Structural brain network topology in migraine vs. healthy subjects: A graph theory study

Abstract

There is compelling evidence that migraine is associated with a decline in grey matter in the brain. We postulate that these changes might affect the architecture of the structural brain networks and normal wiring leading to altered functioning of the brain. Therefore, our goal was to compare the global brain network topology of patients with migraines and healthy subjects using grey matter structural networks. The study involved 45 patients with migraine and 46 healthy subjects. 3D, T1-weighted brain images were obtained using a 3 Tesla MRI scanner. Images were preprocessed, and grey matter volume images were generated. Group-level structural connectivity matrices were created using Pearson correlation, and the matrices were binarized by applying a series of sparsity thresholds to compute global network topologies. According to the between-group results, patients with migraines showed increases in small-worldness and global efficiency while local efficiency and synchronization did not differ significantly between patients and healthy subjects (p< 0.05). Assortativity values were widely dispersed across different levels of sparsity and were significantly higher in the healthy network compared to the migraine network at sparsities of 0.4 and 0.5 (p< 0.05). Furthermore, as network sparsity increased, there was a noticeable trend towards a hierarchy property among patients. According to our findings, patients with migraines exhibit better integration and poorer segregation of information processing in the human brain. Graph theory-based approach provides valuable information on network topological metrics in migraine.