Alteration of multilayer network perspective on gray and white matter connectivity in obstructive sleep apnea.

ABSTRACT

PURPOSE: The objective of this research was to examine changes in the neural networks of both gray and white matter in individuals with obstructive sleep apnea (OSA) in comparison to those without the condition, employing a comprehensive multilayer network analysis. METHODS: Patients meeting the criteria for OSA were recruited through polysomnography, while a control group of healthy individuals matched for age and sex was also assembled. Utilizing T1-weighted imaging, a morphometric similarity network was crafted to represent gray matter, while diffusion tensor imaging provided structural connectivity for constructing a white matter network. A multilayer network analysis was then performed, employing graph theory methodologies. RESULTS: We included 40 individuals diagnosed with OSA and 40 healthy participants in our study. Analysis revealed significant differences in various global network metrics between the two groups. Specifically, patients with OSA exhibited higher average degree overlap and average multilayer clustering coefficient (28.081 vs. 23.407, p < 0.001; 0.459 vs. 0.412, p = 0.004), but lower multilayer modularity (0.150 vs. 0.175, p = 0.001) compared to healthy controls. However, no significant differences were observed in average multiplex participation, average overlapping strength, or average weighted multiplex participation between the patients with OSA and healthy controls. Moreover, several brain regions displayed notable differences in degree overlap at the nodal level between patients with OSA and healthy controls. CONCLUSION: Remarkable alterations in the multilayer network, indicating shifts in both gray and white matter, were detected in patients with OSA in contrast to their healthy counterparts. Further examination at the nodal level unveiled notable changes in regions associated with cognition, underscoring the effectiveness of multilayer network analysis in exploring interactions across brain layers.