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使用小波前沿量化的小鼠清醒和睡眠脑电图的无标度动力学

Scale-Free Dynamics of the Mouse Wakefulness and Sleep Electroencephalogram Quantified Using Wavelet-Leaders.

作者信息

Lina Jean-Marc, O'Callaghan Emma Kate, Mongrain Valérie

机构信息

Research Centre and Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal (CIUSSS-NIM), 5400 Gouin West blvd., Montreal, QC H4J 1C5, Canada.

Centre de Recherches Mathématiques, Université de Montréal, C.P. 6128, succ. Centre-Ville, Montreal, QC H3C 3J7, Canada.

出版信息

Clocks Sleep. 2018 Oct 20;1(1):50-64. doi: 10.3390/clockssleep1010006. eCollection 2019 Mar.

DOI:10.3390/clockssleep1010006
PMID:33089154
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7509677/
Abstract

Scale-free analysis of brain activity reveals a complexity of synchronous neuronal firing which is different from that assessed using classic rhythmic quantifications such as spectral analysis of the electroencephalogram (EEG). In humans, scale-free activity of the EEG depends on the behavioral state and reflects cognitive processes. We aimed to verify if fractal patterns of the mouse EEG also show variations with behavioral states and topography, and to identify molecular determinants of brain scale-free activity using the 'multifractal formalism' (Wavelet-Leaders). We found that scale-free activity was more anti-persistent (i.e., more different between time scales) during wakefulness, less anti-persistent (i.e., less different between time scales) during non-rapid eye movement sleep, and generally intermediate during rapid eye movement sleep. The scale-invariance of the frontal/motor cerebral cortex was generally more anti-persistent than that of the posterior cortex, and scale-invariance during wakefulness was strongly modulated by time of day and the absence of the synaptic protein Neuroligin-1. Our results expose that the complexity of the scale-free pattern of organized neuronal firing depends on behavioral state in mice, and that patterns expressed during wakefulness are modulated by one synaptic component.

摘要

大脑活动的无标度分析揭示了同步神经元放电的复杂性,这种复杂性不同于使用经典节律量化方法(如脑电图(EEG)频谱分析)所评估的情况。在人类中,EEG的无标度活动取决于行为状态并反映认知过程。我们旨在验证小鼠EEG的分形模式是否也随行为状态和地形而变化,并使用“多重分形形式主义”(小波-领导者)确定大脑无标度活动的分子决定因素。我们发现,无标度活动在清醒时更具反持续性(即时间尺度之间差异更大),在非快速眼动睡眠时反持续性较小(即时间尺度之间差异较小),而在快速眼动睡眠时通常处于中间状态。额叶/运动皮层的尺度不变性通常比后皮层更具反持续性,清醒时的尺度不变性受一天中的时间和突触蛋白神经连接蛋白-1缺失的强烈调节。我们的结果表明,有组织的神经元放电的无标度模式的复杂性取决于小鼠的行为状态,并且清醒时表达的模式受一种突触成分的调节。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a8a/7509677/06c8440c13bc/clockssleep-01-00006-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a8a/7509677/9d5f055db844/clockssleep-01-00006-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a8a/7509677/87f94c0571e3/clockssleep-01-00006-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a8a/7509677/704c90cdb78e/clockssleep-01-00006-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a8a/7509677/06c8440c13bc/clockssleep-01-00006-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a8a/7509677/9d5f055db844/clockssleep-01-00006-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a8a/7509677/87f94c0571e3/clockssleep-01-00006-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a8a/7509677/704c90cdb78e/clockssleep-01-00006-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a8a/7509677/06c8440c13bc/clockssleep-01-00006-g004.jpg

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