INTRODUCTION

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION APPENDIX A APPENDIX B REFERENCES

MORE THAN 25 YEARS AGO, Cowley et al. (14) reported that compared with normal dogs, the blood pressure (BP) distributions of sinoaortic denervated (SAD) dogs "exhibited curves with twice the 24-h standard deviation." There have since been similar observations in various species, including humans. One interpretation of these observations is that the baroreflexes normally restrain minute-to-minute BP variability; this contrasts with a view of the baroreflexes as provoked to only occasional action by specific perturbations such as thermal stress, postural shifts, or hemorrhage. The physiology of the postSAD-BP variability is not known. Although the variability is reversed by ganglionic block and, thus probably neurally mediated, in unrestrained rats, brain lesions as extensive as precollicular decerebration do not eliminate it (39). Furthermore, ventilated, intensively maintained, neuromuscular-blocked (NMB) rats show similar ganglionic block-dependent and increased postSAD variability (16), indicating that it does not depend on fluctuating respiration or the skeletal activity of general behavior.

As expected for random data, the SAD variability is independent of the sampling interval (2, 7). However, we found that averaging observations over 30 s (16) also did not attenuate the variance and that suggested that the beat-to-beat variability was not uniformly random. A mean is effectively a time-domain filter, and taking differences between successive 30-s systolic BP (SBP) means (16) amounts to applying a 0.005- to 0.025-Hz band-pass filter to the data (see APPENDIX A). That postSAD variability was unattenuated by this averaging strongly suggested that, although random, its spectral power was concentrated in the very low frequencies (VLF).¹

Because a negative feedback element constrains variability, noise increases when it is removed. Furthermore, it is fundamental that an element can oppose and neutralize noise only where its transfer function (TF) and the noise spectrum coincide; it is a corollary that the spectral change that occurs, when an element is removed, delineates the closed-loop system's TF. Dog- and rabbit-baroreflex response curves have corner frequencies at ~0.05 Hz (23, 25, 32), and the spectral effects of SAD in the rat predict a similar TF.

In the companion paper (16), we described the statistical variability of the BP in the NMB preparation, showed that it resembled the patterns of ambulatory rats, and then, by directly activating the carotid sinus (SINUS) and aortic depressor nerve (ADN) with hydraulic and electrical stimuli, measured the steady-state baroreflex responses of arterial (ABP) and venous BP (VBP), interbeat interval (IBI), and the skin (skBF), mesenteric (msBF), and femoral (fmBF) blood flow. In the studies described here, with the same preparations, stimulus modes, and response measures, we applied both step and periodic stimuli and, with the use of several straightforward methods, determined the open-loop frequency and phase response of the carotid and aortic reflexes, estimated the upper limit of the "central" lag for vagus and peroneal nerve activity, and calculated the absolute gain of the TF.

The diagrams in Fig. 1 represent the cardiovascular system with and without the baroreceptor feedback path [H(s)]. In both, the source of variability or noise input [N₁(s)] is the same and located in the central nervous system (CNS); and feedback is from the BP through the baroreceptors. There is a neural summing point (_CNS) in the CNS, which combines N₁(s) with the baroafferents' output, and a hydraulic point at the baroreceptors that reflects the net action of the cardiovascular effectors relative to the sensory reference or adaptation level. The implied hypothesis is that, normally, the baroreflex counteracts endogenous variability propagated from N₁(s) and that the difference between the Pre and Post spectra is due to the action of the baroreflex.

View larger version (15K): [in this window] [in a new window]   Fig. 1.   System diagrams of the baroreflex. Anatomically, H is the feedback path from the baroreceptor stretch endings (BR) to the summing point (CNS). G(s) converts the neural outflow to blood pressure (BP) via cardiac and vascular mechanisms. Endogenous sources of BP variability are represented by N1(s). Experimentally, Pre(s) and Post(s) are BP in the abdominal aorta; the sympathetic (peroneal) nerve recording is between CNS and G(s), and  is the application point for the test stimuli.

An alternative hypothesis is that noise is a de novo experimental artifact introduced by random firing of damaged baroreceptors, which, although severed from their receptive endings, remain connected to nucleus of the solitary tract (NTS) target neurons. Primary afferents in other sensory systems, e.g., dorsal root ganglion cells, show spontaneous activity after distal axotomy (9, 29); however, on the basis of several kinds of neurophysiological and statistical evidence, random activity of axiotomized baroreceptors is not a likely source of the postSAD variability (see analysis in APPENDIX B).

