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The Pattern of Sympathovagal Balance Explored in the Frequency Domain

Alberto Malliani

A. Malliani is Professor of Internal Medicine at the University of Milan, Hospital "L. Sacco," via G. B. Grassi, 74, 20157 Milan, Italy.

	Abstract

 
In most physiological conditions, sympathetic and vagal activities modulating heart period undergo a reciprocal regulation, leading to the concept of sympathovagal balance. This pattern can be indirectly quantified by computing the spectral powers of the oscillatory components corresponding to respiratory acts (high frequency) and to vasomotor waves (low frequency) present in heart rate variability.

	Introduction

Top Introduction The sympathovagal balance Heart rate variability The frequency-domain analysis Physiological interpretation Recent acquisitions Concluding remarks References

 
General concepts are often quite useful in interpreting the complexity of neural mechanisms. For instance, Sherrington (14) conceived the "simple reflex" (although considered purely abstract) and the "dominance of the brain" as two pillars of the neural integrative action. The autonomic nervous system is no exception and can be viewed from a similar perspective (6). Relatively simple cardiovascular reflexes can be identified in experimental conditions; however, they are even more unlikely to act as such when, in closed-loop conditions, a natural hemodynamic event is sensed by different reflexogenic areas. For example, the baroreflex, which is usually conceived as arising exclusively from delimited arterial sites, is normally modulated by other, more widespread reflexes, such as those, largely excitatory in nature, mediated by cardiovascular sympathetic afferents (4, 6). As to the dominance of the brain, it is largely recognizable in the structure of patterns, each subserved by multiple reflexes and shaped to fulfill a precise behavioral purpose.

For instance, during quiet resting conditions, an increase in arterial pressure induced by a vasopressor drug usually elicits a baroreflex-mediated bradycardia, whereas during physical exercise or an emotionally charged state, a comparable increase in arterial pressure is accompanied by tachycardia. Thus the central integration modifies the gain of the individual reflexes during various behaviors, according to a pattern organization.

A black box seems the most appropriate paradigm for all these possibilities, its main constituents being 1) the peripheral inhibitory and 2) excitatory reflex mechanisms modulated by 3) the central integration (4). The peripheral target functions, such as heart rate, reflect the whole of this interaction but provide no information on the individual components.

During the last two decades exploration in the frequency domain of cardiovascular neural regulation (1, 10) has provided a novel insight into the interplay of sympathetic and vagal cardiovascular modulations, without the need for artificially isolating the influence of either outflow (8).

	The sympathovagal balance

Top Introduction The sympathovagal balance Heart rate variability The frequency-domain analysis Physiological interpretation Recent acquisitions Concluding remarks References

 
In most physiological conditions, the activation of either sympathetic or vagal outflow is accompanied by the inhibition of the other (therefore the concept of "balance," as a horizontal beam pivoted at its center). This is true for reflexes arising not only predominantly from the arterial baroreceptive areas but also from the heart. For instance, the stimulation of cardiac sympathetic afferents induces reflex sympathetic excitation and vagal inhibition, whereas the opposite effect is elicited by stimulating cardiac vagal afferents; this reciprocal reflex organization, alluding to a synergistic design, was demonstrated by recording the activity of single sympathetic or vagal efferent fibers isolated from the same mixed nerve impinging upon the heart (13).

The concept of sympathovagal balance has been challenged recently (2) because the reciprocity would not always be present, as in the case of the "diving reflex." This peculiar neural response found in expert divers, like seals or ducks, is triggered by the mechanical stimulation of nostril receptors, consists of vagal bradycardia and sympathetic peripheral vasoconstriction (resulting in a sort of heart-brain circuit), is designed for the search of food, and is unlikely to be relevant to the human condition. In this regard, acute myocardial ischemia is a more interesting pathophysiological model, in which a simultaneous reflex excitation of both vagal and sympathetic outflows can occur (8). However, pathophysiological mechanisms are often characterized by their loss of a finalistic purpose; more generally, biological rules, and not only these, might have their exceptions. Yet the most persuasive argument in favor of the concept of sympathovagal balance is the fact that sympathetic excitation and simultaneous vagal inhibition, or vice versa, are both presumed to contribute to the increase or decrease of cardiac performance to implement various behaviors. Accordingly, this concept of reciprocity was part of traditional physiological thinking, not implying that it was a paradigm and that its nature was linear or simple throughout its range. It is our hypothesis that the complex regulation of sympathovagal balance modulating heart period (Fig. 1) can be explored in the frequency domain (8, 10).

