| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
1 Department of Physiology and
Clinical Pharmacology, To study spontaneous cardiac baroreflex at
different timescales, a new method has been developed that identifies
such episodes. Mean arterial pressure (MAP) and heart rate (HR) were
recorded beat to beat over 1 h in freely moving control
(n = 10) and acutely (1 day before
study, n = 7) and chronically (2 wk
before study, n = 10)
sinoaortic-denervated (SAD) 12- to 14-wk-old male Sprague-Dawley rats.
These beat-to-beat time series were successively low-pass filtered
seven times and resampled at different time intervals from 0.1 to 6.4 s, allowing different timescales to be scanned. With the use of the
Z coefficient, the statistical
relationship was estimated for the associations of inverse MAP and HR
variations when these inverse MAP and HR variations occurred
simultaneously or were time shifted. In control rats and for timescales
statistical dependence; cardiovascular; blood pressure; heart rate
MANY STUDIES OF THE BAROREFLEX have been
reported using pharmacological or mechanical methods to stimulate the
baroreceptors. These studies enable pulse interval or heart rate (HR)
to be correlated to blood pressure during changes induced by
administration of vasoactive drugs (7, 12, 19) or by stimulation of the
carotid baroreceptors by neck suction (14, 15). Although validated, these methods assess nonspontaneous phenomena that are slow compared with HR. Consequently, they must be insufficient to appraise the regulatory functions under normal conditions.
These limitations led to the development of other methods to assess
baroreflex in spontaneous conditions, such as cross-spectral analysis
(8, 18) or sequence method (2, 10, 16). However, these methods are
limited to short-term oscillatory phenomena that occur in the so-called
high- and low-frequency ranges (6) and for which oscillatory functions
are difficult to understand at the present time.
The aim of this work was to develop a tool allowing phenomena of much
longer duration to be analyzed. For that purpose, we used the
previously developed Z-coefficient
method (5, 9) with a major improvement, a method of low-pass filtering
to study the relationships between mean arterial pressure (MAP) and HR variations at different timescales instead of being restricted to
beat-to-beat analysis. Indeed, the previous
Z-coefficient method using
beat-to-beat analysis did not show dynamic functioning of the
baroreflex. In addition, the study of time series at different timescales may disclose different physiological phenomena related to
the baroreflex that may act with different time responses. To identify
the baroreflex origin of observed relationships, the current method was
applied to blood pressure recordings obtained under basal conditions in
freely moving control or sinoaortic-denervated (SAD) rats.
Animals and Experimental Protocol
ABSTRACT
Top
Abstract
Introduction
Methods
Results
Discussion
References
0.4 s, the highest Z
coefficient(0.38) was obtained when MAP variations preceded inverse HR
variations by one sampling interval. The baroreflex origin of this link
was demonstrated by its disappearance after acute SAD. In conclusion,
this method enabled spontaneous baroreflex episodes to be identified
for unusually long timescales without limiting the study to fast,
linear, stationary, or oscillating phenomena.
INTRODUCTION
Top
Abstract
Introduction
Methods
Results
Discussion
References
METHODS
Top
Abstract
Introduction
Methods
Results
Discussion
References
All rats were maintained on a 12:12-h light-dark cycle and were housed in controlled conditions (21 ± 1°C). They received a standard rat chow and tap water ad libitum. After a 5-h period of habituation to the recording environment, blood pressure was continuously recorded for 1 h.
