| dc.description.abstract | Migraine is a common primary brain disorder which is characterized by recurrent pulsating
and throbbing head pain. Magnetic Resonance Imaging (MRI) - based neuroimaging
has been extensively used to detect structural brain changes in migraines. We hypothesize
that the above- mentioned changes may alter structural brain network topology in the
brain, resulting in poor information transfer. Therefore, we aimed to characterize the
global network topology of patients with migraine and healthy subjects using gray matter
structural networks. The study was performed using 3D T1-weighted MRI images of
the brains of 45 migraine patients and 46 healthy controls. Then group-level structural
connectivity matrices were developed using Pearson correlation and the matrices
were binarized by applying a series of sparsity thresholds and global network topologies
(small worldness, network efficiency, hierarchy, synchronization, and assortativity) were
computed. Between-group di erences of global topological metrics were tested using
nonparametric statistics (permutation tests, n=1000). According to between-group results,
patients with migraine showed increases in small-worldness, and global e ciency
while local e ciency and synchronization did not di er signi cantly between patients
and healthy subjects (p<0.05). Assortativity values were largely dispersed among their
sparsities and were considerably higher in the healthy network than in the migraine
network at sparsities of 0.4 and 0.5 (p<0.05). In addition, with the increasing network
sparsity there was an increasing trend for hierarchy property of patients. Our ndings
imply that migraine could alter the topological properties of structural brain networks
and graph theory-based approach provides valuable information about them. | en_US |
