Discharge Pattern of Renal Sympathetic Nerve Activity in the Conscious Rat: Spectral Analysis of Integrated Activity

Takato Kunitake and Hiroshi Kannan

Department of Physiology, Miyazaki Medical College, Miyazaki 889-1692, Japan


    ABSTRACT
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Kunitake, Takato and Hiroshi Kannan. Discharge Pattern of Renal Sympathetic Nerve Activity in the Conscious Rat: Spectral Analysis of Integrated Activity. J. Neurophysiol. 84: 2859-2867, 2000. We investigated the periodic characteristics of bursting discharge in renal sympathetic nerve activity (RSNA) in conscious rats. Employing a discrete fast Fourier transform algorithm, a power spectrum analysis was used to quantify periodicities present in rectified and integrated RSNA whose signal-to-noise ratio in the recordings was greater than six. In conscious rats with intact baroreceptors, RSNA was characterized by four frequency components occurring at about 0.5, 1.5, 6, and 12 Hz, which corresponded to the low-frequency fluctuation of heart rate, respiration, and frequency of heart beat, and its harmonics, respectively. After intravenous infusion of sodium nitroprusside (SNP) to elicit reflex increases in RSNA and heart rate, the power for the component at 6 Hz followed the changes in heart beat frequency and was significantly increased, while those for the three other components were attenuated or experienced no change. In sino-aortic denervated (SAD) conscious rats, all four components were abolished, and the power spectrum was well fitted by a flat or Lorentzian curve, suggesting an almost random pattern. Only a respiratory-related component, which suggested common central modulation, appeared sporadically for short periods but was absent for the most part. Therefore most of this component together with the low-frequency component was also likely due to the baroreceptor-dependent peripheral modulation. The activity was sorted in 15 subgroups on the basis of spike amplitudes in the RSNA. Each subgroup showed frequency characteristics similar to the whole nerve activity. These results suggest that all periodicity in the RSNA of conscious rats with intact baroreceptors is caused by the baroreceptor input.


    INTRODUCTION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

The aim of the present study is to evaluate the oscillating characteristics of the multiple frequency components concomitantly appearing in the sympathetic vasomotor tone when animals rest while conscious or perform homeostatic regulation to small perturbations within the physiological range. As first shown by Adrian, Bronk, and Phillips (1932) and Bronk et al. (1936) sympathetic nerve activity in multi-fiber preparations from anesthetized cats is characterized by a marked grouping of impulses often forming rhythmic volleys in phase with cardiac pulse and/or respiration. Since then, numerous investigators have focused on the periodic characteristics found in sympathetic nerve activity, but controversy surrounding these results remains. Many researchers employed an averaging technique triggered by an electrocardiogram (ECG) R-wave, arterial pressure pulse, or phrenic nerve activity. In addition to cardiac- and respiratory-related periodicity, low-frequency (lower than respiratory frequency) (Montano et al. 1992; Persson et al. 1992), 2- to 6-Hz (Gebber 1990; Kenney 1994), and "fundamental" or 10-Hz rhythm (Cohen and Gootman 1970; Green and Heffron 1967; Ninomiya et al. 1989; Ootsuka et al. 1995) has recently been discussed under several experimental conditions and with certain animals. The differing use of a specific frequency component among these studies, however, makes it difficult to evaluate the entire aspect of sympathetic vasomotor tone regulating cardiovascular homeostasis, i.e., the composition and amount of each periodic component in sympathetic nerve activity. Additional differences in the recording quality, the processing of original nerve activity for further analysis, the analytical method, the use of anesthetics, and the intervention of neural pathways such as decerebration and vagotomy might have significantly contributed to the discrepancies in the results.

These reports could be divided into two according to whether rectification-integration was applied or not. In a series of reports measuring the inter-peak intervals in renal sympathetic nerve activity (RSNA), the "rectification-integration procedure" was applied to sympathetic nerve activity (Malpas and Ninomiya 1992; Ninomiya et al. 1989, 1990). To the contrary, in another series of reports, sympathetic nerve activity was considered as a slow wave, and its frequency spectrum was examined without the rectification-integration procedure (Gebber 1980, 1990). An interval histogram of peak-to-peak sympathetic nerve activity characterizes only time intervals between two bursts that occur in succession. It does not provide easy access to the various periodic components contained in the nerve activity or to their relative amplitude. It is difficult to access the total periodicity of the various kinds of independent periodicity contained in the nerve activity and evaluate the amplitude of each periodic fluctuation. In particular, the bursting activity in the sympathetic nerve is concomitantly related to cardiac- and respiratory-related rhythms. If the individual spikes could be recognized in the bursting activity without applying rectification-integration, the spectral analysis would demonstrate not only the periodicity made by the time series of the presence or absence of bursting but also the frequency characteristics of each spike of which the burst consists. Therefore spectral analysis without rectification-integration makes it difficult to extract a periodicity in bursts from the multi-fiber recordings of the sympathetic nerve. Should the time series data be of a higher frequency than the maximum of the analytical resultant obtained from spectral analysis, an aliasing error, which is an unexpected spectral component appended within the spectral range, is expected to occur (Oppenheim and Schafer 1975).

