Complexity of middle cerebral artery blood flow
velocity: effects of tilt and autonomic failure
A. P. BLABER,1 R. L. BONDAR,1 F. STEIN,2 P. T. DUNPHY,2
P. MORADSHAHI,2 M. S. KASSAM,2 AND R. FREEMAN3
1School of Kinesiology, Faculty of Health Sciences, The University of Western Ontario, London,
Ontario N6A 3K7; 2Centre for Advanced Technology Education, Ryerson Polytechnic University,
Toronto, Ontario, Canada M5B 2K3; and 3Department of Neurology, Peripheral Nerve and
Autonomic Laboratory, Beth Israel Deaconess Medical Center and Harvard Medical School,
Boston, Massachusetts 02215
Blaber, A. P., R. L. Bondar, F. Stein, P. T. Dunphy, P.
Moradshahi, M. S. Kassam, and R. Freeman. Complexity
of middle cerebral artery blood flow velocity: effects of tilt and
autonomic failure. Am. J. Physiol. 273 (Heart Circ. Physiol.
42): H2209–H2216, 1997.—We examined spectral fractal
characteristics of middle cerebral artery (MCA) mean blood
flow velocity (MFV) and mean arterial blood pressure adjusted to the level of the brain (MAPbrain ) during graded tilt (5
min supine, 210°, 10°, 30°, 60°, 210°, supine) in eight
autonomic failure patients and age- and sex-matched controls. From supine to 60°, patients had a larger drop in
MAPbrain (62 6 4.7 vs. 23 6 4.5 mmHg, P , 0.001; means 6
SE) and MFV (16.4 6 3.8 vs. 7.0 6 2.5 cm/s, P , 0.001) than in
controls. From supine to 60°, there was a trend toward a
decrease in the slope of the fractal component (b) of MFV
(MFV-b) in both the patients and the controls, but only the
patients had a significant decrease in MFV-b (supine:
patient 5 2.21 6 0.18, control 5 1.99 6 0.60; 60°: patient 5
1.46 6 0.24, control 5 1.62 6 0.19). The b value of MAPbrain
(MAPbrain-b; 2.19 6 0.05) was not significantly different between patients and controls and did not change with tilt.
High and low degrees of regulatory complexity are indicated
by values of b close to 1.0 and 2.0, respectively. The increase
in fractal complexity of cerebral MFV in the patients with tilt
suggests an increase in the degree of autoregulation in the
patients. This may be related to the drop in MAPbrain. The
different response of MFV-b compared with that of MAPbrain-b
also indicates that MFV-b is related to the regulation of
cerebral vascular resistance and not systemic blood pressure.
spectral analysis; cerebral autoregulation; fractal
SYNCOPE IN PATIENTS with autonomic nervous system
failure may be the result of insufficient regulation of
mean arterial blood pressure (MAP), altered or impaired cerebral autoregulation, or some combination of
the two. Autonomic failure is frequently associated
with reduced beat-to-beat heart rate variability (HRV)
(9, 10, 28). We have recently shown that beat-to-beat
blood pressure variability (BPV), both systolic and
diastolic, is also reduced in these patients (3). Specifically, low-frequency HRV and BPV power, associated
with autonomic nervous system control, was affected.
This is a consequence of neurological degeneration of
the sympathetic and/or parasympathetic nervous systems.
Although some previous studies (27) of patients with
autonomic failure have shown autoregulation to remain intact, with impaired blood pressure regulation,
there may be changes in cerebral autoregulation. Exam-
ining the pattern of cerebral blood flow and MAP
variability with tilt may provide useful information by
allowing some insight into the behavior of the cerebral
autoregulation mechanism in these patients.
Recent observations (7, 20) of both HRV and BPV
have indicated that, underlying the harmonic components analyzed by spectral analysis, there is a pattern
of fractal variability. The fractal property of a physiological system is identified by a characteristic 1/f b function
of spectral power. It has been speculated (20) that this
pattern is important for the maintenance of cardiovascular homeostasis. Although not yet characterized as
fractal, cerebral blood flow would not be expected to be
different in this respect from other physiological signals.
