Insights into chronic migraine pathophysiology – what measures of gray matter reveal

To the Editor:

We read with enthusiasm the recent article by Lai and colleagues describing the use of surface- and voxel-based MRI methods to assess gray matter structure in a cohort of patients with chronic migraine (CM) (1). Of the measures assessed, significant differences in surface-based cortical thickness measures were detected, such that patients had thinner cortices in the bilateral insular cortex, caudal middle frontal gyrus, precentral gyrus, and parietal lobes compared to controls. Additionally, cortical thickness within the right insula was negatively associated with monthly migraine frequency at baseline. While the authors acknowledge that these findings are not specific to CM, having also been reported in episodic migraine and across the pain literature in general, their study nonetheless provides support for the greater sensitivity of surface-based over voxel-based methods in detecting structural brain alterations and adds to the literature demonstrating a role for brain regions involved in pain processing in CM pathophysiology.

There are several strengths to the Lai et al. study. First, patients with a history of medication overuse headache or major depression were excluded, providing a unique opportunity to assess subtle gray matter alterations in the absence of common clinical confounds. Additionally, multiple methods to assess gray matter structure were included, providing a more comprehensive assessment of gray matter structure compared to using either method alone.

However, there is now a growing literature investigating brain structures as part of larger networks as opposed to examining them focally. The rationale for a network approach stems from research demonstrating that coordinated variations in structural brain measures differ across development and disease and often comprise networks underlying behavioral and cognitive functions. One framework to investigate networks of structural covariance is graph theory, whereby brain regions are represented as nodes and their interconnections (e.g. correlations in gray matter structure) as edges. Network analyses involve descriptive measures of local and global features and generally provide information about network integration and segregation (2).

Recently, we applied graph theory to surface- and voxel-based measures of gray matter structure to examine structural network alterations in patients with CM (3). Compared to controls, we showed that patients with CM had altered local and global network properties that were characterized as less integrated, less efficient, and abnormally segregated. These network differences were most prominent in the limbic and insular cortices, in line with the findings by Liu et al., and also spanned frontal, temporal and brainstem regions. While our findings suggest the involvement of certain brain regions in CM pathophysiology, the novelty of our results is the demonstration of how these brain regions are differentially connected to neighboring brain areas (local) and to the rest of the brain (global). These network alterations were detected in the absence of group differences in focal brain structure, suggesting that network approaches may provide a more sensitive means to detect subtle differences in brain structure when clinical comorbidities cannot be excluded. Future studies investigating structural and functional network alterations in CM may provide further insight into migraine-specific pathophysiology.