Dynamic cerebral autoregulation during exhaustive exercise in humans

Dynamic cerebral autoregulation during exhaustive exercise in humans

Shigehiko Ogoh1, Mads K. Dalsgaard2, Chie C. Yoshiga2, Ellen A. Dawson2, David M.

Keller1, Peter B. Raven1, and Niels H. Secher2

1Department of Integrative Physiology, University of North Texas Health Science Center,

Fort Worth, Texas 76107, U.S.A. and 2Department of Anesthesia, The Copenhagen

Muscle Research Center, Rigshospitalet, University of Copenhagen, DK-2100

Copenhagen, Denmark

Running Title: Dynamic CA during exhaustive exercise

Keywords: carbon dioxide, cerebral blood flow, cerebral metabolism

Please send correspondence to:

Shigehiko Ogoh, Ph.D.

Department of Integrative Physiology

University of North Texas Health Science Center

3500 Camp Bowie Boulevard

Fort Worth, Texas, 76107, U.S.A.

Phone: 817-735-2088

Fax: 817-735-5084

e-mail: [email protected]

Articles in PresS. Am J Physiol Heart Circ Physiol (October 21, 2004). doi:10.1152/ajpheart.00948.2004

Copyright © 2004 by the American Physiological Society.

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ABSTRACT

We investigated whether dynamic CA is affected by exhaustive exercise using transfer

function gain and phase shift between oscillations in mean arterial pressure (MAP) and

middle cerebral artery mean blood flow velocity (MCA Vmean). Seven subjects were

instrumented with a brachial artery catheter for measures of MAP and determination of

PaCO2, while jugular venous oxygen saturation (SvO2) was determined to assess changes

in whole brain blood flow. After a 10 min resting period, the subjects performed

dynamic leg cycle ergometry at 168 ± 5 W (mean ± SE) continued to exhaustion with a

group average time of 26.8 ± 5.8 min. Despite no significant change in MAP during

exercise, MCA Vmean decreased from 70.2 ± 3.6 to 57.4 ± 5.4 cm/s and SvO2 from 68 ±

1% to 58 ± 2% at exhaustion and both correlated to PaCO2 (5.5 ± 0.2 to 3.9 ± 0.2 kPa; r =

0.47, P = 0.04 and r=0.74, P<0.001, respectively). An effect on brain metabolism was

indicated by a decrease in the cerebral metabolic ratio of O2/ (glucose + ½ lactate) from

5.6 to 3.8 (P<0.05). At the same time, the normalized low frequency gain between MAP

and MCA Vmean was increased (P<0.05), while the phase shift tended to decrease. These

findings suggest that dynamic CA was impaired by exhaustive exercise despite a

hyperventilation-induced reduction in PaCO2.

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INTRODUCTION

Blood flow to the brain is controlled by regional changes in neural activity versus global

influences including cerebral autoregulation (CA; 28). However, CA is itself subjected to

global influences which help to maintain a stable flow despite marked changes in whole

body metabolism and arterial blood pressure (ABP), e.g. during exercise. For instance,

CA is attenuated by hypercapnia and enhanced by hypocapnia (1, 8, 9, 27), while

sympathetic activation shifts the CA curve to the right (22) and prevents cerebral hyper-

perfusion during exercise (13). During the progression towards a maximal effort, with

either a constant or an incremental increase in workload, there is a lowering of arterial

carbon dioxide tension (PaCO2) which occurs as a result of hyperventilation (16) and

there is an exponential increase in plasma catecholamines (19). Collectively, the

decrease in PaCO2 and the sympathetic activation suggest that, especially during intense-

exercise, CA will be enhanced.

Although acute changes in ABP are transmitted to the cerebral circulation, under

normal conditions the cerebral blood flow tends to return to its original value within a

few seconds (1, 26). This is usually referred to as dynamic CA and it correlates well with

assessments of static autoregulation (26, 37). During mild to heavy exercise, dynamic

CA is maintained and may reflect a balance between the influence of sympathetic activity

and PaCO2 (5). However, it remains unknown whether dynamic CA becomes enhanced

during exhaustive exercise when PaCO2 decreases. To test this question subjects

exercised to exhaustion on a bicycle ergometer, while measures of middle cerebral artery

mean blood flow velocity (MCA Vmean) and mean arterial pressure (MAP) were made

beat-to-beat and analyzed using linear dynamic analysis (26, 39, 41). At the same time

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jugular venous oxygen saturation (SvO2) was determined to assess changes in whole

brain blood flow.

