• Danielle D DeSouza, Yohannes W Woldeamanuel, Bharati M Sanjanwala, Daniel A Bissell, James H Bishop, Addie Peretz, and Robert P Cowan.
• Department of Neurology and Neurological Sciences, Stanford University Headache and Facial Pain Program, 300 Pasteur Drive, Palo Alto, CA, USA. desouzad@stanford.edu.
• Brain Struct Funct. 2020 Jan 1; 225 (1): 161-172.

AbstractDespite its prevalence and high disease burden, the pathophysiological mechanisms underlying chronic migraine (CM) are not well understood. As CM is a complex disorder associated with a range of sensory, cognitive, and affective comorbidities, examining structural network disruption may provide additional insights into CM symptomology beyond studies of focal brain regions. Here, we compared structural interconnections in patients with CM (n = 52) and healthy controls (HC) (n = 48) using MRI measures of cortical thickness and subcortical volume combined with graph theoretical network analyses. The analysis focused on both local (nodal) and global measures of topology to examine network integration, efficiency, centrality, and segregation. Our results indicated that patients with CM had altered global network properties that were characterized as less integrated and efficient (lower global and local efficiency) and more highly segregated (higher transitivity). Patients also demonstrated aberrant local network topology that was less integrated (higher path length), less central (lower closeness centrality), less efficient (lower local efficiency) and less segregated (lower clustering). These network differences not only were most prominent in the limbic and insular cortices but also occurred in frontal, temporal, and brainstem regions, and occurred in the absence of group differences in focal brain regions. Taken together, examining structural correlations between brain areas may be a more sensitive means to detect altered brain structure and understand CM symptomology at the network level. These findings contribute to an increased understanding of structural connectivity in CM and provide a novel approach to potentially track and predict the progression of migraine disorders.This study is registered on ClinicalTrials.gov (Identifier: NCT03304886).

24 keywords...

hide…