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Am J Physiol Heart Circ Physiol 295: H1081-H1089, 2008. First published June 27, 2008; doi:10.1152/ajpheart.00023.2008
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Muscle mechanoreflex augments arterial baroreflex-mediated dynamic sympathetic response to carotid sinus pressure

Kenta Yamamoto,1,2 Toru Kawada,2 Atsunori Kamiya,2 Hiroshi Takaki,2 Toshiaki Shishido,2 Kenji Sunagawa,3 and Masaru Sugimachi2

1Consolidated Research Institute for Advanced Science and Medical Care, Waseda University, Tokyo; 2Department of Cardiovascular Dynamics, Advanced Medical Engineering Center, National Cardiovascular Center Research Institute, Osaka; and 3Department of Cardiovascular Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan

Submitted 8 January 2008 ; accepted in final form 19 June 2008


   ABSTRACT
TOP
 ABSTRACT
METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 GRANTS
 REFERENCES
 
Although the muscle mechanoreflex is one of the pressor reflexes during exercise, its interaction with dynamic characteristics of the arterial baroreflex remains to be quantitatively analyzed. In anesthetized, vagotomized, and aortic-denervated rabbits (n = 7), we randomly perturbed isolated carotid sinus pressure (CSP) using binary white noise while recording renal sympathetic nerve activity (SNA) and arterial pressure (AP). We estimated the transfer functions of the baroreflex neural arc (CSP to SNA) and peripheral arc (SNA to AP) under conditions of control and muscle stretch of the hindlimb (5 kg of tension). The muscle stretch increased the dynamic gain of the neural arc while maintaining the derivative characteristics [gain at 0.01 Hz: 1.0 ± 0.2 vs. 1.4 ± 0.6 arbitrary units (au)/mmHg, gain at 1 Hz: 1.7 ± 0.6 vs. 2.7 ± 1.4 au/mmHg; P < 0.05, control vs. stretch]. In contrast, muscle stretch did not affect the peripheral arc. In the time domain, muscle stretch augmented the steady-state response at 50 s (–1.1 ± 0.3 vs. –1.7 ± 0.7 au; P < 0.05, control vs. stretch) and negative peak response (–2.1 ± 0.5 vs. –3.1 ± 1.5 au; P < 0.05, control vs. stretch) in the SNA step response. A simulation experiment using the results indicated that the muscle mechanoreflex would accelerate the closed-loop AP regulation via the arterial baroreflex.

muscle stretch; transfer function; exercise pressor reflex; exercise; arterial pressure

THE ARTERIAL BAROREFLEX SYSTEM plays an important role in stabilizing arterial pressure (AP) during daily activity. Knowledge of the open-loop static and dynamic characteristics of the arterial baroreflex is essential for a systematic understanding of how the baroreflex system regulates AP. The static characteristics provide information on the operating point of the baroreflex system (19, 34, 48), whereas the dynamic characteristics determine the stability and quickness of the baroreflex system (14, 22, 23). Importantly, many previous studies showed that exercise resets the baroreflex function (3, 5, 6, 29, 30, 32, 35, 36, 40, 45, 47). However, only a few investigations focused on the dynamic characteristics of the arterial baroreflex during exercise (10, 36, 38, 57). The dynamic characteristics of the arterial baroreflex determine how quickly or slowly the system would response to baroreceptor pressure perturbations. Such information cannot be obtained from the static characteristics alone.

The neural mechanisms responsible for changes in the baroreflex function during exercise are considered to be mediated by central command (6, 13, 29, 39, 46) and by afferent inputs from metabolic and mechanical-sensitive skeletal muscle receptors (11, 12, 17, 41, 43, 44, 49). Regarding the static interaction between the muscle mechanoreflex and arterial baroreflex, we performed a baroreflex open-loop study and reported that muscle stretch extended the response range of sympathetic nerve activity (SNA) to baroreceptor pressure input (58, 59). Based on the results, we hypothesized that the activation of the muscle mechanoreflex would augment the dynamic SNA response to baroreceptor pressure input under open-loop conditions. To the best of our knowledge, however, the effects of the muscle mechanoreflex on the dynamic characteristics of the arterial baroreflex have never been reported.

