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目的 通过24 h睡眠剥夺试验,探讨24 h睡眠剥夺耐受人群和不耐受人群在静息态和Flanker任务下的脑电活动差异及其对认知控制功能的影响。方法 面向社会招募39名志愿者,剔除和脱落7例,纳入统计32例(耐受组16例,不耐受组16例),分别在24 h睡眠剥夺前后进行静息态和Flanker任务下的脑电测量。结果 不耐受组在睡眠剥夺后认知功能明显下降,而耐受组在睡眠剥夺后仍能保持稳定的认知表现。脑网络分析显示,不耐受组在睡眠剥夺前表现出高频段(Beta、Gamma)的超链接特征,而在睡眠剥夺后,这些连接性显著下降,尤其是Gamma频段的连接显著减弱。耐受组在任务下表现出Theta和Alpha频段的连接增强。通过结合空间模式网络特征,线性判别分析的分类性能在睡眠剥夺前后均显著提升,特别是在Flanker任务不一致的条件下,进一步验证了空间模式网络特征在鉴别耐受与不耐受人群方面的有效性。结论 本研究提供了睡眠剥夺对认知功能影响的新视角,为睡眠剥夺后耐受与不耐受人群的鉴别提供了新依据。
Abstract:Objectives In this study, we examined the differences in EEG activity between tolerant and intolerant subjects during the resting state and Flanker task, and the effects of 24-hour sleep deprivation on cognitive control. Methods In this study, a total of 39 volunteers were recruited. Seven cases were excluded or dropped out, and 32 cases were included in the statistical analysis(16 in the tolerance group and 16 in the intolerance group). Electroencephalogram measurements were conducted in the resting state and under the Flanker task before and after 24-hour sleep deprivation, respectively. Results The intolerant group showed significant cognitive decline after sleep deprivation, whereas the tolerant group maintained stable cognitive performance after sleep deprivation. Brain network analysis showed that the intolerance group showed hyperconnectivity characteristics in high frequency bands(Beta and Gamma) at baseline, while these connectivity were significantly decreased after sleep deprivation, especially in Gamma band. The tolerant group showed enhanced connectivity in the Theta and Alpha bands under the task. By combining spatial pattern network(SPN) features, the classification performance of linear discriminant analysis(LDA) was significantly improved before and after SD, especially in the Flanker task inconsistency condition, which further verified the effectiveness of SPN features in distinguishing tolerant and non-tolerant populations. Conclusion this study reveals a new perspective on the effects of sleep deprivation on cognitive function, and provides a new basis for the identification of tolerant and non-tolerant populations after sleep deprivation.
[1]LIM J, DINGES DF. A meta-analysis of the impact of short-term sleep deprivation on cognitive variables.[J]. Psychological Bulletin,2010, 136(0033-2909):375-389.
[2]侯成,卢光照,鲁莹,等.军事人员对抗睡眠剥夺策略的研究进展[J].第二军医大学学报, 2020,41(9):1012-1020.
[3]LIEBERMAN HR, AGARWAL S, CALDWELL JA, et al. Demographics, sleep, and daily patterns of caffeine intake of shift workers in a nationally representative sample of the US adult population[J].Sleep, 2020, 43(3):zsz240.
[4]MAIRE M, REICHERT C, GABEL V, et al. Sleep ability mediates individual differences in the vulnerability to sleep loss:Evidence from a PER3 polymorphism[J]. Cortex, 2014, 52(0010-9452):47-59.
[5]GOEL N, BASNER M, DINGES DF. Phenotyping of neurobehavioral vulnerability to circadian phase during sleep loss[J]. Methods Enzymol, 2015, 552:285-308.
[6]ZHANG T, LIU T, LI F, et al. Structural and functional correlates of motor imagery BCI performance:Insights from the patterns of fronto-parietal attention network[J]. NeuroImage, 2016, 134:475-485.
[7]ZHANG R, YAO D, VALDéS-SOSA PA, et al. Efficient resting-state EEG network facilitates motor imagery performance[J]. J Neural Eng,2015, 12(6):66024.
[8]ZHANG Y, XU P, GUO D, et al. Prediction of SSVEP-based BCI performance by the resting-state EEG network[J]. J Neural Eng, 2013,10(6):66017.
[9]SONG T, DU F, XU L, et al. Total sleep deprivation selectively impairs motor preparation sub-stages in visual search task:Evidence from lateralized readiness potentials[J]. Front Neurosci, 2023,17:989512.
[10]GUO Y, ZHAO X, LIU X, et al. Electroencephalography microstates as novel functional biomarkers for insomnia disorder[J]. General Psychiatry, 2023,36(6):e101171.
[11]GOOL JK, FRONCZEK R, BOSMA P, et al. Enhanced visual cortex activation in people with narcolepsy type 1 during active sleep resistance:an fMRI-EEG study[J]. Front Neuroscie, 2022,16:904820.
[12]SALEHINEJAD MA, GHANAVATI E, REINDERS J, et al. Sleep-dependent upscaled excitability, saturated neuroplasticity, and modulated cognition in the human brain[J]. ELife, 2022,11:e69308.
