(Stroke. 1998;29:2341-2346.)
© 1998 American Heart Association, Inc.
Original Contributions |
Grading of Cerebral Dynamic Autoregulation From Spontaneous Fluctuations in Arterial Blood Pressure
From the Division of Medical Physics, University of Leicester Faculty of Medicine (R.B.P., D.H.E.), and Department of Clinical Neurosciences, King's College School of Medicine and Dentistry and the Institute of Psychiatry, London (R.P.W., H.S.M.) (UK).
Correspondence to Dr R.B. Panerai, Department of Medical Physics, Leicester Royal Infirmary, Leicester LE1 5WW, UK. E-mail rp9{at}le.ac.uk
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Abstract |
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Background and Purpose—Assessment of cerebral autoregulation has been traditionally performed with static changes in arterial blood pressure. Newer dynamic methods require the induction of sudden drops in arterial blood pressure with the sudden release of bilateral thigh cuffs. An alternative method is proposed, based on the spontaneous variability of arterial blood pressure that does not require its manipulation. We compared this method with the established thigh cuff method in patients with carotid artery stenosis.
Methods—Cerebral blood flow velocity (determined by transcranial Doppler) and arterial blood pressure (determined by noninvasive servo-controlled plethysmograph) were recorded in 20 patients with carotid artery stenosis and 18 age-matched controls. At rest, grading of dynamic autoregulation was estimated from the impulse response of the blood pressure–velocity dynamic relationship. This was compared with the autoregulatory index (ARI) provided by the thigh cuff method and with the degree of stenosis. The critical closing pressure was derived from the fitted models and was also correlated with degree of stenosis.
Results—The 2 ARIs were significantly correlated (r=0.76) and reduced in subjects with carotid stenosis (baseline ARI, 3.65±3.11 versus 6.68±1.88, P<0.0001; thigh cuff ARI, 3.78±2.32 versus 6.35±1.06, P<10-5). The critical closing pressure (relative to mean arterial blood pressure) was also significantly reduced (-0.24±1.06 versus 0.50±0.31, P<0.0001) and correlated with the thigh cuff ARI (r=0.68). Both the baseline ARI and critical closing pressure were correlated with degree of stenosis (P<10-6).
Conclusions—Grading of dynamic autoregulation with the use of undisturbed recordings of arterial blood pressure and cerebral blood flow velocity might provide a safer technique for assessment of patients in whom a sudden drop of arterial blood pressure is not desirable, such as patients with heart or autonomic failure.
Key Words: autoregulation • blood pressure • carotid artery diseases • carotid endarterectomy • ultrasonography, Doppler, transcranial
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Introduction |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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Impaired cerebral autoregulation may be important in the pathogenesis of both stroke and global cerebral ischemia occurring during periods of hypoperfusion, such as intraoperatively. Risk factors for stroke, such as hypertension,1 are associated with altered cerebral autoregulation. An acute impairment of autoregulation is also found in a variety of disease states, including head injury, ischemic stroke, and vasospasm secondary to subarachnoid hemorrhage.2 A profound impairment of autoregulation is found in some patients with carotid artery stenosis or occlusion.3 This may be associated with increased stroke risk4 and with increased stroke risk during hypotensive therapy or perioperatively. Conventionally static autoregulation has been measured. Cerebral blood flow itself, or an estimate such as cerebral blood flow velocity (CBFV), is measured during a large change in blood pressure that is usually induced pharmacologically. Such techniques are not suitable for many patients at risk of stroke because the blood pressure change induced could result in cerebral ischemia. Therefore, most investigators have used an indirect measure of cerebral autoregulation such as the vasodilatory response to hypercapnia. Although this does correlate with cerebral autoregulation,3 it measures a slightly different physiological response, and the 2 may not always correlate. More recently, noninvasive methods for measuring dynamic autoregulation have been proposed. These study the response of cerebral blood flow or CBFV to small changes in arterial blood pressure (ABP) and therefore are suitable for use in patients at risk of stroke. Aaslid et al5 suggested the use of bilateral leg cuffs inflated suprasystolically and then suddenly deflated to induce a transient fall in blood pressure, and they correlated the temporal pattern of the change in blood pressure with the change in middle cerebral artery (MCA) CBFV determined using transcranial Doppler. Such a technique may be more clinically relevant than static autoregulation in patients at risk of stroke because the transient blood pressure changes are likely to be more similar to those occurring in patients on a day-to-day basis than the large blood pressure changes induced during static autoregulatory testing. Tiecks et al6 have demonstrated an excellent agreement between this method and the classic assessment of static autoregulation.
