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Department of Cardiovascular Dynamics, National Cardiovascular Center Research Institute, Osaka 565-8565, Japan
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ABSTRACT |
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 TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
APPENDIX
REFERENCES
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Although regional difference in sympathetic efferent nerve activity has been well investigated, whether this regional difference exists in the dynamic baroreflex regulation of sympathetic nerve activity remains uncertain. In anesthetized, vagotomized, and aortic-denervated rabbits, we isolated carotid sinuses and randomly perturbed intracarotid sinus pressure (CSP) while simultaneously recording cardiac (CSNA) and renal sympathetic nerve activities (RSNA). The neural arc transfer function from CSP to CSNA and that from CSP to RSNA revealed high-pass characteristics. The increasing slope of the transfer gain in the frequencies between 0.03 and 0.3 Hz was significantly greater for CSNA than for RSNA (2.96 ± 0.72 vs. 1.64 ± 0.73 dB/octave, P < 0.01, n = 9). The difference was hardly explained by the difference in static nonlinear characteristics of CSP-CSNA and CSP-RSNA relationships or by the difference in conduction velocities in the multifiber recording. These results indicate that the central processing in the brain stem differs between CSNA and RSNA. The neural arc of the baroreflex may exert differential effects on the heart and kidney in response to dynamic baroreflex activation.
systems analysis; transfer function; white noise; carotid sinus baroreflex; rabbits
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INTRODUCTION |
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TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
APPENDIX
REFERENCES
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REGIONAL DIFFERENCE in sympathetic efferent nerve activity has been a great concern of many investigators. Ninomiya et al. (25) reported differential arterial baroreflex control of sympathetic nerve activity among neural districts directed to the spleen, kidney, and heart in anesthetized cats. Karim et al. (10) demonstrated that activation of left atrial receptors causes an increase in cardiac sympathetic efferent nerve activity (CSNA), a decrease in renal sympathetic efferent nerve activity (RSNA), and no change in lumbar and splenic sympathetic efferent nerve activities in anesthetized dogs. Iriki et al. (9) demonstrated that hypoxia shifts the cardiac and renal baroreflex curves in an opposite direction in anesthetized rabbits. Kostreva et al. (16) reported that hepatic baroreceptor activation through caval occlusion resulted in the increase of CSNA and RSNA in the anesthetized dog. In their study, an anterior hepatic plexus section eliminates the RSNA response while preserving the CSNA response, suggesting that CSNA and RSNA are differently regulated through different hepatic afferent pathways. Matsukawa et al. (20) reported differential effects of anesthesia on CSNA and RSNA in cats. These lines of evidence indicate that CSNA and RSNA respond differentially depending on the type of exogenous perturbations. The differential central pathways in the rostral ventrolateral medulla are considered to underlie the regional difference of sympathetic nerve activity (4, 21).
Aside from the regional difference, a quickness of regulation is another hallmark of the neural control in contrast to hormonal and humoral controls. The quickness of neural control may be best described by analyzing its dynamic characteristics. In a previous paper (8), we estimated the dynamic characteristics of the carotid sinus baroreflex in anesthetized rabbits. We divided the total baroreflex loop into two principal arcs: a neural arc from baroreceptor pressure input to CSNA and a peripheral arc from CSNA to arterial pressure. In the study, we found that the neural arc shows high-pass characteristics in frequencies above 0.1 Hz. That is to say, the magnitude of CSNA response to baroreceptor pressure input becomes greater as the input frequency increases. A simulation study indicated that the fast neural arc compensates for the slow peripheral arc to achieve quickness and stability of arterial pressure regulation. However, whether the concept of the fast neural arc is commonly applicable to the dynamic baroreflex regulation of sympathetic nerve activity other than CSNA remains unclear. Kubo et al. (17) investigated the RSNA response to aortic depressor nerve stimulation and constructed a diagram indicating that most of the high-pass characteristics of the neural arc is attributable to the dynamic transduction properties of baroreceptors. In their diagram, central processing in the brain stem is a simple all-pass filter rather than a high-pass filter and does nothing in particular except for inverting the sign of signal and adding some lag time. The discrepancy between their and our data lead us to hypothesize that the dynamic baroreflex regulation differs between CSNA and RSNA.