View larger version (18K): [in this window] [in a new window]   FIGURE 1. Schematic representation of opposing feedback mechanisms that, in addition to central integration, subserve neural control of the cardiovascular system. Baroreceptive and vagal afferent fibers from the cardiopulmonary region mediate negative feedback mechanisms (exciting the vagal outflow and inhibiting the sympathetic outflow), whereas positive feedback mechanisms are mediated by sympathetic afferent fibers (exciting the sympathetic outflow and inhibiting the vagal outflow). From Ref. 8 with permission.

	Heart rate variability

Top Introduction The sympathovagal balance Heart rate variability The frequency-domain analysis Physiological interpretation Recent acquisitions Concluding remarks References

View larger version (19K): [in this window] [in a new window]   FIGURE 2. Top: R-R interval series (tachogram) of a healthy subject in supine and upright (passive 90° tilt) positions. Bottom: power spectrum autoregressive analysis (note 2 different scales). The power of each component (spectral decomposition) is indicated by dotted lines. PSD, power spectral density; VLF, very low frequency; LF, low frequency; HF, high frequency. For other details, see text. From Ref. 7 with permission.

	The frequency-domain analysis

Top Introduction The sympathovagal balance Heart rate variability The frequency-domain analysis Physiological interpretation Recent acquisitions Concluding remarks References

Various algorithms can be used to extract from the tachogram the characteristics of the rhythmic components embedded in its variability. Most studies have relied on either the fast Fourier transform (1) or an autoregressive approach, as in our case (8, 10). Autoregressive algorithms can automatically furnish the number, center frequency, and associated power of oscillatory components.

In the power spectra represented in Fig. 2, three components are highly evident: a high frequency (HF) at 0.33 Hz (corresponding to respiratory activity), a low frequency (LF) at 0.09 Hz (usually corresponding to vasomotor waves), and a very low frequency (VLF) around 0 Hz. Under normal conditions, the VLF component cannot be properly assessed with short time series but only with longer periods of uninterrupted data.

The power of each individual spectral component is expressed graphically by its area in absolute units (ms²). However, because the absolute values of spectral components are highly correlated to variance (corresponding to total power), some further indexes focusing mainly on the fractional distribution of power and independent of the absolute values of variance are also necessary. This is accomplished by calculating the LF-to-HF ratio or LF and HF in normalized units (nu). These are obtained by dividing LF or HF components by the total power from which the VLF has been subtracted (to minimize the influence of noise and slow trends affecting mainly the VLF) and multiplying by 100. Although the sum of LFnu and HFnu should approximate 100 nu, it usually falls short of this value (Fig. 2) because of the presence of smaller components. In Fig. 2, 10 variables of interest are reported: mean R-R, variance, the absolute value of VLF, the center frequencies of LF and HF, their absolute and normalized values, and LF/HF.

In the supine position (Fig. 2) healthy subjects always present LF and HF components, the latter often being greater in adolescence and smaller in adulthood. In the active upright position (or during passive tilt), in addition to an increase in heart rate and to small adjustments in blood pressure, a marked change occurs, as a rule, in the spectral profile; the LF component is increased, whereas the HF component is reduced. Variance usually decreases in the upright position, causing a reduction in the absolute value of both spectral components. Hence, in the upright position, LF tends to be decreased, in its absolute values, by the reduction of variance but also tends to be increased, in nu, by the greater concentration of power in this part of the spectrum (Fig. 2).