Transcranial Doppler measurement of MCA CBFV is only a
suitable technique if there is no change in MCA diameter during the
change in blood pressure. Newell et al7 compared
internal carotid artery absolute flow values during this step change in
blood pressure with transcranial Doppler MCA CBFV
changes and found an extremely close correlation, suggesting that
transcranial Doppler is an appropriate technique to use
in this setting. We have previously shown that this method of assessing
dynamic autoregulation may identify a subgroup of patients with carotid
stenosis who have significant impairments of cerebral
autoregulation.3 However, in some patients the
use of thigh cuffs to induce a fall in blood pressure is not ideal for
a number of reasons. First, in 
20% of cases, it is not possible to
induce a sufficient fall in blood pressure with this
method.3 Second, inflation of thigh cuffs may be
uncomfortable in some patients. Third, particularly in patients with
critical cerebrovascular hemodynamics, such as
premature newborns and individuals suffering from heart or autonomic
failure, there may be a risk associated with the induction of drops in
blood pressure. For these reasons alternative methods of providing an
assessment of dynamic autoregulation in humans, which do not require
induction of ABP disturbances, would be highly desirable.
One attractive possibility is to explore the spontaneous variability in ABP that is observed in most individuals at rest. The feasibility of this approach was demonstrated by Panerai et al8 in neonates using a coherent averaging method to improve the signal-to-noise ratio of the CBFV response to transient elevations in ABP. A more general approach is the use of linear systems analysis methods to describe the dynamic relationship between fluctuations in ABP and CBFV as an input-output model in either the frequency domain or the time domain. The frequency domain approach was pioneered by Giller9 and was also adopted by others.10 11 12 13 14 In the time domain, the most usual approach is to express the relationship between ABP and CBFV by the impulse response function (IRF).10 13 14 The IRF represents the CBFV temporal response to a very short, impulselike disturbance in ABP. The usefulness of the IRF is that, once it is known, it can be used to predict the CBFV response to ABP changes of any temporal pattern, including step changes, as induced by the thigh cuff method.5 6 10 13 14 Therefore, from baseline, spontaneous fluctuations in ABP and CBFV, it should be possible to estimate the step response and to grade it with the same mathematical model proposed by Tiecks et al.6 13 14
In a previous study, White and Markus3 applied the thigh cuff method to assess cerebral autoregulation in patients with carotid artery stenosis. Using baseline recordings from the same study, we have been able to compare the degree of agreement between the autoregulation index (ARI ) values obtained by the thigh cuff test with those estimated by the IRF method.
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Subjects and Methods |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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Twenty subjects with carotid stenosis >60% were studied. Grading of stenosis was based on ultrasound Doppler velocities in combination with B-mode imaging15 with a color-flow duplex ultrasound system (Acuson XP). Eighteen healthy age-matched nonsmoking volunteers with carotid stenosis excluded on duplex ultrasound were also studied as the control group.
Measurements were performed with subjects in a supine position and with their heads slightly elevated. CBFV was recorded bilaterally simultaneously through the transtemporal window with 2-MHz transducers (DWL, Langerach). The MCA was insonated at a mean±SD depth of 50.2±3.5 mm for the control population and 52.6±3.4 mm for the carotid stenosis group. Continuous ABP recording was made with a noninvasive servo-controlled plethysmograph (Finapres 2300, Ohmeda), with the subject's hand maintained at the same level as the head. Baseline measurement of resting ABP was made by automated arm cuff (Omega 1400 series, In Vivo Laboratories Inc). A sudden stepwise drop in ABP was induced by the rapid release of bilateral thigh cuffs that had been inflated suprasystolically for 3 minutes. Drops in ABP of <10 mm Hg were not accepted for analysis. Continuous recordings of bilateral CBFV and ABP were stored on the transcranial Doppler machine for a baseline period of 2 minutes preceding the cuff release and for another 1 minute after the sudden deflation. Five cycles of inflation/deflation were performed per subject with a 3-minute rest interval between cycles. Grading of autoregulation based on the CBFV response to the stepwise drop in ABP was provided by a software program supplied by the transcranial Doppler manufacturers, as described previously.3 6 For each integer value of ARIcuff, ranging from 0 to 9, the predicted CBFV response for the mathematical model proposed by Tiecks et al6 was compared with the actual CBFV tracing, and the ARIcuff value corresponding to the curve with the least square error over a 30-second interval was selected as the best estimate. The critical closing pressure (CrCPcuff) was manually selected to improve model fitting. We calculated mean values of ARIcuff and CrCPcuff for each subject using the individual estimates from each acceptable inflation/deflation cycle.
