ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)

For citation:

Dick O. Е. Synchronization analysis of time series obtained from anesthetized rats during painful action. Izvestiya VUZ. Applied Nonlinear Dynamics, 2024, vol. 32, iss. 2, pp. 209-222. DOI: 10.18500/0869-6632-003093, EDN: PKSHOK

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Full text PDF(Ru):
PDF icon and_2024-2_dick_209-222.pdf
(downloads: 0)
Language: 
Russian
Heading: 
Nonlinear Dynamics and Neuroscience
Article type: 
Article
UDC: 
530.182
DOI: 
10.18500/0869-6632-003093
EDN: 
PKSHOK

Synchronization analysis of time series obtained from anesthetized rats during painful action

Autors: 
Dick Olga Евгеньевна,
Abstract: 

The purpose of this work is to determine the possibility of detecting changes in the relationships between such physiological rhythms as the activity of neurons in the reticular formation of the medulla oblongata, fluctuations in the blood pressure and respiration in anesthetized rats before and during the development of a pathological state associated with painful colorectal distension. This stretch mimics the pain localized in the lower abdomen in patients with irritable bowel syndrome and it is accompanied by responses of the brain neurons, fluctuations in the blood pressure and respiration. The analysis of changes in the relationships of these rhythms consisted in identifying phase synchronization between the time series of the variability of neuronal activity intervals and the variability of blood pressure intervals at the respiratory rate before and during pain exposure.

Methods. To solve this problem, the synchrosqueezed wavelet transform method was applied, which makes it possible to effectively calculate the instantaneous frequencies and phases of non-stationary signals. As indicators of synchronization, we used the values of the index and the duration of phase synchronization as a time interval during which the value of the synchronization index is close to 1.

Results. It has been established that the pain effect provides an adjustment of the frequency of the neuronal activity variability and the occurrence of synchronization between this activity and the blood pressure variability at the respiratory rate or causes an adjustment of the frequency of the blood pressure variability and the occurrence of synchronization between the blood pressure variability and the respiratory rhythm. It was found that the pain effect increases the duration of phase synchronization between the variability of the blood pressure and the respiratory rhythm or reduces the duration of phase synchronization between the variability of neuronal activity and the respiratory rhythm.

Conclusion. The effect of painful colorectal distension on changes in the parameters of phase synchronization between physiological rhythms in anesthetized rats was studied in detail.

