Multilayer network changes in patients with migraine

Abstract

Introduction: To investigate changes in the multilayer network in patients with migraine compared to healthy controls.

Methods: This study enrolled 82 patients with newly diagnosed migraine without aura and 53 healthy controls. Brain magnetic resonance imaging (MRI) was conducted using a 3-tesla MRI scanner, including three-dimensional T1-weighted and diffusion tensor imaging (DTI). A gray matter layer matrix was created with a morphometric similarity network using T1-weighted imaging and the FreeSurfer program. A white matter layer matrix was also created with structural connectivity using the DTI studio (DSI) program. A multilayer network analysis was then performed by applying graph theory using the BRAPH program.

Results: Significant changes were observed in the multilayer network at the global level in patients with migraines compared to the healthy controls. The multilayer modularity (0.177 vs. 0.160, p = .0005) and average multiplex participation (0.934 vs. 0.924, p = .002) were higher in patients with migraines than in the healthy controls. In contrast, the average multilayer clustering coefficient (0.406 vs. 0.461, p = .0005), average overlapping strength (56.061 vs. 61.676, p = .0005), and average weighted multiplex participation (0.847 vs. 0.878, p = .0005) were lower in patients with migraine than in the healthy controls. In addition, several regions showed significant changes in the multilayer network at the nodal level, including multiplex participation, multilayer clustering coefficients, overlapping strengths, and weighted multiplex participation.

Conclusion: This study demonstrated significant changes in the multilayer network in patients with migraines compared to healthy controls. This could aid an understanding of the complex brain network in patients with migraine and may be associated with the pathophysiology of migraines. Patients with migraine show multilayer network changes in widespreading brain regions compared to healthy controls, and specific brain areas seem to play a hub role for pathophysiology of the migraine.

Keywords: magnetic resonance imaging; migraine; neural network.