Data Analysis
Estimation of the ARI with the use of the IRF method was
obtained from the baseline recordings preceding the thigh cuff
deflation. The 120-second-long records were transferred to a
personal computer at a rate of 200 samples per second for subsequent
analysis. Each record was inspected visually for the
presence of artifact or ectopic beats. Recordings with >4
ectopic beats were rejected. Narrow spikes in the CBFV signals were
removed by linear interpolation. Time series of mean values of ABP and
bilateral CBFV were obtained by low-pass filtering these signals with a
cutoff frequency of 1 Hz (8th order zero-phase Butterworth) and
decimating the sampling rate to 5 samples per second.
The IRF was estimated with a fast Fourier transform (FFT) algorithm.16 Before the direct FFT transform was computed, each signal was normalized by its mean value, and a cosine (Hanning) window was applied to the data.16 Two segments of data with 256 samples each were used to estimate the cross-spectra and the transfer function between the mean ABP (MABP) and CBFV signals with a frequency resolution of 0.0195 Hz. The amplitude spectra was smoothed with a 3-element triangular window, and the IRF was computed from the inverse FFT with a cutoff frequency of 0.5 Hz. Nyquist theorem16 states that this cutoff frequency is appropriate for signals that have been low-pass filtered at 1 Hz. The final IRF for each MCA was obtained as the average of all IRF available for each side, and it is termed IRFbase. A numerical estimate of the baseline step response can be obtained by integration of the IRF. For technical reasons, however, grading of autoregulation based on baseline recordings was attempted by using the IRFbase rather than the step response. The reasons for this choice will be given in the Discussion.
With the use of the model of Tiecks et al,6 for
each step response a corresponding IRF (IRFmodel)
was obtained by calculating the numerical derivative. To compare
IRFbase with IRFmodel, it
is necessary to take into account the parameter CrCP
introduced by Tiecks et al6 in their original
formulation. As discussed later, this parameter might not
reflect the true critical closing pressure of the cerebral circulation.
For simplicity, assume that autoregulation is impaired. In this case,
percent changes in ABP will induce velocity changes with unit gain,
that is, 
Vbase=P, where V and P are
percent CBFV and ABP changes, respectively. The model of Tiecks et
al,6 however, assumes that flow (or velocity) can
become 0 for ABP values >0 (ie, when ABP=CrCP), as
represented in Figure 4
. In this case, percent changes in
ABP will lead to percent changes in velocity, as shown in the following
equation:
 | (1) |
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For an impaired autoregulation, the peak value of the IRF is given by
the ratio V/P, and the relative amplitude
(Arel) between IRFmodel and
IRFbase is then
 | (2) |
From Equation 2
it is possible to calculate the equivalent CrCP during
baseline measurements, from the relative amplitude of the 2 impulse
responses:
 | (3) |
In summary, Arel was computed from the ratio of peak values of IRFmodel and IRFbase, leading to an estimate of the equivalent CrCPbase and also of the relative CrCPrel=CrCPbase/MABP. CrCPbase estimates were compared with values of CrCPcuff manually selected to improve model fitting for the thigh cuff data.
The temporal pattern of IRFbase was graded by identifying the best fit with 10 model curves, generated with the same set of parameters proposed by Tiecks et al,6 providing a value of ARIbase. Fractional values of ARIbase were obtained by parabolic interpolation around the point with least square error.
Agreement between parameters derived from IRFbase and ARIcuff was assessed by the correlation coefficient and Bland-Altman plots.17 The Mann-Whitney U test was used to test for differences in mean values. ANOVA with Scheffé's test for post hoc analysis was used to test the relationship between model-derived parameters and the degree of stenosis. Linear regression analysis was performed to test for linear dependence between variables. A level of P<0.05 was considered significant.