Numerous data, collected in various experimental conditions involving human and animal studies, have been summarized previously (8) to support the assumptions that 1) the respiratory rhythm of heart period variability (HF) is a marker of vagal modulation (an issue widely accepted); 2) the rhythm corresponding to vasomotor waves and present in heart period and arterial pressure variability (LF) is a marker of sympathetic modulation of, respectively, heart period and vasomotion; and 3) the reciprocal relation existing in the R-R variability spectrum between LFnu and HFnu is a marker of the state of the sympathovagal balance modulating sinus node pacemaker activity (also deducible from LF/HF, which, like any ratio, can emphasize the opposite changes).

This hypothesis does not imply that LF and HF components should be confined to sympathetic and vagal activities, respectively: actually, the opposite is true, because they are simultaneously present in the discharge of both autonomic outflows (8). However, a rhythm, being a flexible and dynamic property of neural networks, should not necessarily be restricted to one specific neural pathway to carry a functional significance (8) (as in the case of different electroencephalogram patterns).

	Physiological interpretation

Top Introduction The sympathovagal balance Heart rate variability The frequency-domain analysis Physiological interpretation Recent acquisitions Concluding remarks References

 
The puzzle exists: if both LF and HF components are present simultaneously in sympathetic and vagal discharges, it is likely that these same spectral components characterizing R-R variability are mixed in nature, i.e., arise from a sympathovagal interaction, and, in this regard, it might be too simplistic to hypothesize that HF is purely vagally mediated and that the HF sympathetic rhythm cannot influence pacemaker activity (for instance, interact with vagal transmission) in view of a low-filtering property of sympathetic transmission. However, in the case of a mixed origin, how is it possible that LFnu and HFnu can assess the changes in sympathovagal balance?

With an observational strategy, it was indeed quite clear that LFnu increased and HFnu decreased whenever a sympathetic excitation occurred and vice versa during vagal excitation. This pattern was present independently of the mechanism leading to a given state of sympathovagal balance. For instance, in animal and human experiments (8, 10), LFnu of R-R variability increases not only during a sympathetic excitation induced by baroreceptive deactivation (such as that occurring in the upright posture or in response to the action of vasodilators) but also during a sympathetic excitation accompanied by a rise in arterial pressure and hence by an increased baroreceptive stimulation (such as that occurring during mental stress or mild physical exercise). In this latter case, however, it should be pointed out that with current methodology, an adequate spectral analysis can be performed only in the absence of too unstable conditions and too numerous transients. Finally, an increase in LFnu can also occur during a reflex sympathetic excitation from the heart [such as that elicited by experimental coronary occlusion (4)], independently of arterial pressure changes.

Hence, although the possible contribution of baroreceptive mechanisms to LF components has been convincingly demonstrated (12), this mechanism cannot be envisaged as exclusive. Moreover, conscious dogs (8), when quiet and acquainted with the laboratory, most often present only an HF component in R-R variability, although an LF component is present in arterial pressure variability and baroreflexes are known to be extremely active in this species. On the other hand, some tetraplegic patients have an LF component in R-R variability in the absence of an LF component in arterial pressure variability (5).

In terms of neurophysiological thinking, an increase in the rhythmicity of a neural substratum should be induced either by increasing a rhythmic input to it or by reducing some tonic inhibitory activity restraining its autochthonous rhythmicity. In the case of baroreceptive deactivation, which is the mechanism leading to an increased LF rhythmicity? Bioengineering modeling would simply explain the phenomenon by increasing the gain of the closed-loop baroreceptor circuit.

Our view is rather based on the redundancy of neural mechanisms and on the widespread distribution of neural rhythms and considers too simplistic a hypothesis based on a single reflex mechanism (2). LF and HF rhythms can be found in the discharge variability of medullary neurons recorded in animals deprived of sinoaortic afferents (5) as well as in the cardiac sympathetic discharge of spinal animals. Thus numerous findings point to their central representation. Obviously, in closed-loop conditions, central and peripheral circuits have the potentiality to reinforce this rhythmicity throughout appropriate reflex actions and central integration.