Anesthetics have some effect on periodicity in sympathetic nerve activity. In rabbits, the effects of anesthesia on sympathetic nerve rhythm were examined by means of spectral analysis (Suzuki et al. 1993). Suzuki et al. demonstrated that the cardiac-related oscillation of renal sympathetic nerve activity in conscious rabbits disappeared or was markedly attenuated in the anesthetized condition. Furthermore, an electrical stimulation study demonstrated that renal functions such as the release of renin, the reabsorption of sodium, and vasoconstriction are regulated by the frequency-dependent modulation of RSNA (Kirchheim 1991). Therefore from a functional point of view, it is important to elucidate the basic characteristics of renal sympathetic nerve activity, such as a periodicity, in conscious animals.

Using baroreceptor-denervated cats, some investigators reported that 2- to 6-Hz and/or 10-Hz rhythmic activity remained stable or augmented (Gebber 1990; Ninomiya et al. 1989, 1990) and that those periodicities are an intrinsic or "fundamental" rhythm generated via the central neural network. Hedman et al. (1994) suggested that the cardiac-related rhythm in cardiac sympathetic nerve activity is produced by reflexly inhibiting the transmission of the fundamental rhythmicity due to periodic baroreceptor input. The cardiac-related rhythm is also thought to be due to the reflex ability of pulse-synchronous baroreceptor afferent-nerve activity to entrain a centrally generated 2- to 6-Hz rhythm to a cardiac cycle (Gebber 1990).

These periodicities have been investigated under several experimental conditions, including the use of decerebrated and vagal-denervated animals. A study of the changes in periodicity of sympathetic nerve activity within "physiological" conditions is, at least to our knowledge, lacking. We therefore investigated the change in periodicity of renal sympathetic nerve activity as a result of the baroreceptor unloading elicited by the administration of sodium nitroprusside (SNP).

We investigated the periodic characteristics of RSNA in conscious freely moving rats from the following four standpoints in particular: 1) how many components are concomitantly included in RSNA; 2) how much variation of each component is observed over individual animals; 3) what are the changes in each component during activation of the baroreflex; and 4) what is the difference in periodicity between intact and discontinued baroreceptor input. Subsequently, we sorted the multi-fiber recordings of sympathetic nerve activity into subgroups according to impulse amplitude to determine whether they showed the same frequency characteristics as whole multi-fibers activity. The spectrum of the fluctuation in the number of impulses was then calculated in each subgroup.

A preliminary communication of this work has been published in abstract form (Kunitake et al. 1995).


    METHODS
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ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Male Wistar rats (6 intact, 6 sino-aortic denervated), weighing 350-550 g, were kept at constant room temperature, a humidity of 60% and on light/dark cycle of 12 h. Standard laboratory chow and water were given ad libitum. All experiments were performed in accordance with the guidelines of the Committee on Animals of Miyazaki Medical College.

Surgical methods

For implantation of electrodes and catheters, animals were anesthetized with pentobarbital sodium (50 mg/kg ip). Arterial and venous catheters were inserted into the right femoral artery and vein, respectively. Both catheters, filled with heparinized saline (10 U/ml), were tunneled under the skin to exit at the nape of the neck. The left kidney was exposed retroperitoneally by means of an aseptic technique. The left renal nerve bundle was isolated and dissected free from surrounding connective tissue and fat as it coursed to the left renal hilum. This renal nerve was never cut. To raise the freed nerve to a height about 1 mm from body, three fine stainless-steel wires (No. AS633, Cooner Wire, Chatsworth, CA) were inserted between two. One wire attached only to the body acted as an electrical ground, while the remaining two wires, acting as bipolar electrodes, were used to raise the freed nerve without touching the body or dissected connective tissue. Once an optimal nerve recording had been obtained, the nerve and the electrode were covered with silicon rubber (Silgel 604, Wacker Chemie, Munich, Germany). The silicone was then allowed to harden. For recording the ECG, a pair of electrodes was implanted at upper and lower levels of the sternum. Lead wires from the recording electrodes were tunneled under the skin and exteriorized through the nape of the neck. All wounds were closed by suture. On recovery from anesthesia, the rats were placed in cages that allowed full movement.