To test the above hypothesis, we identified the dynamic characteristics of the baroreflex during muscle stretch in anesthetized rabbits under baroreflex open-loop conditions (14, 22, 23). The transfer functions from baroreceptor pressure input to SNA (the baroreflex neural arc) and from SNA to AP (the baroreflex peripheral arc) were estimated by a white noise approach (51). The "whiteness" is essential for the system identification of the arterial baroreflex because it is equivalent mathematically to test the system with all possible pressure changes within the frequency range of interest.


   METHODS
TOP
 ABSTRACT
 METHODS
RESULTS
 DISCUSSION
 APPENDIX
 GRANTS
 REFERENCES
 
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. All protocols were approved by the Animal Subjects Committee of the National Cardiovascular Center. Seven Japanese White rabbits weighing 2.6–3.0 kg were anesthetized via an intravenous injection (2 ml/kg) of a mixture of urethane (250 mg/ml) and {alpha}-chloralose (40 mg/ml) and were mechanically ventilated with oxygen-enriched room air. Supplemental anesthetics (0.2–0.3 ml·kg–1·h–1) were administered continuously to maintain stable AP and heart rate levels during intervals of experimental protocols, which were indicative of an appropriate level of anesthesia. Arterial blood was sampled from the left common carotid artery. Rabbits were slightly hyperventilated to suppress chemoreflexes (arterial PCO2 ranged from 30 to 35 mmHg, arterial PO2 > 300 mmHg). Arterial blood pH was within the physiological range when examined at the end of the surgical preparation as well as at the end of the experiment. The body temperature of each animal was maintained at ~38°C with a heating pad. AP was measured using a high-fidelity pressure transducer (Millar Instruments, Houston, TX) inserted from the right femoral artery to the aortic arch.

We isolated bilateral carotid sinuses from the systemic circulation by ligating the internal and external carotid arteries and other small branches originating from the carotid sinus region. Isolated carotid sinuses were filled with warmed physiological saline via catheters inserted through the common carotid arteries. Intra-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 neck to minimize reflexes from the cardiopulmonary region and from 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, Chatsworth, CA) to record SNA. The nerve bundle peripheral to the electrodes was tightly ligated and crushed to eliminate afferent signals from the kidney. The nerve and electrodes were secured with silicone glue (Kwik-Sil, World Precision Instruments, Sarasota, FL). The preamplified nerve signal was band-pass filtered at 150–1,000 Hz, full-wave rectified, and low-pass filtered with a cutoff frequency of 30 Hz to quantify the nerve activity.

With the rabbit in the prone position, the sacrum, left ankle, and knee were clamped with a custom-made apparatus to prevent body trunk and hindlimb movement during muscle stretch. The left triceps surae muscle, Achilles tendon, and calcaneus bone were exposed. The left triceps surae muscle was isolated from the surrounding tissue. The Achilles tendon was severed from the calcaneus bone and attached to a force transducer (Load Cell LUR-A-SA1, Kyowa Electronic Instruments, Tokyo, Japan). During muscle stretch, the other side of the force transducer was connected to a 5-kg weight via a pulley.

Protocols. To obtain operating pressure values, the carotid sinus baroreflex negative feedback loop was effectively closed by adjusting CSP to AP. Mean AP (and thus mean CSP) at steady state was treated as the operating pressure under control conditions. We then performed muscle stretch for 1 min while the carotid sinus baroreflex was effectively closed. Mean AP during the last 10 s of muscle stretch was treated as the operating pressure under muscle stretch conditions.

To estimate the baroreflex dynamic characteristics, CSP was assigned either high (+20 mmHg) or low (–20 mmHg) pressure values around the operating pressure according to a binary white noise sequence. The switching interval of the binary white noise signal was set at 500 ms so that the CSP power spectrum was fairly flat up to 1 Hz. We confirmed that the muscle stretch produced a sustained SNA increase for at least 7 min (58). To limit the maximum duration of muscle stretch within this time period, a 6-min CSP perturbation was performed twice using different binary sequences, and the two sets of data were pooled for analyses under both control and muscle stretch conditions. The order of control and muscle stretch conditions was randomized across the animals.

Data analysis. We recorded CSP, muscle tension, SNA, and AP at a sampling rate of 200 Hz using a 12-bit analog-to-digital converter. Data were stored on a dedicated laboratory computer system.