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METHODS

Seven men with a mean age of 23 ± 2 yr, height of 180 ± 10 cm, and weight of 72 ± 9 kg

(mean ± SD) were recruited to participate in the study. All subjects were free of

cardiovascular and pulmonary disorders and were not using prescribed or over the

counter medications. Each subject provided written informed consent as approved by the

Ethics Committee of Copenhagen (KF 01-369/97) and they were requested to abstain

from caffeinated beverages for 12 h and strenuous physical activity and alcohol for at

least 24 h before the experiment.

Protocol

The subjects arrived at the laboratory after an overnight fast. Following catherization,

they were seated in a semi-recumbent position on a modified Krogh cycle ergometer.

After a 10 min resting period, the subjects began cycling at 10 W, which was

subsequently adjusted to the target heart rate (HR; 160 beats·min-1, i.e. to 168 ± 5 W;

mean ± SE) which was continued until they could no longer maintain a pedaling

frequency of 60 r.p.m. despite strong verbal encouragement.

Measurements

A catheter (1.1 mm ID, 20 gauge) was placed in the brachial artery of the non-dominant

arm and ABP was measured with a Bentley transducer (Uden, The Netherlands)

positioned at the level of the right atrium and connected to a pressure monitoring system

(Hewlett Packard M1275A, Germany). Beat-to-beat data acquisition of cardiovascular

variables was collected by a personal computer with customized software. The HR,

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stroke volume (SV) and thus cardiac output (Q) were calculated from the blood pressure

waveform using the Modelflow software program incorporating age, sex, height and

weight (BeatScope version 1.0 software, TNO-TPD, Biomedical Instrumentation,

Amsterdam, The Netherlands). Modelflow provides a reliable estimate of changes in Q

during exercise in healthy young humans (36) and SV and Q were expressed relative to

rest. Arterial blood samples were obtained at rest and during exercise and stored in ice-

water until analyzed for pH, oxygen (PaO2) and carbon dioxide (PaCO2) tensions, oxygen

saturation (SaO2), glucose and lactate (ABL725, Radiometer, Copenhagen, Denmark). In

addition, a catheter (14 gauge, 2.2 mm) was placed in the right internal jugular vein for

the measurement of internal jugular venous pressure, venous pH, oxygen (PvO2) and

carbon dioxide (PvCO2) tensions, oxygen saturation (SvO2), glucose and lactate. The

MCA Vmean was obtained by transcranial Doppler ultrasonography (Multidop X, DWL,

Sipplingen, Germany) with a 2−MHz probe placed over the temporal window and fixed

with an adjustable headband and adhesive ultrasonic gel (Tensive, Parker Laboratories,

Orange, NJ). Cerebral vascular resistance index (CVRi) was expressed as (MAP at MCA

level − the jugular venous pressure)/MCA Vmean. The MAP at the MCA level was

calculated as the vertical distance from the fourth intercostal space in the midclavicular

line (heart level) to the Doppler probe and the mass of blood (1.06 g/cm3): hydrostatic

pressure = the vertical length×0.77 mmHg/cm. To evaluate changes in brain activation,

the cerebral metabolic ratio was calculated from the arterial-venous differences across the

brain for O2/(glucose + ½ lactate) as published (7).

Data analysis

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Analog signals of ABP and the spectral envelope of MCA Vmean were sampled at 100 Hz

and digitized at 12 bits for off-line analysis. Beat-to-beat MAP and MCA Vmean were

obtained by integrating analog signals within each cardiac cycle and linearly interpolated

and re-sampled at 2 Hz for spectral analysis of dynamic CA (39). At rest and during

exercise dynamic CA was calculated as the transfer function gain and phase shift between

fluctuations in MAP and MCA Vmean (39). The transfer gain and phase shift reflect the

relative amplitude and time relationship between the changes in MAP and MCA Vmean

over a specified frequency range. From the temporal sequences of MAP and MCA Vmean,

the frequency-domain transformations are computed with a Fast Fourier Transformation

(FFT) algorithm. The transfer function H(f) between the two signals was calculated as

H(f) = Sxy(f) / Sxx(f), where Sxx(f) is the autospectrum of changes in MAP and Sxy(f) is the

cross-spectrum between the two signals. The transfer function magnitude H(f) and

phase spectrum Ф(f) were obtained from the real part HR(f) and imaginary part HI(f) of

the complex function:

H(f) = [HR(f)2 +HI(f) 2]1/2

Ф (f) = tan-1[HI(f) / HR(f)]

Moreover, the transfer function H(f) was normalized to the mean values of input

(x) and output (y) variables as H’(f) = [Sxy(f)·x] / [Sxx(f)·y] and the normalized gain was

calculated as 20 log H’(f) to provide values in decibels (dB). A value of 0 indicates

that the output varies by the same fraction of the mean value as the input, and a negative

and positive value indicate that the output varies less or more than the input.