The core of what we propose (5, 8) is that two main rhythms, one a marker of excitation and intrinsic in sympathetic activation (LF) and the other a marker of inhibition and quiet and linked to vagal predominance (HF), would be organized, in physiological conditions, in a reciprocal manner. This could also be viewed as a widespread code signaling the balance between excitation and inhibition. The normalization procedure, in this sense, is not the result of serendipity but rather a tool used to explore this hypothesis.

The study of complexity is more advisable on the basis of what occurs rather than how it may occur. With an observational approach, it was found that a graded tilt angle was positively correlated with LFnu and LF/HF and negatively with HF (9). Hence, spectral analysis of HRV was found to be capable of providing a noninvasive, quantitative evaluation of the presumed graded changes in the state of the sympathovagal balance. As to the possibility of shifting the sympathovagal balance toward vagal predominance, controlled respiration at frequencies within the resting physiological range (8, 10) provides a convenient tool to enhance the vagal modulation of heart period, reflected by an increase of HFnu and a decrease of LFnu. This phenomenon obviously does not occur at all possible metronome frequencies, especially when the maneuver tends to be stressful, thus increasing LFnu. It might be worthwhile to recall that in the oriental tradition the control of respiration, mastered to its furthest possibilities, is associated with the intention of reducing what we would refer to as sympathetic tone. Rotation (5) and electrical or mechanical stimulation of the esophagus (15), richly innervated by vagal afferents, represent other maneuvers capable of inducing a prevalence of vagal modulation and hence of HFnu component.

Finally, it should be mentioned that 24-h analysis of HRV has detected the expected circadian cycle consisting of a nocturnal decrease of LFnu and an increase of HFnu (8). Moreover, with spectral methodology it is possible to continuously assess the baroreceptive mechanisms and to identify also in their case a circadian oscillation, with an increased gain during the night (8). As to the applications of spectral methodology to numerous pathophysiological conditions, like ischemic heart disease, arterial hypertension, congestive heart failure, diabetic neuropathy, and others, the advantages and the limitations have been summarized elsewhere (8).

	Recent acquisitions

Top Introduction The sympathovagal balance Heart rate variability The frequency-domain analysis Physiological interpretation Recent acquisitions Concluding remarks References

 
To update this article, three quite recent papers are discussed further, in support of this whole conception. Figure 3 is taken from the paper by Jasson et al. (3) utilizing an algorithm (smoothed pseudo-Wigner Ville transform) capable of detecting transients and of displaying a frequency-domain analysis over time. In spite of its technical complexity, the biological interpretation of this figure is quite simple. In the top two tracings, the HF and LF oscillations can be appreciated in both their frequency and amplitude characteristics. The third tracing is the usual tachogram (R-R), and the fourth tracing represents the instant center frequency (ICF) of the whole spectrum, i.e., the median frequency of the whole power distribution. The bottom tracing is a similar instant center frequency calculated for the LF component (ICF LF). These recordings were obtained from a normal subject who, after an initial period in a resting horizontal position, was passively tilted to an upright position at 90° (between the 7th and 8th minutes). During tilt the following simultaneous changes occurred and were maintained throughout its duration: the amplitude of HF decreased; that of LF increased; heart period also decreased indicating a tachycardia response; and ICF underwent a decrease, indicating that spectral power was redistributed toward the lower frequencies. In short, all the changes indicated a shift in sympathovagal balance toward sympathetic excitation and vagal inhibition.

View larger version (39K): [in this window] [in a new window]   FIGURE 3. Time-frequency analysis of heart rate variability of a healthy subject in supine position followed by passive tilt at 90°. Metronome breathing at 0.25 Hz. Amplitude of oscillations was obtained by finite impulse response filtering. Top to bottom: HF and LF amplitude (ms), R-R interval (ms), and instant center frequency (in Hz) of whole spectral power (ICF) and of LF (ICF LF). Tilt between 7 and 8 min. Modified from Ref. 3 with permission.

