Closed-Loop Identification of Carotid Sinus Baroreflex Transfer Characteristics Using Electrical Stimulation.

Kawada, Toru; Sato, Takayuki; Inagaki, Masumi; Shishido, Toshiaki; Tatewaki, Teiji; Yanagiya, Yusuke; Zheng, Can; Sugimachi, Masaru; Sunagawa, Kenji

doi:10.2170/jjphysiol.50.371

Cited by 20 publications

(26 citation statements)

References 25 publications

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“…P OP might have been lower than P MID owing to the anesthetic condition. Taken together, the peak-to-peak amplitude of CSP perturbation (40 mmHg) was greater than the native pulse pressure (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), lowering mean AP below P OP .…”

Section: Discussionmentioning

confidence: 91%

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Intravenous Angiotensin II Does Not Affect Dynamic Baroreflex Characteristics of the Neural or Peripheral Arc

Kashihara

Takahashi²,

Chatani³

et al. 2003

JJP

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The arterial baroreflex system is one of the most important negative-feedback systems that stabilizes arterial pressure (AP) against exogenous pressure perturbation. The arterial baroreflex may be divided into the neural and peripheral arc subsystems [1][2][3]. The neural arc represents the signal transduction pathway from baroreceptor pressure input to efferent sympathetic nerve activity (SNA). The peripheral arc represents the regulatory pathway from SNA to AP. Static characteristics of the two arcs provide information on the operating point of AP regulation [2,4]. The dynamic characteristics of the two arcs determine the stability and quickness of AP regulation [1,5]. Because the baroreflex operates dynamically under the ordinary conditions of daily activity, changes in baroreflex dynamic characteristics critically affect the performance of AP regulation and consequently have an impact on quality of life. An assessment of baroreflex dynamic characteristics in conjunction with altered hormonal regulation of the cardiovascular sys- Key words: renal sympathetic nerve activity, transfer function, systems analysis, rabbits, carotid sinus baroreflex.Abstract: Although the elevation of angiotensin II (Ang II) associated with cardiovascular diseases has been considered to suppress the arterial baroreflex function, how Ang II affects dynamic arterial pressure (AP) regulation remains unknown. The aim of the present study was to elucidate the acute effects of Ang II on dynamic AP regulation by the arterial baroreflex. In seven anesthetized Japanese white rabbits, we randomly perturbed intra-carotid sinus pressure (CSP) according to a binary white noise sequence while recording renal sympathetic nerve activity (RSNA) and AP. We estimated the neural arc transfer function from CSP to RSNA and the peripheral arc transfer function from RSNA to AP before and after 30-min intravenous administration of Ang II (100 ng/kg/min). Ang II increased mean

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Section: Discussionmentioning

confidence: 91%

“…We then parameterized the peripheral arc transfer function [H P ( f )] using a second-order low-pass filter as in previous studies [1,23]:…”

Section: Methodsmentioning

confidence: 99%

Intravenous Angiotensin II Does Not Affect Dynamic Baroreflex Characteristics of the Neural or Peripheral Arc

Kashihara

Takahashi²,

Chatani³

et al. 2003

JJP

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“…In this study, all subjects maintained a spontaneous breathing rate above 0.15 Hz (in the HF range); however, respiratory frequencies below 0.15 Hz would cause additional respiratory input in the LF range, and an LF model based solely on LF spike rate could not accurately predict changes in LF SBP . The present model takes into account only open-loop properties of the baroreflex system and ignores its closed-loop properties, i.e., feedback from the baroreceptors, which may also contribute to changes in BP and spike rate fluctuations (24). It has also been suggested that CO contributes to mean sympathetic activity (6) and BP fluctuations (31,50).…”

Section: Predictive Modelmentioning

confidence: 99%

“…One limitation of most models is that they do not include sympathetic activity as a direct input parameter. Modeling of BP fluctuations from sympathetic activity in animals has been successfully applied using a system identification approach (5,22,24). Kamiya et al (23) adapted a transfer function approach used in rabbits to explain the LF fluctuations in human BP and sympathetic activity during tilt but applied this model only to simulated data.…”