Measurements and data collection

The arterial catheter was connected to a pressure transducer and an infusion pump that supplied 2 µl/min of heparinized saline (10 U/ml) throughout the period of recording. The ECG was amplified by a differential amplifier (San-ei, 1253A, Japan). Cardiac cycles were determined by detecting the electrocardiographic QRS complex after digitization of the ECG signal. Blood pressure (BP) was determined on a beat-to-beat basis using a peak-trough detection procedure. The RSNA was amplified by a differential amplifier (Nihon Koden, AVB-9, Japan) with a low- and high-frequency cutoff at 50 and 3,000 Hz, respectively. All data were stored on a DAT magnetic recorder (SONY, PC-208, Japan). The noise level of RSNA was determined by infusion of hexamethonium chloride (2-4 mg iv). The magnitude of the RSNA remaining after intravenous injection of hexamethonium was similar to the instrumental noise level observed postmortem recordings.

All data were taken after a judgment of the signal-to-noise ratio was determined. To calculate the signal-to-noise ratio, each signal and noise level was determined by our method as follows. The signal level was obtained from averaging the largest 100 peak values of integrated RSNA obtained during the first 180 s following a SNP perfusion. In addition, the noise level was calculated as the mean value of 100 integrated values chosen randomly during infusion of hexamethonium or as the mean of the 100 lowest peaks obtained during the recording period mentioned above. The two types of noise level were not significantly different; therefore the latter calculation was used to define the signal-to-noise ratio (S/N), which was simply calculated from the signal level divided by the noise level.

Sino-aortic baroreceptor denervation

Sino-aortic baroreceptor denervation was performed in 6 rats according to the technique described by Krieger (1964). Briefly, after a ventral mid-line incision of the neck and bilateral retraction of the sternohyoideus muscles, aortic baroreceptor denervation was performed by resection of the superior cervical ganglia and section of the superior laryngeal nerve and aortic depressor nerve (when the latter were apparent). Carotid-sinus baroreceptor denervation was achieved by stripping all fibers from the carotid sinus and subsequently applying 10% phenol (in 95% ethanol) to the external, internal, and common carotid arteries and occipital artery.

Experimental protocol

Data were collected at 2 days after surgery when the rats were in their home cages. To change the tonic level of RSNA, SNP was perfused intravenously at 25 µg/min for 5 min. The ECG, BP, and RSNA were recorded simultaneously for 15 min, including each 5-min period before, during, and after infusion of SNP. This recording was repeated twice on each rat within an interval of 24 h.

Data analysis

Each of the 12 trials of intact and baroreceptor-denervated conscious rats were analyzed. After the RSNA stored on tape had been digitized at a frequency of 5 kHz (0.2 ms per sample), full-wave rectification and integration were performed using a fully digital procedure. This integration, which was a simple summation of 100 recorded samples, was calculated every 0.2 ms. The outcome appeared to be comparable with that of a conventional analog integrator whose time constant is 20 ms. Since some spiky peaks were observed on the integrated wave, a digital high cut filter (40 Hz) was applied. After convoluting the data in the time domain using a Hanning window, we conducted the spectral analysis on the time-series data of integrated RSNA that were re-sampled every 20 ms by the discrete fast Fourier transform (FFT) algorithm (decimation-in-frequency) (Oppenheim and Schafer 1975). The data of 1,024 points were analyzed, and at least 32 segments that had shifted 64 points were observed and averaged to evaluate its consistency and change. Such a small shift was due to the Hanning window, since the amount of spectral power can be evaluate correctly at only the central portion of the segment.

To confirm that procedures of full-wave rectification and integration correctly evaluated nerve activity, the number of spikes every 20 ms was counted and analyzed. Furthermore, each amplitude of spikes in RSNA were measured, and then individual spike were assigned to subgroups according to amplitude.

Signal processing was carried out using a Concurrent RTU computer (Series 5000, Concurrent Corp, Ft. Lauderdale, FL) based on the Real Time Unix operating system. Laboratory Work Bench (LWB) and an appended user function that we programmed were used for all analyses in the present study.

To assess the changes in RSNA spectral power associated with SNP-induced baroreceptor unloading, an ANOVA and post hoc analysis were performed. A P value of <0.05 was considered to be statistically significant. Values are expressed as means ± SE.


    RESULTS
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ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Figure 1 presents examples of original recordings of 5 s duration, showing the electrocardiogram, arterial blood pressure, original RSNA, and integrated RSNA in intact and sino-aortic denervated (SAD) conscious rats (Fig. 1, A and B, respectively). The bottom traces (Integ.) were filtered signals of the above (the cutoff frequency was 40 Hz). In an intact rat (Fig. 1A), synchronized activity was observed in RSNA; however, such synchronization disappeared in a SAD rat (Fig. 1B). The time-series data for spectral analysis were obtained from integrated RSNA every 20 ms. It should be mentioned that, in SAD, it was difficult to find a pause in or absence of activity of nonbursting discharge in RSNA within 5 s.