Although Harada et al. (7) demonstrated that arterial baroreflex control of CSNA and RSNA is uniform in the frequency domain in anesthetized cats, we think the study is incomplete with respect to the following aspects. First, they applied a conventional open-loop transfer function analysis despite the fact that the arterial baroreflex loop was partially closed in their experimental setting (17). Second, the input power spectra were not quite white in the frequency range examined (0.01-0.7 Hz). Therefore, there is room for argument over whether the neural arc transfer functions they estimated were precise enough for uncovering the difference in dynamic baroreflex regulation between CSNA and RSNA. Third, the dynamic responses of CSNA and RSNA were not directly compared in individual animals, which might have made the differentiation between the CSNA and RSNA responses difficult. To circumvent these problems, we performed a baroreflex open-loop experiment in anesthetized rabbits while simultaneously recording CSNA and RSNA. We isolated bilateral carotid sinuses from the systemic circulation so as to accurately impose desired pressure perturbation (8, 11-14, 22, 23, 27, 29). The results indicated that the high-pass characteristics of the neural arc was significantly more enhanced in CSNA than in RSNA.
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MATERIALS AND METHODS |
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TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
APPENDIX
REFERENCES
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Surgical Preparations
Animals were cared for in strict accordance with the Guiding Principles for the Care and Use of Animals in the Field of Physiological Sciences approved by the Physiological Society of Japan. Nine Japanese White rabbits weighing 2.4-3.8 kg were anesthetized via intravenous injection (2 ml/kg) of a mixture of urethane (250 mg/ml) and
-chloralose (40 mg/ml) and mechanically ventilated with
oxygen-enriched room air. Supplemental anesthetics were injected as
necessary (0.5 ml/kg) to maintain an appropriate level of anesthesia. Aortic pressure (AoP) was measured using a high-fidelity pressure transducer (Millar Instruments; Houston, TX) inserted via the right
femoral artery. We isolated the bilateral carotid sinuses vascularly
from the systemic circulation by ligating the internal and external
carotid arteries and other small branches originating from the carotid
sinus regions. The isolated carotid sinuses were filled with warmed
physiological saline through catheters inserted via the common carotid
arteries. The intracarotid sinus pressure (CSP) was controlled by a
servo-controlled piston pump (model ET-126A, Labworks; Costa Mesa, CA).
Bilateral vagal and aortic depressor nerves were sectioned at the
middle of the neck to eliminate baroreflexes from the cardiopulmonary
region and the aortic arch. We exposed the left renal sympathetic nerve
retroperitoneally and attached a pair of stainless steel wire
electrodes (Bioflex wire AS633, Cooner Wire) to record RSNA. The nerve
fibers peripheral to the electrodes were tightly ligated and crushed to
eliminate afferent signals from the kidney. We also recorded CSNA from
the left cardiac sympathetic nerve through a midline thoracotomy. The
nerve fibers peripheral to the electrodes were sectioned to eliminate
afferent signals from the heart. To insulate and fix the electrodes,
the nerve and electrodes were covered with a mixture of silicone gel
(Semicosil 932A/B, Wacker Silicones) and white petrolatum (Vaseline).
The preamplified nerve signal was band-pass filtered at 150-1,000
Hz. It was then full-wave rectified and low-pass filtered with a cutoff
frequency of 30 Hz to quantify the nerve activity. We exchanged the
recording systems for CSNA and RSNA occasionally and confirmed that the
recording systems had identical characteristics. Pancuronium bromide
(0.3 mg/kg) was administered to prevent contamination of muscular
activity in the CSNA and RSNA recordings. Body temperature was
maintained at ~38°C with a heating pad.