To estimate the neural arc transfer function of the carotid sinus baroreflex, we treated CSP as the input and SNA as the output of the system. In the peripheral arc transfer function, we treated SNA as the input and AP as the output of the system. In the total loop transfer function, we treated CSP as the input and AP as the output of the system. Data analysis was started from 90 s after the initiation of each trial to process the stationary portion of data without the effects of transition from closed-loop CSP waveform to open-loop binary white noise CSP input and the transition from nonstretch to stretch of muscle mechanoreceptors. The input-output data pairs were resampled at 10 Hz and segmented into 50%-overlapping bins of 1,024 points each. For each segment, a linear trend was subtracted, and a Hanning window was applied. A fast Fourier transform was performed to obtain the frequency spectra of the input and output signals. The ensemble averages of input power spectral density [SXX(f)], output power spectral density [SYY(f)], and cross-spectral density between the input and output [SYX(f)] were obtained over eight segments derived from two sets of data, where f represents frequency. Finally, we calculated the transfer function from input to output [H(f)] using the following equation (27):

Formula 1(1)
Hereinafter, we denote the modulus as the dynamic gain of the transfer function. To quantify the linear dependence between input and output signals in the frequency domain, we calculated a magnitude-squared coherence function [Coh(f)] using the following equation (27):

Formula 2(2)
The coherence value ranges from zero to unity. Unity coherence indicates perfect linear dependence between input and output signals, whereas zero coherence indicates total independence between the two signals.

To facilitate an intuitive understanding of the transfer function, the step response corresponding to the transfer function was also calculated as follows. The system impulse response was derived from the inverse Fourier transform of H(f). The step response was obtained from the time integral of the impulse response.

Statistical analysis. All data are presented as means ± SD. Because the amplitude of SNA varied depending on recording conditions, such as the physical contact between the nerve and electrodes, SNA was presented in arbitrary units (au). Neural and peripheral arc transfer functions were normalized in each animal so that the average gain values below 0.03 Hz in the control trial became unity. To compare the transfer functions between two conditions, a transfer gain value at 0.01 Hz (G0.01), 0.1 Hz (G0.1), 0.5 Hz (G0.5), and 1 Hz (G1) were calculated. In the step response of the neural arc, the steady-state step response at 50 s (S50), the negative peak value (Speak), and the time to negative peak (Tpeak) were calculated. The effects of muscle stretch on these parameters were examined using the paired t-test. Differences were considered significant when P < 0.05.


   RESULTS
TOP
 ABSTRACT
 METHODS
 RESULTS
DISCUSSION
 APPENDIX
 GRANTS
 REFERENCES
 
Figure 1 shows a typical time series of CSP, muscle tension, SNA, and AP under control (left) and muscle stretch (right) conditions. Although the same binary sequence was applied for two conditions in each animal, different binary sequences were applied for different animals to reduce possible systematic errors in system identification caused by a bias in whiteness specific to a selected binary sequence. The mean CSP during muscle stretch conditions (Fig. 1, right) was set higher than that during the control conditions (Fig. 1, left) to mimic the increase in the operating pressure during muscle stretch under baroreflex closed-loop conditions (i.e., the AP increase by muscle stretch increases the mean input pressure to the baroreceptors) . Muscle stretch increased mean levels of SNA and AP compared with control conditions during the experiment (Table 1).


Figure 1
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  		Fig. 1. Typical time series of intracarotid sinus pressure (CSP), muscle tension, sympathetic nerve activity [SNA; in arbitrary units (au)], and arterial pressure (AP) under control (left) and muscle stretch (right) conditions. CSP was perturbed according to a binary white noise sequence. Muscle stretch increased mean levels of SNA and AP under muscle stretch conditions compared with the control conditions.

 

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  		Table 1. Mean levels and CVs of CSP, SNA, and AP at 1, 2, 4, and 6 min under control and muscle stretch conditions