The squared coherence function MSC(f) was estimated as:

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MSC(f) = Sxy(f)2 / [Sxx(f)·Syy(f)],

where Syy(f) is the autospectrum of changes in MCA Vmean. The squared coherence

function reflects the fraction of output power (MCA Vmean) that can be linearly related to

the input power (MAP) at each frequency. Similarly to a correlation coefficient, it varies

between 0 and 1.

Spectral power of MAP, MCA Vmean, mean value of transfer function gain, phase,

and coherence function were calculated in the very low (VLF, 0.02 to 0.07 Hz), low (LF,

0.07 to 0.20 Hz), and high (HF, 0.20 to 0.30 Hz) frequency ranges to reflect different

patterns of the dynamic pressure-flow relationship (39, 40). The ABP fluctuations in the

HF range, including those induced by the respiratory frequency, are transferred to MCA

Vmean, whereas ABP fluctuations in the LF range are independent of the respiratory

frequency and the LF transfer analysis reflects CA mechanisms (8, 39). Furthermore, the

VLF range of both the flow and the pressure variabilities appears to reflect multiple

physiological mechanisms that confound interpretation. Thus, we used the LF range for

the spectral analysis to identify the dynamic CA during exercise.

Resting measurements were made during a 3-min data collection segment, while

during exercise, the data segments of the 6-9th min, 12-15th min and the 3-min period

prior to exhaustion were used. The power spectra, transfer function gain, phase and

coherence of 12-15th min segment could be calculated for only five subjects because in 2

subjects the period prior to exhaustion overlapped.

Statistical Analysis

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One-way analysis of variance (ANOVA; SigmaStat; Jandel Scientific Software, SPSS

Inc., Chicago, IL, USA) with repeated measures was used to assess the differences in the

steady-state hemodynamic variables, spectral power of HR, MAP and MCA Vmean,

transfer function gain, phase and coherence function in each frequency range between

rest and the three exercise segments. A Student Newman-Keul test was employed post

hoc when main effects were significant, i.e. P<0.05. Data are expressed as mean ± SE

and the relationships between SvO2, MCA Vmean and PaCO2 were described using linear

regression analysis.

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RESULTS

Exercise increased ABP, HR, SV and Q (Table 1) and exhaustion was reached after 26.8

± 5.8 min. The HR continued to increase with time from 5-min (166 ± 4 bpm) to

exhaustion (185 ± 3 bpm) (P<0.05), while SV and Q did not change significantly during

exercise.

Blood gas variables

Throughout exercise the arterial lactate concentration increased and pH decreased

(P<0.05), while PaO2 remained stable (Table 1). However, SaO2 decreased at the

beginning of exercise (P<0.05), while SvO2 increased at minute five (P<0.05) and then

decreased to a value smaller than at rest (P<0.05). Thus, the arterial-venous difference

(a-v diff.) across the brain in SO2 decreased at the beginning of exercise (P<0.05) and

then increased at 15-min and at exhaustion to values similar to those obtained at rest.

The a-v diff. across the brain for lactate demonstrated a small release at rest (−0.45 ± 0.25

mmol/L), however at 15-min and at exhaustion there was an uptake (a-v diff.; 1.06 ± 0.23

and 1.00 ± 0.24 mmol/L, respectively; P<0.05). Thus, the cerebral metabolic ratio of

O2/(glucose + ½ lactate) decreased from 5.6 ± 0.7 at rest to 3.0 ± 0.4 and 3.8 ± 0.7 at 15-

min and at exhaustion, respectively (P<0.05).

MAP, MCA Vmean, SvO2 and PaCO2

Representative changes in MAP and MCA Vmean during exercise are outlined for one

subject (Fig. 1). Despite no significant change in MAP throughout exercise to

exhaustion, MCA Vmean decreased gradually towards exhaustion. The increase in MAP

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from rest to exercise was by 23.5 ± 3.2% at 5-min, 19.4 ± 3.8% at 15-min and 18.3 ±

4.3% at exhaustion (P<0.05), while the changes in MCA Vmean were by 19.7 ± 5.3%, 12.8

± 7.5% and -2.0 ± 9.0%, respectively (Table 1, P<0.05). Thus, CVRi tended to increase

from 1.16 ± 0.06 mmHg/cm/s at rest to 1.51 ± 0.23 mmHg/cm/s (P = 0.11) at exhaustion.