Protocols
Dynamic protocol.
After the surgical preparation was completed, CSP was equilibrated with
mean AoP to obtain the operating pressure (OP). To estimate the dynamic
characteristics of the baroreflex in regulating CSNA and RSNA, we
randomly assigned CSP to either high (OP + 20 mmHg) or low
(OP 
20 mmHg) pressure every 500 ms according to a binary white
noise sequence (12-14, 19, 28, 29). The input power
spectra of CSP were relatively flat up to 1 Hz (Fig.
1). We recorded CSP, CSNA, RSNA, and AoP
for 10 min at a sampling rate of 200 Hz using a 12-bit
analog-to-digital converter. The data were stored on the hard disk
drive of a dedicated laboratory computer system for later
analysis.
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Static protocol. Because static nonlinear characteristics of the neural arc are thought to affect dynamic baroreflex regulation of sympathetic nerve activity (11, 30), we estimated the static characteristics of the neural arc in six of nine animals. CSP was first decreased to 40 mmHg. After CSNA and RSNA reached steady state, CSP was changed stepwise from 40 to 180 mmHg with an increment of 20 mmHg. Each pressure step was maintained for 60 s. When CSNA and RSNA were completely suppressed and AoP decreased below 50 mmHg at the CSP level of 160 mmHg, the CSP level of 180 mmHg was omitted to avoid deterioration of the animal's condition.
Data Analysis
In the dynamic protocol, to estimate the neural arc transfer function of the carotid sinus baroreflex, we treated CSP as the input and CSNA or RSNA as the output of the system. We resampled input-output data pairs at 10 Hz and segmented them into eight sets of 50% overlapping bins of 1,024 data points each. For each segment, a linear trend was subtracted, and a Hanning window was applied. We then performed a fast Fourier transform to obtain frequency spectra of the input and output. We ensemble averaged the input power [SXX(f)], output power [SYY(f)], and cross-power between the input and output [SYX(f)] over the eight segments, where f represents frequency. Finally, we calculated the transfer function [H(f)] from the input to output with the use of the following equation (19)
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(1) |
(f)] of the transfer function were
derived from its real
[Hreal(f)] and imaginary parts
[Himag(f)] with
the use of the following equations
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(2) |
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(3) |
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(4) |
Hereafter in the present paper, HCSNA and HRSNA denote the transfer function from CSP to CSNA and from CSP to RSNA, respectively. The system impulse response was derived from the inverse Fourier transform of H(f). To facilitate intuitive understanding of the transfer characteristics, we calculated the system step response from a time integral of the system impulse response up to 10 s. To compare the dynamic responses of CSNA and RSNA directly during CSP perturbation, we also calculated the transfer function from RSNA to CSNA. In this context, the transfer function represented the amplitude ratio and phase difference between RSNA and CSNA rather than the input-output relationship between the two signals.
In the static protocol, we calculated mean sympathetic nerve activity
during the last 10 s of each CSP level and performed a regression
analysis for the four-parameter logistic curve as follows (11,
15, 28)
![y=<FR><NU>P<SUB>1</SUB></NU><DE>1+exp[<IT>P<SUB>2</SUB></IT>(CSP<IT>−P<SUB>3</SUB></IT>)]</DE></FR><IT>+P<SUB>4</SUB></IT>](/content/vol280/issue4/fulltext/H1581/img005.gif)
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(5) |
Statistical Analysis
All data are presented as means ± SD. In all of the following statistics, difference was considered significant when P < 0.05. Because the magnitude of sympathetic nerve activity varied depending on such recording conditions as the physical contact between the nerve and the electrodes, CSNA and RSNA were presented in arbitrary units. In the dynamic protocol, we normalized CSNA and RSNA by the gain values of HCSNA and HRSNA below 0.03 Hz, respectively. To examine the difference between HCSNA and HRSNA, we obtained the gain and phase values at 0.01, 0.1, 0.5, and 1 Hz in each animal. Because the baroreceptors were exposed to a broad frequency bandwidth perturbation in the range from 0.01 and 1 Hz, we arbitrarily chose the frequencies for the statistical analysis within this frequency range. To minimize the variance associated with the estimation of the transfer function, values between 0.45 and 0.5 Hz were averaged to represent the value around 0.5 Hz. Similarly, values between 0.9 and 1 Hz were averaged to represent the value around 1 Hz. After the gain and phase values at each frequency in each animal were obtained, group differences in these parameters between HCSNA and HRSNA were examined by paired t-test (6). We also calculated a slope of the transfer gain in the frequencies between 0.03 and 0.3 Hz in each animal and examined its group difference between HCSNA and HRSNA by paired t-test.The coherence values at 0.01, 0.1, 0.5 (averaged from 0.45 to 0.5 Hz), and 1 Hz (averaged from 0.9 to 1 Hz) were obtained in each animal. The group difference in the coherence value at each frequency was then examined by the Wilcoxon signed-ranks test, because the normal distribution was not assumed for the coherence values (6).