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Fig. 1. Examples of original renal sympathetic nerve activity taken from intact (A) and sino-aortic denervated (B) conscious rats. In intact rats, synchronized activity was clearly demonstrated; however, it was completely absent in SAD. ECG, electrocardiogram; BP, blood pressure; RSNA, renal sympathetic nerve activity; SAD, sino-aortic denervated.

To investigate accurately the analysis of periodicity in nerve activity, it is important to record the activity with high fidelity. The S/N ratio of recordings for current investigations ranged from 6.1 to 27.1 (12.6 ± 1.2, mean and SE, n = 12) and from 6.5 to 18.3 (10.2 ± 2.3, n = 12) in intact and SAD rats, respectively.

To observe the consistency of and change in the spectral peak, the frequency spectrum of all 32 segments, each with power indicated on the energy scale, was arranged (Fig. 2). Each segment, 20.48 s or 1,024 points in duration, was placed with a 1.28-s or 64-point delay from the beginning of the preceding segments. In Fig. 2A, RSNA in conscious rats with intact baroreceptors was characterized by four frequency components occurring at about 0.5, 1.5, 6, and 12 Hz, which corresponded to the low-frequency fluctuation of heart rate (LF), respiration (HF), and the frequency of the cardiac cycle (CF) and its harmonics (C2F). The power for the component at 12 Hz was smaller than those for the three other components. In the recordings of relatively low signal-to-noise ratio, this component was often absent. As illustrated in Fig. 2B, all four components disappeared in the SAD conscious rat. These results suggest that the four frequency components exist in the synchronized RSNA of conscious rats and arterial baroreceptors are responsible for their generation.



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Fig. 2. Graphs demonstrate the results of power spectrum analysis of the records shown in Fig. 1. The length of integrated activity was about 60 s and divided into 32 segments to adopt the fast Fourier transform (FFT). Renal sympathetic nerve activity showed 4 frequency components occurring at approximately 0.5, 1.5, 6, and 12 Hz in an intact rat (A). In contrast to this, no frequency component could be found in a SAD rat (B).

The superimposed plots of 12 periodgrams obtained by averaging the successive 32 spectra of individual trials in each rat that were shown in Fig. 2 are presented in Fig. 3A. However, the characteristics of spectral power for each fluctuation, especially cardiac-related activity, were not localized on the axis of frequency because this frequency depended on the periodicity of the heart beat, which differed from one rat to another even after the same postoperative period.



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Fig. 3. These graphs show the frequency characteristics of RSNA in intact and SAD conscious rats. Top panels show the overlapped spectrum from different intact-baroreceptor (A; n = 12) and SAD (B; n = 12) conscious rats. Inset in B: example of the respiratory frequency component that appears sporadically in certain SAD rats. To elucidate the spectral component of cardiac-related activity, partial spectra with a 4-Hz band in which the cardiac frequency (D and G) and its double (E and H) were centered were extracted from the above whole spectrum. Graphs (C and F) represent simple averaging of <4 Hz. Inset in G and H: the same spectra in B are plotted on logarithmic scales. The solid line indicates the Lorentzian curve fitted by SAD spectra.
<IT>S</IT>(<IT>f</IT>) = <FR><NU>0.374</NU><DE>1 + (<IT>f</IT>/14.119)<SUP>2</SUP></DE></FR>

Therefore to compare each peak across different animals, a partial spectrum around the frequency of heart rate and its second harmonics with ±2-Hz range were extracted from the whole spectrum (Fig. 3A) and then averaged in Fig. 3, D and E, respectively. Figure 3C shows the lower-frequency and the respiratory-frequency spectra, which also averaged lower portion of Fig. 3A.

It should be noticed that two small peaks are observable in Fig. 3D. These peaks were placed at approximately the same distance from the frequency of cardiac-related activity. Furthermore, another small peak was located at a frequency twice as high as that of the cardiac-related activity in Fig. 3E. This small peak corresponded to the peak at 10 Hz in Fig. 2A. In Fig. 3C, individual heart rates differed; however, this peak was elucidated after processing the extraction from the whole spectrum. These small peaks were presumably related to the cardiac activity. The occurrence of these three small peaks in the spectrum will be explained in considerable detail later on. In contrast, there was no significant frequency peak in the power spectrum of SAD rats, as shown in the superimposed plot (Fig. 3B) and partial spectra (Fig. 3, F-H). As shown in the inset of Fig. 3, G and H, the power spectrum was well fitted by a flat or Lorentzian curve, suggesting an almost random pattern. In almost period, the respiratory-related peaks were abolished; however, they sporadically appeared within a short-duration (inset of Fig. 3B).

