急性睡眠剥夺协同运动对心脑轴的影响研究

【作者】 张越；

【导师】 池爱平；

【作者基本信息】 陕西师范大学 ， 体育教育训练学， 2022， 硕士

【摘要】 研究目的:随着工作和学习时间的增多,越来越多的人选择牺牲睡眠时间早起进行体育锻炼,这不仅严重降低了运动效率,还加剧了疲劳的累积。为科学的评估身体状态,及时的了解各个时间段的身体机能,避免猝死等意外事故的发生,有效的监测机体的各项生理指标就显得尤为重要。因此,本研究选取具备一定运动能力的人群,通过监测其单次睡眠不足安静状态下以及该状态下运动后脑电和心电水平,考察此类人群不同疲劳状态下的脑、心激活特征,并进一步探究心-脑轴的激活水平和疲劳之间的关系,为睡眠不足对运动能力的影响提供一定的理论依据。研究方法:选取20名健康男性青少年(18～22岁)为实验对象,前后两次(≥7天)随机在正常睡眠和急性睡眠剥夺的状态下,于次日上午参与跑台的布鲁斯(Bruce)运动方案,并利用身体机能监测仪(GTX9,Acti Life,美国)和32导电极帽的脑电采集系统(Brain Vision Recorder,Neuroscan,美国)采集其运动前后5min的静息态心率变异性信号(heart rate variability,HRV)以及运动前后6min的静息态脑电信号(electroencephalogram,EEG),基于Matlab软件对EEG进行功率谱和微状态分析,基于Kubios软件对HRV进行时域分析、频域分析以及非线性分析。在对HRV进行分析时,时域指标选择:RR间期标准差(SDNN)、相邻RR间期差的均方根(RMSSD)及RR50除以总的RR间期个数,再乘以100(p NN50);频域指标选择:低频输出功率的均值(LF)、高频输出功率的均值(HF);非线性指标选择:庞莱恩散点图(Poincare)中的短轴(SD1)、长轴(SD2)。EEG功率谱分析采用编程批处理法,将头部分为额区、顶区、中央区、顶区、颞区和枕区,对于每个受试者和每个分段,使用快速傅立叶变换(FFT)获得EEG信号的频谱,产生的功率谱(μV~2)范围为1～100Hz,主要包括δ、θ、α、β、γ波段的功率值变化;EEG微状态分析采用聚类分析法聚集4种典型的脑电地形图,选取参数持续时间(Duration)、时间覆盖率(Contribution)、发生频率(Occurrence)以及转换概率进行分析;同时,对不同波段的功率值以及微状态参数和HRV指标进行Spearman相关性分析。所有统计结果均采用SPSS 20.0统计软件进行最终的处理与分析。行为学数据和两种睡眠状态下不同波段的功率值以及HRV的变化均采用配对样本T检验,而两种睡眠状态下微状态参数的变化采用2(正常睡眠,睡眠剥夺)×4(微状态A,B,C,D)双因素重复测量方差分析。两种睡眠状态下运动后的HRV和不同波段功率值的变化均采用2(正常睡眠,睡眠剥夺)×2(运动前,运动后)的双因素重复测量方差分析,而微状态参数的变化采用2(正常睡眠,睡眠剥夺)×4(微状态A,B,C,D)×2(运动前,运动后)的三因素重复测量方差分析,脑电和心电之间的相关性采用斯皮尔曼(Spearman)检验。