Pharmacological Estimation of Baroreflex Sensitivity
The extent of denervation was evaluated in each rat at the time of the study by measuring the changes in MAP and pulse interval in response to two intravenous bolus injections of phenylephrine (3 µg/kg) in a volume of 250 µl/kg. In these conditions, cardiac baroreflex sensitivity was given by the average ratio of the peak changes in pulse interval to MAP.Blood Pressure Recording
Polyethylene catheters made of a piece of heat-stretched PE-10 fused to a PE-50 extension were inserted under halothane anesthesia via the femoral artery and vein into the lower abdominal aorta and inferior vena cava for blood pressure recording and intravenous injection, respectively. The femoral catheter was sutured to the vessels, filled with heparinized saline (50 IU/ml), and led subcutaneously to emerge between the scapulae. Blood pressure was continuously measured in freely moving rats by connecting the femoral catheter to a precalibrated pressure transducer (Statham P23ID, Gould, Cleveland, OH) coupled to an amplifier recorder (model 8802, Gould). Analog outputs of pulsatile blood pressure were then digitalized at 500 Hz and processed by our previously described technique (11) on a computer (Motorola MVME SYS 147, Motorola, Tempe, AZ). Cardiac cycles were first recognized, and then MAP and HR were computed for each beat.Off-Line Data Analysis Methods
The off-line statistical algorithms ran on a SparcClassic SUN workstation (SUN Microsystems, Mountain View, CA) under Solaris 2.4.Timescale and low-pass filtering.
To analyze time evolution of MAP and HR at different timescales, we
developed a "pyramidal" technique of successive low-pass filters.
Before filtering, MAP and HR beat-to-beat time series were equally
resampled (sampling interval:
T1 = 0.1 s).
These two equally sampled time series were then filtered a first time by a classical digital low-pass (null phased) filter at the cutoff frequency (CF1) = 2.5 Hz (17).
T2 = 0.2 s = 2T1.
Consequently, a new time series of MAP and HR was obtained at a new
timescale in which the number of points was reduced by one-half and the
fluctuations of frequency >CF2 were eliminated. The same process was successively applied seven times
to obtain seven MAP and HR time series at seven different timescales
giving a large temporal point of view as described in Table
1.
|
T1 = 0.1 s
and CF1 = 2.5 Hz) to the third
(T3 = 0.4 s
and CF3 = 0.63 Hz) timescale. The fluctuations caused by respiration (frequency > 0.63 Hz) are visible on the timescale for
T1 (0.1 s) for
HR. They completely disappear on the timescale for
T3 (0.4 s),
revealing low-frequency fluctuations (0.25-0.75 Hz in rats; Ref.
6) that are partially masked in the first timescale.
|
Definition of statistical events and event associations.
With the Z-coefficient method, events
that would allow characterization of baroreflex episodes had to be
defined. For that purpose, for each MAP and HR time series, variations
between two successive points were computed and defined as positive or
negative variations according to variation thresholds that were fixed
at 1 mmHg for MAP and 3 beats/min for HR at any sampling interval (2).
Three possible events could be distinguished for each parameter:
increase (
), decrease (
), or stability.
Because cardiac baroreflex was the object of the study, the
associations of MAP and HR inverse variations were examined for all
sampling intervals.
Estimation of Z coefficient for association of events. As previously described (9), the Z coefficient computed for two events gives the strength of their statistical relationship. Briefly, Z is defined from probabilities of each event and their association. It varies from zero in the case of total independence to unity for total dependence. It is negative in the case of exclusion. In practice, the probability of events was estimated by their observed frequency.
To test the significance of Z values estimated for each group of animals, the Z sampling fluctuation was simulated by the Monte Carlo approach under the null hypothesis Z = 0 (20). This simulation was performed for each sampling interval with its statistical constraints (number of data, observed frequency of events). The confidence interval of Z mean under the null hypothesis was built at the risk P = 0.05. Data are expressed as means ± SE. Nonparametric variance analysis (Kruskal-Wallis) was performed to study the influence of the time shift between MAP and HR variations and to compare control and SAD rats. A Wilcoxon rank test was performed to compare two data groups. P < 0.05 was considered as statistically significant.| |
RESULTS |
|---|
|
Top
Abstract
Introduction
Methods
Results
Discussion
References
|
|---|
Influence of Sampling Interval on Z Values in Control Rats
Figure 2 gives the Z value in control rats as a function of sampling interval in the case of MAP and HR being
either simultaneous or time shifted by one sampling interval. In all
cases, Z values were outside the
confidence interval of Z mean under
the null hypothesis, showing their significance. The highest
Z values (>0.25) were obtained when
MAP preceded HR and for sampling intervals between 0.4 and 6.4 s. Z values were also positive
when MAP and HR appeared simultaneously, but they were
much lower. On the other hand, when HR preceded MAP,
Z values were negative for sampling
intervals >0.4 s, suggesting an exclusion.