In the present study, the waveform of the RSNA in multi-fiber recordings differed from those with slow waves, which were obtained from band-pass filters with cutoff at very low frequency (Gebber 1990). As shown in an expanded trace of Fig. 4A, individual impulse-like activity from multi-fibers that formed a burst is easier to identify than that in slow waves. Therefore to examine whether each impulse-like activity showed the same frequency characteristics as the whole activity, the RSNA was divided into sub-groups on the basis of the amplitude of the impulse-like activity. Figure 4 shows a schematic diagram of the processing. The amplitude of each spike in the impulse-like activity was measured (see the vertical line in the 2nd panel of Fig. 4A) and was plotted as a function of time. Each amplitude of the impulse-like activity was measured, and the vertical line adjusted to equivalent height was plotted on a horizontal line (middle panel, Fig. 4A). The right side of the bottom trace in Fig. 4A indicates the level of 15 windows by which each impulse-like activity was sorted. In Fig. 4B, the time course of the number of the impulse-like activity in each window every 20 ms is plotted in relation to the height of 15 ribbons during 1 s. The time course in each window from w1 to w5 was relatively similar in appearance (Fig. 4B, left). If specific patterns for occurrence exist in a range of RSNA amplitudes, this plot should clarify it. However, no specific patterns could be detected on this graph, nor in those produced from 5 other animals (data not shown). In contrast, there was no synchronization in SAD rats (Fig. 4B, right).



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Fig. 4. Graphs demonstrating the procedure for grouping the activities from multi-fiber preparation according to the amplitude of impulse-like activity. Each amplitude of an impulse-like form was measured and sorted by matching windows whose level differed equally in height. Bottom panels show the changes in number of spikes within each window every 20 ms for 1 s.

The spectral analysis was also performed on the time series obtained by this windowing procedure. Figure 5 shows results produced by w1, w2, w4, and w10-14 in B, C, D, and E, respectively. For comparison, the power spectrum calculated for the original time series is illustrated in Fig. 5A.



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Fig. 5. The spectra of sorted impulse-like activity (B-E) were compared with that of whole activity (A), which was taken from the integration procedure. It should be noted that the general characteristics from A to E were almost the same in spite of small differences in the power of each spectral component between graphs.

The spectral analysis calculated for all sorted activity is appended in Fig. 5A to compare the frequency component. Generally, all subgroups showed similar spectral densities, although some differences could be recognized. In Fig. 5B, the power for cardiac-related activity (CF) was slightly smaller than for others. The power for the harmonics of cardiac-related activity (C2F) was slightly smaller in B, D, and E than in C. However, no other new components could be found in Fig. 5, B-E, which suggests that all of the activities sorted on the basis of amplitude have similar periodicities. Data for SAD rats are not shown, but all spectra calculated from the time course of the number of impulse-like activity sorted by each window were the same as those shown in Fig. 2B. It should be noted that there was no periodicity among any fibers in renal sympathetic nerves in SAD conscious rats.

Sympathetic nerve activity is augmented via the baroreceptor reflex when blood pressure is lowered. To elucidate the change in periodicity of RSNA in rats, SNP was intravenously perfused for 5 min (25 µg/min). Figure 6 presents digitized records of ECG, BP, and integrated RSNA before, during, and after perfusion of SNP. The heart rate and RSNA started to increase gradually after the onset of perfusion. It should be mentioned that it took about 2 min to increase the heart rate to its maximum level (Fig. 6C). The expanded recordings of ECG, BP, RSNA, and integrated RSNA before (A) and during (B) perfusion of SNP are shown in the top panels of Fig. 6. The synchronized RSNA was augmented during SNP.



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Fig. 6. C shows the time course of changes in blood pressure (BP), heart rate (HR), and integrated renal sympathetic nerve activity (RSNA) after sodium nitroprusside (SNP) perfusion over 5 min. Each point represents the averaged value for 5 s. A and B show expanded original records taken at approximately 50 s before and 100 s after the start of SNP perfusion, respectively.

