Three Dimensions of Association Link Migraine Symptoms and Functional Connectivity

Abstract

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.

Migraine Disability, Pain Catastrophizing, And Headache Severity Are Associated With Evoked Pain And Targeted By Mind-Body Therapy

“…Importantly, the strength of the canonical correlation was fairly small (final model Rc was 0.24), which suggests that complementary approaches are needed to better understand migraine symptom variability. Consistent with this, our group recently identified a much larger canonical correlation with migraine symptoms when using resting-state functional MRI connectivity in this same sample [21].…”

“…Instead of performing correlations between two variables, CCA identifies associations between two domains of variables [17]. CCA has gained renewed interest in the neuroimaging field [41] and has been used to successfully link demographic characteristics and pathology with biological variables [21,26,35,43]. In the current study we associate clinical symptoms and pain ratings simultaneously in over 100 episodic migraine patients recruited for a clinical trial assessing enhanced mindfulnessbased stress reduction (MBSR+) treatment of headaches [34].…”

Migraine disability, pain catastrophizing, and headache severity are associated with evoked pain and targeted by mind–body therapy

“…Consistent with this, we recently identified a much larger canonical correlation with migraine symptoms when using resting-state functional MRI connectivity in this same sample. 24 The present study is not without its limitations. First, there is always the danger of overfitting in CCA, which will create a model that fails to generalize.…”

“…Consistent with this, we recently identified a much larger canonical correlation with migraine symptoms when using resting-state functional MRI connectivity in this same sample. 24…”

“…20 Canonical correlation analysis has gained renewed interest in the neuroimaging field 49 and has been used to successfully link demographic characteristics and pathology with biological variables. 24,31,43,51 In the current study, we sought to determine if there exists any relationship between migraine symptoms and experimental evoked pain ratings. To do this, we used CCA to simultaneously test for association between QST thermal pain ratings and 12 clinical measures relating to disability, headache frequency, pain severity, allodynia, sleep quality, pain-related cognition, somatization, and affect.…”