{"title":"Combining static and dynamic functional connectivity analyses to identify male patients with obstructive sleep apnea and predict clinical symptoms.","authors":"Lifeng Li, Liming Song, Yuting Liu, Muhammad Ayoub, Yucheng Song, Yongqiang Shu, Xiang Liu, Yingke Deng, Yumeng Liu, Yunyan Xia, Haijun Li, Dechang Peng","doi":"10.1016/j.sleep.2024.12.013","DOIUrl":null,"url":null,"abstract":"<p><strong>Background and purpose: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":21874,"journal":{"name":"Sleep medicine","volume":"126 ","pages":"136-147"},"PeriodicalIF":3.8000,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sleep medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.sleep.2024.12.013","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0
摘要
背景和目的:阻塞性睡眠呼吸暂停(OSA)患者会经历慢性间歇性缺氧和睡眠片段化,从而导致大脑缺血和神经功能障碍。因此,确定可将 OSA 患者与健康对照组(HC)区分开来的特征并深入了解与 OSA 相关的潜在脑部改变非常重要。本研究旨在以小脑为种子区域,利用小脑-全脑静态和动态功能连接(分别为sFC和dFC)来区分OSA患者和健康人,并预测临床症状的改变:方法:研究对象包括60名男性OSA患者和60名年龄、教育程度和性别匹配的男性HC患者。使用 27 个小脑种子区进行滑动窗口分析,计算小脑和全脑之间的 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患者和HC患者,并预测OSA患者的临床症状:结果:OSA患者的小脑亚区与颞上回和颞中回之间的dFC增加,而与额中回之间的dFC减少。相反,小脑亚区与小脑第六小叶、扣带回、额中回、顶叶下叶、岛叶和颞上回之间的sFC增加。结合动态和静态FC特征的支持向量机在区分OSA和HC方面表现出了卓越的分类性能。在临床症状预测方面,FC改变对认知障碍的影响高达30.11%,对过度嗜睡的影响高达55.96%,对焦虑和抑郁的影响高达27.94%:结论:结合小脑sFC和dFC分析可对OSA进行高精度分类和预测。异常的FC模式反映了大脑的代偿性重组和认知网络整合的中断,突显了OSA的潜在神经影像标记。
Combining static and dynamic functional connectivity analyses to identify male patients with obstructive sleep apnea and predict clinical symptoms.
Background and purpose: 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.
Methods: 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.
Results: 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.
Conclusions: 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.
期刊介绍:
Sleep Medicine aims to be a journal no one involved in clinical sleep medicine can do without.
A journal primarily focussing on the human aspects of sleep, integrating the various disciplines that are involved in sleep medicine: neurology, clinical neurophysiology, internal medicine (particularly pulmonology and cardiology), psychology, psychiatry, sleep technology, pediatrics, neurosurgery, otorhinolaryngology, and dentistry.
The journal publishes the following types of articles: Reviews (also intended as a way to bridge the gap between basic sleep research and clinical relevance); Original Research Articles; Full-length articles; Brief communications; Controversies; Case reports; Letters to the Editor; Journal search and commentaries; Book reviews; Meeting announcements; Listing of relevant organisations plus web sites.