• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

脑电图的莱姆普尔-齐夫复杂度随睡眠阶段而变化,但似乎与梦境体验无关。

EEG Lempel-Ziv complexity varies with sleep stage, but does not seem to track dream experience.

作者信息

Aamodt Arnfinn, Sevenius Nilsen André, Markhus Rune, Kusztor Anikó, HasanzadehMoghadam Fatemeh, Kauppi Nils, Thürer Benjamin, Storm Johan Frederik, Juel Bjørn Erik

机构信息

Brain Signalling Lab, Division of Physiology, Faculty of Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.

National Centre for Epilepsy, Oslo University Hospital, Oslo, Norway.

出版信息

Front Hum Neurosci. 2023 Jan 10;16:987714. doi: 10.3389/fnhum.2022.987714. eCollection 2022.

DOI:10.3389/fnhum.2022.987714
PMID:36704096
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9871639/
Abstract

In a recent electroencephalography (EEG) sleep study inspired by complexity theories of consciousness, we found that multi-channel signal diversity progressively decreased from wakefulness to slow wave sleep, but failed to find any significant difference between dreaming and non-dreaming awakenings within the same sleep stage (NREM2). However, we did find that multi-channel Lempel-Ziv complexity (LZC) measured over the posterior cortex increased with more perceptual ratings of NREM2 dream experience along a thought-perceptual axis. In this follow-up study, we re-tested our previous findings, using a slightly different approach. Partial sleep-deprivation was followed by evening sleep experiments, with repeated awakenings and immediate dream reports. Participants reported whether they had been dreaming, and were asked to rate how diverse, vivid, perceptual, and thought-like the contents of their dreams were. High density (64 channel) EEG was recorded throughout the experiment, and mean single-channel LZC was calculated for each 30 s sleep epoch. LZC progressively decreased with depth of non-REM sleep. Surprisingly, estimated marginal mean LZC was slightly higher for NREM1 than for wakefulness, but the difference did not remain significant after adjusting for multiple comparisons. We found no significant difference in LZC between dream and non-dream awakenings, nor any significant relationship between LZC and subjective ratings of dream experience, within the same sleep stage (NREM2). The failure to reproduce our own previous finding of a positive correlation between posterior LZC and more perceptual dream experiences, or to find any other correlation between brain signal complexity and subjective experience within NREM2 sleep, raises the question of whether EEG LZC is really a reliable correlate of richness of experience as such, within the same sleep stage.

摘要

在一项近期受意识复杂性理论启发而开展的脑电图(EEG)睡眠研究中,我们发现多通道信号多样性从清醒状态到慢波睡眠状态逐渐降低,但在同一睡眠阶段(NREM2)的做梦与非做梦觉醒之间未发现任何显著差异。然而,我们确实发现,沿着思维-感知轴,后皮质区域测量得到的多通道莱姆尔-齐夫复杂度(LZC)随着对NREM2梦境体验更多的感知评分而增加。在这项后续研究中,我们采用了略有不同的方法对之前的发现进行重新测试。先进行部分睡眠剥夺,随后在晚上进行睡眠实验,包括多次觉醒并立即报告梦境。参与者报告他们是否在做梦,并被要求对其梦境内容的多样程度、生动程度、感知程度和类似思维的程度进行评分。在整个实验过程中记录高密度(64通道)EEG,并为每个30秒的睡眠时段计算平均单通道LZC。LZC随着非快速眼动睡眠深度的增加而逐渐降低。令人惊讶的是,NREM1阶段的估计边际平均LZC略高于清醒状态,但在进行多重比较调整后,差异不再显著。在同一睡眠阶段(NREM2),我们发现做梦与非做梦觉醒之间的LZC没有显著差异,LZC与梦境体验的主观评分之间也没有任何显著关系。未能重现我们之前关于后皮质LZC与更多感知性梦境体验之间存在正相关的发现,或者在NREM2睡眠中未发现脑信号复杂性与主观体验之间的任何其他相关性,这就引发了一个问题:在同一睡眠阶段,EEG LZC是否真的是体验丰富程度的可靠相关指标。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0cc/9871639/33a5249b303d/fnhum-16-987714-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0cc/9871639/5f0f41ff95f6/fnhum-16-987714-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0cc/9871639/e4021a25923d/fnhum-16-987714-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0cc/9871639/91135f485496/fnhum-16-987714-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0cc/9871639/7406887bfd82/fnhum-16-987714-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0cc/9871639/c4662614e5a6/fnhum-16-987714-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0cc/9871639/6c9426ca483c/fnhum-16-987714-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0cc/9871639/1e268b57a619/fnhum-16-987714-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0cc/9871639/04e67ba308fe/fnhum-16-987714-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0cc/9871639/a69d204c1c34/fnhum-16-987714-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0cc/9871639/33a5249b303d/fnhum-16-987714-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0cc/9871639/5f0f41ff95f6/fnhum-16-987714-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0cc/9871639/e4021a25923d/fnhum-16-987714-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0cc/9871639/91135f485496/fnhum-16-987714-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0cc/9871639/7406887bfd82/fnhum-16-987714-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0cc/9871639/c4662614e5a6/fnhum-16-987714-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0cc/9871639/6c9426ca483c/fnhum-16-987714-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0cc/9871639/1e268b57a619/fnhum-16-987714-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0cc/9871639/04e67ba308fe/fnhum-16-987714-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0cc/9871639/a69d204c1c34/fnhum-16-987714-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0cc/9871639/33a5249b303d/fnhum-16-987714-g010.jpg

