Ke Ming, Li Jianpan, Wang Lubin
College of Computer and Communication, Lanzhou University of Technology, Gansu, China.
Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences, Beijing, China.
Front Hum Neurosci. 2021 Apr 12;15:636252. doi: 10.3389/fnhum.2021.636252. eCollection 2021.
The cognitive effects of total sleep deprivation (TSD) on the brain remain poorly understood. Electroencephalography (EEG) is a very useful tool for detecting spontaneous brain activity in the resting state. Quasi-stable electrical distributions, known as microstates, carry useful information about the dynamics of large-scale brain networks. In this study, microstate analysis was used to study changes in brain activity after 24 h of total sleep deprivation. Twenty-seven healthy volunteers were recruited and underwent EEG scans before and after 24 h of TSD. Microstate analysis was applied, and six microstate classes (A-F) were identified. Topographies and temporal parameters of the microstates were compared between the rested wakefulness (RW) and TSD conditions. Microstate class A (a right-anterior to left-posterior orientation of the mapped field) showed lower global explained variance (GEV), frequency of occurrence, and time coverage in TSD than RW, whereas microstate class D (a fronto-central extreme location of the mapped field) displayed higher GEV, frequency of occurrence, and time coverage in TSD compared to RW. Moreover, subjective sleepiness was significantly negatively correlated with the microstate parameters of class A and positively correlated with the microstate parameters of class D. Transition analysis revealed that class B exhibited a higher probability of transition than did classes D and F in TSD compared to RW. The observation suggests alterations of the dynamic brain-state properties of TSD in healthy young male subjects, which may serve as system-level neural underpinnings for cognitive declines in sleep-deprived subjects.
完全睡眠剥夺（TSD）对大脑的认知影响仍知之甚少。脑电图（EEG）是检测静息状态下大脑自发活动的非常有用的工具。准稳定的电分布，即微状态，携带有关大规模脑网络动态的有用信息。在本研究中，微状态分析被用于研究24小时完全睡眠剥夺后大脑活动的变化。招募了27名健康志愿者，在24小时完全睡眠剥夺前后进行脑电图扫描。应用微状态分析，识别出六个微状态类别（A-F）。比较了静息觉醒（RW）和完全睡眠剥夺（TSD）条件下微状态的地形图和时间参数。微状态A类（映射场从右前到左后的方向）在完全睡眠剥夺（TSD）状态下的全局解释方差（GEV）、出现频率和时间覆盖率低于静息觉醒（RW）状态，而微状态D类（映射场额中央极端位置）在完全睡眠剥夺（TSD）状态下的全局解释方差（GEV）、出现频率和时间覆盖率高于静息觉醒（RW）状态。此外，主观嗜睡与A类微状态参数呈显著负相关，与D类微状态参数呈正相关。转换分析显示，与静息觉醒（RW）相比，在完全睡眠剥夺（TSD）状态下，B类向其他状态转换的概率高于D类和F类。该观察结果表明健康年轻男性受试者在完全睡眠剥夺（TSD）状态下脑动态状态属性发生了改变，这可能是睡眠剥夺受试者认知能力下降的系统水平神经基础。
