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Objective To identify brain regions having aberrant pain-induced activation in migraineurs, thereby gaining insight into particular aspects of pain processing that are atypical in migraineurs. Methods Functional magnetic resonance imaging assessed whole brain responses to painful heat in 24 adult episodic migraineurs who were at least 48 hours pain free and 27 healthy controls. Regions differentially activated in migraineurs compared to controls were identified. Activation intensities in these regions were correlated with headache frequency, number of migraine years, and time to next migraine attack. Results Migraineurs had greater pain-induced activation of lentiform nucleus, fusiform gyrus, subthalamic nucleus, hippocampus, middle cingulate cortex, premotor cortex, somatosensory cortex and dorsolateral prefrontal cortex, and less activation in precentral gyrus and superior temporal gyrus. There were significant correlations between activation strength and headache frequency for middle cingulate (r=0.627, p=.001), right dorsolateral prefrontal cortex (r=.568, p=.004), left fusiform gyrus (r=0.487, p=.016), left precentral gyrus (r=.415, p=.044), and left hippocampus (r=0.404, p=.050) and with number of migraine years for left fusiform gyrus (r=0.425, p=.038). There were no significant correlations between activation strength and time to next migraine attack. Conclusions The majority of regions with enhanced pain-induced activation in headache-free migraineurs participate in cognitive aspects of pain perception such as attending to pain and pain memory. Enhanced cognitive pain processing by migraineurs might reflect cerebral hypersensitivity related to high expectations and hypervigilance for pain.

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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.