Citations

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.

Background Research imaging of brain structure and function has helped to elucidate the pathophysiology of medication overuse headache (MOH). Methods This is a narrative review of imaging research studies that have investigated brain structural and functional alterations associated with MOH. Studies included in this review have investigated abnormal structure and function of pain processing regions in people with MOH, functional patterns that might predispose individuals to development of MOH, similarity of brain functional patterns in patients with MOH to those found in people with addiction, brain structure that could predict headache improvement following discontinuation of the overused medication, and changes in brain structure and function after discontinuation of medication overuse. Results and Conclusions MOH is associated with atypical structure and function of brain regions responsible for pain processing as well as brain regions that are commonly implicated in addiction. Several studies have shown “normalization” of structure and function in pain processing regions following discontinuation of the overused medication and resolution of MOH. However, some of the abnormalities in regions also implicated in addiction tend to persist following discontinuation of the overused medication, suggesting that they are a brain trait that predisposes certain individuals to medication overuse and MOH.

Background This study used machine-learning techniques to develop discriminative brain-connectivity biomarkers from resting-state functional magnetic resonance neuroimaging ( rs-fMRI) data that distinguish between individual migraine patients and healthy controls. Methods This study included 58 migraine patients (mean age = 36.3 years; SD = 11.5) and 50 healthy controls (mean age = 35.9 years; SD = 11.0). The functional connections of 33 seeded pain-related regions were used as input for a brain classification algorithm that tested the accuracy of determining whether an individual brain MRI belongs to someone with migraine or to a healthy control. Results The best classification accuracy using a 10-fold cross-validation method was 86.1%. Resting functional connectivity of the right middle temporal, posterior insula, middle cingulate, left ventromedial prefrontal and bilateral amygdala regions best discriminated the migraine brain from that of a healthy control. Migraineurs with longer disease durations were classified more accurately (>14 years; 96.7% accuracy) compared to migraineurs with shorter disease durations (≤14 years; 82.1% accuracy). Conclusions Classification of migraine using rs-fMRI provides insights into pain circuits that are altered in migraine and could potentially contribute to the development of a new, noninvasive migraine biomarker. Migraineurs with longer disease burden were classified more accurately than migraineurs with shorter disease burden, potentially indicating that disease duration leads to reorganization of brain circuitry.

Background Migraine attacks manifest with hypersensitivities to light, sound, touch and odor. Some people with migraine have photosensitivity between migraine attacks, suggesting persistent alterations in the integrity of brain regions that process light. Although functional neuroimaging studies have shown visual stimulus induced “hyperactivation” of visual cortex regions in migraineurs between attacks, whether photosensitivity is associated with alterations in brain structure is unknown. Methods Levels of photosensitivity were evaluated using the Photosensitivity Assessment Questionnaire in 48 interictal migraineurs and 48 healthy controls. Vertex-by-vertex measurements of cortical thickness were assessed in 28 people with episodic migraine who had interictal photosensitivity (mean age = 35.0 years, SD = 12.1) and 20 episodic migraine patients without symptoms of interictal photosensitivity (mean age = 36.0 years, SD = 11.4) using a general linear model design. Results Migraineurs have greater levels of interictal photosensitivity relative to healthy controls. Relative to migraineurs without interictal photosensitivity, migraineurs with interictal photosensitivity have thicker cortex in several brain areas including the right lingual, isthmus cingulate and pericalcarine regions, and the left precentral, postcentral and supramarginal regions. Conclusion Episodic migraineurs with interictal photosensitivity have greater cortical thickness in the right parietal-occipital and left fronto-parietal regions, suggesting that persistent light sensitivity is associated with underlying structural alterations.