 
Figure 2 shows the transfer functions of the neural (left) and peripheral (right) arcs estimated under the control and muscle stretch conditions; gain plots (top), phase plots (middle), and Coh(f) (bottom) are also presented. The thin and thick solid lines in Fig. 2 indicate control and muscle stretch conditions, respectively. In the neural arc, the dynamic gain increased as the frequency of input modulation increased under both conditions, indicating derivative characteristics of the neural arc. Muscle stretch caused an approximately parallel upward shift of the gain plot. The phase approached –{pi} radians (–180°) at the lowest frequency (0.01 Hz) under both conditions, reflecting the negative feedback character of the baroreflex neural arc (i.e., an increase in CSP decreased SNA). Phase plots were nearly superimposed between the two conditions. Coherence values did not differ between both conditions. In the peripheral arc, the dynamic gain decreased in the frequency range from 0.05 to 1 Hz as the frequency of input modulation increased under both conditions, indicating the low-pass characteristics of the peripheral arc. The phase approached 0 radians at the lowest frequency (0.01 Hz) under both conditions, reflecting the fact that an increase in SNA increased AP. The phase lagged with increasing frequency up to 1 Hz. The gain plot, phase plot, and Coh(f) did not differ between both conditions.


Figure 2
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  		Fig. 2. Transfer functions of the neural (left) and peripheral (right) arcs under control and muscle stretch conditions. In the neural arc, the input was CSP and the output was SNA. In the peripheral arc, the input was SNA and the output was AP. The mean level of CSP input to the neural arc was set higher under muscle stretch conditions than under control conditions to mimic the physiological condition (i.e., baroreflex closed-loop conditions). Gain plots (top), phase plots (middle) and coherence (Coh) functions (bottom) are shown. Thin and thick solid lines indicate control and muscle stretch conditions, respectively. In the neural arc (left), muscle stretch caused an approximately parallel upward shift of the gain plot. Solid and dashed lines represent means and means ± SD values, respectively.

 
Table 2 summarizes gains of the transfer functions. In the neural arc, G0.01, G0.1, G0.5, and G1 were higher under muscle stretch compared with control conditions. In the peripheral arc, G0.01, G0.1, G0.5, and G1 were unchanged between control and muscle stretch conditions.


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  		Table 2. Gains of the transfer functions

 
Figure 3 shows the total baroreflex loop transfer functions (CSP to AP) under control and muscle stretch conditions. The thin and thick solid lines in Fig. 3 indicate control and muscle stretch conditions, respectively. The dynamic gain decreased as the frequency of input modulation increased under both conditions, indicating low-pass characteristics. The dynamic gain under muscle stretch conditions was higher than that under control conditions in frequency from 0.01 to 0.5 Hz (Table 2). The phase plot and Coh(f) did not differ between both conditions.


Figure 3
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  		Fig. 3. Total loop transfer functions from CSP to AP under control and muscle stretch conditions. Gain plots (top), phase plots (middle) and coherence functions (bottom) are shown. Thin and thick solid lines indicate control and muscle stretch conditions, respectively. The dynamic gain decreased as the frequency of input modulation increased under both conditions, indicating low-pass characteristics. Muscle stretch caused an approximately parallel upward shift of the gain plot. Solid and dashed lines represent means and means ± SD values, respectively.

 
Figure 4 shows step responses of SNA corresponding to the transfer functions in the neural arc shown in Fig. 2. The initial drop in the SNA response as well as the steady-state response was augmented during muscle stretch (Table 3). Tpeak did not differ between control and muscle stretch conditions (Table 3).


Figure 4
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  		Fig. 4. Step responses corresponding to transfer functions of the neural arc obtained from Fig. 2, showing the SNA response to a 1-mmHg increase in input pressure. Thin and thick solid lines indicate control and muscle stretch conditions, respectively. The initial drop in the SNA response as well as the steady-state response was augmented by the muscle stretch. Solid and dashed lines represent means and means ± SD values, respectively.

 

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  		Table 3. Parameters of step responses

 

   DISCUSSION
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
APPENDIX
 GRANTS
 REFERENCES
 
The key new findings of the present study are as follows. Muscle stretch increased the dynamic gain of the carotid sinus baroreflex neural arc as estimated by binary white noise input (Fig. 2). In contrast, the peripheral arc transfer function remained unchanged irrespective of the muscle stretch (Fig. 2). These results suggest that during muscle mechanoreflex activation, the dynamic SNA response to CSP perturbation is augmented.