The second study is by Pagani et al. (11), who reported, as the main finding, a tight average correlation between the LFnu component of R-R variability and the LFnu component of muscle sympathetic nerve activity (MSNA) during graded changes in arterial pressure induced by vasoactive drugs. At an individual level, a significant correlation was present in seven of eight subjects, whereas a clear trend was observed in the remaining subject. The general phenomenon was that during sympathetic excitation, in normal humans, there was a predominance of the coherent LF oscillations present in heart period, arterial pressure, and MSNA.

However, the most indisputable proof of the pragmatic value of both the concept of sympathovagal balance and the corollary normalization procedure has been furnished by a study (7), the protocol of which included 350 healthy subjects from whom ECG and respiratory recordings were obtained in controlled laboratory conditions. Each subject was studied both in supine and upright positions. Individual data were ordered consecutively in their historical sequence, and, subsequently, odd and even rank positions were assigned to a Training or Test set, respectively. Hence, the Training and Test sets each held 350 patterns characterized by 10 power spectrum variables belonging to 175 subjects studied both in supine and upright positions (Fig. 2). The features related to both postures were considered as independent.

A forecasting linear method concentrated the information distributed in the various spectral variables into a normalized activation index (AI) (ranging from –1 for supine to +1 for upright posture). During the Training set the algorithm had to match the target, i.e., the posture, which was classified by the experimenter, with the information that could be extracted from the interaction of the variables of interest. A pattern was correctly discriminated when the supine position corresponded to an AI between 0 and –1 and the upright position to an AI between 0 and +1. During the Test set, as well, a negative value of the AI was intended to recognize the supine and a positive value the upright position. Such a blind forecasting on the Test set was capable of correctly assigning 83.4% (146 of 175) of features to the supine group and 86.3% (151 of 175) to the upright group, when 10 variables were evaluated simultaneously. Three variables (R-R, LFnu, and HFnu) were found to hold almost all the information content and could recognize an overall 84.0% of patterns, with a comparably good performance in both supine and upright groups (Fig. 4). When one of these three variables was not considered, the forecasting provided inconsistent results.

View larger version (24K): [in this window] [in a new window]   FIGURE 4. Activation Index (AI) of each individual feature belonging to either the Training or the Test set. Negative Al were normalized, for supine posture, between 0 and –1, and positive Al were normalized, for upright posture, between 0 and +1. Three variables (R-R and LF and HF in normalized units) were used for discrimination and recognition rates. In the Test set, 33 features were unrecognized in the supine group and 23 in the upright group, whereas all others were correctly assigned. From Ref. 7 with permission.

	Concluding remarks

Top Introduction The sympathovagal balance Heart rate variability The frequency-domain analysis Physiological interpretation Recent acquisitions Concluding remarks References

 
The pattern of sympathovagal balance is nothing but a concept reflecting, hopefully, a piece of reality. In its turn, a concept is nothing but a tool, and its value should be established on a sound heuristic base. In a stage of research in which the increasing reductionistic nature of experimental models is largely prevailing and in which integrative functional studies are progressively less and less numerous, it is remarkable that a quite sophisticated computerized approach, coupled with a traditional physiological concept, has furnished a sort of holistic view that has been defined as sympathovagal balance but that could as well be defined as "excitation-inhibition" balance. Hence, on the one hand, a new way of thinking seems necessary to address a concept that can be only partly quantified, just like intelligence, stress, or homeostasis. On the other hand, only future research will establish how much the study of a patterned rhythmic code will represent a useful approach to complexity.

	References

Top Introduction The sympathovagal balance Heart rate variability The frequency-domain analysis Physiological interpretation Recent acquisitions Concluding remarks References

 

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