Figure 7 presents the changes in frequency and power of spectral components during reflexly increased heart rate in response to lowering the BP by SNP perfusion. To visualize successive changes in the spectral power associated with increasing the HR, 192 spectra were divided chronologically into 6 arrays and arranged from 1 to 6 in Fig. 7. In the middle (white triangle) of 3, the spectral component of CF started to shift to the right, indicating that the heart rate increased gradually, and this shift continued up to the end of 5. The LF and HF bands were defined as high at 1.0 Hz and from 1 to 3 Hz, respectively. It was found that the peak frequency of the CF component was extremely close to the current heart rate and that irregular small deflections existed around the peak. It should be noted that the peak frequency of the CF component successively increases in parallel to the increase in heart rate during SNP perfusion, as depicted in Fig. 6. The peak frequency of the C2F component changed concomitantly with CF component, which suggests that the C2F component fully depends on the cardiac-related activity. The existence of another specific periodic component such as the "fundamental" or 10-Hz rhythm was not observed. Since the frequency ranges of the CF and C2F components were not fixed and their center frequencies were equal to the frequency of heartbeat and its double, this suggested that CF and C2F were caused by heartbeat. On two occasions, the range of significant power did not exceed 0.5 Hz from the center frequencies of CF and C2F. Furthermore, the power of the CF component increased in parallel to the tachycardia induced via the baroreceptor reflex, whereas the other three components did not exhibit significant changes during the same period.



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Fig. 7. Spectra were calculated by the discrete FFT algorithm. From the bottom left (1) to the top right (6) panels, all sequential spectra are shown. Note that after SNP perfusion, the power for the component at 6 Hz significantly increased, and the frequency of this component shifted to the right (white triangle, 3), following the increase in heart rate. Although the powers for the 3 other components were attenuated, no other new component was detected.

The left side of Fig. 8 summarizes changes in BP (A), HR (B), and mean RSNA (C) before, during, and after intravenous SNP perfusion. On the right side of this figure, from top (D) to bottom (H), graphs represent the magnitude of changes in the power for the C2F, CF and HF, LF components, and total power fluctuations of RSNA, respectively, in response to the SNP-induced lowering of blood pressure (represented by the shaded box at the bottom of Fig. 8, C and H). To evaluate quantitative changes in the spectral power during perfusion of SNP, the integral over the area occupied by the four frequency components of RSNA was expressed relative to the mean value of the integral over the area of the respective component in the prestimulus period. For each segment of data (numbered from 1 to 32), the relative power for each frequency component are shown using a logarithmic scale for the ordinate in Fig. 8, D-H, by the function of segment numbers. Integrated and total power were expressed as the averaged value of each of the 12 segments. When the systolic and diastolic blood pressures (A) were significantly decreased by SNP perfusion, a significant increase in both HR (B) and mean RSNA (C) was induced by the baroreflex. While total power (H) increased constantly during SNP perfusion, CF power (E) showed a similar pattern, but was accompanied by a significant transient decrease during the initial increase in mean RSNA. After the termination of perfusion, mean RSNA returned to near or slightly below the control level and total, CF, and C2F powers showed a significant decrease. The figure illustrates that no appreciable change in the power for the HF component occurred regardless of baroreceptor unloading, whereas lowering the blood pressure resulted in a temporal decrease and a transient increase after perfusion in the power for the LF component (G) of RSNA.



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Fig. 8. Graphs in A, B, and C exhibit changes in the blood pressure, heart rate, and RSNA, respectively, associated with a lowering of blood pressure by SNP (n = 12). Two traces in A reflect systolic and diastolic blood pressure. Corresponded profiles of integrated power for harmonics (C2F), frequency of the cardiac cycle (CF), respiration (HF), low-frequency fluctuation of heart rate (LF), and total powers fluctuations are depicted on D, E, F, G, and H, respectively. Integrated and total power were expressed as the averaged value of each of the 12 segments. The time scale of horizontal axes on both sides are equivalent. The error bars on the left and right are expressed as SE and SD, respectively.

To analyze the mechanisms responsible for the generation of the frequency components in the spectrum of RSNA, two types of simulated signal were generated using the following equations
<IT>S</IT>(<IT>t</IT>) = &agr; + <IT>g</IT>(<IT>t</IT>) (1)

<IT>S</IT>′(<IT>t</IT>) = <FENCE><AR><R><C>&agr; + <IT>g</IT>(<IT>t</IT>)</C><C> for <IT>g</IT>(<IT>t</IT>) ≥ 0</C></R><R><C>&agr;</C><C> for <IT>g</IT>(<IT>t</IT>) < 0</C></R></AR></FENCE> (2)

<IT>g</IT>(<IT>t</IT>) = <LIM><OP>∑</OP><LL><IT>i</IT>=1</LL><UL>3</UL></LIM> &bgr;<SUB><IT>i</IT></SUB> sin (2&pgr;<IT>f<SUB>i</SUB>t</IT>)
where alpha  is the noise level and beta i and fi are the amplitude and frequency of each component, respectively, the applied values of which are as follows: alpha : 60; beta 1-3: 0.5, 0.5, and 1.25, respectively; and f1-3: 0.2, 1.3, and 6 Hz, respectively. The combination of three pure sine curves from Eq. 1 makes only three frequency peaks occurring at 0.2, 1.3, and 6 Hz in Fig. 9B. Because the nerve activity was modified by the rectification-integration, the RSNA used for these procedures never decreased its value either to the negative or beneath the noise level. Equation 2 simulates this situation. As shown in Fig. 9C, all values below 60 are rounded up to 60. Figure 9D demonstrates the frequency components calculated from this simulation. It should be noted that three small peaks at 4.5, 7.5, and 12 Hz are newly appended to those of Fig. 9B.