所有数据统计结果使用Greenhouse-Geisser法校正,统计检验的显著水平设置为P<0.05。研究结果:睡眠剥夺状态下:和正常睡眠状态下相比较,受试者睡眠剥夺状态下的Ln-RMSSD、Ln-p NN50、Ln-HFn、Ln-SD1显著减小(P<0.05),受试者大脑δ波功率值在左额-中央区(FC3)、左中央区(C3)、中央区(CZ)以及右额区(F4)显著降低(P<0.05),大脑θ波功率值在左额-颞区(FT7)、左中央区(C3)以及右额区(F4)显著降低(P<0.05或P<0.01),微状态C的Duration和Contribution显著增大(P<0.05),微状态C的Occurrence显著减小(P<0.05),微状态B,A→C的转换概率显著增加(P<0.05)。睡眠剥夺状态下运动后:和正常睡眠状态下运动后相比较,受试者睡眠剥夺状态下运动后的Ln-RMSSD、Ln-SDNN、Ln-p NN50、Ln-HFn、Ln-SD1显著减小(P<0.05或P<0.01),受试者大脑δ波功率值在左中央区(C3)、左顶区(P3和P7)显著降低(P<0.05或P<0.01),大脑α波功率值在左顶区(P7)和左颞-顶区(TP7)显著降低(P<0.05),大脑γ波功率值在左中央-顶区(CP3)、左顶区(P7)、左颞-顶区(TP7)显著降低(P<0.05),微状态C的Duration和Contribution显著增大(P<0.001),微状态D的Duration和Occurrence显著减小(P<0.05),微状态B,A→C的概率显著增加(P<0.05),微状态C,B→D的概率显著减小(P<0.05)。心脑互动:正常睡眠状态下,左额-中央区(FC3)以及微状态C与心电的激活水平有关且具有同步化趋势;而在睡眠剥夺后,左额-中央区(FC3)和右额区(F4)以及微状态C和微状态D与心电的激活水平有关且具有同步化趋势;受试者在正常睡眠状态下的运动表现较好,并且运动后,左中央区(C3)、左顶区(P3和P7)、左颞-顶区(TP7)以及微状态C和微状态D与心电的激活水平有关且具有同步化趋势;而受试者在睡眠剥夺状态下的运动表现较差,并且运动后,左中央区(C3)、左顶区(P3和P7)、左中央-顶区(CP3)、左颞-顶区(TP7)以及微状态C和微状态D与心电的激活水平有关且具有同步化趋势。研究结论:(1)正常睡眠状态下,心迷走神经和左额-中央区以及凸显网络的联系较为紧密,而睡眠剥夺在此基础上使得心迷走神经与右额区和注意网络的联系增强。睡眠剥夺不仅导致心迷走神经活性减弱和心自主神经系统的紊乱,还破坏了脑内神经电活动的动态平衡,使得两者相互作用起来,共同对机体进行调节。(2)两种睡眠状态下进行高强度运动都会导致心自主神经与左中央区、左顶区、左中央-顶区、左颞-顶区以及凸显网络和注意网络的相互作用增强,不过睡眠剥夺使得心脑的联系更为紧密,从而来缓解和消除机体累积的疲劳。(3)睡眠剥夺不仅会损害最终的运动表现,还会造成运动中的身体机能下降,增加猝死和受伤的风险。更多还原