System identification by the white noise approach. To identify the dynamic characteristics of arterial baroreflex function quantitatively, we described the carotid sinus baroreflex control of SNA and AP in terms of system identification using the white noise technique. Compared with the traditional approach of testing dynamic properties of the physiological system with step and sine wave stimuli, the white noise approach has definite advantages, as follows (27). First, if a step stimulus is applied, we learn the response of the system to this step and have little notion of the response of the system to any other type of stimulus. If a sinusoidal pulse is applied, then we know the response of the system to such a stimulus and little else. The same applies for any other specific waveform. Theoretically speaking, the system is tested with every possible stimulus in the white noise approach. The white noise stimulus is a very rich stimulus. It should be emphasized that the white noise method is perfectly suited to the analysis of linear systems. As shown in Figs. 2 and 3, high coherence values close to unity indicate the validity of our method for system identification. Second, the identification of the physiological system through the white noise technique is largely unaffected by the types of contaminating noise usually present in such a system. Our study provides the first and quantitative description of the dynamic characteristics of the carotid sinus baroreflex during isolated activation of mechanosensitive afferents from skeletal muscle.

Effects of the muscle mechanoreflex on dynamic characteristics of the carotid sinus baroreflex. The effects of activation of afferents from skeletal muscle, such as those occurring during exercise, on the arterial baroreflex have been extensively studied (5, 13, 29, 42, 43, 49, 58, 59). These studies have demonstrated that the afferent input from muscle resets the baroreflex control of AP, heart rate, and SNA. However, the dynamic characteristics of the arterial baroreflex during isolated activation of muscle mechanosensitive afferents have never been analyzed. In the present study, muscle stretch increased dynamic gain in every frequency (Fig. 2 and Table 2), whereas it did not affect the peripheral arc. These data are the first to provide quantitative evidence demonstrating that the dynamic SNA response to CSP perturbation is augmented during isolated activation of the muscle mechanoreflex. Although an increase in dynamic gain in the lowest frequency (0.01 Hz) was expected from the results of our previous studies showing an increase in static gain by muscle stretch (58, 59), the information was insufficient to perform a simulation study to examine the effects of muscle stretch on the closed-loop dynamic AP regulation (see Physiological implications). The present study extended our previous work by providing additional information on the dynamic interaction over a wide range of frequencies between 0.01 and 1 Hz in the carotid sinus baroreflex.

The static characteristics of the arterial baroreflex determine an operating point of the baroreflex system. Furthermore, the static characteristics described by a modeled sigmoid function provide the parameters of threshold, saturation, and maximal gain at the centering point. However, the static characteristics alone cannot provide the information on the changes over time in the response of the baroreflex system. On the other hand, dynamic analysis techniques such as transfer function analysis estimated by the white noise approach provide information on the stability and quickness of the system response. The dynamic SNA response to baroreceptor pressure input became greater as the frequency of input modulation increased, suggesting derivative characteristics (i.e., high-pass characteristics) of the baroreflex neural arc (Fig. 2, left, thin solid line). In contrast, the dynamic AP response to SNA became smaller as the frequency of SNA modulation increased, indicating low-pass characteristics of the baroreflex peripheral arc (Fig. 2, right, thin solid line). The total loop transfer function (CSP to AP) is determined by a product of the neural and peripheral arc transfer functions (Fig. 3, thin solid line). Therefore, the decreasing slope of dynamic gain in the total loop transfer function was shallower than that in the corresponding peripheral arc. In other words, the fast neural arc effectively compensates for the slow peripheral arc to accelerate dynamic AP regulation by the baroreflex negative-feedback loop (14). During muscle stretch, the dynamic gain in the neural arc was increased by ~50% in every frequency under study (Fig. 2 and Table 2), indicating that the derivative characteristics of the neural arc were maintained. As a result, the effect of the neural arc compensating for the slow AP response was preserved during the activation of muscle mechanoreflex (Fig. 3 and Table 2). Furthermore, the total loop dynamic gain was augmented during the muscle stretch due to the upward shift of the neural arc transfer function.

Because we used passive muscle stretch as the input for the muscle mechanoreflex, the physiological significance of the present results should be interpreted carefully. Several studies have examined the arterial baroreflex control of SNA during static and dynamic exercise. Static and heavy dynamic exercise resets the baroreflex control of SNA to higher SNA levels with an increase in its sensitivity (9, 11, 17, 32). On the other hand, mild to moderate dynamic exercise resets the baroreflex control of SNA without any change in its sensitivity (3, 24, 38). Because the muscle mechanoreflex is activated during mild to moderate dynamic exercise (4), our results indicate that the muscle mechanoreflex may contribute to increasing the baroreflex gain of SNA during mild to moderate dynamic exercise. In addition to differences in the measured SNA (renal vs. muscle), analytic methods of baroreflex function, modes of mechanoreflex activation, and/or species between the present study and previous studies, the cardiopulmonary baroreflex should be taken into account. Charkoudian et al. (1) demonstrated that increasing central venous pressure via head-down tilt or saline infusion attenuated the baroreflex sensitivity in the control of SNA. The activation of cardiopulmonary baroreceptors induced by increasing central venous pressure may influence the arterial baroreflex control during dynamic exercise (37). In the present study, however, the cardiopulmonary baroreflex did not operate due to bilateral vagotomy.