Coarse graining spectral analysis (CGSA) has been
used (29) to characterize the complexity of blood pressure and heart rate regulation. This is determined
using the fractal spectral exponent b. High and low
degrees of regulatory complexity are indicated by values of b close to 1.0 and 2.0, respectively. Complexity is
thought to be related to the number of mechanisms
interacting to produce the measured physiological signal. Recent investigation in our laboratory (3) of HRV
and BPV in these patients showed reduced fractal
power but similar complexity to age- and sex-matched
controls. Furthermore, in these patients HRV complexity was reduced with tilt, whereas BPV complexity was
unaffected by tilt. It was suggested that these results
indicated reduced cardiovascular regulation but unaffected neural integration.
In this paper we examine complexity of MAP adjusted to the level of the brain (MAPbrain ) and mean
blood flow velocity (MFV) of the middle cerebral artery
(MCA) of these autonomic failure patients and agematched controls during graded tilt. CGSA of MAPbrain
and MFV was used to explore the underlying physiological changes in the cardiovascular and cerebrovascular
systems occurring with autonomic dysfunction. Our
objective was to investigate the harmonic and fractal
components of MAPbrain and MFV in these patients and
to measure their response to tilt. On the basis of the
results of the previous study (3), we expected reduced
MAPbrain and cerebral MFV low-frequency power. We
hypothesized that the beat-by-beat variability of cerebral blood flow velocity would be fractal and that this
fractal variability would be related to the maintenance
of cerebral blood flow (i.e., related to cerebral autoregu-
0363-6135/97 $5.00 Copyright r 1997 the American Physiological Society
H2209
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COMPLEXITY OF MIDDLE CEREBRAL ARTERY BLOOD FLOW VELOCITY
lation). We proposed that a decrease in b (an increase in
complexity) for MFV would indicate an increase in the
number of mechanisms involved with cerebral autoregulation. With the increased orthostatic stress and decreased MAP (MAPbrain ) associated with tilt in the
patients, we predicted an increase in the number of
mechanisms involved with cerebral autoregulation and
therefore an increase in MCA-MFV complexity.
METHODS
Subjects. Eleven patients (5 males and 6 females), previously described (3) (Table 1), with autonomic failure were
observed during graded tilt in the autonomic function laboratory at Beth Israel Deaconess Medical Center (Boston, MA).
Patients who were taking medication were asked to discontinue their medication at least 24 h before testing. Control
subjects were selected to match the age and sex of each
patient. Patients and control subjects gave informed consent
to undergo the International Review Board-approved protocol. This protocol was been presented in a previous paper (3)
but will be briefly reported along with additional information
pertaining to the measurement and analysis of cerebral blood
flow.
Experimental procedure. A motorized tilt bed with a foot
plate but no saddle support was used (Ratiotrol 3530-VS;
Laberne, Columbia, SC). Subjects were instrumented in the
supine position over a period of ,10 min. After this period, 5
min of baseline data were collected in the supine position.
This was followed by 5 min in each of the following positions:
210° (head-down tilt, HDT), 10°, 30°, 60° (head-up tilt, HUT),
210°, and supine recovery. Subjects who became presyncopal
during the HUT protocol were returned immediately to the
210° position for 5 min, followed by a 5-min supine recovery
period.
Data collection. Hematocrit was determined for each subject at rest. R-R interval was obtained continuously from a
standard four-lead electrocardiogram (ECG). Blood pressure
was obtained by the noninvasive finger cuff method (Finapres
2300; Ohmeda, Englewood, CO) to provide beat-to-beat estimates of arterial blood pressure with the arm of the subject
supported at the level of the left ventricle. Percent end-tidal
CO2 (PETCO2) and respiratory rate were measured by sampling exhaled CO2 using a nasal adaptor (Datex Normocap
200, Helsinki, Finland). MFV of the MCA was measured
noninvasively using transcranial Doppler (TCD) ultrasound
(Transpect, Medasonics). The MCA was insonated through
the temporal window using a 2-MHz probe. All TCD measurements were performed by the same operator.
Table 1. Autonomic failure patient characteristics
Subject
Gender
Age, yr
Diagnosis
1
2
3
4
5
6
7
8
9
10
11
M
F
F
M
M
F
M
F
F
F
M
59
51
57
54
68
72
61
56
42
61
82
SDS
PAF
PAF
PAF
PAF
SDS
IDDM
PAF
IDDM
PAF
PAF
M, male; F, female; SDS, Shy-Drager syndrome; PAF, pure autonomic failure; IDDM, insulin-dependent diabetes mellitus.