[13]ZHANG Q, HOU Y, DING H, et al. Alterations of sleep deprivation on brain function:A coordinate-based resting-state functional magnetic resonance imaging meta-analysis[J]. World J Psychiatry, 2024,14(2):315-329.
[14]CHUA EC, YEO S, LEE IT, et al. Individual differences in physiologic measures are stable across repeated exposures to total sleep deprivation[J]. Physiol Rep, 2014, 2(9):e12129.
[15]LI F, PENG W, JIANG Y, et al. The dynamic brain networks of motor imagery:time-varying causality analysis of scalp EEG[J]. Int Journal Neural Syst, 2019, 29(1):1850016.
[16]LI F, CHEN B, LI H, et al. The Time-varying networks in P300:a task-evoked EEG study[J]. IEEE Trans Neural Syst Rehabil Eng,2016, 24(7):725-733.
[17]SRINIVASAN R, WINTER WR, DING J, et al. EEG and MEG coherence:Measures of functional connectivity at distinct spatial scales of neocortical dynamics[J]. J Neurosci Methods, 2007, 166(1):41-52.
[18]SUN J, HONG X, TONG S. Phase synchronization analysis of EEG signals:an evaluation based on surrogate tests[J]. IEEE Trans Biomed Eng, 2012, 59(8):2254-2263
[19]SAKKALIS V. Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG[J]. Comput Biol Med,2011:41(12); 1110-1117.
[20]ZHANG Z, LIAO W, CHEN H, et al. Altered functional–structural coupling of large-scale brain networks in idiopathic generalized epilepsy[J]. Brain, 2011, 134(10):2912-2928.
[21]XU P, XIONG XC, XUE Q, et al. Recognizing mild cognitive impairment based on network connectivity analysis of resting EEG with zero reference[J]. Physiol Meas, 2014, 35(7):1279-1298.
[22]LI F, WANG J, LIAO Y, et al. Differentiation of schizophrenia by combining the spatial EEG brain network patterns of rest and task P300[J]. IEEE Trans Neural Syst Rehabil Eng, 2019, 27(1534-4320):594-602.
[23]ZHANG Y, MIAO H, WANG C, et al. Effects of acute sleep deprivation on post-error adjustments and error processing[J]. Int J Psychophysiol, 2025, 211:112554.
[24]SUN J, LI Z, TONG S. Inferring functional neural connectivity with phase synchronization analysis:a review of methodology[J]. Comput Math Methods Med, 2012, 2012:239210..
[25]XU P, KASPROWICZ M, BERGSNEIDER M, et al. Improved noninvasive intracranial pressure assessment with nonlinear kernel regression[J]. IEEE Trans Inf Technol Biomed, 2010, 14(4):971-978.
[26]RUPP TL, WESENSTEN NJ, BALKIN TJ. Trait-like vulnerability to total and partial sleep loss[J]. Sleep, 2012, 35(8):1163-1172.
[27]BRIEVA TE, CASALE CE, YAMAZAKI EM, et al. Cognitive throughput and working memory raw scores consistently differentiate resilient and vulnerable groups to sleep loss[J]. Sleep, 2021,44(12):197.
[28]CUI J, TKACHENKO O, GOGEL H, et al. Microstructure of frontoparietal connections predicts individual resistance to sleep deprivation[J]. NeuroImage, 2015,106:123-133.
[29]BUSH BJ, DONNAY C, ANDREWS EA, et al. Non-rapid eye movement sleep determines resilience to social stress[J]. ELife, 2022,11:e80206.
[30]RIVOLTA D, HEIDEGGER T, SCHELLER B, et al. Ketamine dysregulates the amplitude and connectivity of high-frequency oscillations in cortical–subcortical networks in humans:evidence from resting-state magnetoencephalography-recordings[J]. Schizophr Bull,2015, 41(5):1105-1114.
[31]PANG J, TANG X, NIE Q, et al. Resolving the Electroencephalographic correlates of rapid goal-directed chunking in the frontal-parietal network[J]. Front Neurosci, 2019, 13:744.
[32]ADAMANTIDIS AR, GUTIERREZ HERRERA C, GENT TC. Oscillating circuitries in the sleeping brain[J]. Nat Rev Neurosci, 2019,20(12):746-762.
[33]WANG X, TALEBI N, ZHOU X, et al. Neurophysiological dynamics of metacontrol states:EEG insights into conflict regulation[J]. NeuroImage, 2024, 302:120915.
[34]许敏鹏,李榕,明东.选择性注意与节律性神经振荡关系综述[J].生物医学工程学杂志, 2019, 36(2):320-324.
基本信息:
DOI:10.16289/j.cnki.1002-0837.2025.06001
中图分类号:R740
引用信息:
[1]杨昊平,叶珈妤,秦海波,等.睡眠剥夺前后不同耐受组脑电网络功能链接特征差异性分析[J].航天医学与医学工程,2025,36(06):513-520.DOI:10.16289/j.cnki.1002-0837.2025.06001.
基金信息:
国家重点研发计划(2023YFF1203705)