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  Detrended fluctuation analysis of cerebrovascular responses to abrupt changes in peripheral arterial pressure in rats

Pavlov, A. N., Abdurashitov, A. S., Koronovskii, A. A., Pavlova, O. N., Semyachkina-Glushkovskaya, O. V., Kurths, J. (2020): Detrended fluctuation analysis of cerebrovascular responses to abrupt changes in peripheral arterial pressure in rats. - Communications in Nonlinear Science and Numerical Simulation, 85, 105232.
https://doi.org/10.1016/j.cnsns.2020.105232

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

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 Abstract: We study scaling features in the reactions of cereral blood vessel network to sudden “jumps” in peripheral arterial pressure in rats. Using laser speckle contrast imaging (LSCI) to measure the relative velocity of cerebral blood flow (CBF) and detrended fluctuation analysis (DFA) for processing experimental data, we investigate distinctions in the responses of veins and capillaries. To quantify short-term reactions associated with transients, we propose an extension of the conventional DFA approach, which estimates an additional scaling exponent reflecting the effect of nonstationarity. We also consider the ability of characterizing vascular dynamics with multifractal DFA in terms of the degree of multiscality. Based on statistical analysis, we report significant distinctions in the responses of small network of microcerebral blood vessels compared to veins such as the sagittal sinus, which are quite insensitive to variations in peripheral blood circulation.

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 Dates: 2020-02-122020-02-132020
 Publication Status: Finally published
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.cnsns.2020.105232
PIKDOMAIN: RD4 - Complexity Science
MDB-ID: No data to archive
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

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Title: Communications in Nonlinear Science and Numerical Simulation
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 85 Sequence Number: 105232 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/201610061
Publisher: Elsevier