The analog signals from these devices were recorded
simultaneously at 12 kHz per channel using an eight-channel
digital tape recorder (TEAC RD-111T, Montebello, CA). Beatby-beat analysis of these data was performed off line. MAP
was adjusted to the level of the brain (MAPbrain ) by applying a
hydrostatic correction. A single beat for both blood pressure
and cerebral blood flow velocity was described by the pressure
and velocity envelope tracings, respectively, between the
initial increases in blood pressure or velocity after successive
ECG R-waves. MAP and MFV were calculated as the arithmetic averages of the data points in each beat. The Doppler
and blood pressure tracings were manually reviewed for
anomalies and movement artifact. Any unusable blood flow
velocity or blood pressure data were removed, and the time
series was adjusted by interpolating new values from the two
valid points surrounding the excluded segment. If more than
20% of any 256-beat segment was interpolated, the results
were deemed invalid.
Cerebral MFV and MAPbrain complexity. The beat-by-beat
variability and fractal complexity of MFV and MAPbrain were
evaluated by CGSA (29). This method is a modification of the
fast Fourier transform used by some investigators in studies
of HRV. The advantage of this method over other techniques
of spectral analysis is its ability to extract fractal components
from the harmonic components (7, 29). Blood pressure signals
have been shown (6, 7, 16, 29) to be fractal, that is, a
broad-band, nonwhite signal underlies the (harmonic) variations that are normally taken to indicate parasympathetic
and sympathetic regulatory activities.
The algorithm has been described in detail, along with a
demonstration of its efficiency in extracting harmonic from
fractal components (29). Briefly, the extraction of fractal and
harmonic components is achieved by rescaling the data. The
data are twice rescaled, once by sampling every second data
point and once by sampling every data point twice before
cross-correlation with the original data. Only the fractal
components are retained after cross-correlation because of
their self-similar properties, whereas the harmonic components are lost. A subtraction of the rescaled cross-correlation
from an autocorrelation of the original therefore yields the
harmonic spectral power.
The total power (PTOT ) can therefore be divided into fractal
(PFrac ) and harmonic (PHarm ) components. The harmonic component can be divided into two frequency regions: high
frequency (HI; $0.15 Hz) and low frequency (LO; 0–0.15 Hz).
The high-frequency region is respiratory related, whereas the
low-frequency region is a consequence of several factors.
From the harmonic component, or the total harmonic power
(PHarm ), the integrated power at 0.0–0.15 Hz (PLO ) and
0.15–0.50 Hz (PHI ) can be calculated.
The fractal component has linear scaling across a wide
range of frequencies when the data are plotted as log spectral
power versus log frequency in what is commonly called the
1/f b relationship (20, 24, 26, 29). The b value is the slope of the
linear regression applied to these data. When the value of b is
close to 1, there is a high level of complexity because the data
frequently change direction toward or away from the mean.
In contrast, b close to 2 represents a low level of complexity
with less complex changes of the measured data (6, 20, 29). In
a less complex system, feedback control is probably dominated by a reduced number of inputs (17).
Yamamoto and Hughson (30) investigated the effects of
data length on the interpretation of data from CGSA. Less of
the fractal signal was extracted at small data lengths (256
compared with 8,500 beats), and as a consequence the
magnitude of the harmonic powers was found to also be
dependent on data length. On the other hand, the parameter
H2211
COMPLEXITY OF MIDDLE CEREBRAL ARTERY BLOOD FLOW VELOCITY
Table 2. Mean arterial blood pressure and spectral power for autonomic failure patients and controls
Patients
Controls
P (group)
P (tilt)*
MAPbrain ,
mmHg
PTOT ,
mmHg2
PHarm ,
mmHg2
PLO ,
mmHg2
PHI ,
mmHg2
PFrac ,
mmHg2
b
93.6 6 2.9
80.9 6 2.7
0.008
,0.001
6.65 6 2.26
7.48 6 2.26
0.800
0.325
0.73 6 0.15
0.96 6 0.15
0.309
0.043
0.50 6 .013
0.84 6 0.13
0.084
0.455
0.25 6 0.08
0.12 6 0.08
0.271
0.001
5.70 6 2.24
6.80 6 2.24
0.733
0.333
2.16 6 0.13
2.46 6 0.13
0.126
0.150
Values for patients and controls are means 6 SE. MAPbrain , mean arterial blood pressure adjusted to brain level; PTOT , total spectral power;
PHarm , harmonic spectral power; PLO , harmonic low-frequency spectral power; PHI , harmonic high-frequency spectral power; PFrac , fractal
spectral power; and b, slope of fractal component of MAP. P , 0.05 indicates statistical significance. P (group), comparison of main effects
between patients and control; P (tilt), comparison of main effects over tilt. * If P (tilt) , 0.05, then post hoc analyses (Student-Newman-Keuls
test) between tilt conditions were conducted. MAPbrain and PHI had significant post hoc differences, shown in Figs. 1 and 3, respectively.
for describing the fractal component, b, and the ratio of low to
high power were less susceptible to changes in data length.