Relationship Between the Pre and PostSAD Models

(1)

(2)

	METHODS

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION APPENDIX A APPENDIX B REFERENCES

The subjects, surgery, and general methods are identical to and described in the companion paper (16). All actual surgery or possibly irritating manipulation was done under controlled and carefully monitored, deep isoflurane anesthesia. The protocol is supervised and certified to be in compliance with National Institutes of Health Guidelines by the Pennsylvania State University College of Medicine Institutional Animal Use and Care Committee. The specific protocols and data analysis are as follows.

Noise Spectra

SBP. For each rat, 50 randomly chosen 90-s postSAD-SBP samples were automatically extracted to binary files from 4-h 6-kHz digital audio tape (DAT) records of undisturbed baseline. Systole was algorithmically detected and backward-step interpolated into a 1-kHz array, and the spectra were obtained by a fast Fourier transform (FFT) (Hanning; 8.3-mHz resolution) of the detrended data. For rat EH, both pre- and postSAD 6-kHz samples (within 24 h at 0.15% isoflurane) were analyzed, and the VLF (0.01-0.2) and low-frequency (LF) (0.2-0.6) power were measured by integration. For all rats, 2.5-s/sample data pre- and postSAD (within 24 h at 0.15% isoflurane) were analyzed for VLF power. Differences due to SAD were evaluated by ANOVA and post hoc t-tests.

Sympathetic (peroneal) nerve. Fifty parallel 90-s postSAD sympathetic trials for each rat were extracted from 6-kHz records, and the spectra were calculated.

Open-Loop Transfer Function

Modulation frequency analysis. ADN. Current levels were selected to activate A or A + C fibers (17, 18; see Ref. 16 for criteria). A fibers were stimulated with the use of a 100-µs pulse width (PW) at 50-60 µA; A + C fibers with 300 µs at 80-100 µA. The test parameters were 110% of the minimum and 90% of the maximum linear range (see Ref. 16): 20-50 impulses/s for A; 3-20 ips for A + C. The periodic stimuli were symmetrical on-off cycles of 0.02-0.4 Hz for 120 s. ADN modulation analysis was done in three rats. SINUS. Stimulation was done by inflating a microballoon in a vascularly isolated sinus (16); the range was determined as above 1.7-3.2 µl (peak-to-peak) at frequencies of 0.02-0.4 Hz. Complete analyses were done in two rats.

Power spectral analysis (SBP). The test stimulus modes were SINUS (2 rats), ADN-A (3 rats), and ADN-A + C (1 rat). The test frequencies for SINUS were 0.02, 0.03, 0.0375, 0.045, 0.055, 0.0625, 0.0875, 0.1, 0.1125, 0.1375, 0.15, 0.1625, 0.175, 0.2, 0.25, and 0.4 Hz; for ADN-A: 0.02, 0.025, 0.0375, 0.05, 0.0625, 0.075, 0.0875, 0.1, 0.1375, 0.175, 0.25, 0.3, and 0.4 Hz; and for ADN-A + C: 0.025, 0.03, 0.0375, 0.05, 0.0625, 0.075, 0.0875, 0.1, 0.125, 0.1375, 0.175, 0.2, and 0.4 Hz. For each rat, stimulus mode, and test frequency, 5 to 27 spectra were averaged (see Table 1). Each spectrum was obtained by an FFT (8.3-mHz resolution) on the 120-s interpolated, Hanning-windowed responses. The amplitude TF were calculated from the normalized square-root power and interpolated to estimate the 3- and 20-dB frequencies and 0.4-Hz amplitude.

Sinusoidal fit. The ensemble-averaged signals were iteratively fit to a sine function. The variables of the fit were the amplitude, phase lag, and frequency. The amplitude TF were directly estimated by calculating the output-to-input amplitude ratio and extrapolated as above.

Step-frequency analysis. ADN. The parameters were the maximums used for periodic stimulation. For A fibers, it was 20-50 ips; and, for A + C fibers, it was 9-20 impulses/s. Analyses were completed for five rats. SINUS. Balloon volume was 80% of the linear range maximum (2.25-3.2 µl); analyses were completed for three rats.