The initial response (i.e., maximum negative response) and steady-state response of the step response (averaged between 9 and 10 s) were also calculated in each animal. Group differences in the initial and steady-state responses between the CSNA and RSNA step responses were then examined by paired t-test (6).
In the static protocol, because mean sympathetic nerve activity was represented in arbitrary units, the absolute P1 and P4 values had no particular biological meanings. Therefore, we compared only the P2 and P3 values between the CSP-CSNA and CSP-RSNA relationships by paired t-test (6). Note that the P2 value has a reciprocal relationship with the operating range in the CSP axis independent of the other parameters of the logistic function (see APPENDIX).
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RESULTS |
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TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
APPENDIX
REFERENCES
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Figure 2 presents typical time
series obtained from the dynamic protocol. CSP, CSNA, RSNA, and AoP are
shown. CSP was perturbed according to a binary white noise sequence.
When CSP was increased, CSNA and RSNA decreased. AoP then decreased
with some delay. When CSP was decreased, the opposite responses were
observed. Because the carotid sinuses were isolated, changes in AoP did
not affect CSP, thereby validating a conventional open-loop transfer
function analysis under this experimental condition (12, 14,
19). Although the shape of each burst differs between CSNA and
RSNA, global characteristics of dynamic changes are similar between the
two activities. A simple inspection of the time series data does not
allow us to differentiate dynamic CSNA and RSNA responses to CSP
perturbation.
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Figure 3A shows the neural arc
transfer functions estimated using CSNA (left) and RSNA
(right). Gain plots (top), phase plots (middle), and coherence functions (bottom) are
shown. As a result of normalization, the gain value approximated unity
at the lowest frequency in both HCSNA and
HRSNA. The gain values increased as the input
frequency of CSP perturbation increased between 0.03 and 0.3 Hz,
indicating high-pass characteristics of the neural arc. The increasing
slope of the transfer gain was steeper in HCSNA
than HRSNA. The gain values were relatively
constant in the frequencies between 0.4 and 0.8 Hz and gradually
decreased as the frequency approached 1 Hz in both
HCSNA and HRSNA. The phase plots indicated an out-of-phase relationship between CSP and
sympathetic nerve activity in the lowest frequencies in both HCSNA and HRSNA,
reflecting negative feedback in the neural arc. The coherence values
were between 0.6 and 0.9 in the frequency range between 0.01 and 0.4 Hz
in both HCSNA and HRSNA.
Figure 3B shows the step responses corresponding to
HCSNA and HRSNA. The
initial decrease of the step response was significantly more negative
in CSNA than in RSNA, whereas the steady-state values did not differ
between the two.