相似文献

1
EEG Lempel-Ziv complexity varies with sleep stage, but does not seem to track dream experience.脑电图的莱姆普尔-齐夫复杂度随睡眠阶段而变化,但似乎与梦境体验无关。
Front Hum Neurosci. 2023 Jan 10;16:987714. doi: 10.3389/fnhum.2022.987714. eCollection 2022.
2
EEG Signal Diversity Varies With Sleep Stage and Aspects of Dream Experience.脑电图信号多样性随睡眠阶段和梦境体验的不同方面而变化。
Front Psychol. 2021 Apr 23;12:655884. doi: 10.3389/fpsyg.2021.655884. eCollection 2021.
3
EEG Frontal Alpha Asymmetry and Dream Affect: Alpha Oscillations over the Right Frontal Cortex during REM Sleep and Presleep Wakefulness Predict Anger in REM Sleep Dreams.脑电图额部α不对称与梦境影响:快速眼动睡眠和睡前清醒期右侧额皮质的α 振荡预测快速眼动睡眠期梦境中的愤怒。
J Neurosci. 2019 Jun 12;39(24):4775-4784. doi: 10.1523/JNEUROSCI.2884-18.2019. Epub 2019 Apr 15.
4
Lempel-Ziv complexity of cortical activity during sleep and waking in rats.大鼠睡眠和清醒期间皮质活动的莱姆尔-齐夫复杂度
J Neurophysiol. 2015 Apr 1;113(7):2742-52. doi: 10.1152/jn.00575.2014. Epub 2015 Feb 25.
5
Multiscale Lempel-Ziv complexity for EEG measures.用于脑电图测量的多尺度莱姆尔-齐夫复杂度
Clin Neurophysiol. 2015 Mar;126(3):541-8. doi: 10.1016/j.clinph.2014.07.012. Epub 2014 Jul 18.
6
Dreaming in NREM Sleep: A High-Density EEG Study of Slow Waves and Spindles.非快速眼动睡眠期的梦境:一项关于慢波和纺锤波的高密度脑电图研究。
J Neurosci. 2018 Oct 24;38(43):9175-9185. doi: 10.1523/JNEUROSCI.0855-18.2018. Epub 2018 Sep 10.
7
Effects of the series length on Lempel-Ziv Complexity during sleep.睡眠期间序列长度对莱姆尔-齐夫复杂度的影响。
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:693-6. doi: 10.1109/EMBC.2014.6943685.
8
Beyond the neuropsychology of dreaming: Insights into the neural basis of dreaming with new techniques of sleep recording and analysis.超越梦境的神经心理学:借助新的睡眠记录和分析技术,深入了解梦境的神经基础。
Sleep Med Rev. 2017 Oct;35:8-20. doi: 10.1016/j.smrv.2016.07.005. Epub 2016 Jul 28.
9
Neural complexity in patients with poststroke depression: A resting EEG study.脑卒中后抑郁患者的神经复杂性:一项静息态 EEG 研究。
J Affect Disord. 2015 Dec 1;188:310-8. doi: 10.1016/j.jad.2015.09.017. Epub 2015 Sep 11.
10
Multiple characteristics analysis of Alzheimer's electroencephalogram by power spectral density and Lempel-Ziv complexity.基于功率谱密度和莱姆尔-齐夫复杂度的阿尔茨海默病脑电图多特征分析
Cogn Neurodyn. 2016 Apr;10(2):121-33. doi: 10.1007/s11571-015-9367-8. Epub 2015 Nov 12.