We wanted to compare the harmonic and the fractal components and chose a constant data length. Because of the time
spent at each condition, we used a data length of 256 beats.
Statistical analysis. Statistical analysis of variables across
the tilt levels (baseline, 210°, 10°, 30°, 60°, 210°, and
recovery), comparing patients with age-matched healthy
controls, was performed using a nested (subjects within tilt)
two-way repeated-measures analysis of variance. All data
were analyzed with SigmaStat (Jandel Scientific, San Rafael,
CA) using the Kolmogorov-Smirnov test for normality and the
Levene median test for equal variance. If main effects or
interactions (P , 0.05) were detected, subsequent post hoc
analysis using a Student-Newman-Keuls test was performed
(P , 0.05). All data are expressed as means 6 SE.
RESULTS
Because of technical difficulties and noise, only eight
of the patients had sufficient TCD data for spectral
analysis (subjects 1–8). None of these patients presented presyncopal symptoms during the tilt test. For
statistical purposes, only the eight matched control
subjects were used in the final analysis.
End-tidal CO2 responses were similar in both patients and controls, with patients dropping from 6.13 6
0.5% while supine to 5.77 6 0.4% at 60° HUT and
controls dropping from 6.06 6 0.4% while supine to
5.80 6 0.6% at 60° HUT. Resting hematocrit levels also
were not significantly different between the patients
(39.7 6 4%) and controls (40.7 6 3%).
There were significant differences in the mean values, averaged over the 256 beats, of MAPbrain (Table 2)
and MCA MFV (Table 3) between patients and controls
and with tilt. From supine to 60° (Fig. 1), patients had a
significant decrease in MAPbrain (105 6 4.7 to 43 6 4.5
mmHg, P , 0.01) and MFV (61 6 3.6 to 44 6 5.2 cm/s,
P , 0.001) and controls had a significant decrease in
MAPbrain (88 6 4.5 to 65 6 4.7 mmHg, P , 0.001) and a
nonsignificant decrease in MFV (51 6 2.8 to 44 6 3.1
cm/s). Furthermore, MAPbrain was significantly higher
in patients in the supine position and significantly
lower in patients in the 60° HUT position. Overall, the
patients had a significantly higher MFV, but post hoc
analysis revealed no differences between patients and
controls over the levels of tilt. In both groups the
smaller change in MFV with tilt compared with MAPbrain
indicates a decrease in cerebrovascular resistance when
estimated as MAPbrain/ MFV (1).
Plots of MAPbrain and MFV harmonic power spectra
with tilt for a patient and age-matched control are
shown in Fig. 2. The low- and high-frequency components are observable in all the power spectra. As
expected, there are marked differences between the
patient and control power spectra. Most notable is the
increased MAPbrain and MFV high-frequency power in
the patients.
Spectral power. Both MAPbrain and MFV PTOT and
PFrac were not different between patients and controls
and were unaffected by tilt (Tables 2 and 3). Percent
fractal for MAPbrain (controls 5 82 6 2.5%, patients 5
86 6 2.2%) and MFV (controls 5 86 6 1.8%, patients 5
85 6 1.7%) was not significantly different between
patients and controls. Although MAPbrain PHarm showed
a significant main effect change with tilt (P 5 0.043;
Table 2), subsequent post hoc analysis did not indicate
differences between the levels of tilt. MFV PHarm was
not different between patients and controls or over the
different levels of tilt (Table 3). There was a trend (P 5
0.084; Table 2) toward reduced MAPbrain, but not MFV
(P 5 0.698; Table 3), PLO. Both MAPbrain (P 5 0.001;
Table 2) and MFV (P 5 0.002; Table 3) PHI were
significantly affected by tilt. Subsequent analysis revealed that only the patients had a significant increase
in PHI between the supine and the 60° HUT positions
Table 3. Middle cerebral artery blood flow velocity spectral power for autonomic failure patients and controls
Patient
Control
P (group)
P (tilt)*
MFV,
cm/s
PTOT ,
cm2/s2
PHarm ,
cm2/s2
PLO ,
cm2/s2
PHI ,
cm2/s2
PFrac ,
cm2/s2
b
57.2 6 2.6
47.5 6 2.6
0.021
,0.001
6.53 6 1.44
3.23 6 0.29
0.111
0.451
0.65 6 0.12
0.44 6 0.12
0.221
0.432
0.41 6 0.07
0.37 6 0.07
0.698
0.716
0.25 6 0.08
0.07 6 0.08
0.151
0.002
5.83 6 1.43
2.80 6 0.27
0.155
0.398
1.82 6 0.09
1.88 6 0.06
0.894
0.032
MFV, mean blood flow velocity. * If P (tilt) , 0.05, then post hoc analyses (Student-Newman-Keuls test) between tilt conditions were
conducted. MFV, PHI , and b had significant post hoc differences, shown in Figs. 1, 3, and 4, respectively.