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Results |
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Of the 38 subjects, 1 patient and 1 control subject were rejected because of the high incidence of ectopic beats. As a result, 72 IRFbase were available for analysis. The mean±SD relative error for fitting the Tiecks model to IRFbase was 7.2±3.1% of the IRFbase peak value. Figure 1
shows typical
IRFbase from 1 subject and the corresponding
IRFmodel best fits. The negative wave observed
after t=0 in Figure 1A was present in all control subjects.
Similarly, this negative wave was significantly reduced or absent in
most patients with carotid artery disease (CAD) (Figure 1B).
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Mean±SD values for the main variables studied are given in Table 1 for the control and patient
groups, together with the P values for the Mann-Whitney
test. Highly significant differences were found between the 2 groups of
individuals in relation to ARIcuff,
ARIbase, CrCPcuff,
CrCPrel, and CrCPbase. The
MABP was also significantly different for the 2 groups. For the control
group, ARIcuff had a smaller coefficient of
variation (16.7%) than ARIbase (28.1%), but the
difference was not significant.
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Highly significant correlation coefficient values were obtained between the ARIcuff and the model-derived parameters ARIbase (r=0.764), CrCPrel (r=0.68), CrCPbase (r=0.70), and Arel (r=0.69).
Figure 2 presents a scatter diagram
between ARIbase and
ARIcuff. A linear regression between these 2
parameters had a highly significant slope
(P<0.00001) and residuals that were normally distributed.
Figure 2 suggests the presence of 2 distinct groups of arteries. When
we performed a linear regression using only data from control subjects,
the correlation coefficient was reduced to r=0.406, but the
regression slope was still significant (P<0.017).
Bland-Altman analysis17 of the agreement
between ARIbase and ARIcuff
indicated a bias value of -0.09 and limits of agreement of -3.89 and
3.71. For the control group, CrCPrel
(r=0.484, P=0.004) and
CrCPbase (r=0.45, P=0.007)
were also significantly correlated with
ARIcuff.
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Patients were split into 4 subgroups according to the classification of
stenosis15 with the following number of
vessels in each subgroup: <60% stenosis (9 vessels), 60% to
79% (8), 80% to 99% (7), and 100% (12). One subject with a carotid
artery bypass was not included. The mean±SEM of
ARIbase and CrCPrel are
represented in Figure 3 for these 4 patient subgroups and
for the control group, which contained 34 vessels.
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ANOVA of the 5 patient subgroups represented in Figure 3
yielded very significant results for both ARIbase
and CrCPrel
(P<10-6). Scheffé's test also
indicated that these parameters can distinguish between
some of these subgroups, with the P values given in Table 2. Significant differences between the 5
subgroups were also obtained for the ANOVA of
CrCPbase
(P=1.5x10-5) and
CrCPcuff
(P=7x10-6), but
Sheffé's test indicated inferior discrimination
between individual subgroups compared with the results obtained for
CrCPrel (Table 2). On the other hand, ANOVA
results were nonsignificant for mean CBFV (MCBFV), MABP, and fitting
least square error for the different degrees of stenosis
considered.
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Discussion |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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The possibility of assessing the status of cerebral pressure autoregulation with the spontaneous variability of ABP as the stimulus to perturb CBFV has been demonstrated by previous studies,9 10 11 12 13 14 but hitherto it has been restricted to the dichotomous classification of normal/impaired autoregulation. In the present investigation we have demonstrated that the model proposed by Tiecks et al,6 to describe the CBFV response to ABP drops induced by the sudden deflation of thigh cuffs, can also be used to grade autoregulation using baseline recordings of ABP and CBFV. The ARI obtained during undisturbed conditions (ARIbase) has shown a very high correlation with ARIcuff and considerable sensitivity to the degree of stenosis in a population of CAD patients (Figure 3
). In a previous study, Zhang et
al14 have also shown that baseline estimates of
IRF can be used to predict CBFV changes after the sudden release of
thigh cuffs in normal subjects, thus suggesting that the same IRF might
apply to both situations.