Previous studies (7, 25) have suggested that the muscle mechanoreflex has a dominant role in pressor reflexes during muscle contraction in anesthetized or decerebrate cats. Although we believe that the mechanoreflex is one of the pressor reflexes during exercise, the functional importance of the muscle mechanoreflex in cardiovascular regulation during exercise in conscious conditions is debatable. Matsukawa et al. (28) recently reported that blockade of the muscle mechanoreflex by gadolinium did not alter AP responses to isometric exercise in conscious cats. Moreover, they found that gadolinium significantly diminished the pressor responses to passive muscle stretch in anesthetized cats. These observations suggest that, under the experimental design, the muscle mechanoreflex would not be activated during exercise or, even if it was activated, it has no functional importance in cardiovascular responses to exercise in conscious conditions. One criticism for the study is that there is always a possibility that changes in the central command in conscious conditions had compensated for the lack of muscle mechanoreflex. Further studies are needed to better understand the role of the muscle mechanoreflex on neural cardiovascular responses during exercise.

High-pass characteristics of the baroreflex neural arc. It is likely that the dynamic characteristics of the baroreflex neural arc actually reflect the intrinsic and synaptic properties of central nervous system neurons and neural circuits that transmit baroreceptor input. However, the central baroreceptor synapses are characterized as a low-pass filter (26). The difference between high-pass characteristics of the neural arc transfer gain and low-pass characteristics of the central baroreceptor synaptic transmission could be attributable to the difference of estimated frequency ranges. Frequency-dependent depression (FFD) of synaptic transmission in the baroreflex central pathways is the phenomenon that the probability of excitatory postsynaptic potentials progressively reduces as the frequency of afferent input increases beyond 1 Hz (2, 33). Although FDD and transfer gain should be discriminated in theory, interactions between FDD and transfer gain may occur when the modulation frequency of afferent fiber stimulation approached the frequency range of FDD. Indeed, Kawada et al. (23) found high-cut characteristics of the baroreflex neural arc in the frequency range above ~1 Hz. In the present study, the transfer gain was derived from 0.01 to 1 Hz. Whether the dynamic interaction between carotid sinus baroreflex and muscle mechanoreflex exists in the frequency range beyond 1 Hz awaits further studies.

Part of the high-pass characteristics in the baroreflex neural arc is attributable to the derivative nature observed in the baroreceptor transduction from CSP input to baroreceptor afferent nerve activity (i.e., mechanoneural transduction) (21). However, we think there exists high-pass characteristics in the transduction from baroreceptor afferent input to efferent SNA, because the magnitude of high-pass characteristics slightly differs between cardiac and renal SNAs in response to the same baroreceptor pressure perturbation (18).

In an electrical circuit, we can design a high-pass filter only from low-pass filter elements using a feedback loop (Fig. 5) . Although the main forward path of the baroreflex neural arc from afferent nerve activity to efferent SNA is considered to be the nucleus tructus solitarius, caudal ventrolateral medulla, and rostral ventrolateral medulla (53), there could be feedback connections between these areas. Therefore, it is possible that synaptic connection has basically low-pass characteristics, whereas the baroreflex neural arc reveals high-pass characteristics as a neural circuit. The speculation also needs to be verified experimentally in the future.


Figure 5
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  		Fig. 5. An example that a circuit consisting of only low-pass elements yields high-pass characteristics as a circuit. A: block diagram of a single low-pass element (triangle) and its transfer function. Units for gain and frequency are arbitrary. B: block diagram of a circuit with a negative feedback loop with the same low-pass element (triangles). Because gain in the lower frequency range is attenuated more by the low-pass characteristics of the feedback path, the transfer function from input to output reveals high-pass characteristics.

