| dc.description.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. | en_US |