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Table 1 summarizes the parameters of the
transfer function and step response shown in Fig. 3. The gain value at
0.01 Hz approximated unity due to the normalization of sympathetic
nerve activity. Although the gain value at 0.1 Hz did not differ
between HCSNA and HRSNA,
the gain values at 0.5 and 1 Hz were significantly greater in
HCSNA than in HRSNA. The
phase value at 0.01 Hz did not differ between
HCSNA and HRSNA. The
phase values at 0.1, 0.5, and 1 Hz were significantly less negative in
HCSNA than in HRSNA. The
coherence values were slightly but significantly lower in HCSNA than in HRSNA at
0.5 and 1 Hz. The increasing slope of the transfer gain was
significantly greater in HCSNA than in
HRSNA. The initial decrease in the step response
was significantly more negative in CSNA than in RSNA, reflecting the
differential high-pass characteristics. The steady-state value of the
step response did not differ between CSNA and RSNA.
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Figure 4 shows the amplitude ratio and
phase difference between CSNA and RSNA. Although the amplitude ratio of
CSNA to RSNA approximated unity in the frequencies below 0.1 Hz, it
became greater than unity in the higher frequencies up to 1 Hz. Despite the frequency-dependent difference in the amplitude ratio of CSNA to
RSNA, the phase approximated 0 rad over the frequency range under
study. If we take a close look at Fig. 4, however, the phase difference
is slightly positive in the higher frequencies, indicating that CSNA
preceded RSNA. The phase difference at 1 Hz approximated 
/10 rad,
which corresponded to a lag time of ~50 ms. Given that the recording
positions for CSNA and RSNA were separated by ~20-25 cm, the
average conduction velocity between CSNA and RSNA was 4-5 m/s,
which fell among the conduction velocities of preganglionic sympathetic
neurons (3-15 m/s), postganglionic sympathetic neurons (0.7-2.3 m/s) (26), and spinal sympathetic conduction
(1.6-8 m/s) (31). The coherence function between RSNA
and CSNA ranged from 0.7 to 0.9.
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Figure 5 presents typical results
obtained from the static protocol. Figure 5A is the time
series of CSP, CSNA, and RSNA. Mean CSNA and RSNA were decreased in
response to a pressure increment in CSP. Figure 5B
illustrates the relationship between CSP and mean CSNA and between CSP
and mean RSNA calculated from data shown in Fig. 5A. The
fitted logistic functions were almost superimposable by the arbitrary
scaling of the ordinate. The group-averaged parameters of
P2 and P3 did not differ
between the CSP-CSNA and CSP-RSNA curves (P2:
0.110 ± 0.028 vs. 0.106 ±0.031 mmHg
1;
P3: 114.6 ± 11.9 vs. 112.1 ± 14.2 mmHg, n = 6).
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DISCUSSION |
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TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
APPENDIX
REFERENCES
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The present study demonstrated that the neural arc transfer function of the carotid sinus baroreflex showed high-pass characteristics regardless of whether CSNA or RSNA was used as the output of the neural arc (Fig. 3). However, the extent of the high-pass filter or the increasing slope of the transfer gain was significantly greater in HCSNA than HRSNA (Table 1). To our knowledge, this is the first demonstration of regional difference in the dynamic baroreflex regulation between CSNA and RSNA.