Despite these initially encouraging results, several limitations of the
methods adopted need to be kept in perspective. One major limitation of
using baseline recordings to grade autoregulatory
performance is the lack of ABP variability at the spectral
frequency bands, which can stimulate an autoregulatory
response.13 Although the pressure drop produced
by the deflation of thigh cuffs is not a perfect "step function"
and returns to its original value 10 to 15 seconds after the sudden
release of the cuffs, it provides much more low-frequency power (<0.2
Hz)13 than normally available in a 120-second
recording of undisturbed fluctuations in ABP. The amount of
signal power available in the pertinent frequency band is important to
overcome the noise levels, thus providing reliable estimates of the
input-output relationship.16 This deficiency in
low-frequency power becomes more of a problem if one is estimating the
step response because of its higher relative content of low-frequency
power compared with the IRF. For this reason, the analysis and
fitting of the Tiecks et al6 model were performed
on the IRF. Although the IRF provides a less intuitive "feeling" of
the autoregulatory response corresponding to different values of ARI,
its temporal pattern can also reflect grading of autoregulation. At
t=0, when a hypothetical impulselike disturbance in ABP takes
place, there is an immediate direct response in CBFV. If the IRF
remains flat, this means a lack of feedback reaction in CBFV that will
then tend to follow ABP changes. This situation is characteristic of an
absence of autoregulation. On the other extreme, if the positive
immediate change in CBFV is counteracted by a negative wave in the IRF
(Figure 1A), this will induce a return of CBFV to its original level,
representing the case of a perfect autoregulation. Although
the occurrence of this temporal pattern was in excellent agreement with
the reference values of ARIcuff, the limits of
agreement between this index and ARIbase (-3.89
to 3.71) should be considered inadequate for clinical applications of
the baseline method at this stage.
A number of different reasons might contribute to explain this poor agreement. First, it is appropriate to question the accuracy and precision of ARIcuff as a "gold standard" for assessment of dynamic autoregulation. More studies on the reproducibility of the thigh cuff technique are undoubtedly needed, but initial results suggest a possible problem in this area.18 Second, the transcranial Doppler equipment allows the operator to select values of CrCPcuff to improve the fitting between model and data, but different values of ARIcuff could have been obtained if an objective and automatic procedure was adopted for this purpose, such as we have used to fit the model during baseline recordings. Third, the model of Tiecks et al6 is an obvious first choice to fit and grade the IRFbase, but we have not explored other alternatives that could lead to better fitting and reduced least square errors. Future research on this topic is important to arrive at more accurate and sensitive mathematical models to describe the dynamic pressure-velocity relationship during baseline recordings. Fourth, and related to this previous point, is the problem of performing the FFT analysis on segments of data that are long enough to yield accurate estimates of IRF and to maximize the low-frequency power spectral content. IRF estimates with higher signal-to-noise ratios than hitherto available are necessary to allow further investigation of alternative mathematical models to fit and grade the IRFbase. To summarize this point of the discussion, it is important that more work is performed to understand the sources of variability of both ARIcuff and ARIbase, before either is prematurely discarded or uncritically accepted.
Newell et al7 have shown that CBFV changes after the sudden deflation of thigh cuffs provide an accurate estimate of the corresponding changes in cerebral blood flow. Their results suggest that the diameter of the MCA does not change significantly during the thigh cuff test, but it is not possible to assume that the same holds true for the spontaneous variability of CBFV recorded during baseline measurements. If the diameter of the MCA remains constant, the observed variability of CBFV is a true reflection of spontaneous or pressure-induced fluctuations in flow. In this case, if autoregulation is intact, the negative transient of the IRF is a reflection of adjustments in small-vessel resistance, possibly involving a metabolic mechanism.2 13 14 On the other hand, as observed by Zhang et al,14 if flow is constant during baseline and the velocity fluctuations are due to small changes in MCA diameter, then the IRF might be reflecting a myogenic mechanism involving the direct action of ABP variability on large-vessel diameter. Methods based on the power of the reflected Doppler signal might be able to shed light on this problem in the near future.5 19
The CrCP parameter, as introduced by Tiecks et
al,6 is important to explain the finding that
IRFs can have markedly different amplitudes. As shown by Figure 4, a sudden change in ABP (P) can
