disorders and (2) network abnormalities distinct to each headache disorder. In order to focus the section on migraine, the headache disorder that has been most extensively studied, we chose to include articles that interrogated functional connectivity: (i) during the attack phase; (ii) in migraine patients with aura compared to migraine patients without aura; and (iii) of regions within limbic, sensory, motor, executive and default mode networks and those which participate in multisensory integration. The results of this review show that headache disorders are associated with atypical functional connectivity of regions associated with pain processing as well as atypical functional connectivity of multiple core resting state networks such as the salience, sensorimotor, executive, attention, limbic, visual, and default mode networks.

Objective To investigate differences in cortical thickness in patients with persistent post‐traumatic headache (PPTH) relative to healthy controls and to interrogate whether cortical morphology relates to headache burden (headache frequency, years with post‐traumatic headache, PTH) in patients with PPTH. Background PTHs are one of the most common symptoms following concussion. In some patients, PTHs continue for longer than three months and are classified as PPTH. This study has two main goals: (1) To delineate the neuropathology of PPTH, by interrogating differences in cortical thickness in patients with PPTH relative to healthy controls. (2) To interrogate potential associations between brain morphology and headache burden in patients with PPTH by examining whether cortical thickness relates to frequency of headaches or years lived with PTH. Methods Adults with PPTH diagnosed according to ICHD 3 beta diagnostic criteria and healthy controls underwent brain MRI on a 3 Tesla scanner. Vertex‐by‐vertex whole brain estimates of cortical thickness were automatically calculated using FreeSurfer v5.3. Differences in cortical thickness in patients with PPTH relative to healthy controls were determined using a general linear model design. Associations were explored between regional clusters where patients with PPTH showed cortical thickness differences compared with healthy controls with headache frequency and years lived with PPTH. Results This study included 33 patients with PPTH and 33 healthy control subjects (healthy controls: median age = 33.0, IQR = 15.5; patients with PPTH: median age = 36.0, IQR = 20.5; P = .56). Patients with PPTH had less cortical thickness relative to healthy controls in the left and right superior frontal, caudal middle frontal, and precentral cortex as well as less cortical thickness in the right supramarginal, right superior and inferior parietal, and right precuneus region ( P < .05, Monte Carlo corrected for multiple comparisons). There were no regions where patients with PPTH had more cortical thickness relative to healthy controls. A correlation analysis of regions that showed less cortical thickness in patients with PPTH demonstrated a negative correlation between left and right superior frontal thickness with headache frequency ( P < .05). There was no association between regional cortical thickness and years lived with PPTH. Conclusion Compared with healthy controls, patients with PPTH had less cortical thickness in bilateral frontal regions and right hemisphere parietal regions. For patients with PPTH, more frequent headaches were related to less thickness in the left and right superior frontal regions, potentially indicating that brain morphology changes in the superior frontal regions in patients with PPTH are modified by headache frequency.

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 mm 3 , SD = 425.3 mm 3 , migraine patients: left hippocampal volume = 4089.5 mm 3 , SD = 453.9 mm 3 , P = .02) and less total (right plus left) hippocampal volume (healthy controls: total hippocampal volume= 8690.8 mm 3 , SD = 855.1 mm 3 ; migraine patients: total hippocampal volume = 8341.8 mm 3 , SD = 917.9 mm 3 ; 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.

Background The current subclassification of migraine is according to headache frequency and aura status. The variability in migraine symptoms, disease course, and response to treatment suggest the presence of additional heterogeneity or subclasses within migraine. Objective The study objective was to subclassify migraine via a data‐driven approach, identifying latent factors by jointly exploiting multiple sets of brain structural features obtained via magnetic resonance imaging (MRI). Methods Migraineurs ( n = 66) and healthy controls ( n = 54) had brain MRI measurements of cortical thickness, cortical surface area, and volumes for 68 regions. A multimodality factor mixture model was used to subclassify MRIs and to determine the brain structural factors that most contributed to the subclassification. Clinical characteristics of subjects in each subgroup were compared. Results Automated MRI classification divided the subjects into two subgroups. Migraineurs in subgroup #1 had more severe allodynia symptoms during migraines (6.1 ± 5.3 vs. 3.6 ± 3.2, P = .03), more years with migraine (19.2 ± 11.3 years vs 13 ± 8.3 years, P = .01), and higher Migraine Disability Assessment (MIDAS) scores (25 ± 22.9 vs 15.7 ± 12.2, P = .04). There were not differences in headache frequency or migraine aura status between the two subgroups. Conclusions Data‐driven subclassification of brain MRIs based upon structural measurements identified two subgroups. Amongst migraineurs, the subgroups differed in allodynia symptom severity, years with migraine, and migraine‐related disability. Since allodynia is associated with this imaging‐based subclassification of migraine and prior publications suggest that allodynia impacts migraine treatment response and disease prognosis, future migraine diagnostic criteria could consider allodynia when defining migraine subgroups.