|
Similar results were obtained in the reverse case, i.e., with
MAP and HR. Z values
remained >0.2 when MAP preceded HR for sampling
intervals >0.4 s.
Figure 3 shows how isolation of different baroreceptor episodes with different durations can be seen. Different areas of baroreflex activity are selected at these two successive timescales. Only three episodes with a duration of 0.4 s are included with the seven episodes with a duration of 0.8 s.
|
Influence of Sampling Interval on Z Values in SAD Animals
Table 2 indicates the characteristics of the animals after acute and chronic SAD. SAD induced hypertension and tachycardia that were more noticeable 1 day after surgery than they were 2 wk after surgery. SAD significantly enhanced MAP variability. The cardiac baroreflex sensitivity measured using a pharmacological method was unmeasurable after acute SAD and was significantly decreased after chronic SAD.
|
According to the data obtained in control rats, the analysis of
Z was restricted to the condition in
which it reached its highest values, i.e., MAP precedes
HR by one sampling interval.
Figure 4 shows that
Z values at sampling intervals of 0.4 and 0.8 s were close to 0 after acute SAD. At the other sampling intervals Z values were negative,
suggesting an exclusion. In chronic SAD rats weak, significant positive
Z values reappeared when the sampling
interval was >1.6 s, but they remained lower than in control rats.
Similar results were obtained in the case of MAP and
HR.
|
| |
DISCUSSION |
|---|
|
Top
Abstract
Introduction
Methods
Results
Discussion
References
|
|---|
The present work shows that the method we developed allowed activity caused by cardiac baroreflex to be identified at different timescales in spontaneous conditions by direct computation of the statistical relationship between associations of inverse MAP and HR variations. Although the physiological mechanisms underlying increases in MAP followed by decreases in HR and decreases in MAP followed by increases in HR are different, the statistical link obtained for these two cases was in close resemblance, allowing us to simply consider inverse MAP and HR variations together. Interestingly, spontaneous cardiac baroreflex seems to be active at unusual timescales up to 6.4 s.
Methodological Aspects
Three kinds of methods were used in this study: 1) a filtering method that took into account a large range of timescales, 2) a reduction of data to simple and relevant events, and 3) a direct analysis of the statistical relationship between two events.Choice of filtering. It was interesting to apply the filtering method for study of spontaneous cardiac baroreflex to separate the different temporal components of this regulation. In all time series, slow fluctuations are always masked by faster ones, and filtering is the most powerful method of isolating different temporal aspects of dynamic responses of a system. A low-pass band filter was chosen because cardiac baroreflex might be sensitive to the level of blood pressure, and it was important to select a filter that allowed the absolute values of parameters to be preserved. Therefore, a low-pass, null-phased filter that conserved the general shape of the curves and the continuous component was used (17).
Choice of events. To characterize baroreflex episodes, simple events made up of inverse variations of parameters were chosen. This simplification was necessary to validate our method, which combined filtering and statistical events analysis. In the previously developed Z-coefficient method (5), the chosen events were absolute beat-to-beat MAP and HR values. Such events allowed the relationship between MAP and HR levels to be studied without taking into account their chronological evolution.