【Abstract】 Objective:With the increase of work and study time,more and more people choose to sacrifice sleep time to get up early for physical exercise,which not only severely reduces exercise efficiency,but also exacerbates the accumulation of fatigue.In order to scientifically evaluate the physical state,understand the body functions in various time periods in time,and avoid accidents such as sudden death,it is particularly important to effectively monitor the body’s various physiological indicators.Therefore,this study selects people with certain exercise ability to monitor their brain and heart activation characteristics under different fatigue states by monitoring their EEG and ECG activities in a quiet state and under this state after exercise.The relationship between the activation level of the heart-brain axis and fatigue provides a theoretical basis for the effect of lack of sleep on exercise capacity.Methods:20 healthy male adolescents(18～22 years old)were selected as subjects and randomized twice before and after(≥7 days)to participate in a Bruce exercise protocol on a running table the following morning under normal sleep and acute partial sleep deprivation,and their resting state heart rate variability(HRV)signals were collected for 5 min before and after exercise using a physical performance tester(GTX9,Acti Life,USA)and a 32 conductive polar cap electroencephalography system(Brain Vision Recorder,Neuroscan,USA)to collect resting heart rate variability(HRV)signals before and after 5 min of exercise and resting electroencephalogram(EEG)signals before and after 6 min of exercise.The power spectrum and microstate analysis of the EEG were performed based on Matlab software,and time domain analysis,frequency domain analysis and non-linear analysis of HRV were performed based on Kubios software.While analyzing HRV;time domain metrics selected: standard deviation of RR intervals(SDNN),root mean square of the difference between adjacent RR intervals(RMSSD)and RR50 divided by the total number of RR intervals and multiplied by 100(p NN50);frequency domain metrics selected: mean value of low frequency output power(LF),mean value of high frequency output power(HF);non-linear metrics selected: short axis(SD1)and long axis(SD2)in the Poincare scatter plot.EEG power spectrum analysis was performed using the programmed batch method,dividing the head into frontal,parietal,central,parietal,temporal and occipital regions.For each subject and each segment,the spectrum of the EEG signal was obtained using the fast fourier transform(FFT),generating a power spectrum(μV2)ranging from 1 to100 Hz,mainly including the δ,θ,α,β and γ bands of The EEG microstates were analysed using cluster analysis to aggregate four typical EEG topographies,and the parameters Duration,Time Coverage,Occurrence and Transition Probability were selected for analysis;meanwhile,the power values of different bands as well as the microstates parameters and HRV indicators were Spearman correlation analysis was performed.All statistical results were subjected to final processing and analysis using SPSS20.0 statistical software.The variability between behavior data,power values in different bands and changes in HRV in the two sleep states were analyzed using paired samples ttests,while changes in microstate parameters in the two sleep states were analyzed using a 2(normal sleep,sleep deprivation)× 4(microstate A,B,C,D)two-factor repeated measures ANOVA.Changes in HRV and power values in different bands after exercise in both sleep states were analyzed using a two-factor repeated-measures ANOVA of 2(normal sleep,sleep deprivation)× 2(pre-exercise,post-exercise),while changes in microstate parameters were analyzed using a three-factor repeated-measures ANOVA of 2(normal sleep,sleep deprivation)× 4(microstates A,B,C,D)× 2(pre-exercise,postexercise),and the correlation between EEG and ECG was tested using Spearman’s test.All data statistics were corrected using the Greenhouse-Geisser method and the significance level of the statistical test was set at P < 0.05.Results:Under sleep deprivation state: compared to the normal sleep state,subjects’ sleep deprivation state showed significant decreases in Ln-RMSSD,Ln-p NN50,Ln-HFn,LnSD1(P < 0.05),subjects’ brain delta-wave power values were significantly lower in the left frontal-central region(FC3),left central region(C3),central region(CZ),and right frontal region(F4)(P < 0.05),brain theta-wave power values were significantly lower in the left frontal-temporal area(FT7),left central zone(C3),and right frontal zone(F4)(P< 0.05 & 0.01),significantly increased Duration and Contribution in microstate C(P <0.05),significantly decreased Occurrence in microstate C(P < 0.05),and significantly increased probability of transition from A→C in microstate B(P < 0.05).Under sleep deprivation state after exercise: compared to normal sleep state after exercise,subjects’ Ln-RMSSD,Ln-SDNN,Ln-p NN50,Ln-HFn,and Ln-SD1 were significantly reduced after exercise in the SD state(P < 0.05 & 0.01),subjects’ brain delta-wave power values were significantly reduced in the left central-parietal region(C3)and left parietal region(P3 and P7)(P < 0.05 & 0.01),brain alpha-wave power values were significantly reduced in the left parietal region(P7)and left temporo-parietal region(TP7)(P < 0.05),brain gamma-wave power values significantly decreased in left centralparietal region(CP3),left parietal region(P7),and left temporo-parietal region(TP7)(P< 0.05),Duration and Contribution significantly increased in microstate C(P < 0.001),Duration and Occurrence significantly decreased in microstate D(P < 0.05),microstate B,A→C was significantly increased(P < 0.05)and the probability of microstate C,B→D was significantly decreased(P < 0.05).Heart-brain interaction: In the normal sleep state,the left frontal-central region(FC3)and microstate C were associated with ECG activation levels and tended to be synchronized,whereas in the sleep deprivation state,the left frontal-central region(FC3)and the right frontal region(F4),as well as microstates C and D,were associated with ECG activation levels and tended to be synchronized.After exercise,the left central zone(C3),left parietal zone(P3 and P7),left temporo-parietal zone(TP7),and microstates C and D were associated with activation levels of ECG and had a tendency to synchronize;while subjects performed poorly in the sleep deprivation state and after exercise,the left central zone(C3),left parietal zone(P3 and P7),left central-parietal zone(CP3),left temporo-parietal zone(TP7)and microstates C and D were associated with activation levels of ECG and tended to be synchronized.Conclusion:(1)Under the normal sleep condition,the vagus nerve is more closely connected to the left frontal-central region and the projection network,whereas sleep deprivation strengthens the connection between the vagus nerve and the right frontal region and the attentional network on this basis.The two interact to regulate the body.(2)High-intensity exercise in both sleep states leads to enhanced cardiac autonomic interactions with the left central,left parietal,left central-parietal and left temporalparietal regions,as well as the projection and attention networks,although sleep deprivation leads to a closer connection between the mind and brain to relieve and eliminate accumulated fatigue in the organism.(3)sleep deprivation not only impairs eventual athletic performance but also causes a decrease in physical performance during exercise,increasing the risk of sudden death and injury.更多还原