 
Physiological implications. Under physiological conditions, the baroreflex is closed as a negative feedback system. In the following discussion, we will focus on the effect of the augmentation of dynamic SNA modulation in the neural arc on the closed-loop dynamic AP regulation. Figure 6 A illustrates a simulator consisting of the linear neural arc transfer function (HN) and linear peripheral arc transfer function (HP) followed by the nonlinear sigmoidal components (see the APPENDIX for details). A closed-loop AP response to a stepwise pressure perturbation (–40 mmHg) with pulsatile pressure was simulated, and the result is shown in Fig. 6B. Muscle stretch shortened the time to 95% of steady state by ~33% from 7.2 to 4.8 s (shaded and solid arrows in Fig. 6B). This result suggests that, under baroreflex closed-loop conditions, the rate of recovery in AP following a pressure perturbation occurs sooner when accompanied by the muscle mechanoreflex. Increasing the quickness of the negative-feedback system can be caused by augmentation and/or acceleration of the open-loop transfer function of the system. In our baroreflex open-loop experiment, S50 and Speak in the step responses of SNA were augmented by the muscle stretch (Fig. 4 and Table 3). On the other hand, Tpeak did not differ between control and muscle stretch conditions (Fig. 4 and Table 3). These results suggest that the improvement in the quickness of the AP restoration via the baroreflex observed in the closed-loop simulation was induced by augmentation, rather than acceleration, of the dynamic SNA response in the neural arc. However, further experimental studies are needed to verify the simulation model.


Figure 6
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  		Fig. 6. A: simulator of the baroreflex system during activation of the muscle mechanoreflex. A stepwise perturbation with pulsatile pressure was applied to the baroreflex negative feedback system (see the APPENDIX for details). HN, neural arc transfer function; HP, peripheral arc transfer function. B: simulation results of the closed-loop AP response to the stepwise pressure perturbation (–40 mmHg). Muscle stretch shortened the time to 95% of steady state by ~33% (shaded and solid arrows). Shaded and solid thick lines indicate mean AP (MAP) resampled at 1 Hz. {Delta}MAP, change in MAP from baseline.

 
Limitations. The present study has several limitations. First, we performed the experiment in anesthetized animals. Previous studies have suggested that any anesthetic could alter the baroreflex regulation in AP (54–56). The gain of the baroreflex is reported in the conscious state to be higher (~2-fold) than in the anesthetized state. A previous study (52) suggested that {alpha}-chloralose anesthesia could alter the dynamic characteristics of the baroreflex regulation around the frequency of 5 Hz. However, the anesthesia was convenient for the elimination of the central command. Furthermore, we compared the baroreflex gain between muscle stretch and nonstretch conditions both under anesthesia. Therefore, a reasonable interpretation would be that the increased baroreflex gain is attributable to muscle stretch in this experiment.

Second, stretching of skeletal muscle provides a stimulus for the activation of mechanoreceptors that is different from that which occurs during muscle contraction. During contraction, mechanoreceptors are activated by a shortening of skeletal muscle and by compression of the receptors. Thus, mechanoreceptors may be stimulated in a very different manner during stretch, which would likely affect the magnitude of the corresponding reflex response. In addition, the level of muscle stretch used in our experiment was relatively high (50). The stretch may activate different afferents than contraction (8). Furthermore, the discharge profile of mechanosensitive afferents adapt during static muscle stretch (31). Accordingly, during the muscle stretch for 6 min in the present study, the firing level from the mechanoreceptors might have been steadily diminishing. In fact, the increase in SNA and AP induced by muscle stretch gradually decreased from 90 s to 6 min after the initiation of the muscle stretch, which was used for data analysis (Table 1). However, SNA and AP remained significantly higher under muscle stretch conditions than control conditions over the protocol for 6 min. Thus, we believe that the mechanoreflex remained activated in this protocol. Further studies are required to elucidate the dynamic interactions between baroreflex and mechanoreflex induced by different modes of activation, such as cyclic activation of the mechanoreflex.

Third, the transfer function analysis is useful in identifying the linear input-output relationship of the baroreflex at a given operating point. However, the transfer function cannot characterize the nonlinear input-output relationship of the system. In the presence of nonlinear system behavior such as the baroreflex system, the transfer function analysis is partly compromised, indicating that the absolute output values of the nonlinear system to given input signals cannot be predicted accurately by the transfer function alone. Combining a linear transfer function with a nonlinear sigmoidal element would increase the accuracy to reproduce dynamic characteristics observed in the baroreflex neural arc (20, 22).