Key words: 
synchrosqueezed wavelet transform
phase synchronization
physiological rhythms
Acknowledgments: 
This work was supported by the Ministry of Science and Higher Education of the Russian Federation within the framework of Research Topics No. 0134-2019-0001
Reference: 
  1. Ticos CM, Rosa Jr E, Pardo WB, Walkenstein JA, Monti M. Experimental real-time phase synchronization of a paced chaotic plasma discharge. Phys. Rev. Lett. 2000;85(14):2929–2932. DOI: 10.1103/PhysRevLett.85.2929.
  2. DeShazer DJ, Breban R, Ott E, Roy R. Detecting phase synchronization in a chaotic laser array. Phys. Rev. Lett. 2001;87(4):044101.DOI: 10.1103/PhysRevLett.87.044101.
  3. Boccaletti S, Kurths J, Osipov G, Valladares DL, Zhou CS. The synchronization of chaotic systems. Physics Reports. 2002;366(1–2):1–101. DOI: 10.1016/S0370-1573(02)00137-0.
  4. Boccaletti S, Allaria E, Meucci R, Arecchi FT. Experimental characterization of the transition to phase synchronization of chaotic CO2 laser systems. Phys. Rev. Lett. 2002;89(19):194101. DOI: 10.1103/PhysRevLett.89.194101.
  5. Ponomarenko VI, Prokhorov MD, Bespyatov AB, Bodrov MB, Gridnev VI. Deriving main rhythms of the human cardiovascular system from the heartbeat time series and detecting their synchronization. Chaos, Solitons & Fractals. 2005;23(4):1429–1438. DOI: 10.1016/j.chaos. 2004.06.041.
  6. Bespyatov AB, Bodrov MB, Gridnev VI, Ponomarenko VI, Prokhorov MD. Experimental observation of synchronization between the rhythms of cardiovascular system. Nonlinear Phenomena in Complex Systems. 2003;6(4):885–893.
  7. Hramov AE, Koronovskii AA, Ponomarenko VI, Prokhorov MD. Detecting synchronization of self-sustained oscillators by external driving with varying frequency. Phys. Rev. E. 2006;73(2):026208. DOI: 10.1103/PhysRevE.73.026208.
  8. Hramov AE, Koronovskii AA, Ponomarenko VI, Prokhorov MD. Detection of synchronization from univariate data using wavelet transform. Phys. Rev. E. 2007;75(5):056207. DOI: 10.1103/ PhysRevE.75.056207.
  9. Moskalenko OI, Koronovskii AA, Hramov AE, Zhuravlev MO. Estimate of the degree of synchronization in the intermittent phase synchronization regime from a time series (model systems and neurophysiological data). Jetp Lett. 2016;103(8):539–543. DOI: 10.1134/S0021364016080105.
  10. Dik OE, Glazov AL. Parameters of phase synchronization in electroencephalographic patterns as markers of cognitive impairment. Tech. Phys. 2021;66(4):560–570. DOI: 10.1134/S106378422 1040058.
  11. Dick OE, Glazov AL. Estimation of the synchronization between intermittent photic stimulation and brain response in hypertension disease by the recurrence and synchrosqueezed wavelet transform. Neurocomputing. 2021;455:163–177. DOI: 10.1016/j.neucom.2021.05.038.
  12. Rangaprakash D, Pradhan N. Study of phase synchronization in multichannel seizure EEG using nonlinear recurrence measure. Biomedical Signal Processing and Control. 2014;11:114–122. DOI: 10.1016/j.bspc.2014.02.012.
  13. Kiselev AR, Mironov SA, Karavaev AS, Kulminskiy DD, Skazkina VV, Borovkova EI, Shvartz VA, Ponomarenko VI, Prokhorov MD. A comprehensive assessment of cardiovascular autonomic control using photoplethysmograms recorded from the earlobe and fingers. Physiol. Meas. 2016;37(4):580–595. DOI: 10.1088/0967-3334/37/4/580.
  14. Borovkova EI, Karavaev AS, Kiselev AR, Shvartz VA, Mironov SA, Ponomarenko VI, Prokhorov MD. Method for diagnostics of synchronization of 0.1 Hz rhythms of cardiovascular system autonomic regulation in real time. Annals of Arrhythmology. 2014;11(2):129–136 (in Russian). DOI: 10.15275/annaritmol.2014.2.7.
  15. Hoyer D, Leder U, Hoyer H, Pompe B, Sommer M, Zwiener U. Mutual information and phase dependencies: measures of reduced nonlinear cardiorespiratory interactions after myocardial infarction. Medical Engineering & Physics. 2002;24(1):33–43. DOI: 10.1016/S1350-4533(01) 00120-5.
