Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, China.
Department of Radiology, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan Province, China.
CNS Neurosci Ther. 2024 Jun;30(6):e14786. doi: 10.1111/cns.14786.
To investigate dynamic functional connectivity (dFC) within the cerebellar-whole brain network and dynamic topological properties of the cerebellar network in obstructive sleep apnea (OSA) patients.
Sixty male patients and 60 male healthy controls were included. The sliding window method examined the fluctuations in cerebellum-whole brain dFC and connection strength in OSA. Furthermore, graph theory metrics evaluated the dynamic topological properties of the cerebellar network. Additionally, hidden Markov modeling validated the robustness of the dFC. The correlations between the abovementioned measures and clinical assessments were assessed.
Two dynamic network states were characterized. State 2 exhibited a heightened frequency, longer fractional occupancy, and greater mean dwell time in OSA. The cerebellar networks and cerebrocerebellar dFC alterations were mainly located in the default mode network, frontoparietal network, somatomotor network, right cerebellar CrusI/II, and other networks. Global properties indicated aberrant cerebellar topology in OSA. Dynamic properties were correlated with clinical indicators primarily on emotion, cognition, and sleep.
Abnormal dFC in male OSA may indicate an imbalance between the integration and segregation of brain networks, concurrent with global topological alterations. Abnormal default mode network interactions with high-order and low-level cognitive networks, disrupting their coordination, may impair the regulation of cognitive, emotional, and sleep functions in OSA.
研究阻塞性睡眠呼吸暂停（OSA）患者小脑-全脑网络内的动态功能连接（dFC）和小脑网络的动态拓扑特性。
纳入 60 名男性患者和 60 名男性健康对照者。采用滑动窗口法检测 OSA 患者小脑-全脑 dFC 和连接强度的波动。此外，采用图论指标评估小脑网络的动态拓扑特性。另外，隐马尔可夫模型验证了 dFC 的稳健性。评估了上述测量指标与临床评估之间的相关性。
两个动态网络状态的特征。状态 2 在 OSA 中表现出更高的频率、更长的分数占用和更大的平均停留时间。小脑网络和脑-小脑 dFC 的改变主要位于默认模式网络、额顶叶网络、躯体运动网络、右侧小脑 CrusI/II 和其他网络中。全局性质表明 OSA 中存在小脑拓扑异常。动态特性主要与情绪、认知和睡眠相关的临床指标相关。
男性 OSA 中的异常 dFC 可能表明脑网络的整合和分离之间存在不平衡，同时伴有全局拓扑改变。异常的默认模式网络与高级和低级认知网络的相互作用，破坏了它们的协调，可能会损害 OSA 中认知、情绪和睡眠功能的调节。