Statistical analysis. The use of a coefficient that evaluates the statistical link for discrete events is not usual in the study of relationships between the values of a time series. Intercorrelation methods are generally used, but stationarity must be assumed. The Z coefficient enables us to evaluate the strength of the determinism that links two events without any hypothesis of linearity or stationarity. The efficiency of this statistical approach has already been demonstrated (5, 9).
It can be noted that Z mean fluctuation obtained with the Monte Carlo method remained weak for any sampling interval. It increased with sampling interval. This is mainly explained by the fact that the sampling fluctuation increases when the number of data decreases. With pyramidal filtering the number of data was reduced by one-half from one sampling interval to the next. To avoid this problem, it would have been possible to increase sampling time, but this solution was not used because it would have increased the fluctuations of the physiological states of the animals. When MAP preceded HR by a time shift outside the range
of baroreflex regulation (1-10 min),
Z values were not significantly different from 0 (data not shown). This allows us to
conclude that no artifact and no artificial link were introduced by the numerical processing.
The methods based on cross-spectral analysis detected low-frequency
oscillations related to the baroreflex (4, 8). These methods are well
adapted to the study of the oscillatory effect of the baroreflex, but,
on the other hand, they may miss isolated nonoscillating episodes also
due to the baroreflex that can be slow.
Another method usually chosen for the study of the spontaneous
baroreflex consists of the selection of sequences made up of several
consecutive beats with progressively increasing or decreasing systolic
blood pressure and pulse interval (2, 16). An indirect analysis must be
performed to verify that these sequences were not due to chance,
because no direct statistical link between the systolic blood pressure
and pulse interval changes was computed. The observed frequencies of
the sequences obtained in control conditions were compared with those
obtained in other animals without baroreflex (2). Surrogate data
analysis enabled us to verify that the sequence method did not only
extract chance events (3). This method was applied to beat-to-beat data
without any filtering and selected sequences over 3-5 beats, i.e.,
lasting <1 s on average in rats. Therefore, it was limited to fast
and linear sequences selected principally from respiratory
fluctuations, contrary to our method, which allows statistical links to
be disclosed between slow variations at different sampling intervals up
to 6.4 s.
Physiological Aspects
Because the statistical link between inverse MAP and HR variations disappeared after acute SAD, it can reasonably be postulated that it was related to baroreflex. In the same way, the disappearance of this link or even the occurrence of negative Z values when HR variation preceded MAP variation by one sampling interval suggested that MAP variation caused HR variation, which is in accordance with the baroreflex function.After chronic SAD and for long sampling intervals (3.2 and 6.4 s), slight but significant Z values were observed. This is in good agreement with the unexplained but usually observed reappearance of a slight cardiac baroreflex activity as measured after phenylephrine injection. Therefore, it can be postulated that a long time after SAD, compensatory mechanisms enabled the recovery of part of the reflex regulation, suggesting a possible role of the cardiopulmonary receptor reflex (1).
The maximum Z values for the baroreflex episodes were detected at sampling intervals of 0.4 and 0.8 s. Therefore, they may be partly related to low-frequency fluctuations that are centered at 0.4 Hz in rats (4). Moreover, baroreflex episodes could still be identified at sampling intervals >1.6 s in conditions that exclude low-frequency fluctuations because of low-pass filtering. The existence of these slow "nonoscillating" spontaneous cardiac baroreflex episodes is an unexpected finding that is compatible with the time response of the inhibition of sympathetic activity observed during phenylephrine infusion (7).
These slow episodes appeared on chronograms in a nonoscillating way, and 70% of these episodes that were identified at a given timescale were not selected at the same time at the previous timescale. This indicates that the different timescales disclose in great part baroreflex episodes that have different durations.
