Three Dimensions of Association Link Migraine Symptoms and Functional Connectivity

Abstract

Migraine is a heterogeneous disorder with variable symptoms and responsiveness to therapy. Because of previous analytic shortcomings, variance in migraine symptoms has been inconsistently related to brain function. In the current analysis, we used data from two sites (n = 143, male and female humans), and performed canonical correlation analysis, relating resting-state functional connectivity (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. The first dimension 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 postintervention, were associated with 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. In addition to performing canonical correlation analysis, 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.

SIGNIFICANCE STATEMENT Using a large multisite dataset of migraine patients, we identified three dimensions of multivariate association between symptoms and functional connectivity. This analysis revealed neural networks that relate to all measured symptoms, but also to specific symptom ensembles, such as patient propensity to catastrophize painful events. Using these three dimensions, we found four biotypes of migraine informed by clinical and neural variation together. Such findings pave the way for precision medicine therapy for migraine.