produce different V transients and therefore distinct peak values of
the IRF, depending on the CrCP parameter. The peak value of
the IRF, represented at t=0 in Figure 1, reflects the
immediate perturbation of CBFV to a sudden change in ABP, before the
few seconds required for the autoregulatory response to be
manifested.2 5 13 14
The fact that CrCP values required to fit the Tiecks model to
IRFbase are spread over a relatively wide range
and show extremely high correlation with the degree of stenosis
(Figure 3) was not expected at the outset of our study. From the work
of Burton,20 the concept of CrCP has been
formulated as the point at which reductions in perfusion pressure lead
to a stagnation in blood flow or velocity. For a number of reasons,
however, the use of this concept in the model of Tiecks et
al6 can be misleading, and it is not possible to
assume that the resulting values reflect the true critical closing
pressure of the cerebral circulation. Values of CrCP >0 have been
obtained in animals21 and
humans10 22 by using the instantaneous
pressure–velocity relationship for a complete cardiac
cycle.10 Unfortunately, in humans it is not yet
possible to measure the ABP of large cerebral vessels, and this raises
questions about the accuracy of CrCP estimates based on ABP
measurements in the aorta or radial arteries. This problem becomes
worse when the Finapres is used to record the ABP waveform. Note
that the CrCP parameter in the formulation of Tiecks et
al6 bears no relationship to estimates based on
instantaneous pressure–velocity relationships, because the signals in
Equation 1 represent mean values of CBFV and ABP for each
cardiac cycle. In addition, this parameter has not been
estimated by the extrapolation of the linear regression of velocity
against ABP, as adopted by other studies.10 21 22
Despite these differences and the risk of misleading interpretations,
pressure-velocity relationships, as depicted in Figure 4, can still
provide a useful model to interpret the role of the CrCP
parameter, as introduced by Tiecks et
al,6 particularly regarding the unusual finding
of large negative values of CrCP (Figure 3). For a step or impulselike
change in ABP of amplitude P, in the case of
CrCP1 >0, the relative change will result
in (V1/MCBFV)>(P/MABP), as observed
in control subjects and CAD patients with stenosis <60%
(Figure 3). On the other hand, for CrCP2 <0, the
same change (P) will lead to much smaller relative changes in
velocity and will result in
(V2/MCBFV)<(P/MABP), as exemplified by the
patients with >80% stenosis. The most likely explanation for
the latter is that negative CrCP values are an illusion resulting from
significant degrees of stenosis causing downstream falls in
perfusion pressure. As a consequence, the "true" perfusion pressure
change at the MCA will be P'
P (Figure 4), and the relative
changes in velocity can then be compatible with positive, albeit small,
values of CrCP. On the other hand, the reduction in CrCP with
diminishing autoregulatory capacity seems to be a real phenomenon since
it is also observed in the control group.
Further discussion about the physiology of elevated CrCP, as observed in some control subjects, is beyond the scope of this report. Nevertheless, the fact that CrCP is correlated with ARIcuff and the degree of stenosis and the large negative values encountered in some patients deserve further investigation.
Grading of IRFbase can be obtained with empirical
parameters, such as the area of the negative wave in the
interval 0 to 5 seconds, but this could lead to confounding of the
differential effects of cerebral autoregulation and CAD on the IRF. By
separating changes in amplitude (ie, CrCP) from the morphology of the
IRF (ie, ARIbase), it was possible to confirm the
hypothesis initially explored by White and
Markus3 that CAD has a significant depressing
effect on cerebral autoregulation. The combined use of the 2
parameters (ARIbase and
CrCPrel) seems to allow a better noninvasive
discrimination of degree of stenosis (Table 1) than obtained in
their original study with
ARIcuff.3
Although further studies are necessary, the initial results described above suggest that system analysis of spontaneous fluctuations in ABP might be useful as a convenient and trouble-free test of dynamic autoregulation. The relative simplicity of this approach might facilitate more comprehensive studies of several patient subgroups in which cerebral autoregulation is disturbed, particularly for critically ill patients in whom inducing a blood pressure reduction might be inadequate. Such patient groups might include acute cerebral ischemia, head injury, and neonates.
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Acknowledgments |
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The collection of these data was supported by a Medical Research Council (UK) project grant (G9512159). Data analysis was supported in part by the Engineering and Physical Sciences Research Council (UK) (GR 46163).
Received May 13, 1998; revision received July 24, 1998; accepted August 4, 1998.