H2212
COMPLEXITY OF MIDDLE CEREBRAL ARTERY BLOOD FLOW VELOCITY
Fig. 1. Mean arterial blood pressure adjusted to brain level (MAPbrain;
top) and mean blood flow velocity (MFV; bottom) with tilt for controls
and patients. Values are means 6 SE. BL, supine baseline; RC,
supine recovery. * Significantly different from baseline (P , 0.05);
# significantly different from control (P , 0.05).
(Fig. 3).The patients also had a significantly greater PHI
at 60° HUT compared with controls (Fig. 3).
b, Index of complexity. The spectral exponent for
blood pressure (MAPbrain-b) was determined for both
the controls and patients but was found not to be
different between patients and controls and did not
change with tilt (Table 2). MFV-b was significantly
reduced with tilt (P 5 0.032; Table 3), and subsequent
post hoc analysis of the effect of tilt revealed that only
the patients had a significant reduction from baseline
to 60° HUT (Fig. 4). Regression analysis also revealed
that MFV-b decreased significantly with MAPbrain [r 5
0.33, P 5 0.021; MFV-b 5 (0.0067 3 MAPbrain ) 1 1.19].
DISCUSSION
With autonomic failure both sympathetic and parasympathetic nervous system control of circulation can
be affected (2, 8, 21–23). Our laboratory (3) recently
reported that low-frequency HRV and BPV are both
reduced in these patients. We also reported that systolic (SAP) and diastolic arterial blood pressure (DAP)
signal complexities were not different between patients
and controls and did not change with tilt. Heart rate
complexity was also not different between patients and
controls but decreased with increased tilt.
In this paper we have shown that cerebral blood flow
velocity has a variability signal that in appearance is
similar to that of blood pressure. We have also shown
that the response of MFV complexity to tilt is not the
same as that of heart rate or blood pressure; however,
the response is predictable based on the autoregulatory
model presented in the introduction. In the baseline
(supine) position, both the signal complexity of MAPbrain
and the signal complexity of MFV are not different;
however, with tilt the MFV signal complexity increases.
Although this increase was only significant in the
patients, the controls also showed the same trend. We
hypothesize that this increase in complexity is due to
an increased number of mechanisms involved with
cerebral blood flow regulation in the face of reduced
MAPbrain. This is supported by the significant correlation between MFV-b and MAPbrain in the patients. This
change in complexity was greater in the patients due to
a larger change in MAPbrain related to their autonomic
dysfunction. Furthermore, we have shown a larger
increase in autoregulation from supine to 60° HUT in
these patients (4). We predict that, in a healthy population, this increase in complexity would be significant
given a sufficient drop in MAPbrain.
The fall in CO2 from supine to 60° HUT was similar
in both patients and controls and was not large enough
to make a significant difference in MFV. Even in
patients who experienced a slightly larger change in
CO2, the drop in CO2 from supine to 60° HUT was only
0.36% (2.7 mmHg). Furthermore, this drop occurred at
a time when MAPbrain was very low, at which point the
effect of CO2 on MFV is known to be diminished (12).
Methodological considerations. The use of continuous
noninvasive blood pressure monitoring has extended
our ability to observe beat-to-beat BPV. The finger
monitor can be used for spectral analysis of BPV, but
caution is needed when interpreting the low-frequency
region. Omboni et al. (19) found increased low-frequency power with the finger monitor compared with
intra-arterial measurements, although they were unable to determine whether this was due to measurement error or amplification with the blood pressure
monitor. In the present study, the same blood pressure
monitor was used for all subjects, with comparisons
made between the patient and control groups. Interpretations were only made on the basis of these intergroup
comparisons and not from absolute values, and therefore there should be no reason to doubt the validity of
these comparisons.