There was a small increase in PaCO2 at the beginning of exercise, but PaCO2 decreased to

below the resting value at exhaustion (P<0.05). Thus, during exercise the reduction in

MCA Vmean was related to the decrease in PaCO2 (MCA Vmean = 33 + 6.8 × PaCO2;

r=0.47, P=0.04; Fig. 2A). Similarly, SvO2 decreased from 68 ± 1% to 58 ± 2% at

exhaustion and was also related to the decrease in PaCO2 (SvO2 = 34 + 5.0 × PaCO2;

r=0.74, P<0.001; Fig. 2B).

Spectral and transfer function analyses

During exercise to exhaustion, there were no significant changes in the spectral power of

MCA Vmean across all frequency ranges nor with the spectral power of MAP, with the

exception of the HF range at exhaustion (Table 2, P<0.05). The transfer function phase

shift tended to decrease over time (Figs. 3, 4), but at the 12-15th min and at exhaustion the

transfer function gain, as well as the normalized values for each frequency range,

increased from the resting value (P<0.05). The coherence between MAP and MCA Vmean

decreased compared to rest, but there was no significant difference between the values

obtained during exercise (Fig. 4). Despite this, throughout exercise the coherence

remained above 0.5, i.e. it remained statistically significant.

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DISCUSSION

The main finding of the present investigation was that the effectiveness of dynamic CA

was reduced by exhaustive exercise despite a reduction in PaCO2 and a presumed marked

increase in sympathetic activity. Furthermore, the slope of the linear relationship

between MCA Vmean and PaCO2 throughout the range of PaCO2 (5.5~3.9 kPa) that was

developed by exercise to exhaustion appeared to be markedly less than at rest, i.e.

12%/kPa vs. 29%/kPa (12).

The CA maintains a constant cerebral blood flow and is considered to operate within a

MAP range of 60 to 150 mmHg as long as Q and PaCO2 remain stable (28). However,

cerebral blood flow is highly sensitive to alterations in PaCO2 (1, 8, 9, 12, 14, 27, 31).

For example, at rest, increases in PaCO2 dilates the cerebral resistance vessels and

promotes blood flow, whereas a decrease causes vasoconstriction of the cerebral

resistance vessels (14, 17, 31), which consequently produce marked changes in MCA

Vmean without changes in diameter (12, 14, 31). The relationship between MCA Vmean and

PaCO2 is unclear and has been reported to be linear (12), sigmoidal (31) or exponential

(14). The cerebral metabolic demand for oxygen also contributes to changes in MCA

Vmean (4). Throughout exercise to exhaustion, SvO2 decreased and correlated to the

decrease in PaCO2 (Fig. 2B) suggesting that PaCO2 maintains its ability to cause

vasoconstriction of the cerebral resistance vessels during exhaustive exercise. With both

MAP and Q remaining stable during exercise, the relationship between individual MCA

Vmean and PaCO2 ranging from 5.5 to 3.9 kPa was linear (Fig. 2A), but the slope was

approximately half of that found both at rest (12) and during exercise combined with the

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administration of CO2 to the inspired air (24). Moreover, during exercise MCA Vmean was

higher than expected for a given PaCO2.

The cerebral metabolic rate of oxygen remains stable during cerebral activation

(23). Therefore, during the initial five minutes of exercise, a decrease in the a-v diff. in

blood oxygen saturation across the brain indicates an enhanced perfusion (Table 1).

However, at 15 min and at exhaustion, the a-v diff. in SO2 across the brain was not

significantly different from rest, suggesting normalization of cerebral blood flow. In

addition, the a-v diff. of lactate concentration across the brain indicated an increased

uptake as the subject became tired, particularly at exhaustion. Moreover, the cerebral

metabolic ratio decreased during exercise to exhaustion. These results substantiate the

hypothesis that cerebral metabolism is enhanced by intense exercise. Ide et al. (13, 15)

found that changes in Q, independent of MAP, affect MCA Vmean. However, in the

present study both Q and MAP remained stable during exhaustive exercise and the

decreases in PaCO2 with exercise-induced hyperventilation therefore played a major role

in regulation of MCA Vmean.