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Fig. 9. Graphs show the 2 types of simulated signal (A and C) and their spectra (B and D). The simulated signal (A) is constructed simply from 3 sine functions with different amplitude and frequency. A signal less than its average is replaced with the average value (C). Note the smaller peaks appearing in D. Further explanation can be found in the text.


    DISCUSSION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

In the present study, application of the spectral analysis techniques to rectified and integrated renal sympathetic nerve activity obtained from conscious freely moving rats allowed us to analyze periodicity in RSNA under various conditions: resting state, increased activity caused by baroreceptor unloading, and return to rest after cessation of baroreceptor input.

In conscious rats with intact baroreceptors, data collected on the basis of S/N ratio indicated that RSNA with a high S/N ratio was characterized by four frequency components occurring at about 0.5, 1.5, 6, and 12 Hz, corresponding to the low-frequency fluctuation of heart rate (LF), respiration (HF), and frequency of heart beat (CF) and its harmonics (C2F), respectively. It is worth noting that C2F was difficult to identify if the S/N ratio was lower, which suggests that the presence of the frequency components might be strongly dependent on the recording quality. These findings were obtained from integrated multi-fiber signals. Therefore we cannot ascertain whether these frequency characteristics are common to each nerve fiber recorded in our preparation. However, as shown in Fig. 5, a profile of the sorted spike-like activity showed similar periodicity in each band with small inter-band differences. No other frequency component was detected, at least in the present study.

If the C2F band corresponds to a specific period independent of heart beat, it should remain fixed. However, when the heart rate was increased or decreased, the spectral peak and the two neighboring side bands were shifted accordingly. Therefore one possibility is that these two bursts in one cycle will contribute to the harmonics of cardiac frequency (C2F). To interpret the three small spectral peaks appearing at twice the cardiac frequency and at equally distant sides from the CF component (Fig. 3C), the spectrum of simulated RSNA was calculated. As shown in Fig. 9, the side bands and harmonics of the spectral peak were made artificially by a processing procedure using a FFT algorithm. Therefore these small peaks are likely to be one sort of artifact, which indicates that periodicity in sympathetic activity is slightly different from a complete sine curve. If another procedure, such as the maximum-entropy method, is employed to study the periodicity, the C2F component may be attenuated. However, we elected to use the FFT algorithm because of its exact estimation of the frequency width and the power of periodicity in RSNA. Ando et al. (1994) performed a similar spectrum analysis for renal sympathetic nerve activity associated with simultaneously stimulating both the carotid sinus using artificial pulsatile pressure at 1 Hz and the peroneal nerve by means of an electrical stimulator at 3 Hz. Powers at 1 and 3 Hz with two small peaks at 2 and 4 Hz and at 6 Hz, respectively, are shown in Fig. 2B of the study by Ando et al. Using a mathematical model, they suggested that these small peaks at 2 and 4 Hz might be side bands resulting from the interaction between the 3- and 1-Hz oscillation. The spectra show some similarity to those shown in Fig. 3 in the present study. This means that our CF component may be comparable to the 3-Hz component, which appears to be a somatosympathetic reflex response, since both our CF and their 3-Hz component were attended by similar side bands and harmonic components. In the present study, we considered that respiratory- and cardiac-related fluctuations corresponded to their 1- and 3-Hz oscillations, respectively. This may suggest that the drive of the baroreceptor input to the sympathetic nervous system may be a simple mechanism such as that of the somatic afferent nerve by artificial electrical stimulation. Furthermore, the cessation of baroreceptor input resulted in the disappearance of periodicity in the sympathetic nerve and a change in the sympathetic activity pattern to random discharge.

The CF and C2F bands were concentrated at the fundamental and first harmonic frequencies of heart beat, respectively. Kezdi and Geller (1968) have shown that generation of cardiac cycle-related oscillation of sympathetic nerve discharges is dependent on the frequency of the cardiac cycle. When the CF and C2F components were normalized to the fundamental frequency of heart beat (Fig. 3), the full width at base of the CF and C2F component peaks was <0.5 Hz; in particular, the full width at half-height of the CF peak was close to 0.1 Hz. This finding suggests that CF and C2F are highly dependent on the cardiac cycle. On the contrary, the full width at base of the HF peak, corresponding to the respiratory-related activity, was slightly wider at about 1.2 Hz. If the respiratory rate is measured exactly, the HF peak could also be normalized and adjusted at the frequency of the individual respiratory rate. This procedure may make its bandwidth narrower.