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J. A. H. R. Claassen, B. D. Levine, and R. Zhang Dynamic cerebral autoregulation during repeated squat-stand maneuvers J Appl Physiol, January 1, 2009; 106(1): 153 - 160. [Abstract] [Full Text] [PDF]
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 M. Reinhard, M. Roth, T. Muller, B. Guschlbauer, J. Timmer, M. Czosnyka, and A. Hetzel Effect of Carotid Endarterectomy or Stenting on Impairment of Dynamic Cerebral Autoregulation Stroke, June 1, 2004; 35(6): 1381 - 1387. [Abstract] [Full Text] [PDF]
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H.-Y. Hsu, Y.-T. Chen, H.-H. Hu, R. Aaslid, and D. W. Newell Limitations in Estimating Critical Closing Pressure by Noninvasive Blood Pressure Measurements * Response: Limitations in Estimating Critical Closing Pressure by Noninvasive Blood Pressure Measurements Stroke, May 1, 2004; 35(5): e91 - e92. [Full Text] [PDF]
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B. J. Carey, R. B. Panerai, and J. F. Potter Effect of Aging on Dynamic Cerebral Autoregulation During Head-Up Tilt Stroke, August 1, 2003; 34(8): 1871 - 1875. [Abstract] [Full Text] [PDF]
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C. W. Park, M. Sturzenegger, C. M. Douville, R. Aaslid, and D. W. Newell Autoregulatory Response and CO2 Reactivity of the Basilar Artery Stroke, January 1, 2003; 34(1): 34 - 39. [Abstract] [Full Text] [PDF]
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B. Schmidt, M. Czosnyka, A. Raabe, H. Yahya, J. J. Schwarze, D. Sackerer, D. Sander, and J. Klingelhofer Adaptive Noninvasive Assessment of Intracranial Pressure and Cerebral Autoregulation Stroke, January 1, 2003; 34(1): 84 - 89. [Abstract] [Full Text] [PDF]
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J. M. Serrador, S. J. Wood, P. A. Picot, F. Stein, M. S. Kassam, R. L. Bondar, A. H. Rupert, and T. T. Schlegel Effect of acute exposure to hypergravity (GX vs. GZ) on dynamic cerebral autoregulation J Appl Physiol, November 1, 2001; 91(5): 1986 - 1994. [Abstract] [Full Text] [PDF]
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R. L. Hughson, M. R. Edwards, D. D. O'Leary, and J. K. Shoemaker Critical Analysis of Cerebrovascular Autoregulation During Repeated Head-Up Tilt Stroke, October 1, 2001; 32(10): 2403 - 2408. [Abstract] [Full Text] [PDF]
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 R. B. Panerai, S. L. Dawson, P. J. Eames, and J. F. Potter Cerebral blood flow velocity response to induced and spontaneous sudden changes in arterial blood pressure Am J Physiol Heart Circ Physiol, May 1, 2001; 280(5): H2162 - H2174. [Abstract] [Full Text] [PDF]
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B. J. Carey, P. J. Eames, M. J. Blake, R. B. Panerai, and J. F. Potter Dynamic Cerebral Autoregulation Is Unaffected by Aging Stroke, December 1, 2000; 31(12): 2895 - 2900. [Abstract] [Full Text] [PDF]
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P. J. Mahony, R. B. Panerai, S. T. Deverson, P. D. Hayes, and D. H. Evans Assessment of the Thigh Cuff Technique for Measurement of Dynamic Cerebral Autoregulation Stroke, February 1, 2000; 31(2): 476 - 480. [Abstract] [Full Text] [PDF]
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B. Meyer, C. Schaller, C. Frenkel, B. Ebeling, and J. Schramm Distributions of Local Oxygen Saturation and Its Response to Changes of Mean Arterial Blood Pressure in the Cerebral Cortex Adjacent to Arteriovenous Malformations Stroke, December 1, 1999; 30(12): 2623 - 2630. [Abstract] [Full Text] [PDF]
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R. B. Panerai, S. L. Dawson, and J. F. Potter Linear and nonlinear analysis of human dynamic cerebral autoregulation Am J Physiol Heart Circ Physiol, September 1, 1999; 277(3): H1089 - H1099. [Abstract] [Full Text] [PDF]
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