引用本文的文献

1
We are the Sensors of Consciousness! A Review and Analysis on How Awakenings During Sleep Influence Dream Recall.我们是意识的感知者!关于睡眠中的觉醒如何影响梦境回忆的综述与分析
Nat Sci Sleep. 2025 Apr 30;17:709-729. doi: 10.2147/NSS.S506461. eCollection 2025.
2
Entropy of difference works similarly to permutation entropy for the assessment of anesthesia and sleep EEG despite the lower computational effort.尽管计算量较小,但差异熵在评估麻醉和睡眠脑电图方面的工作方式与排列熵类似。
J Clin Monit Comput. 2024 Dec 26. doi: 10.1007/s10877-024-01258-8.
3
Psychedelics and disorders of consciousness: the current landscape and the path forward.

本文引用的文献

1
Consciousness and complexity: a consilience of evidence.意识与复杂性:证据的一致性
Neurosci Conscious. 2021 Aug 30;2021(2):niab023. doi: 10.1093/nc/niab023. eCollection 2021.
2
Distinct EEG signatures differentiate unconsciousness and disconnection during anaesthesia and sleep.不同的脑电图特征可区分麻醉和睡眠期间的无意识和意识中断。
Br J Anaesth. 2022 Jun;128(6):1006-1018. doi: 10.1016/j.bja.2022.01.010. Epub 2022 Feb 9.
3
Consciousness is supported by near-critical slow cortical electrodynamics.意识由近临界慢皮质电动力学支持。
迷幻剂与意识障碍:当前形势与未来方向
Neurosci Conscious. 2024 Jun 15;2024(1):niae025. doi: 10.1093/nc/niae025. eCollection 2024.
4
The Constrained Disorder Principle May Account for Consciousness.受限紊乱原理或可解释意识。
Brain Sci. 2024 Feb 23;14(3):209. doi: 10.3390/brainsci14030209.
5
Altered brain dynamics index levels of arousal in complete locked-in syndrome.改变的大脑动力学指数水平在完全闭锁综合征中觉醒。
Commun Biol. 2023 Jul 20;6(1):757. doi: 10.1038/s42003-023-05109-1.
6
Sensory modality defines the relation between EEG Lempel-Ziv diversity and meaningfulness of a stimulus.感觉模式定义了 EEG 勒贝格多样性与刺激意义之间的关系。
Sci Rep. 2023 Mar 1;13(1):3453. doi: 10.1038/s41598-023-30639-3.
Proc Natl Acad Sci U S A. 2022 Feb 15;119(7). doi: 10.1073/pnas.2024455119.
4
Decomposing Spectral and Phasic Differences in Nonlinear Features between Datasets.分解数据集之间非线性特征的谱和相位差异。
Phys Rev Lett. 2021 Sep 17;127(12):124101. doi: 10.1103/PhysRevLett.127.124101.
5
EEG Signal Diversity Varies With Sleep Stage and Aspects of Dream Experience.脑电图信号多样性随睡眠阶段和梦境体验的不同方面而变化。
Front Psychol. 2021 Apr 23;12:655884. doi: 10.3389/fpsyg.2021.655884. eCollection 2021.
6
Parameterizing neural power spectra into periodic and aperiodic components.将神经功率谱参数化为周期性和非周期性成分。
Nat Neurosci. 2020 Dec;23(12):1655-1665. doi: 10.1038/s41593-020-00744-x. Epub 2020 Nov 23.
7
The Dream Catcher experiment: blinded analyses failed to detect markers of dreaming consciousness in EEG spectral power.捕梦网实验:盲法分析未能在脑电图频谱功率中检测到梦境意识的标志物。
Neurosci Conscious. 2020 Jul 15;2020(1):niaa006. doi: 10.1093/nc/niaa006. eCollection 2020.
8
Neural correlates of the DMT experience assessed with multivariate EEG.使用多元脑电评估 DMT 体验的神经相关因素。
Sci Rep. 2019 Nov 19;9(1):16324. doi: 10.1038/s41598-019-51974-4.
9
Understanding the Higher-Order Approach to Consciousness.理解意识的高阶方法。
Trends Cogn Sci. 2019 Sep;23(9):754-768. doi: 10.1016/j.tics.2019.06.009. Epub 2019 Jul 30.
10
ICLabel: An automated electroencephalographic independent component classifier, dataset, and website.ICLabel:一种自动化的脑电图独立成分分类器、数据集和网站。
Neuroimage. 2019 Sep;198:181-197. doi: 10.1016/j.neuroimage.2019.05.026. Epub 2019 May 16.