Differential High-Pass Characteristics of Neural Arc Transfer Function
Although high-pass characteristics were common for HCSNA and HRSNA, the extent of the high-pass filter or increasing slope of transfer gain was significantly greater in HCSNA than HRSNA (Fig. 3 and Table 1). The differential CSNA and RSNA responses to CSP perturbation were also supported by the apparent transfer function between RSNA and CSNA where the amplitude ratio of CSNA to RSNA increased greater than unity in the frequencies above 0.1 Hz (Fig. 4). Because arterial pressure regulation via changes in cardiac output is much faster than via changes in urine excretion (24), the CSNA response would be more sensitive to rapid changes in baroreceptor pressure input than would the RSNA response. The neural arc transfer function of the carotid sinus baroreflex is a cascade of the transfer function from pressure input to carotid sinus afferent nerve activity and from carotid sinus afferent nerve activity to sympathetic efferent nerve activity. According to our previous studies (28, 30), the amplitude of aortic depressor nerve activity increased by about two times when the frequency of baroreceptor pressure input increased from 0.01 to 0.5 Hz. If we assume that these dynamic characteristics are also applicable to the carotid sinus baroreceptors, a major part of the high-pass characteristics of HRSNA would be attributable to the dynamic transduction properties at baroreceptors, being consistent with the interpretation proposed by Kubo et al. (17). On the other hand, the high-pass characteristics were more exaggerated in HCSNA than in HRSNA. The amplitude of the CSNA response increased by about four times when the frequency of CSP perturbation increased from 0.01 to 0.5 Hz (Table 1). Therefore, the dynamic transduction properties of the baroreceptors alone cannot account for the high-pass characteristics of HCSNA. The central processing in the brain stem would play an important role in enhancing the high-pass characteristics of HCSNA compared with HRSNA.The coherence values associated with the neural arc transfer function ranged from 0.6 to 0.9 in the frequency range below 0.4 Hz, suggesting that there existed a significant linear dependence between CSP and CSNA (or RSNA) (Fig. 3). At the same time, however, coherence values less than unity suggest the existence of CSNA and RSNA that could not be described by linear dynamics with baroreceptor pressure input. The reduction of coherence values is attributable to several factors: a nonlinear system response, a central command component of sympathetic nerve activity uncoupled with baroreceptor pressure input, and physical noise in the nerve activity recording procedure. Among these possibilities, the central command component would be the most significant factor. For instance, the sympathetic preganglionic nuclei are known to receive inputs from sources other than the rostral ventral medulla (4). Such inputs generate physiological signals in nerve activity independent of the arterial baroreflex, which are then treated as the inherent noise of the system in terms of linear system analysis.
Effect of Static Nonlinear Characteristics of Neural Arc on Dynamic Baroreflex Regulation
We examined whether mechanisms other than the central processing could account for the observed differential high-pass characteristics between HCSNA and HRSNA. The neural arc of the carotid sinus baroreflex has a nonlinear sigmoidal relationship between CSP and steady-state sympathetic nerve activity (9, 24, 27). Because of the sigmoidal nonlinearity, the response of sympathetic nerve activity is saturated when the input amplitude increases. Note that the high-pass characteristics of baroreceptor transduction properties augment the amplitude of baroreflex afferent signal in the higher frequencies, even when the amplitude of input pressure remains constant (28, 30). If the central processing in the brain stem also has a sigmoidal nonlinearity between baroreflex afferent signal and sympathetic efferent nerve activity, the saturation effect on sympathetic nerve activity will become pronounced in the higher frequencies of CSP perturbation. Therefore, if the operating range of the CSP-RSNA curve is narrower than that of the CSP-CSNA curve, it will result in the saturation of dynamic RSNA response relative to dynamic CSNA response in the higher frequencies. However, the coherence values associated with HRSNA were slightly but significantly greater than those associated with HCSNA at 0.5 and 1 Hz (Table 1), suggesting that the linearity between CSP and RSNA during the dynamic protocol was not less than that between CSP and CSNA. These results were against the presumption that the operating range of the CSP-RSNA curve was narrower than that of the CSP-CSNA curve.We also directly examined whether the operating range differed between the CSP-CSNA and CSP-RSNA curves in the static protocol (Fig. 5). There were no significant differences in P2 and P3 between the CSP-CSNA and CSP-RSNA curves. In other words, the operating range did not differ between the CSP-CSNA and CSP-RSNA curves (see APPENDIX). Therefore, the differential high-pass characteristics between HCSNA and HRSNA cannot be attributed to the difference in the static nonlinear characteristics between the CSP-CSNA and CSP-RSNA curves. The fact that the differential high-pass characteristics between HCSNA and HRSNA were persistently observed when the peak-to-peak amplitude of CSP perturbation was reduced to 20 mmHg in the dynamic protocol (data not shown) also supports the interpretation that the differential high-pass characteristics are independent of the saturation effect via the static nonlinearity.