Background The International Classification of Headache Disorders provides criteria for the diagnosis and subclassification of migraine. Since there is no objective gold standard by which to test these diagnostic criteria, the criteria are based on the consensus opinion of content experts. Accurate migraine classifiers consisting of brain structural measures could serve as an objective gold standard by which to test and revise diagnostic criteria. The objectives of this study were to utilize magnetic resonance imaging measures of brain structure for constructing classifiers: (1) that accurately identify individuals as having chronic vs episodic migraine vs being a healthy control; and (2) that test the currently used threshold of 15 headache days/month for differentiating chronic migraine from episodic migraine. Methods Study participants underwent magnetic resonance imaging for determination of regional cortical thickness, cortical surface area, and volume. Principal components analysis combined structural measurements into principal components accounting for 85% of variability in brain structure. Models consisting of these principal components were developed to achieve the classification objectives. Tenfold cross validation assessed classification accuracy within each of the 10 runs, with data from 90% of participants randomly selected for classifier development and data from the remaining 10% of participants used to test classification performance. Headache frequency thresholds ranging from 5‐15 headache days/month were evaluated to determine the threshold allowing for the most accurate subclassification of individuals into lower and higher frequency subgroups. Results Participants were 66 migraineurs and 54 healthy controls, 75.8% female, with an average age of 36 +/− 11 years. Average classifier accuracies were: (1) 68% for migraine (episodic + chronic) vs healthy controls; (2) 67.2% for episodic migraine vs healthy controls; (3) 86.3% for chronic migraine vs healthy controls; and (4) 84.2% for chronic migraine vs episodic migraine. The classifiers contained principal components consisting of several structural measures, commonly including the temporal pole, anterior cingulate cortex, superior temporal lobe, entorhinal cortex, medial orbital frontal gyrus, and pars triangularis. A threshold of 15 headache days/month allowed for the most accurate subclassification of migraineurs into lower frequency and higher frequency subgroups. Conclusions Classifiers consisting of cortical surface area, cortical thickness, and regional volumes were highly accurate for determining if individuals have chronic migraine. Furthermore, results provide objective support for the current use of 15 headache days/month as a threshold for dividing migraineurs into lower frequency (ie, episodic migraine) and higher frequency (ie, chronic migraine) subgroups.

Background Specific white-matter tract alterations in migraine remain to be elucidated. Using diffusion tensor imaging (DTI), this study investigated whether the integrity of white-matter tracts that underlie regions of the “pain matrix” is altered in migraine and interrogated whether the number of years lived with migraine modifies fibertract structure. Methods Global probabilistic tractography was used to assess the anterior thalamic radiations, the corticospinal tracts and the inferior longitudinal fasciculi in 23 adults with migraine and 18 healthy controls. Results Migraine patients show greater mean diffusivity (MD) in the left and right anterior thalamic radiations, the left corticospinal tract, and the right inferior longitudinal fasciculus tract. Migraine patients also show greater radial diffusivity (RD) in the left anterior thalamic radiations, the left corticospinal tract as well as the left and right inferior longitudinal fasciculus tracts. No group fractional anisotropy (FA) differences were identified for any tracts. Migraineurs showed a positive correlation between years lived with migraine and MD in the right anterior thalamic radiations ( r = 0.517; p = 0.012) and the left corticospinal tract ( r = 0.468; p = 0.024). Conclusion Results indicate that white-matter integrity is altered in migraine and that longer migraine history is positively correlated with greater alterations in tract integrity.

Background/Objective Between attacks, migraine is associated with hypersensitivities to sensory stimuli. The objective of this study was to investigate hypersensitivity to pain in migraineurs between attacks. Methods Cutaneous heat pain thresholds were measured in 112 migraineurs, migraine free for ≥48 hours, and 75 healthy controls. Pain thresholds at the head and at the arm were compared between migraineurs and controls using two-tailed t-tests. Among migraineurs, correlations between heat pain thresholds and headache frequency, allodynia symptom severity, and time interval until next headache were calculated. Results Migraineurs had lower pain thresholds than controls at the head (43.9℃ ± 3.2℃ vs. 45.1℃ ± 3.0℃, p = 0.015) and arm (43.2℃ ± 3.4℃ vs. 44.8℃ ± 3.3℃, p < 0.001). There were not significant correlations between pain thresholds and headache frequency or allodynia symptom severity. For the 41 migraineurs for whom time to next headache was known, there were positive correlations between time to next headache and pain thresholds at the head ( r = 0.352, p = 0.024) and arm ( r = 0.312, p = 0.047). Conclusions This study provides evidence that migraineurs have low heat pain thresholds between migraine attacks. Mechanisms underlying these lower pain thresholds could also predispose migraineurs to their next migraine attack, a hypothesis supported by finding positive correlations between pain thresholds and time to next migraine attack.