Transient response. The output variables were systolic BP (SBP), IBI, mesenteric vascular conductance (msVC), and femoral vascular conductance (fmVC). For each rat, for ADN-A, A + C, and SINUS, 20 stimuli at each of 2-4 strengths were ensemble averaged. With the use of the difference between the stimulation period and mean of the 12-s prestimulus baseline, the initial 50 s of the averaged response was iteratively fit to y(t) = A(1  e^t/T), where A is the asymptotic response amplitude, T is the time constant, and the TF of the step is defined as sY(s).

Transportation lag estimates. SBP. For each of five rats, SINUS, ADN-A, and A + C, open-loop transportation lags were measured. Each data set was composed of 50 prestimulus and 80 stimulus-on cardiac cycles. A least-squares line was fit to the prestimulus data, and an exponential was fit to the stimulus data (see Fig. 6); simultaneous solution relative to t₀ gave the transportation lag (see Fig. 6). CNS. Fifteen 3.0-µl SINUS step responses were extracted from 6-kHz DAT; the IBI was measured, back interpolated, and represented as instantaneous frequencies (f_i). The balloon volume, heart frequency, and absolute value of the vagus and sympathetic neurograms were ensemble averaged and smoothed by a 10-point second-order Savitzky-Golay algorithm. The stimulus onset was defined with respect to the peak volume rate of change and threshold volume. The response onsets were defined at 3 SD above the baseline.

Gain-Scaling Factor [k]

Rat EH. For each of the n  20-modulation test frequencies, f_i, the normalized RMS amplitudes, GH_i were obtained from FFT modulation TF estimates. Spectral ratio was determined by division of the preSAD by the postSAD amplitude spectra, and the lumped open-loop system lag, _lag, and estimated first-order phase lag, arctan(2fT), were used to calculate the phase. The error, , between the TF and spectral measurements (Eq. 3) was differentiated with respect to k, and the value of k, corresponding to the minimum, was determined for each kind of stimulation.

(3)

	RESULTS

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION APPENDIX A APPENDIX B REFERENCES

PostSAD spectra. BLOOD PRESSURE. Figure 2D shows that the normalized high-resolution postSAD-SBP spectra of five undisturbed NMB rats (including rat EH) are very similar. The corresponding postSAD and preSAD spectra for rat EH are shown in the main panel of Fig. 2. The key feature is the large increase in VLF power (PSD = 1.2 × 10⁵ mmHg²/Hz, df = 49, t = 3.65, P < 0.001); there is also a smaller decrease in LF power¹ [change in power spectral density (PSD) = 1.5 × 10³, df = 49, t = 2.02, P < 0.05]. Similar analysis of 2.5 s/sample data (Nyquist 0.2 Hz) for all five rats showed a large increase in VLF power (PSD = 0.75 × 10⁵ mmHg²/Hz, df = 4, t = 3.69, P < 0.01), which probably accounts for most increased postSAD variability (see Fig. 2 in Ref. 16). PERONEAL (SYMPATHETIC) NERVE. The postSAD spectra plotted in Fig. 3 estimate the postSAD N₁(s) (Fig. 1) and shows that the nerve activity spectrum is nearly flat; thus the LF skew of the SBP spectrum is most likely due to G(s) (See Fig. 5).

View larger version (32K): [in this window] [in a new window]   Fig. 2.   Main: high-resolution pre- and postsinoaortic denervation (SAD) spectra of rat EH with 0.15% isoflurane analgesia. [At this level, undisturbed neuromuscular-blocked (NMB) rats have normal basal electroencephalogram, heart rate (HR), and BP, but they have distinct reactions to sounds or light touch.] A: detail of the spectrum showing the low-frequency (LF) peak. Note that the pre- and postspectra cross at 0.15 Hz in the main panel. B: postSAD, there was a large increase in very low-frequency (VLF) power (PSD = 1.2 × 105, df = 49, t = 3.65, P < 0.001), and a small, but reliable, decrease in LF power (PSD = 1.5 × 103, df = 49, t = 2.02, P < 0.05) (see Ref. 4). C: PostSAD spectrum in the main panel compared with an identically processed spectrum obtained 3 wk later, but without isoflurane (between-spectra LANOVA: regression m = 0.995, b = <1% of peak, r2 = 0.965, P < 0.0001, K-S for 0.1 Hz, 2 = 1.4, P > 0.999). D: highly similar normalized postSAD spectra of 5 different rats (including EH) without isoflurane.

View larger version (21K): [in this window] [in a new window]   Fig. 3.   The postSAD sympathetic (peroneal) nerve activity (relative) power spectra. The heavy solid line is the renormalized grand average.