Effect of Multifiber Recording on High-Pass Characteristics
The amplitude of the compound action potential decreases and the duration increases as the conduction distance is prolonged in the multifiber recording of nerve activity (3, 5, 26). This is due to the difference in conduction velocities among the nerve fibers that make up the nerve bundle. Because the conduction distance between the brain stem and nerve is longer for the RSNA than for the CSNA recordings, we examined, with the use of a simulation, whether the difference in conduction distance and the nature of multifiber recording could affect the dynamic baroreflex regulation of sympathetic nerve activity. According to previous studies (8, 14, 22), we modeled the neural arc transfer function as a first-order high-pass filter with a lag time as follows
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(6) |
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(7) |
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Open-Loop Versus Closed-Loop Experiments
The differential high-pass characteristics between CSNA and RSNA responses to CSP perturbation are inconsistent with the results by Harada et al. (7) in anesthetized cats. The difference may be explained as follows. First, as mentioned in the introduction, the arterial baroreflex was partially closed in their study. In other words, changes in AoP by aortic balloon inflation and deflation altered sympathetic nerve activity, whereas changes in sympathetic nerve activity, in turn, affected the AoP. Under these conditions, a closed-loop system identification method rather than a conventional open-loop transfer function analysis should be employed to avoid any biased estimation (12, 14). In the present study, the carotid sinus baroreflex loop was opened so that the conventional open-loop analysis was applied. Second, the power spectra of AoP perturbation were not quite white in the frequencies between 0.01 and 0.7 Hz in their study, possibly due to a mechanical damping by the arterial compliance. The lack of input power might have affected the precise assessment of high-pass characteristics of the CSNA and RSNA responses. In contrast, because we isolated the carotid sinuses, we were able to impose CSP perturbation of relatively flat power spectra up to 1 Hz (Fig. 1). Third, they normalized CSNA and RSNA by the respective values under control conditions, whereas we normalized CSNA and RSNA by the gain values below 0.03 Hz in the respective neural arc transfer functions. Because sympathetic nerve activity under control conditions consisted not only of phasic activity (dynamic component) but also of tonic activity (direct current component) and because the transfer function dealt with the dynamic component alone, their normalization procedure might have masked differential high-pass characteristics between HCSNA and HRSNA. Finally, the species difference between cats and rabbits should also be taken into account.Although we focused on the frequency range from 0.01 to 1 Hz on the basis of the frequency bandwidth of the total baroreflex function (8), the baroreceptors are normally exposed to higher frequency input mainly associated with the heart rate component (3-4 Hz in rabbits) and its harmonics. Frequency-dependent depression of synaptic transmission has been reported at the first synapse of the baroreflex in the nucleus tractus solitarii mainly in the frequencies above 1 Hz (2, 18). The apparent contradiction between our high-pass filter characteristics and the phenomenon of frequency-dependent depression may be explained as follows. First, the frequency range tested is different. Second, because we defined the input frequency as the frequency of baroreceptor pressure perturbation, it corresponds to a modulation frequency of stimulation rather than stimulation frequency itself of baroreflex afferent fibers. Further open-loop experiments are clearly needed where input frequencies of CSP perturbation are expanded beyond 1 Hz to integrate the different aspects of dynamic characteristics of signal transduction in the neural arc.
Limitations
There are several limitations to this study. First, we investigated the carotid sinus baroreflex in anesthetized rabbits. Although we chose an anesthetic agent that is less suppressive to cardiovascular regulation, the absolute gain values of the carotid sinus baroreflex might have been affected to some degree. However, because we recorded CSNA and RSNA simultaneously, we believe that the differential high-pass characteristics would exist even in the absence of anesthesia.Second, whether it is CSNA or RSNA that is a better representative of dynamic systemic sympathetic nerve activity remains unclear. Despite a slight but significant difference in the neural arc transfer function, the coherence values were sufficiently high for both HCSNA and HRSNA to accurately describe the linear input-output relationship between CSP perturbation and sympathetic nerve activity (Fig. 3). Therefore, we will be able to use both CSNA and RSNA to describe dynamic baroreflex regulation as long as we recognize the potential difference in the extent of high-pass characteristics. The difference between the CSNA and RSNA responses to CSP perturbation implies that the neural arc transfer function would be differently estimated when other neural districts are examined. Regional difference in the neural arc transfer characteristics among other neural districts awaits further investigation.
