Literature DB >> 8116912

Nonlinear analysis of renal autoregulation under broadband forcing conditions.

V Z Marmarelis1, K H Chon, Y M Chen, D J Marsh, N H Holstein-Rathlou.   

Abstract

Linear analysis of renal blood flow fluctuations, induced experimentally in rats by broad-band (pseudorandom) arterial blood pressure forcing at various power levels, has been unable to explain fully the dynamics of renal autoregulation at low frequencies. This observation has suggested the possibility of nonlinear mechanisms subserving renal autoregulation at frequencies below 0.2 Hz. This paper presents results of 3rd-order Volterra-Wiener analysis that appear to explain adequately the nonlinearities in the pressure-flow relation below 0.2 Hz in rats. The contribution of the 3rd-order kernel in describing the dynamic pressure-flow relation is found to be important. Furthermore, the dependence of 1st-order kernel waveforms on the power level of broadband pressure forcing indicates the presence of nonlinear feedback (of sigmoid type) based on previously reported analysis of a class of nonlinear feedback systems.

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Year:  1993        PMID: 8116912     DOI: 10.1007/bf02368640

Source DB:  PubMed          Journal:  Ann Biomed Eng        ISSN: 0090-6964            Impact factor:   3.934


  10 in total

1.  Wiener analysis of nonlinear feedback in sensory systems.

Authors:  V Z Marmarelis
Journal:  Ann Biomed Eng       Date:  1991       Impact factor: 3.934

2.  Tubuloglomerular feedback dynamics and renal blood flow autoregulation in rats.

Authors:  N H Holstein-Rathlou; A J Wagner; D J Marsh
Journal:  Am J Physiol       Date:  1991-01

3.  A family of quasi-white random signals and its optimal use in biological system identification. Part I: theory.

Authors:  V Z Marmarelis
Journal:  Biol Cybern       Date:  1977-07-08       Impact factor: 2.086

4.  1/f fluctuations in arterial pressure and regulation of renal blood flow in dogs.

Authors:  D J Marsh; J L Osborn; A W Cowley
Journal:  Am J Physiol       Date:  1990-05

5.  A dynamic model of the tubuloglomerular feedback mechanism.

Authors:  N H Holstein-Rathlou; D J Marsh
Journal:  Am J Physiol       Date:  1990-05

6.  Frequency domain analysis of renal autoregulation in the rat.

Authors:  T Sakai; E Hallman; D J Marsh
Journal:  Am J Physiol       Date:  1986-02

7.  On the efficacy of linear system analysis of renal autoregulation in rats.

Authors:  K H Chon; Y M Chen; N H Holstein-Rathlou; D J Marsh; V Z Marmarelis
Journal:  IEEE Trans Biomed Eng       Date:  1993-01       Impact factor: 4.538

8.  Identification of nonlinear biological systems using Laguerre expansions of kernels.

Authors:  V Z Marmarelis
Journal:  Ann Biomed Eng       Date:  1993 Nov-Dec       Impact factor: 3.934

9.  Tubuloglomerular feedback and autoregulation in spontaneously hypertensive rats.

Authors:  F H Daniels; W J Arendshorst; R G Roberds
Journal:  Am J Physiol       Date:  1990-06

10.  Chaos in blood flow control in genetic and renovascular hypertensive rats.

Authors:  K P Yip; N H Holstein-Rathlou; D J Marsh
Journal:  Am J Physiol       Date:  1991-09
  10 in total
  7 in total

1.  Renal blood flow and dynamic autoregulation in conscious mice.

Authors:  Radu Iliescu; Radu Cazan; Gerald R McLemore; Marcia Venegas-Pont; Michael J Ryan
Journal:  Am J Physiol Renal Physiol       Date:  2008-06-25

2.  The identification of nonlinear biological systems: Volterra kernel approaches.

Authors:  M J Korenberg; I W Hunter
Journal:  Ann Biomed Eng       Date:  1996 Mar-Apr       Impact factor: 3.934

3.  Identification of physiological systems: a robust method for non-parametric impulse response estimation.

Authors:  D T Westwick; R E Kearney
Journal:  Med Biol Eng Comput       Date:  1997-03       Impact factor: 2.602

4.  Nonlinear analysis of biological systems using short M-sequences and sparse-stimulation techniques.

Authors:  H W Chen; C J Aine; E Best; D Ranken; R R Harrison; E R Flynn; C C Wood
Journal:  Ann Biomed Eng       Date:  1996 Jul-Aug       Impact factor: 3.934

5.  Linear and nonlinear modeling of cerebral flow autoregulation using principal dynamic modes.

Authors:  Vz Marmarelis; Dc Shin; R Zhang
Journal:  Open Biomed Eng J       Date:  2012-04-26

6.  Detection of Impaired Sympathetic Cerebrovascular Control Using Functional Biomarkers Based on Principal Dynamic Mode Analysis.

Authors:  Saqib Saleem; Yu-Chieh Tzeng; W Bastiaan Kleijn; Paul D Teal
Journal:  Front Physiol       Date:  2017-01-09       Impact factor: 4.566

7.  A Novel Time-Varying Spectral Filtering Algorithm for Reconstruction of Motion Artifact Corrupted Heart Rate Signals During Intense Physical Activities Using a Wearable Photoplethysmogram Sensor.

Authors:  Seyed M A Salehizadeh; Duy Dao; Jeffrey Bolkhovsky; Chae Cho; Yitzhak Mendelson; Ki H Chon
Journal:  Sensors (Basel)       Date:  2015-12-23       Impact factor: 3.576

  7 in total

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