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confidence: 99%

A simplified two-component model of blood pressure fluctuation

Brychta

Shiavi²,

Robertson³

et al. 2007

American Journal of Physiology-Heart and Circulatory Physiology

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September 29, 2006; doi:10.1152/ajpheart.00645.2006.-We propose a simple moving-average (MA) model that uses the low-frequency (LF) component of the peroneal muscle sympathetic nerve spike rate (LFspike rate) and the high-frequency (HF) component of respiration (HFResp) to describe the LF neurovascular fluctuations and the HF mechanical oscillations in systolic blood pressure (SBP), respectively. This method was validated by data from eight healthy subjects (23-47 yr old, 6 male, 2 female) during a graded tilt (15°increments every 5 min to a 60°angle). The LF component of SBP (LFSBP) had a strong baroreflex-mediated feedback correlation with LFspike rate (r ϭ Ϫ0.69 Ϯ 0.05) and also a strong feedforward relation to LFspike rate [r ϭ 0.58 Ϯ 0.03 with LFSBP delay () ϭ 5.625 Ϯ 0.15 s]. The HF components of spike rate (HFspike rate) and SBP (HFSBP) were not significantly correlated. Conversely, HFResp and HFSBP were highly correlated (r ϭ Ϫ0.79 Ϯ 0.04), whereas LFResp and LFSBP were significantly less correlated (r ϭ 0.45 Ϯ 0.08). The mean correlation coefficients between the measured and model-predicted LFSBP (r ϭ 0.74 Ϯ 0.03) in the supine position did not change significantly during tilt. The mean correlation between the measured and model-predicted HFSBP was 0.89 Ϯ 0.02 in the supine position. R 2 values for the regression analysis of the model-predicted and measured LF and HF powers indicate that 78 and 91% of the variability in power can be explained by the linear relation of LFspike rate to LFSBP and HFResp to HFSBP. We report a simple two-component model using neural sympathetic and mechanical respiratory inputs that can explain the majority of blood pressure fluctuation at rest and during orthostatic stress in healthy subjects. wavelet transform; blood pressure variability; Mayer waves; respiration; muscle sympathetic nerve activity VARIOUS RHYTHMIC OSCILLATIONS in human blood pressure (BP) have been proposed to reflect the action of different physiological mechanisms on BP regulation. For instance, it has been suggested that very low-frequency (VLF) trends (period Ͼ25 s, frequency Ͻ0.04 Hz) in BP represent the influence of hormonal regulation and thermoregulation and that high-frequency (HF, 0.15-0.4 Hz) fluctuations mark the effect of respiration on BP. A more hotly debated issue is the idea that low-frequency (LF, 0.04 -0.14 Hz) BP oscillations with a 10-s periodicity, generally referred to as Mayer waves, reflect sympathetically mediated vasomotor BP modulation. It has been proposed that the origin of these waves is a resonance phenomenon of the baroreflex pathway (12), and the LF power has been used as a marker of sympathetic activity (34,35,37), although this practice remains controversial (31, 48).Several lines of indirect evidence have been used to associate the oscillations in BP with oscillations in these other physiological rhythms. For example, physiological maneuvers have identified changes in the oscillatory patterns of sympathetic nerve activity and respiration that correspond to those in BP ...

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“…We and others have identified the linear dynamics of the three baroreflex arcs in the form of transfer functions (i.e., gain and phase as a function of frequency) (3)(4)(5)16). These linear models can capture the dynamic behavior of the systems to a significant extent.…”

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confidence: 99%

Nonlinear identification of the total baroreflex arc: chronic hypertension model

Moslehpour

Kawada

Sunagawa

et al. 2016

American Journal of Physiology-Regulatory, Integrative and Comp

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. Its linear dynamic functioning has been shown to be preserved in spontaneously hypertensive rats (SHR). However, the system is known to exhibit nonlinear dynamic behaviors. The aim of this study was to establish nonlinear dynamic models of the total arc (and its subsystems) in hypertensive rats and to compare these models with previously published models for normotensive rats. Hypertensive rats were studied under anesthesia. The vagal and aortic depressor nerves were sectioned. The carotid sinus regions were isolated and attached to a servo-controlled piston pump. AP and sympathetic nerve activity were measured while CSP was controlled via the pump using Gaussian white noise stimulation. Second-order, nonlinear dynamics models were developed by application of nonparametric system identification to a portion of the measurements. The models of the total arc predicted AP 21-43% better (P Ͻ 0.005) than conventional linear dynamic models in response to a new portion of the CSP measurement. The linear and nonlinear terms of these validated models were compared with the corresponding terms of an analogous model for normotensive rats. The nonlinear gains for the hypertensive rats were significantly larger than those for the normotensive rats [Ϫ0.38 Ϯ 0.04 (unitless) vs. Ϫ0.22 Ϯ 0.03, P Ͻ 0.01], whereas the linear gains were similar. Hence, nonlinear dynamic functioning of the sympathetically mediated total arc may enhance baroreflex buffering of AP increases more in SHR than normotensive rats. arterial baroreflex; Gaussian white noise; system identification; nonlinear model; hypertension THE CAROTID SINUS BAROREFLEX plays a central role in maintaining arterial pressure (AP) in the face of fast-acting, exogenous disturbances and may also contribute to long-term AP regulation (7,20,21). This system responds to increases in carotid sinus pressure (CSP) by decreasing efferent sympathetic nerve activity (SNA), which, in turn, decreases AP. We refer to the aggregate, open-loop system relating CSP to AP as the total baroreflex arc, the "controller" subsystem relating CSP to SNA as the neural arc, and the "effector" subsystem relating SNA to AP as the peripheral arc.We and others have identified the linear dynamics of the three baroreflex arcs in the form of transfer functions (i.e., gain and phase as a function of frequency) (3-5, 16). These linear models can capture the dynamic behavior of the systems to a significant extent. We previously showed that the linear dynamics of the total arc are preserved in spontaneously hypertensive rats (SHR) despite resetting of mean AP (4). However, the nonlinear dynamics of this system, which have been less investigated, could possibly respond differently to the chronic hypertension model.In a recent study (9), we employed the Gaussian white noise approach for nonlinear system identification to develop a second-order, nonlinear dynamic model of the total arc in normotensive Wistar-Kyoto rats (WKY). The model predicted AP 12% better than a linear dynamic model in response to new Gaussian w...

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Closed-Loop Identification of Carotid Sinus Baroreflex Transfer Characteristics Using Electrical Stimulation.

Cited by 20 publications

References 25 publications

Intravenous Angiotensin II Does Not Affect Dynamic Baroreflex Characteristics of the Neural or Peripheral Arc

Intravenous Angiotensin II Does Not Affect Dynamic Baroreflex Characteristics of the Neural or Peripheral Arc

A simplified two-component model of blood pressure fluctuation

Nonlinear identification of the total baroreflex arc: chronic hypertension model

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