We could not find a thickly rounded peak positioned from 2 to 6 Hz to support the findings of those reported by Gebber and his co-worker (Gebber 1980, 1990; Kenney 1994) and Malpas and Ninomiya (1992). This discrepancy may be result of the method of signal processing for spectral analysis, as they did not perform the rectification-integration of the original sympathetic nerve activity for later analysis, and they used a band-pass filter with lower cutoff frequency of below 1 Hz.

Even after interruption of baroreceptor inputs to the CNS, several authors (Gebber 1980; Kenney 1994; Ninomiya et al. 1990; Ootsuka et al. 1995) asserted the persistence of 10-Hz rhythmic activity in the sympathetic nerve in anesthetized or decerebrated animals. Furthermore, they suggested that rhythmic activity remained after abolishing other strong periodic activity such as cardiac- and respiratory-related rhythms. In contrast, in the present study, all frequency components of RSNA in SAD rats were abolished, and the spectrum was almost flat. Furthermore, when the ordinate of the spectra was changed to a logarithmic scale, the spectral pattern could be approximately fitted to a Lorentzian curve, indicating that the discharge pattern of RSNA in SAD is close to the random and lacks any specific periodicity. While some investigators (Åström and Crafoord 1968; Bronk et al. 1936; Pitts et al. 1942) have reported that sympathetic nerve activity showed an essentially continuous or random pattern in character after denervation of baroreceptors, others (Alexander 1945; Downing and Siegel 1963; Koizumi et al. 1971) have observed irregularly occurring oscillations of sympathetic nerve activity characteristic of the synchronized activity of individual fibers. The possibility that whole-nerve recording could mask the periodicity in the neural activity originated from subgroups of single fibers with a rhythmic pattern of activity remains to be examined. Although the sorted spike-like activity shown in Fig. 4 does not represent single-fiber activity, the similarity of individual spike-like activities in the SAD rats seems to support the random fashion of firing.

In a few recordings from SAD rats, the respiratory-related component in RSNA could be seen only sporadically in the spectrum array with short-duration, as shown in the inset of Fig. 3B. However, the probability of the occurrence of this component varied not only among the rats but also within individuals. The central and peripheral components of respiratory modulation of sympathetic nerve activity were reviewed in Häbler et al. (1994). For rats, Häbler et al. (1996) recently reported that the central-driven and baroreceptor-dependent reflex components are present in identical recordings from intact rats, while only the latter component disappeared completely after sino-aortic denervation. Moreover, these researchers demonstrated that the reflex component is significantly smaller than the central-driven one in anesthetized intact rats. However, the present result suggests that the connection between the respiratory center and the sympathetic center in the CNS may be intermittent in the conscious state. Furthermore, it is likely that the reflex modulation is dominant in conscious baroreceptor-intact rats. This and the disappearance after baroreceptor denervation suggest that even respiratory-related rhythmicity may be brought about by a sequence of the presence and absence of a cardiac-related burst.

The current study was performed on conscious resting animals; it will be important to examine the changes occurring under patho-physiological conditions in further investigation on the nature of RSNA periodicity. The regulation of the sympathetic nervous system contributed greatly to the control of the cardiovascular system through the baroreceptor reflex. To compare the changes under the resting state with those in a condition activated by baroreceptor unloading, SNP was intravenously perfused. The frequencies of the spectral peak at CF and C2F shifted following the changes in heart rate. The significant increase in power for CF indicated that the cardiac-related rhythm was augmented during SNP perfusion. However, the power for the HF component did not change during the augmentation. The power for the LF and C2F components decreased temporarily during SNP perfusion, but the numbers of the spectral components observed at rest were the same. Additional specific frequency components did not appear. The characteristics of periodicity in RSNA did not show significant changes. In conclusion, four types of periodicity in the RSNA exist in the rat, and all of these fully depend on arterial baroreceptor inputs.


    ACKNOWLEDGMENTS

This study was supported, in part, by Grants-in-Aid for Scientific Research (05857010, 10557009, and 11470019) from the Japanese Ministry of Education, Science, Sports and Culture.


    FOOTNOTES

Address for reprint requests: H. Kannan, Dept. of Physiology, Miyazaki Medical College, 5200 Kihara, Kiyotake, Miyazaki 889-1692, Japan (E-mail: kannan{at}physiol.miyazaki-med.ac.jp).

Received 13 December 1999; accepted in final form 31 August 2000.


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