  16. Karavaev AS, Prokhorov MD, Ponomarenko VI, Kiselev AR, Gridnev VI, Ruban EI, Bezruchko BP. Synchronization of low-frequency oscillations in the human cardiovascular system. Chaos. 2009;19(3):033112. DOI: 10.1063/1.3187794.
  17. Shiogai Y, Stefanovska A, McClintock PVE. Nonlinear dynamics of cardiovascular ageing. Physics Reports. 2010;488(2–3):51–110. DOI: 10.1016/j.physrep.2009.12.003.
  18. Stefanovska A, Haken H, McClintock PVE, Hozic M, Bajrovic F, Ribaric S. Reversible transitions between synchronization states of the cardiorespiratory system. Phys. Rev. Lett. 2000;85(22): 4831–4834. DOI: 10.1103/PhysRevLett.85.4831.
  19. Lyubashina OA, Mikhalkin AA, Sivachenko IB. Supraspinal neuronal alterations promoting intestinal hyperalgesia in colitis. Integrative Physiology. 2021;2(1):71–78. DOI: 10.33910/2687- 1270-2021-2-1-71-78.
  20. Lyubashina OA, Sivachenko IB, Mikhalkin AA. Impaired visceral pain-related functions of the midbrain periaqueductal gray in rats with colitis. Brain Research Bulletin. 2022;182:12–25. DOI: 10.1016/j.brainresbull.2022.02.002.
  21. Pikovsky A, Rosenblum M, Kurths J. Synchronization: A Universal Concept in Nonlinear Sciences. New York: Cambridge University Press; 2001. 411 p. DOI: 10.1017/CBO9780511755743.
  22. Rosenblum MG, Cimponeriu L, Bezerianos A, Patzak A, Mrowka R. Identification of coupling direction: Application to cardiorespiratory interaction. Phys. Rev. E. 2002;65(4):041909. DOI: 10.1103/PhysRevE.65.041909.
  23. Ponomarenko VI, Prokhorov MD, Bespyatov AB, Bodrov MB, Gridnev VI. Deriving main rhythms of the human cardiovascular system from the heartbeat time series and detecting their synchronization. Chaos, Solitons & Fractals. 2005;23(4):1429–1438. DOI: 10.1016/j.chaos. 2004.06.041.
  24. Kralemann B, Fruhwirth M, Pikovsky A, Rosenblum M, Kenner T, Schaefer J, Moser M. In vivo cardiac phase response curve elucidates human respiratory heart rate variability. Nature Communications. 2013;4:2418. DOI: 10.1038/ncomms3418.
  25. Zhang Q, Patwardhan AR, Knapp CF, Evans JM. Cardiovascular and cardiorespiratory phase synchronization in normovolemic and hypovolemic humans. European Journal of Applied Physiology. 2015;115(2):417–427. DOI: 10.1007/s00421-014-3017-4.
  26. Daubechies I. Ten Lectures on Wavelets. CBMS-NSF Regional Conference Series in Applied Mathematics. Philadelphia, Pennsylvania: SIAM Publication; 1992. 369 p. DOI: 10.1137/1.9781 611970104.
  27. Li D, Li X, Cui D, Li ZH. Phase synchronization with harmonic wavelet transform with application to neuronal populations. Neurocomputing. 2011;74(17):3389–3403. DOI: 10.1016/j.neucom. 2011.05.022.
  28. Daubechies I, Lu J, Wu H-T. Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis. 2011;30(2):243–261. DOI: 10.1016/ j.acha.2010.08.002.
  29. Wu H-T, Chan Y-H, Lin Y-T, Yeh Y-H. Using synchrosqueezing transform to discover breathing dynamics from ECG signals. Applied and Computational Harmonic Analysis. 2014;36(2):354–359. DOI: 10.1016/j.acha.2013.07.003.
  30. Wu H-T, Lewis GF, Davila MI, Daubechies I, Porges SW. Optimizing estimates of instantaneous heart rate from pulse wave signals with the synchrosqueezing transform. Methods. Inf. Med. 2016;55(5):463–472. DOI: 10.3414/ME16-01-0026.
  31. Lyubashina OA, Sivachenko IB, Sokolov AY. Differential responses of neurons in the rat caudal ventrolateral medulla to visceral and somatic noxious stimuli and their alterations in colitis. Brain Research Bulletin. 2019;152:299–310. DOI: 10.1016/j.brainresbull.2019.07.030.
  32. Thakur G, Brevdo E, Fuckar NS, Wu H-T. The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications. Signal Processing. 2013;93(5):1079–1094. DOI: 10.1016/j.sigpro.2012.11.029.
  33. Mormann F, Lehnertz K, David P, Elger CE. Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients. Physica D. 2000; 144(3–4):358–369. DOI: 10.1016/S0167-2789(00)00087-7.
Received: 
10.08.2023
Accepted: 
09.11.2023
Available online: 
09.02.2024
Published: 
29.03.2024
  • 235 reads