Li Lifeng, Song Liming, Liu Yuting, Ayoub Muhammad, Song Yucheng, Shu Yongqiang, Liu Xiang, Deng Yingke, Liu Yumeng, Xia Yunyan, Li Haijun, Peng Dechang
Jiangxi Provincial Key Laboratory for Precision Pathology and Intelligent Diagnosis, Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, China; Department of Radiology, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Hunan Province, China.
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong.
Sleep Med. 2025 Feb;126:136-147. doi: 10.1016/j.sleep.2024.12.013. Epub 2024 Dec 9.
Patients with obstructive sleep apnea (OSA) experience chronic intermittent hypoxia and sleep fragmentation, leading to brain ischemia and neurological dysfunction. Therefore, it is important to identify features that can differentiate patients with OSA from healthy controls (HC) and provide insights into the underlying brain alterations associated with OSA. This study aimed to distinguish patients with OSA from healthy individuals and predict clinical symptom alterations using cerebellum-whole-brain static and dynamic functional connectivity (sFC and dFC, respectively), with the cerebellum as the seed region.
Sixty male patients with OSA and 60 male HC matched for age, education level, and sex were included. Using 27 cerebellar seeds, sliding-window analysis was performed to calculate sFC and dFC between the cerebellum and the whole brain. The sFC and dFC values were then combined and used in multiple machine-learning models to distinguish patients with OSA from HC and predict the clinical symptoms of patients with OSA.
Patients with OSA showed increased dFC between cerebellar subregions and the superior and middle temporal gyri and decreased dFC with the middle frontal gyrus. Conversely, increased sFC was observed between cerebellar subregions and the cerebellar lobule VI, cingulate gyrus, middle frontal gyrus, inferior parietal lobules, insula, and superior temporal gyrus. Combined dynamic-static FC features demonstrated superior classification performance with a support vector machine in discriminating OSA from HC. In clinical symptom prediction, FC alterations contributed up to 30.11 % to cognitive impairment, 55.96 % to excessive sleepiness, and 27.94 % to anxiety and depression.
Combining cerebrocerebellar sFC and dFC analyses enables high-precision classification and prediction of OSA. Aberrant FC patterns reflect compensatory brain reorganization and disrupted cognitive network integration, highlighting potential neuroimaging markers for OSA.
阻塞性睡眠呼吸暂停（OSA）患者经历慢性间歇性缺氧和睡眠片段化，导致脑缺血和神经功能障碍。因此，识别能够区分OSA患者与健康对照（HC）的特征，并深入了解与OSA相关的潜在脑改变非常重要。本研究旨在以小脑为种子区域，通过小脑-全脑静态和动态功能连接（分别为sFC和dFC）来区分OSA患者与健康个体，并预测临床症状改变。
纳入60例年龄、教育水平和性别相匹配的男性OSA患者和60例男性HC。使用27个小脑种子点，进行滑动窗口分析以计算小脑与全脑之间的sFC和dFC。然后将sFC和dFC值合并，并用于多个机器学习模型，以区分OSA患者与HC，并预测OSA患者的临床症状。
OSA患者小脑亚区域与颞上回和颞中回之间的dFC增加，与额中回的dFC降低。相反，在小脑亚区域与小脑小叶VI、扣带回、额中回、顶下小叶、岛叶和颞上回之间观察到sFC增加。动态-静态FC联合特征在支持向量机区分OSA与HC方面表现出卓越的分类性能。在临床症状预测中，FC改变对认知障碍的贡献高达30.11%，对过度嗜睡的贡献为55.96%，对焦虑和抑郁的贡献为27.94%。
结合脑小脑sFC和dFC分析能够对OSA进行高精度分类和预测。异常的FC模式反映了代偿性脑重组和认知网络整合中断，突出了OSA潜在的神经影像学标志物。
