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


For citation:

Pavlova O. N., Pavlov A. N. Rhythmic processes of renal blood flow autoregulation and their interaction in the form of modulation of oscillations. Izvestiya VUZ. Applied Nonlinear Dynamics, 2010, vol. 18, iss. 2, pp. 98-112. DOI: 10.18500/0869-6632-2010-18-2-98-112

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):
(downloads: 159)
Language: 
Russian
Article type: 
Article
UDC: 
57.087

Rhythmic processes of renal blood flow autoregulation and their interaction in the form of modulation of oscillations

Autors: 
Pavlova Olga Nikolaevna, Saratov State University
Pavlov Aleksej Nikolaevich, Saratov State University
Abstract: 

Renal blood flow autoregulation at the level of individual nephrons includes two interacting mechanisms that produce oscillations with different time scales: the tubologlomerular feedback (TGF) and the myogenic response. Based on the wavelet-analysis of experimental data, we study in this work phenomena of amplitude and frequency modulation of myogenic oscillations by the TGF-rhythm. Features of nonlinear dependencies of amplitude and frequency deviation of modulated process versus the amplitude of modulating oscillations are revealed. It is shown that phenomena of modulation are essentially different between normal and hypertensive states.

Reference: 
  1. Schreiber T. Interdisciplinary application of nonlinear time series methods. Physic Reports. 1999;308(1):1–64. DOI: 10.1016/S0370-1573(98)00035-0.
  2. Tsay RS. Detecting and modeling nonlinearity in univariate time series analysis. Statistica Sinica. 1991;1:431–451.
  3. Kaplan DT, Glass L. Direct test for determinism in a time series. Phys. Rev. Lett. 1992;68:427–430. DOI: 10.1103/PhysRevLett.68.427.
  4. Kennel MB, Isabelle S. Method to distinguish possible chaos from colored noise and to determine embedding parameters. Phys. Rev. A. 1992;46:3111–3118. DOI: 10.1103/physreva.46.3111.
  5. Theiler J, Galdrikian B, Longtin A, Eubank S, Farmer JD. Detecting nonlinear structure in time series. Physica D. 1992;58:77–94.
  6. Palus M. Testing for nonlinearity using redundancies: Quantitative and qualitative aspects. Physica D. 1995;80:186–205. DOI: 10.1016/0167-2789(95)90079-9.
  7. Palus M. Detecting nonlinearity in multivariate time series. Phys. Lett. A. 1996;213:138–147. DOI: 10.1016/0375-9601(96)00116-8.
  8. Janson NB, Balanov AG, Anishchenko VS, McClintock PV. Phase synchronization between several interacting processes from univariate data. Phys. Rev. Lett. 2001;86:1749–1752. DOI: 10.1103/PhysRevLett.86.1749.
  9. Rosenblum MG, Pikovsky AS. Detecting direction of coupling in interacting oscillators. Phys. Rev. E. 2001;64:045202(R). DOI: 10.1103/PhysRevE.64.045202.
  10. Palus M, Stefanovska A. Direction of coupling from phases of interacting oscillators: An information-theoretic approach. Phys. Rev. E. 2003;67:055201. DOI: 10.1103/PhysRevE.67.055201.
  11. Smirnov DA, Bezruchko BP. Estimation of interaction strength and direction from short and noisy time series. Phys. Rev. E. 2003;68:046209. DOI: 10.1103/PhysRevE.68.046209.
  12. Cimponeriu L, Rosenblum M, Pikovsky A. Estimation of delay in coupling from time series. Phys. Rev. E. 2004;70:046213. DOI: 10.1103/PhysRevE.70.046213.
  13. Sosnovtseva OV, Pavlov AN, Mosekilde E, Holstein-Rathlou NH, Marsh DJ. Double-wavelet approach to study frequency and amplitude modulation in renal autoregulation. Phys. Rev. E. 2004;70:031915. DOI: 10.1103/PhysRevE.70.031915.
  14. Bezruchko B, Ponomarenko V, Rosenblum MG, Pikovsky AS. Characterizing direction of coupling from experimental observations. Chaos. 2003;13:179–184. DOI: 10.1063/1.1518425.
  15. Palus M, Komarek V, Hrncir Z, Sterbova K. Synchronization as adjustment of information rates: Detection from bivariate time series. Phys. Rev. E. 2001;63:046211. DOI: 10.1103/PhysRevE.63.046211.
  16. Janson NB, Balanov AG, Anishchenko VS, McClintock PV. Phase relationships between two or more interacting processes from one-dimensional time series. II. Application to heart-rate-variability data. Phys. Rev. E. 2002;65:036212. DOI: 10.1103/PhysRevE.65.036212.
  17. Rosenblum MG, Cimponeriu L, Bezerianos A, Patzak A, Mrowka R. Identification of coupling direction: Application to cardiorespiratory interaction. Phys. Rev. E. 2002;65:041909. DOI: 10.1103/PhysRevE.65.041909.
  18. Mrowka R, Cimponeriu L, Patzak A, Rosenblum MG. Directionality of coupling of physiological subsystems: age–related changes of cardiorespiratory interaction during different sleep stages in babies. Am. J. Physiol. Regul. Comp. Integr. Physiol. 2003;285:R1395–R1401. DOI: 10.1152/ajpregu.00373.2003.
