Migraine is a heterogeneous disorder with variable symptoms and responsiveness to therapy. Due to previous analytic shortcomings, variance in migraine symptoms has been weakly and inconsistently related to brain function. Taking advantage of neural network organization measured through resting-state functional connectivity (RSFC) and advanced statistical analysis, sophisticated symptom-brain mapping can now be performed. In the current analysis we used data from two sites (n=102 and 41), and performed Canonical Correlation Analysis (CCA), relating RSFC with a broad range of migraine symptoms ranging from headache characteristics to sleep abnormalities. This identified three dimensions of covariance between symptoms and RSFC. Importantly, none of these dimensions bore any relationship with subject motion. The first dimension was related to headache intensity, headache frequency, pain catastrophizing, affect, sleep disturbances, and somatic abnormalities, and was associated with frontoparietal and dorsal attention network connectivity, both of which are major cognitive networks. Additionally, RSFC scores from this dimension, both the baseline value and the change from baseline to post-intervention, were associated with clinical responsiveness to mind-body therapy. The second dimension was related to an inverse association between pain and anxiety, and to default mode network connectivity. The final dimension was related to pain catastrophizing, and salience, sensorimotor and default mode network connectivity. These unique symptom/brain-mappings over three dimensions provide novel network targets to modify specific ensembles of symptoms. In addition to performing CCA, we evaluated the current clustering of migraine patients into episodic and chronic subtypes, and found no evidence to support this clustering. However, when using RSFC scores from the three significant dimensions, we identified a novel clustering of migraine patients into four biotypes with unique functional connectivity patterns. These findings provide new insight into individual variability in migraine, and could serve as the foundation for novel therapies that take advantage of migraine heterogeneity.

Meta-analysis suggests migraine patients are no more sensitive to experimentally evoked pain than healthy controls. At the same time, studies have linked some migraine symptoms to Quantitative Sensory Testing (QST) profiles. Unfortunately, previous studies associating migraine symptoms and QST have important methodological shortcomings, stemming from inappropriate statistics, small sample sizes, and frequent use of univariate statistics for multivariate research questions. In the current study we seek to address these limitations by using a large sample of episodic migraine patients (n=103) and a multivariate analysis that associates pain ratings from many thermal intensities simultaneously with 12 clinical measures ranging from headache frequency to sleep abnormalities. We identified a single dimension of association between QST and migraine symptoms that relates to pain ratings for all stimulus intensities and a subset of migraine symptoms relating to disability (Headache Impact Trauma 6 and Brief Pain Inventory interference), catastrophizing (Pain Catastrophizing Scale), and pain severity (average headache pain, Brief Pain Inventory severity, and Short Form McGill Pain Questionnaire 2). Headache frequency, allodynia, affect, and sleep disturbances were unrelated to this dimension. Consistent with previous research, we did not observe any difference in QST ratings between migraine patients and healthy controls. Additionally, we found that the linear combination of symptoms that related to QST were modified by mind-body therapy. These results suggest that QST has a selective relationship with pain symptoms even in the absence of between-subjects differences between chronic pain patients and healthy controls.