The present study required continuous, beat-to-beat
measurements of cerebral blood flow dynamics. TCD, a
noninvasive ultrasound method of estimating changes
in cerebral blood flow in the territory of the insonated
vessel, is the only technique that could provide us with
COMPLEXITY OF MIDDLE CEREBRAL ARTERY BLOOD FLOW VELOCITY
H2213
Fig. 2. Sample harmonic MAPbrain (top)
and MFV (bottom) spectra with tilt
from age- and sex-matched control (left)
and patient (right). Note difference in
high-frequency power between patient
and control. MCA, middle cerebral artery.
these data. This method, however, does not measure
flow directly but instead measures flow velocity. The
relationship between the velocity changes measured
and the changes in actual cerebral blood flow depends
on the diameter of the insonated vessel. Peripheral
resistance vessels downstream from the insonated vessel change diameter with autoregulation, which affects
flow, and hence flow velocity, in the insonated vessel.
Previous studies (11, 18) have validated the use of TCD
under a variety of conditions known to affect cerebral
perfusion. This study presents a situation similar to a
previous study (5) in our laboratory based on lower
body negative pressure; because a decrease in MFV
was observed with HUT, this could only be explained
two ways: either blood flow in the territory of the MCA
decreased or the diameter of the MCA increased. Previous studies (15) have found an increase in sympathetic
activity with HUT, which if anything may cause some
vasoconstriction, not dilatation, of the MCA. Although
this increase in sympathetic activity may have been
reduced or even absent in patients, they would most
likely have also experienced a drop in MCA blood flow,
given the severe drop in arterial blood pressure observed during HUT. The presyncopal episodes experienced by two of the patients (3) (not included in this
paper due to insufficient TCD data) further supports
the concept of a reduced MCA flow. We therefore believe
that changes in MCA caliber are likely to be minimal
but that, even if such changes did take place, they
would not have adversely affected our interpretation of
the fall in MFV as a fall in actual MCA blood flow
during HUT.
The other methodological consideration deals with
the use of changes in MAPbrain to represent changes in
cerebral perfusion pressure (CPP). Because CPP 5
MAPbrain 2 intracranial pressure (ICP), this assump-
H2214
COMPLEXITY OF MIDDLE CEREBRAL ARTERY BLOOD FLOW VELOCITY
Fig. 3. MAPbrain (top) and MFV (bottom) harmonic high-frequency
power (PHI ) with tilt for controls and patients. Values are means 6
SE. * Significantly different from baseline (P , 0.05); # significantly
different from control (P , 0.05).
Fig. 4. Spectral exponent of MFV (MFV-b) with tilt for controls and
patients. * Significantly different from baseline (P , 0.05).
tion is valid only if changes in ICP are minimal. The
validity of assuming minimal ICP changes during HUT
has been demonstrated by Rosner et al. (25), who
showed a drop in ICP of only 1 mmHg for every 10° of
HUT. This drop is much lower than the MAPbrain
changes we observed and would not affect the outcome
of the present study. Furthermore, ICP is expected to
remain nearly constant within a given tilt segment, so
it would not be a factor in our analysis of the dynamics
within each segment. Most importantly, because ICP
changes during HUT are thought not to be mediated by
the autonomic nervous system (25), any changes in ICP
should have had the same effect on both the patient and
control groups, and hence our comparison of the two
groups is valid.
Cerebral blood flow variation. Any changes in cerebrovascular blood flow should reflect changes in blood
pressure entering the cerebral vasculature. However,
because of the autoregulatory ability of the cerebrovascular system, signal transduction from pressure to flow
may be modified through changes in the diameter of
cerebrovascular arterioles. Our data clearly show that
the harmonic variations in MAPbrain and MFV are
similar. Low-frequency power was not affected by tilt.
High-frequency power was affected, but only in the
patients. The effect of BPV on MFV variation in the
high-frequency region can be clearly observed in Figs. 2
and 3. The change in high-frequency power in blood
pressure is due to the effects of autonomic failure on
baroreflex activity. In another paper (3), data from our
laboratory were presented showing a significant increase in SAP and DAP PHI power in patients but not in
control subjects. Our observation of increased MAPbrain
PHI is consistent with these findings. The MAPbrain
harmonic variation is directly reflected in the MFV
signal.
