For citation:
Dick O. E. Finding connections between three interacting biological oscillators using phase dynamics modeling. Izvestiya VUZ. Applied Nonlinear Dynamics, 2026, vol. 34, iss. 3, pp. 463-480. DOI: 10.18500/0869-6632-003217, EDN: WSMBBS
Finding connections between three interacting biological oscillators using phase dynamics modeling
The purpose of this work was to identify couplings between three interacting biological oscillators using phase dynamics modeling and to determine the influence of a pathological condition on the configuration of unidirectional triplewise couplings in these systems.
Methods. To identify these couplings, we used phase dynamics modeling for weakly coupled systems. Estimates of the connectivity matrix of the three-oscillator network were used to characterize the interconnections between the systems.
Results. We verified the accuracy of identifying couplings for a model of three chaotic R¨ossler oscillators with known directional couplings. We also identified the possibility of additional influence of one oscillator on another, or the combined influence of two oscillators on a third, with increasing coupling strength. For experimental time series corresponding to respiratory rate fluctuations, blood pressure variability curves, and variability of neuronal activity intervals in the medulla oblongata, we identified the influence of a pathological condition on the emergence of additional couplings in triple interactions between oscillators in the nervous, respiratory, and cardiovascular systems.
Conclusion. The application of phase dynamics modeling to assess the characteristics of the coupling between three interacting biological oscillators demonstrated the importance of physiological state in assessing the predominant influence of one oscillator over another.
- Mokhov II, Kozlenko SS, Smirnov DA, Nakonechny PI, Kurths J. Relationship between El-Niño/Southern oscillation and the Indian monsoon. Izvestiya, Atmospheric and Oceanic Physics. 2012;48(1):47–56.
- Runge J, Heitzig J, Petoukhov V, Kurths J. Escaping the curse of dimensionality in estimating multivariate transfer entropy. Phys. Rev. Lett. 2012;108(25):258701. DOI: 10.1103/PhysRevLett.108.258701.
- Sysoeva MV, Sitnikova E, Sysoev IV, Bezruchko BP, van Luijtelaar G. Application of adaptive nonlinear Granger causality: Disclosing network changes before and after absence seizure onset in a genetic rat model. J. Neuroscience Methods. 2014;226:33–41. DOI: 10.1016/j.jneumeth.2014.01.028.
- Navrotskaya EV, Karavaev AS, Sinkin MV, Borovkova EI, Bezruchko BP. Adaptation of the method of coupling analysis based on phase dynamics modeling to EEG signals during an epileptic seizure in comatose patients. Izvestiya of Saratov University. Physics. 2022;22(1):4–14. DOI: 10.18500/1817-3020-2022-22-1-4-14.
- Prokhorov MD, Borovkova EI, Hramkov AN, Dubinkina ES, Ponomarenko VI, Ishbulatov YM, Kurbako AV, Karavaev AS. Changes in the power and coupling of infra-slow oscillations in the signals of EEG leads during stress-inducing cognitive tasks. Appl. Sci. 2023;13(14):8390. DOI: 10.3390/app13148390.
- 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 and Fractals. 2005;23(4):1429–1438. DOI: 10.1016/j.chaos.2004.06.041.
- 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.
- Dick OE, Glazov AL. Revealing the coupling directionality and synchronization between time series from physiological data by analysis of joint recurrences. Chaos, Solitons and Fractals. 2023;173:113768. DOI: 10.1016/j.chaos.2023.113768.
- Dik OE, Glazov AL. Determination of the directionality of the relationship between time series extracted from biological data of rats using the method of modeling the phase dynamics of periodic processes. Tech. Phys. 2023;93(10):1520–1528. DOI: 10.61011/JTF.2023.10.56291.144-23.
- Chen Y, Rangarajan G, Feng J, Ding M. Analyzing multiple nonlinear time series with extended Granger causality. Phys. Lett. A. 2004;324(1):26–35. DOI: 10.1016/j.physleta.2004.02.032.
- Quiroga RQ, Arnhold J, Grassberge P. Learning driver-response relationships from synchronization patterns. Phys. Rev. E. 2000;61(5):5142–5148. DOI: 10.1103/PhysRevE.61.5142.
- Vejmelka M, Palus M. Inferring the directionality of coupling with conditional mutual information. Phys. Rev. E. 2008;77(2):026214. DOI: 10.1103/PhysRevE.77.026214.
- Stramaglia S, Faes L, Cortes JM, Marinazzo D. Disentangling high-order effects in the transfer entropy. Phys. Rev. Res. 2024;6(3):L032007. DOI: 10.1103/PhysRevResearch.6.L032007.