Third, we could not identify the mechanism for the differential high-pass characteristics between HCSNA and HRSNA. Although a difference in the static nonlinear characteristics of the neural arc could theoretically affect the high-pass characteristics of the neural arc, there were no significant differences between the CSP-CSNA and CSP-RSNA curves. Furthermore, the simulation study indicated that the difference in conduction velocities among the nerve fibers could not account for the differential high-pass characteristics between HCSNA and HRSNA in the frequencies between 0.03 and 0.3 Hz. Further studies focusing on central processing in the brain stem are required to identify the mechanism for the differential high-pass characteristics.
Finally, we filled the isolated carotid sinuses with warm physiological saline. Because the ionic content affects the sensitivity of the baroreceptors (1), the absolute gain values of the carotid sinus baroreflex might have been different from normal physiological values. The extent of chemoreceptor activation might also affect CSNA and RSNA or interfere with the carotid sinus baroreflex. However, because we changed neither intravascular ion content nor oxygen content in the isolated carotid sinuses during CSP perturbation, dynamic changes in CSNA and RSNA should be mainly attributable to baroreceptor pressure input. The baroreflex sensitivity to pressure input was relatively unchanged even in a prolonged protocol under this experimental setting (27). In addition, CSNA and RSNA were simultaneously recorded. We think, therefore, that the comparison of the neural arc transfer functions between the CSNA and RSNA responses is a fair one.
In conclusion, we found differential high-pass characteristics between the CSNA and RSNA responses to dynamic CSP perturbation. The differential high-pass characteristics of the neural arc imply a differential central processing in the brain stem. Although we did not confirm any physiological significance of the differential high-pass characteristics between HCSNA and HRSNA in regulating arterial pressure, we speculate that the neural arc of the baroreflex exerts differential effects on the heart and kidney in response to dynamic baroreflex activation.
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APPENDIX |
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TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
APPENDIX
REFERENCES
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Relationship between coefficient of gain of logistic function and operating range of the system.
In the logistic function, the threshold pressure (Pth) and saturation pressure (Psat) can be described as
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(A1) |
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(A2) |
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(A3) |
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ACKNOWLEDGEMENTS |
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This study was supported by Research Grants for Cardiovascular Diseases (9C-1, 11C-3, and 11C-7) from the Ministry of Health and Welfare of Japan, by a Health Sciences Research Grant for Advanced Medical Technology from the Ministry of Health and Welfare of Japan, by Special Funds to Encourage System of COE from the Science and Technology Agency of Japan, by a Ground-Based Research Grant for the Space Utilization promoted by the National Space Development Agency of Japan and the Japan Space Forum, by a Bilateral International Joint Research Grant from the Science and Technology Agency of Japan, by grants-in-aid for Scientific Research (B-11694337, C-11680862, and C-11670730), by grants-in-aid for the Encouragement of Young Scientists (11770390 and 11770391) from the Ministry of Education, Science, Sports, and Culture of Japan, and by a grant provided by the Ichiro Kanehara Foundation.
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FOOTNOTES |
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Address for reprint requests and other correspondence: T. Kawada, Dept. of Cardiovascular Dynamics, National Cardiovascular Center Research Institute, 5-7-1 Fujishirodai, Suita-shi, Osaka 565-8565, Japan (E-mail: torukawa{at}res.ncvc.go.jp).
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 12 July 2000; accepted in final form 7 November 2000.
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REFERENCES |
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TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
APPENDIX
REFERENCES
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