Fractal nature of MFV and MAPbrain. Previous research supports the concept that heart rate and systolic
blood pressure have fractal variability and that the
fractal component in humans is a large percentage
(70–90%) of both HRV (7, 13, 30) and systolic BPV (6,
13). We have now shown that cerebral blood flow is
fractal and that its complexity changes with blood
pressure. A fractal signal is one that is self similar (29),
that is, when viewed over different time scales, the
pattern would be similar. It has been previously speculated that this fractal information is important in the
maintenance of cardiovascular homeostasis (6, 7, 20).
The complexity of the overall blood pressure and blood
flow velocity variability can be estimated by the slope of
the linear regression of log fractal spectral power
versus log frequency (14). The changes in the slope can
be seen in Fig. 4.
In previous studies (3, 6) in which the fractal components of HRV and BPV were investigated, it was
concluded that the overall central control mechanisms
for HRV and BPV must be independent, because the
slope of the fractal component of BPV remained constant, whereas that for HRV increased as the level of
orthostatic stress was increased. A similar conclusion
can now be made between blood pressure and cerebral
COMPLEXITY OF MIDDLE CEREBRAL ARTERY BLOOD FLOW VELOCITY
blood flow, because the slope of the fractal component
for MFV decreased with increasing HUT, whereas the
slope for MAPbrain remained constant. Furthermore, we
can also conclude that overall central control mechanisms for cerebral blood flow must be different from
heart rate because in these patients (3) the slope of the
HRV fractal component increased as the level of tilt
increased.
It is also significant that, although MFV-b in the
control group did not change significantly with tilt, it
did show a trend in the same direction as in the
patients. If, as previously suggested, the fractal component represented by b is related to the complexity of the
overall MFV signal, then both patients and controls
were not different in this respect. In other words, the
increase in the number of mechanisms involved with
cerebral blood flow variability was a normal response.
The larger change in the patient group was most likely
due to the increased cerebrovascular challenge compared with the controls, as reflected in the larger
change in autoregulatory gain seen in these subjects (4).
These data would then suggest that under normal
resting conditions the number of mechanisms involved
with cerebral blood flow regulation is minimal. Under
these conditions, however, the complexity of heart rate
is maximal and blood pressure is well regulated and
maintained at a level optimal for cerebral perfusion.
During orthostatic stress, heart rate complexity is
reduced and blood pressure is not as well regulated. If
MAPbrain is not maintained, then cerebrovascular
changes are required. This may involve the activation
of local metabolic and wall-tension mechanisms as well
as other humoral and neural mechanisms. The involvement of these mechanisms should increase cerebral
blood flow complexity.
In conclusion, CGSA of cerebral blood flow velocity
provided information with respect to cerebral blood
flow regulation not present in the standard analyses of
cerebral autoregulation. We have demonstrated important differences between blood pressure and blood flow
regulation with orthostatic stress. The harmonic components of blood pressure and blood flow responded
similarly to the tilt protocol. Both had increased highfrequency power in the patients compared with the
controls, although only MAPbrain had a trend toward
reduced low-frequency power in the patient group. The
increase in the high-frequency variation in patient, but
not control, MAPbrain may be linked to a reduced vagal
baroreflex in the patients.
The decrease in MFV-b in the patients was related to
the decrease in MAPbrain with tilt. This response appeared to be normal, because the control group, with a
smaller decrease in MAPbrain, also had a trend toward
increased complexity with tilt. The different response of
MFV-b compared with that of MAPbrain-b indicates that
MFV-b is related to the regulation of cerebral vascular
resistance and not systemic blood pressure regulatory
mechanisms. The increase in complexity suggests that,
with decreased blood pressure and increased cerebrovascular stress, the number of mechanisms involved with
cerebral blood flow regulation increases.
H2215
This research was supported by a joint National Sciences and
Engineering Research Council (NSERC)-Medical Research CouncilCanadian Space Agency grant under NSERC 669–008–93 (R. L.
Bondar) and by Grant FD-R-000393 (R. Freeman) from the Public
Health Service.
Address for reprint requests: R. L. Bondar, Faculty of Health
Sciences, School of Kinesiology, Thames Hall, Rm. 3110, The Univ. of
Western Ontario, London, ON, Canada N6A 3K7.
Received 25 February 1997; accepted in final form 24 July 1997.
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