Modulation time-domain responses. The series of traces in Fig. 4 are examples of the step response (0 Hz) and modulation of the component responses by ADN-A + C stimulation. [The abdominal conductance (msVC) includes the aorta below the superior mesenteric artery.]

View larger version (17K): [in this window] [in a new window]   Fig. 4.   Modulation of the component responses by aortic depressor nerve (ADN-A + C) stimulation plotted in absolute physiological units. The peak-to-peak input modulation amplitude is the same at all frequencies and within the stimulus linear range (16). Each trace is an ensemble average of the 5 median stimulation trials.

Periodic input POWER SPECTRAL ANALYSIS. Figure 5 is the SBP-amplitude spectra (EH, ADN-A, ADN-A + C, SINUS) for eight test frequencies. The procedure was the same as for the power spectra shown in Fig. 2, except the absolute value of the amplitude per square root Hertz is plotted; the corresponding normalized FFT amplitudes for all rats are in Table 1. The solid lines are the average spectra during the specified test stimulus (e.g., ADN-A, 0.1 Hz), and the gray areas are average spectra during the baseline periods that immediately preceded the onset of that kind of test stimulus. The averages are across all trials for the specified mode (e.g., ADN-A + C). The figure illustrates the relationship between the noise and the modulation amplitudes at various frequencies, which are both products of G(s). SINUSOIDAL FITS. An iterative least-squares fit of a sine function to the modulated output is a straightforward measure of the peak-to-peak response amplitude. Table 2 gives the normalized amplitudes for SBP for each rat and stimulus mode; the 3- and 20-dB frequencies were calculated directly from the power ratios.

View larger version (27K): [in this window] [in a new window]   Fig. 5.   Systolic BP (SBP) power spectra. Each cell is the average of 10 FFT on 120-s interpolated, Hanning-windowed responses. For each mode, all stimuli were 80% of the linear range. The stimulation frequencies (label above each peak) and analysis-determined peak frequencies were effectively identical (see Table 2). The individual peaks show the relative magnitude of the transfer function (TF); the figure also shows the relationship to the noise spectrum (the gray areas define the average of the prestimulus baseline spectra for each stimulus mode). Minor peaks, immediately to the right of each major peak, are sampling artifacts; additionally, in the ADN spectra, there are small side lobes due to square-wave modulation. The small consistent peak at the far right, at 1.2 Hz, in each spectrum is at exactly the frequency of the mechanical ventilation.

Step input. For SBP, the mean 3-dB frequency was 0.035 Hz for ADN-A, 0.046 Hz for A + C, and 0.056 Hz for SINUS; the magnitude at 0.4 Hz was 6-18% of the maximum response. fmVC was similar to SBP, but msVC had a reliably lower and IBI had a reliably higher 3-dB frequency. Table 3 summarizes the results and statistical tests for five rats.

Transportation lags. BARORECEPTOR TO SBP. The mean ADN lag was 1.07 s (Table 4 and Fig. 6). After subtracting the transportation lag, cross-correlation analyses of the modulated SBP at each test frequency did not reveal an additional phase shift. However, for >0.15 Hz, the modulation was weak, and at <0.05 Hz, there was considerable VLF noise (see Fig. 2); thus given the close exponential approximation of the response, nonlinear phase effects were assumed to be present. BARORECEPTOR TO VAGUS. Vagus recordings of 15 SINUS stimulations (rat EH) are shown in Fig. 7. The ensemble average of these, of the corresponding heart rate (HR) traces, and of the balloon volume are shown in Fig. 8. The 3-SD (above baseline) increase in vagus activity occurred in <30 ms, and the time to first peak of vagus activity was ~95 ms; thus the estimated maximum delay from balloon inflation to vagus firing was 30-95 ms. BARORECEPTOR TO SYMPATHETIC. The peroneal nerve data in Fig. 8 parallel the vagus data. With the use of the same stimulus onset definition, the 3-SD change was at <20 ms, and the time to the first peak was 84 ms.

View larger version (13K): [in this window] [in a new window]   Fig. 6.   Transportation lag measurement example. The data consist of SBP samples from 50 prestimulus and 80 stimulus-on cardiac cycles. A line was fit to the prestimulus and an exponential to the stimulus-on data. The simultaneous equations were solved for the intersection, and the time from the stimulus onset [to] to the calculated intersection was taken as the transportation lag. The data for 5 rats are in Table 4.