- Rosenblum MG, Pikovsky AS. Detecting direction of coupling in interacting oscillators. Phys. Rev. E. 2001;64(4):045202. DOI: 10.1103/PhysRevE.64.045202.
- Romano MC, Thiel M, Kurths J, Grebogi C. Estimation of the direction of the coupling by conditional probabilities of recurrence. Phys. Rev. E. 2007;76(3):036211. DOI: 10.1103/PhysRevE.76.036211.
- Rosenblum M, Pikovsky A. Inferring connectivity of an oscillatory network via the phase dynamics reconstruction. Front. Netw. Physiol. 2023;3:1298228. DOI: 10.3389/fnetp.2023.1298228.
- Friston KJ. Functional and effective connectivity: a review. Brain Connect. 2011;1(1):13.linebreak. DOI: 10.1089/brain.2011.0008.
- Kralemann B, Pikovsky A, Rosenblum M. Reconstructing phase dynamics of oscillator networks. Chaos. 2011;21(2):025104. DOI: 10.1063/1.3597647.
- Kralemann B, Pikovsky A, Rosenblum M. Reconstructing effective phase connectivity of oscillator networks from observations. New J. Phys. 2014;16:085013. DOI: 10.1088/1367-2630/16/8/085013.
- Dick OE. Analysis of synchronization between time series obtained from anesthetized rats during pain exposure. Izvestiya VUZ. Applied Nonlinear Dynamics. 2024;32(2):209–222. DOI: 10.18500/0869-6632-003093.
- Dick OE. Application of phase dynamics and recurrence modeling methods to assess the characteristics of the relationship between physiological rhythms. Izvestiya VUZ. Applied Nonlinear Dynamics. 2025;33(3):381–398. DOI: 10.18500/0869-6632-003165.
- Daubechies I, Lu J, Wu HT. Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Appl. Comput. Harmon. Anal. 2011;30:243–261. DOI: 10.1016/j.acha.2010.08.002.
- Mokhov II, Smirnov DA. A study of the mutual influence of the El Niño-Southern Oscillation and the North Atlantic and Arctic Oscillation processes using nonlinear methods. Izvestiya, Atmospheric and Oceanic Physics. 2006;42(5):598–614.
- Mokhov II, Smirnov DA. El Nino Southern Oscillation drives North Atlantic Oscillation as revealed with nonlinear techniques from climatic indices. Geophysical Research Letters. 2006;33:L03708. DOI: 10.1029/2005GL024557.
- Thakur G, Brevdo E, Fuckar NS, Wu H-T. The synchrosqueezing algorithm for time-varying spectral analysis: robustness properties and new paleoclimate applications. Signal Process. 2013;93(5):1079–1094. DOI: 10.1016/j.sigpro.2012.11.029.
- Kralemann B, Cimponeriu L, Rosenblum M, Pikovsky A, Mrowka R. Phase dynamics of coupled oscillators reconstructed from data. Phys. Rev. E. 2008;77(6):66205.
- Smirnov DA, Bezruchko BP. Estimation of interaction strength and direction from short and noisy time series. Phys. Rev. E. 2003;68(4):046209. DOI: 10.1103/PhysRevE.68.046209.
- Smirnov DA, Bodrov MB, Bezruchko BP. Estimation of coupling between oscillators from time series via phase dynamics modeling: limits of method’s applicability. Izvestiya VUZ. Applied Nonlinear Dynamics. 2004;12(6):79–92. DOI: 10.18500/0869-6632-2004-12-6-79-92.
- Tass P, Rosenblum MG, Weule J, Kurths J, Pikovsky A, Volkmann J, Schnitzler A, Freund H-J. Detection of n:m phase locking from noisy data: application to magnetoencephalography. Phys. Rev. Lett. 1998;81(15):3291-3294. DOI: 10.1103/PhysRevLett.81.3291.
- Thiel M, Romano MC, Kurths J, Rolfs M., Kliegl R. Generating surrogates from recurrences. Philos. Trans. A Math. Phys. Eng. Sci. 2008;366:545–557. DOI: 10.1098/rsta.2007.2109.
- Ponomarenko VI, Karavaev AS, Borovkova EI, Hramkov AN, Kiselev AR, Prokhorov MD, Penzel T. Decrease of coherence between the respiration and parasympathetic control of the heart rate with aging. Chaos. 2021;31:073105. DOI: 10.1063/5.0056624.
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