Finally, we measured renal SNA as a proxy of systemic sympathetic activity. SNAs to different organs may vary a lot. Although static and dynamic regulations of the baroreflex neural arc are similar among renal, cardiac, and muscle SNAs (15, 16, 18), whether this holds true during muscle stretch remains to be verified. Also, subsystems of the peripheral arc transfer function such as those relating cardiac output and peripheral vascular resistance remain to be identified.

Conclusions. In conclusion, baroreflex open-loop transfer function analysis demonstrated that the activation of mechanosensitive afferents from skeletal muscles augmented the dynamic SNA response in the neural arc. This augmentation of the dynamic SNA response with maintained derivative characteristics of the neural arc may accelerate closed-loop AP regulation via the baroreflex.


   APPENDIX
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
 APPENDIX
GRANTS
 REFERENCES
 
To simulate the closed-loop AP response to stepwise pressure perturbation (Fig. 6), we used the derivative-sigmoidal cascade model. The cascade model consists of a linear derivative filter followed by a nonlinear sigmoidal component (20, 22).

We modeled the sigmoidal nonlinearity in the baroreflex neural arc interacting with the muscle mechanoreflex by the following four-parameter logistic function with threshold according to a previous study (59):

Formula A1(A1)
where x and y are input (in mmHg) and output (in au) values. P1 denotes the response range (in au), P2 is the coefficient of gain, P3 is the midpoint of the input range (in mmHg), P4 is the minimum output value of the symmetric sigmoid curve (in au), and Th is a threshold value for the output (in au). The function max{a,b} gives the greater or equal value between a and b. We set P1 = 135 au, P2 = 0.13, P3 = 110 mmHg, P4 = –40 au, and Th = 0 au. Under muscle stretch conditions, the value of P4 was changed to 5 au. These settings were determined based on the static interaction between the baroreflex and muscle mechanoreflex obtained from previous studies (58, 59).

The sigmoidal nonlinearity in the peripheral arc was modelled by a four-parameter logistic function as follows:

Formula A2(A2)
where y and z are input (in au) and output (in mmHg) values. Q1 denotes the response range (in mmHg), Q2 is the coefficient of gain, Q3 is the midpoint of the input range (in au), and Q4 is the minimum output value (in mmHg). We set Q1 = 120 mmHg, Q2 = –0.05, Q3 = 70 au, and Q4 = 30 mmHg under both conditions, according to a previous study (58).

The neural arc (HN) and peripheral arc (HP) linear transfer functions under control and muscle stretch conditions were obtained from Fig. 2. Because absolute values of the steady-state gains in the neural and peripheral arcs were determined by a sigmoid curve (Eqs. A1 and A2), the steady-state gains of HN and HP under both conditions were normalized to unity.

The input amplitude of the stepwise pressure perturbation was –40 mmHg. To mimic pulsatile pressure, we imposed a sinusoidal input on the output from the peripheral arc. The frequency and zero to peak amplitude of the sinusoidal input were 4 Hz and 15 mmHg, respectively (Fig. 6A). The closed-loop AP response was simulated up to 30 s (Fig. 6B).


   GRANTS
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 GRANTS
REFERENCES
 
This work was supported by Ministry of Health, Labour and Welfare of Japan Health and Labour Sciences Research Grant for Research on Advanced Medical Technology, Health and Labour Sciences Research Grant for Research on Medical Devices for Analyzing, Supporting and Substituting the Function of Human Body, and Health and Labour Sciences Research Grants H18-Iryo-Ippan-023 and H18-Nano-Ippan-003; the Industrial Technology Research Grant Program of the New Energy and Industrial Technology Development Organization of Japan; and Ministry of Education, Culture, Sports, Science and Technology Grant-In-Aid for Scientific Research 18591992.


   FOOTNOTES
 

Address for reprint requests and other correspondence: K. Yamamoto, Consolidated Research Institute for Advanced Science and Medical Care, Waseda Univ., 513 Wasedatsurumakicho, Shinjuku, Tokyo 162-0041, Japan (e-mail: kenta{at}aoni.waseda.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.


   REFERENCES
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 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
 APPENDIX
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 REFERENCES
 

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