  19. Smirnov DA, Bodrov MB, Perez Velazquez JL, Wennberg RA, Bezruchko BP. Estimation of coupling between oscillators from short time series via phase dynamics modeling: Limitations and application to EEG data. Chaos. 2005;15:24102. DOI: 10.1063/1.1938487.
  20. Gonzalez-Fernandez JM, Ermentrout GB. On the origin and dynamics of the vasomotion of small arteries. Math. Biosci. 1994;1190:127–167. DOI: 10.1016/0025-5564(94)90074-4.
  21. Horowitz A, Menice CB, Laporte R, Morgan KG. Mechanisms of smooth muscle contraction. Physiol. Rev. 1996;76:967–1003. DOI: 10.1152/physrev.1996.76.4.967.
  22. Leyssac PP, Holstein-Rathlou NH. Effects of various transport inhibitors on oscillating TFG pressure responses in the rat. Pflugers Arch. 1986;407:285–291. DOI: 10.1007/BF00585304.
  23. Chon KH, Raghavan R, Chen YM, Marsh DJ, Yip KP. Interactions of TGF-dependent and TGF-independent oscillations in tubular pressure. Am. J. Physiol. (Renal Physiol.) 2005;288:F298–F307. DOI: 10.1152/ajprenal.00164.2004.
  24. Casellas D, Moore LC. Autoregulation and tubuloglomerular feedback in juxtamedullary glomerular arterioles. Am. J. Physiol. (Renal Fluid Electrolyte Physiol.) 1990;258:F660–F669. DOI: 10.1152/ajprenal.1990.258.3.F660.
  25. Holstein-Rathlou NH, He J, Wagner AJ, Marsh DJ. Patterns of blood pressure variability in normotensive and hypertensive rats. Am. J. Physiol. (Regul. Integr. Comp. Physiol.) 1995;269:R1230–R1239. DOI: 10.1152/ajpregu.1995.269.5.R1230.
  26. Holstein-Rathlou NH., Leyssac PP. TGF-mediated oscillations in the proximal intratubular pressure: differences between spontaneously hypertensive rats and Wistar-Kyoto rats. Acta Physiol. Scand. 1986;126(3):333–339. DOI: 10.1111/j.1748-1716.1986.tb07824.x.
  27. Leyssac PP, Holstein-Rathlou NH. Tubulo-glomerular feedback response: enhancement in adult spontaneously hypertensive rats and effects of anaesthetics. Pflugers Arch. 1989;413:267–272. DOI: 10.1007/BF00583540.
  28. Yip KP, Holstein-Rathlou NH, Marsh DJ. Chaos in blood flow control in genetic and renovascular hypertensive rats. Am. J. Physiol. (Renal Fluid Electrolyte Physiol.) 1991;261:F400–F408. DOI: 10.1152/ajprenal.1991.261.3.F400.
  29. Yip KP, Marsh DJ, Holstein-Rathlou NH. Evidence of low dimensional chaos in renal blood flow control in genetic and experimental hypertension. Physica D. 1995;80:95–104. DOI: 10.1016/0167-2789(95)90063-2.
  30. Sosnovtseva OV, Pavlov AN, Mosekilde E, Yip KP, Holstein-Rathlou NH, Marsh DJ. Synchronization among mechanisms of renal autoregulation is reduced in hypertensive rats. Am. J. Physiol. (Renal Physiol.). 2007;293:F1545–F1555. DOI: 10.1152/ajprenal.00054.2007.
  31. Sosnovtseva OV, Pavlov AN, Mosekilde E, Holstein-Rathlou NH, Marsh DJ. Double-wavelet approach to study frequency and amplitude modulation in renal autoregulation. Phys. Rev. E. 2004;70:031915. DOI: 10.1103/PhysRevE.70.031915.
  32. Marsh DJ, Sosnovtseva OV, Pavlov AN, Yip KP, Holstein-Rathlou NH. Frequency encoding in renal blood flow regulation. Am. J. Physiol. (Regul. Integr. Comp. Physiol.). 2005;288:R1160–R1167. DOI: 10.1152/ajpregu.00540.2004.
  33. Sosnovtseva OV, Pavlov AN, Mosekilde E, Holstein-Rathlou NH, Marsh DJ. Double-wavelet approach to studying the modulation properties of nonstationary multimode dynamics. Physiological Measurement. 2005;26:351–362. DOI: 10.1088/0967-3334/26/4/002.
  34. Pavlov AN, Makarov VA, Mosekilde E, Sosnovtseva OV. Application of wavelet-based tools to study the dynamics of biological processes. Briefings in Bioinformatics. 2006;7:375–389. DOI: 10.1093/bib/bbl041.
  35. Sosnovtseva OV, Pavlov AN, Pavlova ON, Mosekilde E, Holstein-Rathlou NH. The effect of L-name on intra- and inter-nephron synchronization. European Journal of Pharmaceutical Sciences. 2009;36:39–50. DOI: 10.1016/j.ejps.2008.10.019.
  36. Smedley GT, Yip KP, Wagner AJ, Dubovitsky S, Marsh DJ. A laser Doppler instrument for in-vivo measurements of blood flow in single renal arterioles. IEEE Trans. Biomed. Eng. 1993;40:290–297. DOI: 10.1109/10.216413.
  37. Mallat SG. A wavelet tour of signal processing. New York: Academic Press; 1998.
  38. Addison PS. The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance. Bristol; Philadelphia: IOP Publishing; 2002. 368 p. DOI: 10.1201/9781003040408.
  39. Kaiser G. A friendly guide to wavelets. Boston: Birkhauser; 1994.
  40. Press WH, Flannery BP, Teucolsky SA, Vetterling WT. Numerical recipes: the art of scientific computing. New York: Cambridge University Press; 1986.
Received: 
03.09.2009
Accepted: 
17.02.2010
Published: 
30.04.2010
Short text (in English):
(downloads: 99)