Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea.
Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea.
Sleep Breath. 2024 Aug;28(4):1671-1678. doi: 10.1007/s11325-024-03059-4. Epub 2024 May 11.
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.
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.
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.
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.
本研究旨在通过综合多层网络分析,比较阻塞性睡眠呼吸暂停(OSA)患者和无 OSA 患者的灰质和白质神经网络变化。
通过多导睡眠图招募符合 OSA 标准的患者,同时还招募了年龄和性别相匹配的健康对照组。利用 T1 加权成像构建形态相似网络来表示灰质,利用弥散张量成像构建白质网络的结构连接。然后使用图论方法进行多层网络分析。
我们纳入了 40 名诊断为 OSA 的患者和 40 名健康参与者。分析发现两组之间存在多种全局网络指标的显著差异。具体来说,OSA 患者的平均度重叠和平均多层聚类系数较高(28.081 比 23.407,p<0.001;0.459 比 0.412,p=0.004),但多层模块度较低(0.150 比 0.175,p=0.001)。然而,OSA 患者与健康对照组之间的平均多重参与度、平均重叠强度或平均加权多重参与度无显著差异。此外,在节点水平上,一些脑区的度重叠存在显著差异。
与健康对照组相比,OSA 患者的多层网络发生了显著变化,表明灰质和白质均发生了变化。在节点水平上的进一步检查揭示了与认知相关的区域发生了显著变化,这突显了多层网络分析在探索大脑各层之间相互作用方面的有效性。