View larger version (80K): [in this window] [in a new window]   Fig. 7.   Vagus nerve response to 15 carotid sinus (SINUS) balloon 3.0-µl inflations. The vertical bar marks the start of inflation. Note that the activity occurs in bursts. The first burst has a very consistent latency, and the second burst shows only somewhat more jitter. There are only 4 discrete bursts in the baseline, and more than 60 bursts in the equivalent 2.5-s stimulus-on period (see averages in Fig. 8). Electrical ADN stimulation artifacts in the vagus recording restricted the analysis to SINUS (hydraulic) stimuli.

View larger version (22K): [in this window] [in a new window]   Fig. 8.   Ensemble averages of HR and rectified vagus and sympathetic (peroneal) nerve activity in response to sinus balloon inflation. The balloon volume (solid line) was directly recorded; the dashed line is the calculated derivative. The ramped volume change made the stimulus-onset point ambiguous. However, the time of attaining threshold (1.7 µl) volume (16) and of the maximum volume derivative nearly coincide; from then, to a 3-SD vagus increase was <30 ms, and alternatively, to the vagus peak was 95 ms. Note that typical of SINUS stimulation the maximum HR change is only 12 beats/min.

Estimation of the gain-scaling factor [k]. The values of k that gave the minimum summed squared error between the spectrum-determined pre-to-postSAD ratio and the experimentally determined open-to-closed-loop TF ratio (see equations 1 and 3) are the maximum entries in Table 5. Figure 9 shows the "k corrected" rhs (TF ratio; right-hand side) and the lhs (spectral ratio; left-hand side) of equation 1 plotted against frequency (left panels) and one another (right panels). The plots and correlation analyses show a close correspondence between the theoretically equivalent functions derived from entirely different kinds of data in the same rat (ADN-A: n = 20, r = 0.95, P < 0.0001; ADN-A + C: n = 19, r = 0.89, P < 0.0001; and SINUS: n = 21, r = 0.92, P < 0.0001). It should be particularly noted that, in scaling, the same value of k was used at each frequency; the procedure was thus a linear transformation and did not change the correlation coefficient or its statistical reliability. The conventional correlation coefficients, r, in Fig. 9 are for the best-fitting regression lines. The theoretical absolute identity lines are drawn to show the relationship to the actual data, and the correlations were also calculated with the fit constrained to these (m = 1; b = 0) lines with a result (ADN-A: n = 20, r_I = 0.94, P < 0.0001; ADN-A + C: n = 19, r_I = 0.87, P < 0.0001; and SINUS: n = 21, r_I = 0.90, P < 0.0001) that is very similar to the unconstrained "best-fit" line. Applying the derived k values to the normalized open-loop TF gives estimates of absolute gain at each frequency (Table 5). At >0.3 Hz, the left-hand (lhs) and right-hand side (rhs) ratios of equation 1 conform to one another better for the SINUS than for the ADN (Fig. 9). This is consistent with the established adaptation characteristics of barosensitive stretch endings (24) and is supported by Table 5 and Fig. 10, which show that, for SINUS, the open-loop gain and the feedback gain (|GH| · |N₁| · |Post|¹ = |GH| · |G|¹ = |H|) are comparatively larger at higher frequencies. In the closed-loop, i.e., preSAD, the net phase shift becomes 180° at 0.28 Hz, thus for the SINUS, which has increasing sensitivity in this range, the positive feedback is enhanced and endogenous noise is correspondingly amplified. In that the preSAD spectral estimates (which are the same for all stimulus modes) include stretch endings, the SINUS open-loop measurements, which (unlike the ADN measurements) also include stretch endings, should more authentically emulate the detailed properties of the natural intact system; hence, the better fit at the higher frequencies. RATS DY, EC, AND EF. With the use of the 2.5-s/sample preSAD data for rat EH, the absolute gain was 1.39 ± 0.35 (compared with 1.71 ± 0.52 for the high-resolution estimate); taken overall, on the basis of the 2.5-s data, the mean absolute gain for rats DY, EC, EF, and EH was 1.47 (3 df, 95% CI = ±0.48).

