男性阻塞性睡眠呼吸暂停患者脑网络动态功能连接状态及其影响因素分析  

Analysis of dynamic functional connectivity states and influencing factors of brain network in male patients with obstructive sleep apnea

作　　者：王婧[1] 伋立荣 程超虹 苏桐 韩菲[3] 李晔洲 王二磊[2] 陈锐[1] Wang Jing;Ji Lirong;Cheng Chaohong;Su Tong;Han Fei;Li Yezhou;Wang Erlei;Chen Rui(Department of Respiratory and Critical Care,the Second Affiliated Hospital of Soochow University,Suzhou 215004,China;Department of Medical Imaging,the Second Affiliated Hospital of Soochow University,Suzhou 215004,China;Department of Sleep Center,the Second Affiliated Hospital of Soochow University,Suzhou 215004,China;School of Biological Sciences,University of Manchester,Manchester M146DZ,UK)

机构地区：[1]苏州大学附属第二医院呼吸与危重症科,苏州215004 [2]苏州大学附属第二医院影像科,苏州215004 [3]苏州大学附属第二医院睡眠中心,苏州215004 [4]英国曼彻斯特大学生物科学学院,曼彻斯特M146DZ

出　　处：《中华医学杂志》2023年第48期3938-3945,共8页National Medical Journal of China

基　　金：国家自然科学基金(81770085、82070095);苏州市科教兴卫青年项目(KJXW2021016);苏州市科技发展计划(SKY2023054)

摘　　要：目的分析男性阻塞性睡眠呼吸暂停(OSA)患者脑网络动态功能连接(dFNC)状态及其影响因素。方法前瞻性选取2020年8月至2021年12月因打鼾就诊于苏州大学附属第二医院睡眠门诊,经多导睡眠监测(PSG)诊断的男性OSA及鼾症患者共111例。收集一般资料,根据氧减指数(ODI)将患者分为3组:单纯鼾症组(ODI<5次/h,34例)、轻中度OSA组(5≤ODI<30次/h,43例)、重度OSA组(ODI≥30次/h,34例)。采用蒙特利尔认知评估(MoCA)量表评估认知功能,Epworth嗜睡量表(ESS)评估日间嗜睡状况。采集静息态功能磁共振成像(fMRI)血氧水平依赖序列(BOLD)信号数据并预处理,使用滑动时间窗法构建dFNC矩阵,通过k-均值聚类分析确定动态脑网络dFNC状态数目,使用三种参数[时间分数(FT)、平均停留时间(MDT)、转换次数(NT)]来表征dFNC状态的时间属性,比较组间dFNC状态时间属性的差异,进一步分析时间属性与PSG参数及MoCA、ESS评分等的相关性,并采用逐步多重线性回归分析dFNC状态时间属性的影响因素。结果患者年龄(40.2±8.6)岁(25~65岁),3组患者间年龄、吸烟饮酒史及MoCA评分差异无统计学意义(均P>0.05)。通过k-均值聚类分析提取了3个脑网络dFNC状态:状态1:以视觉、感觉运动网络强连接为特征,出现频率为31.7%(4611/14541);状态2:以默认模式网络、注意网络等认知网络的强连接为特征,出现频率最低(22.1%,3213/14541);状态3:以全脑网络较弱的连接为特征,出现频率最高(46.2%,6717/14541)。重度OSA组状态2的FT[0.28(0.05,0.35)比0.39(0.26,0.53)]与MDT[8.20(4.35,12.54)比11.68(8.50,16.69)]均低于单纯鼾症组(均P<0.05),3组间状态1和状态3的时间属性差异均无统计学意义(均P>0.05)。状态2的FT、MDT与患者的体质指数、呼吸暂停低通气指数、ODI、最低血氧饱和度相关(FT:r值分别为-0.218、-0.230、-0.249、0.198;MDT:r值分别为0.269、-0.253、-0.265、0.209,均P<0.05),与MoCA评分、ESS评分相�Objective To analyze dynamic functional connectivity(dFNC)states and influencing factors of brain network in male patients with obstructive sleep apnea(OSA).Methods A total of 111 male patients diagnosed with obstructive sleep apnea or presenting with simple snoring,who visited the Sleep Clinic at the Second Affiliated Hospital of Soochow University between August 2020 and December 2021,were prospectively selected for this study.General information was collected,and polysomnography(PSG)was performed.Based on the oxygen desaturation index(ODI),the participants were divided into three groups:primary snoring group(ODI<5 events/hour,n=34),mild to moderate OSA group(5 events/hour≤ODI<30 events/hour,n=43),and sever OSA group(ODI≥30 events/hour,n=34).Cognitive function was assessed using the Montreal Cognitive Assessment(MoCA)scale,and daytime sleepiness was evaluated using the Epworth Sleepiness Scale(ESS).Resting-state functional magnetic resonance imaging(fMRI)data were collected and preprocessed.dFNC matrices were constructed using a sliding time window approach.The number of dFNC states was determined using k-means clustering analysis.Three parameters,namely,fractional time(FT),mean dwell time(MDT),and number of transitions(NT),were used to characterize the temporal properties of dFNC states.Differences in the temporal properties of dFNC states among the groups were compared.The correlations between temporal properties and PSG parameters,as well as MoCA and ESS scores,were further analyzed.Multiple stepwise linear regression analysis was performed to identify the influencing factors of the temporal properties of dFNC states.Results The age of the patients was(40.2±8.6)years(range:25-65 years).There were no significant differences in age,smoking history and alcohol history,and MoCA scores among the three groups(all P>0.05).Three dFNC states were extracted through k-means clustering analysis:state 1,characterized by strong connections within the visual and sensorimotor networks with a frequency of 31.7%(4611/145

关 键 词：睡眠呼吸暂停 阻塞性 功能磁共振成像 动态功能连接 时间属性 氧减指数 横断面研究 

分 类 号：R766[医药卫生—耳鼻咽喉科]