Finally, this method combined a technique of successive low-pass filtering and resampling, which allowed different timescales from 0.1 to 6.4 s to be scanned, with the Z-coefficient method, which allowed the strength of the statistical link between two chosen events to be estimated. Focusing on cardiac baroreflex, we elected to calculate the statistical relationship between two events defined as inverse MAP and HR variations in spontaneous conditions. We demonstrated that the variations that were linked because of their high positive Z value were related to baroreflex. These baroreflex episodes appeared at different timescales from 0.4 to 6.4 s. Local slow spontaneous cardiac baroreflex episodes (from 1.6 to 6.4 s) seemed to exist in addition to those already known in the range of low-frequency oscillations (0.25-0.75 Hz in rats). This heuristic method enabled us to extract baroreflex episodes without limiting the study to fast, linear, stationary, or oscillating phenomena and therefore appeared more direct and general than classical methods.
Perspectives
This work appeared to confirm the necessity of improving tools that allow the identification of spontaneous baroreflex. First, to improve spontaneous cardiac baroreflex episode description, these episodes could be more precisely separated according to their different durations with filtering techniques. Different ways of computation could then be tested to define a set of reliable and relevant parameters of description such as gain, difference in phase, coherence, and the strength of their links. Second, these parameters could be evaluated in different physiological situations to provide new observations more directly applicable to research in physiology.| |
ACKNOWLEDGEMENTS |
|---|
We are grateful to Prof. Jean Sassard for help in the review of the manuscript.
| |
FOOTNOTES |
|---|
This research was supported by grants from Centre National de la Recherche Scientifique and Institut National de la Santé et de la Recherche Médicale (Contrat de Recherche Externe 93-0402).
Address for reprint requests: M.-P. Gustin, Dépt. de Physiologie et de Pharmacologie Clinique, CNRS ESA 5014, Faculté de Pharmacie, 8 Ave. Rockefeller, 69373 Lyon Cedex 08, France.
Received 26 June 1997; accepted in final form 6 October 1997.
| |
REFERENCES |
|---|
|
Top
Abstract
Introduction
Methods
Results
Discussion
References
|
|---|
1.
Barrès, C.,
S. J. Lewis,
H. J. Jacob,
and
M. J. Brody.
Arterial pressure lability and renal sympathetic nerve activity are dissociated in SAD rats.
Am. J. Physiol.
263 (Regulatory Integrative Comp. Physiol. 32):
R639-R646,
1992
2.
Bertinieri, G.,
M. Di Rienzo,
A. Cavallazi,
A. U. Ferrari,
A. Pedotti,
and
G. Mancia.
A new approach to analysis of the arterial baroreflex.
J. Hypertens.
3:
S79-S81,
1985.
3.
Blaber, A. P.,
Y. Yamamoto,
and
R. L. Hughson.
Methodology of spontaneous baroreflex relationship assessed by surrogate data analysis.
Am. J. Physiol.
268 (Heart Circ. Physiol. 37):
H1682-H1687,
1995
4.
Cerutti, C.,
C. Barrès,
and
C. Paultre.
Baroreflex modulation of blood pressure and heart rate variabilities in rats: assessment by spectral analysis.
Am. J. Physiol.
266 (Heart Circ. Physiol. 35):
H1993-H2000,
1994
5.
Cerutti, C.,
M. Ducher,
P. Lantelme,
M. P. Gustin,
and
C. Paultre.
Assessment of spontaneous baroreflex sensitivity in rats: a new method using the concept of statistical dependence.
Am. J. Physiol.
268 (Regulatory Integrative Comp. Physiol. 37):
R382-R388,
1995
6.
Cerutti, C.,
M. P. Gustin,
C. Paultre,
M. Lo,
C. Julien,
M. Vincent,
and
J. Sassard.
Autonomic nervous system and cardiovascular variability in rats: a spectral analysis approach.
Am. J. Physiol.
261 (Heart Circ. Physiol. 30):
H1292-H1299,
1991
7.
Coleman, T. G.
Arterial baroreflex control of heart rate in the conscious rat.
Am. J. Physiol.
238 (Heart Circ. Physiol. 7):
H515-H520,
1980
8.