View larger version (23K): [in this window] [in a new window]   Fig. 9.   The ratios from the left-hand side [; lhs; |Post(s)|/|Pre(s)|] and right-hand side [; rhs; TF] of equation 1 plotted as a function of frequency and of one another () for rat EH. The graphs compare the directly measured attenuation of BP variability by an intact baroreflex (lhs) with a predicted ratio that is calculated from the open-loop modulation and phase measurements (rhs). The TF magnitude data were from the normalized FFT with the use of procedures identical to those for the noise spectra. The minimization was over 19-21 frequencies (see Table 1) and was verified with the "FindMinimum" routine of Mathematica 4 (Wofram Research, Champaign, IL). [SINUS involved the complete natural reflex path; and, especially at >0.3 Hz, SINUS tracks the spectral ratios better than the ADN. rI is the identity-constrained correlation; for, ADN-A: m = 1.03, b = 0.13; ADN-A + C: m = 0.88, b = 0.08; and SINUS: m = 0.98, b = 0.09 (for exact agreement  rI = 1; m = 1, b = 0)].

View larger version (14K): [in this window] [in a new window]   Fig. 10.   The approximate shape of the feedback TF, |H|, for the different modes of stimulation. SINUS has increasing gain at >0.3 Hz, probably because, unlike the ADN, its mechanism includes rate-sensitive stretch receptors.

	DISCUSSION

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION APPENDIX A APPENDIX B REFERENCES

To gauge the absolute gain of the intact baroreflex, the open-loop TF was used as a template by constraining the explicitly measured open-loop lag and relative magnitude at each frequency to the ratio of the pre- to postSAD endogenous noise spectra. In contrast to the periodic and step inputs of the open-loop analysis, the properties of the endogenous noise input are not explicitly defined. However, because most postSAD variability is blocked by chlorisondamine, the sympathetic nerve firing rate spectrum (Fig. 3) probably approximates N₁(s), and if the noise is wide band, because the noise terms cancel, the actual spectrum is not critical (see APPENDIX B). The gain estimates obtained for four NMB rats, including the detailed data from rat EH, were similar to one another (1.47 ± 0.48) and to published values for other preparations, including those from the unanesthetized and reversibly isolated-sinus dog (35, 36), the open-loop baroreflex preparation that is most nearly physiological (Ref. 5 gives 1.19 ± 0.24, and Ref. 19 gives 1.36 ± 0.25).

Differences between hydraulic and electrical stimulation. The gain calculation assumed that numerators and denominators on both sides of equation 1 were identical. In principle, this is correct for the SINUS, but not for the ADN, where nerve stimulation bypasses natural stretch endings. If these have a frequency-dependent TF, the lhs and rhs quotients will not multiplicatively scale to superimposable curves; this defect is evident in Fig. 9 for higher frequencies of the ADN-A and A + C.

Implications of the TF for the SBP variability spectrum. The physiological function of the baroreflex is to attenuate BP variability, and its direct manifestation is a trough in the spectrum that corresponds to the passband of the intact reflex. In the VLF (<0.1 Hz) region, the attenuation due to feedback is approximately uniform; this is because the phase is effectively constant, and the feedback TF, H, is flat. Our open-loop estimates of the 3-dB frequency of the NMB rat baroreflex of 0.03-0.07 Hz agree with determinations for the anesthetized dog and rabbit of 0.04-0.05 Hz (23, 25, 32); and, our spectral measurements agree with previous studies in freely moving rats (13, 20). Figure 9 compares the actual attenuation of BP variability, by the baroreflex, with what was calculated from the open-loop determinations of GH. Given that the estimated absolute gain also agrees with applicable published values, the overall correspondence is quite good.

Calculating the gain from the spectra. For rats, the relative gain, lag, and time constant estimates from Tables 1, 3, and 4 can be combined in equation 3 with empirically determined pre- and postSAD amplitudes and  minimized with the use of a least-squares algorithm. In each subject, preSAD measurements can be made with several different treatments; then, after SAD, the baseline spectrum under each treatment determined and the ratios calculated. [Conservatively, to assume that N(s) is stationary, the postSAD treatment effects must be small.] This method can substitute for pharmacological determinations (37), and if recent evidence that HR does not uniformly sample general baroreflex function is correct (6, 16), it might prove to be more valid.

Perspectives

All in all, statistical analysis, computational models, and the experimental findings support the assertion that postSAD-increased variability is caused by removing the restraint of the baroreflex on endogenous sources of noise. This underscores that, rather than being only occasionally exercised, the baroreflex is constantly active, probably making adjustments equivalent to 10-20 mmHg, at least, every few minutes. The purpose, if any, of such ceaseless interplay between endogenous noise and the reflex remains to be elucidated (15, p. 79-84).