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  Extended detrended fluctuation analysis of electroencephalograms signals during sleep and the opening of the blood–brain barrier

Pavlov, A. N., Dubrovsky, A. I., Koronovskii, A. A., Pavlova, O. N., Semyachkina-Glushkovskaya, O. V., Kurths, J. (2020): Extended detrended fluctuation analysis of electroencephalograms signals during sleep and the opening of the blood–brain barrier. - Chaos, 30, 7, 073138.
https://doi.org/10.1063/5.0011823

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Pavlov, A. N.1, Author
Dubrovsky, A. I.1, Author
Koronovskii, A. A.1, Author
Pavlova, O. N.1, Author
Semyachkina-Glushkovskaya, O. V.1, Author
Kurths, Jürgen2, Author              
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1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Detrended fluctuation analysis (DFA) is widely used to characterize long-range power-law correlations in complex signals. However, it has restrictions when nonstationarity is not limited only to slow variations in the mean value. To improve the characterization of inhomogeneous datasets, we have proposed the extended DFA (EDFA), which is a modification of the conventional method that evaluates an additional scaling exponent to take into account the features of time-varying nonstationary behavior. Based on EDFA, here, we analyze rat electroencephalograms to identify specific changes in the slow-wave dynamics of brain electrical activity associated with two different conditions, such as the opening of the blood–brain barrier and sleep, which are both characterized by the activation of the brain drainage function. We show that these conditions cause a similar reduction in the scaling exponents of EDFA. Such a similarity may represent an informative marker of fluid homeostasis of the central nervous system. Natural systems often exhibit complex dynamics with long-range power-law correlations. To quantify this phenomenon, different variants of fluctuation analysis are used with detrended fluctuation analysis (DFA),1,2 representing a universal method that can be applied to both stationary and nonstationary time series. In many practical situations, however, nonstationarity cannot be eliminated by simply removing the slow variation in mean value. For such a case, we have recently proposed an extended method (EDFA), which estimates two scaling exponents: the first exponent is associated with the conventional DFA and the second exponent quantifies the impact of nonstationarity.3 Here, we use EDFA to identify informative markers of changes in electroencephalograms associated with two different factors, namely, open blood–brain barrier (BBB) and sleep, which are characterized by activated brain drainage function. By means of EDFA, we uncover that both factors, sound and sleep, cause similar changes in the low-frequency dynamics of electroencephalograms (EEG). These results offer a hint of the similarity of EEG dynamics related to distinct mechanisms of fluid drainage from the brain during sleep and after the BBB opening

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 Dates: 2020
 Publication Status: Finally published
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1063/5.0011823
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Research topic keyword: Health
Research topic keyword: Nonlinear Dynamics
Model / method: Nonlinear Data Analysis
Organisational keyword: RD4 - Complexity Science
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

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Title: Chaos
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 30 (7) Sequence Number: 073138 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/180808
Publisher: American Institute of Physics (AIP)