Di Rienzo, M.,
G. Parati,
P. Castiglioni,
S. Omboni,
A. U. Ferrari,
A. J. Ramirez,
A. Pedotti,
and
G. Mancia.
Role of sinoaortic afferents in modulating BP and pulse-interval spectral characteristics in unanesthetized cats.
Am. J. Physiol.
261 (Heart Circ. Physiol. 30):
H1811-H1818,
1991
9.
Ducher, M.,
C. Cerutti,
M. P. Gustin,
and
C. Paultre.
Statistical relationships between systolic blood pressure and heart rate and their functional significance in conscious rats.
Med. Biol. Eng. Comput.
32:
649-655,
1994[Medline].
10.
Frankel, R. A.,
P. J. Metting,
and
S. L. Britton.
Evaluation of spontaneous baroreflex sensitivity in conscious dogs.
J. Physiol. (Lond.)
462:
31-45,
1993
11.
Gustin, M. P.,
C. Cerutti,
and
C. Paultre.
Heterogeneous computer network for real-time hemodynamic signal processing.
Comput. Biol. Med.
20:
205-215,
1990[Medline].
12.
Head, G.,
and
R. McCarty.
Vagal and sympathetic components of the baroreceptor-heart rate reflex in conscious rats.
J. Auton. Nerv. Syst.
21:
203-213,
1987[Medline].
13.
Krieger, E. M.
Neurogenic hypertension in the rat.
Circ. Res.
15:
511-521,
1964
14.
Ludbrook, J.,
G. Mancia,
A. Ferrari,
and
A. Zanchetti.
The variable pressure neck-chamber method for studying the carotid baroreflex in man.
Clin. Sci.
53:
165-171,
1977.
15.
Mancia, G.,
G. Parati,
G. Pomidossi,
R. Casadei,
M. Di Rienzo,
and
A. Zanchetti.
Arterial baroreflexes and blood pressure and heart rate variabilities in humans.
Hypertension
8:
147-153,
1986
16.
Parati, G.,
M. Di Rienzo,
G. Bertinieri,
G. Pomidossi,
R. Casadei,
A. Groppelli,
A. Pedotti,
A. Zanchetti,
and
G. Mancia.
Evaluation of the baroreceptor-heart rate reflex by 24-hour intra-arterial blood pressure monitoring in humans.
Hypertension
12:
214-222,
1988
17.
Parks, T. W.,
and
C. S. Burrus.
Digital Filter Design. New York: Wiley, 1987.
18.
Robbe, H. W. J.,
L. J. M. Mulder,
H. Rüddel,
W. A. Langewitz,
J. B. P. Veldman,
and
G. Mulder.
Assessment of baroreceptor reflex sensitivity by means of spectral analysis.
Hypertension
10:
538-543,
1987
19.
Smyth, H. S.,
P. Sleight,
and
G. W. Pickering.
Reflex regulation of arterial pressure during sleep in man. A quantitative method of assessing baroreflex sensitivity.
Circ. Res.
24:
109-121,
1969
20.
Wonnacott, T. H.,
and
R. J. Wonnacott.
Statistics for Business and Economics. New York: Wiley, 1984.
This article has been cited by other articles:
|
|
 |
|
  A. Silvani, D. Grimaldi, S. Vandi, G. Barletta, R. Vetrugno, F. Provini, G. Pierangeli, C. Berteotti, P. Montagna, G. Zoccoli, et al. Sleep-dependent changes in the coupling between heart period and blood pressure in human subjects Am J Physiol Regulatory Integrative Comp Physiol, May 1, 2008; 294(5): R1686 - R1692. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| Visit Other APS Journals Online |
) or shifted by 1 sampling interval when MAP
) and when HR
). Dashed lines show confidence interval of
Z mean under null hypothesis with risk
P = 0.05.
) and chronic (
) sinoaortic-denervated rats as a function
of different sampling intervals for associations of MAP