By using linear dynamic analysis between MAP and MCA Vmean, an increase in

the LF transfer function gain and normalized gain was identified during exhaustive

exercise. Such an increase in transfer function gain between beat-to-beat MAP and

MCA Vmean indicates a reduction in dynamic CA (26, 39, 40), while at rest dynamic CA

increases when PaCO2 is reduced (8, 27). By deflating thigh blood pressure cuffs, the

rate of regulation, which is an index of dynamic CA has been calculated as 0.38, 0.20,

and 0.11/sec in hypocapnia, normocapnia , and hypercapnia, respectively (1). Equally,

Edwards et al. (9) using transfer function analysis demonstrated that dynamic CA is

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improved at a low end-tidal PCO2. In the present investigation the sympathoexcitation

associated with exhaustive exercise (10, 11, 33) and hyperventilatory response to

metabolic acidosis and its resultant hypocapnia (16) would be expected to enhance

dynamic CA (1, 8, 9, 27). However, the transfer function gain was increased indicating

an impairment of dynamic CA. The impairment in dynamic CA may be related to the

altered milieu of cerebral vessels associated with the efflux of metabolites into the

vascular tissue related to increased brain metabolism (7). In the systemic circulation such

increases in metabolites have been found to alter vasomotor tone (18). However, brain

metabolism may not be the only reason why dynamic CA becomes impaired during

exercise.

Another possible explanation of an impaired dynamic CA is acute

hyperammonemia during exhaustive exercise. In patients with acute liver failure,

sympathetic regulation of cerebral blood flow is impaired (21) and CA becomes impaired

in response to stepwise hypotensive stimuli (20), which may be related to ammonia-

induced perturbations of brain metabolism (6, 25). Similarly, intense exercise increases

the blood content of ammonia which easily penetrates the blood-brain-barrier (2). Thus,

impaired dynamic CA and a reduced responsiveness of MCA Vmean to changes in PaCO2

could result from elevated ammonia in the brain during exhaustive exercise.

The range, or set point, of the function representing both static and dynamic CA is

influenced by the prevailing perfusion pressure (35). In chronic hypertension, the limits

of the CA function curve are shifted to the higher MAP (28), while chronic cerebral

hypoperfusion shifts the curve to a lower pressure (38). Acute exposure to orthostatic

stress, such as head-up tilt and lower body negative pressure results in a downward (3) or

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rightward (41, 42) shift in the CA curve. Levine et al. (22) speculate that sympathetic

activation during lower body negative pressure shifts the CA curve to the right and

compromises CA during orthostatic hypotension and may contribute to symptoms of pre-

syncope. However, it has been reported that during heavy exercise the prospect of hyper-

perfusion of the brain was prevented by sympathoexcitation (13) and may be reflective of

a rightward shift in the CA function curve. In the present investigation, although a

rightward shift in the CA curve may have been present, especially at exhaustion, dynamic

CA was impaired. This impairment was associated with the hyperventilatory response to

metabolic acidosis producing hypocapinia, therefore we conclude that the interaction

between sympathoexcitation and decreases in PaCO2 are changed by exhaustive exercise

by an unidentified mechanism.

Potential Limitations

Estimating changes in cerebral blood flow via MCA Vmean could be influenced by

changes in diameter of the insonated vessel. However, MCA diameter remains relatively

constant under a variety of conditions (32, 34). Nonetheless, sympathetic activity may

induce constriction of MCA during submaximal and, more particularly, during maximal

exercise (16). However, sympathetic activation produced during muscle ischemia

following rhythmic handgrip exercise does not change the luminar diameter of a systemic

conduit artery (30). Pott et al. (29) has suggested that the 50% increase of MCA Vmean

during strenuous exercise (at >80% of maximal work capacity) may reflect MCA

constriction when compared with the only 20% increase of MCA Vmean observed in

athletes during low workload exercise. However, these differences can be explain by the

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increase in sympathoexcitation producing a constrictions of the cerebral resistance

vessels more at the higher workload without changing MCA diameter. Hence, we

contend that the beat-to-beat changes in MCA Vmean during steady-state exercise

primarily reflect changes in flow and is confirmed by the changes in SvO2. It should also

be noted that during intense exercise, the MAP profile includes a considerable increase in

pulse pressure. Considering that the fluctuations in MCA Vmean encompass both changes

in peak systolic and diastolic flow velocities, it is important to consider the potential

differences that may occur during these two distinct phases of the MCA V profile. This is

particularly important in regard to the increase in the systolic pressure wave that must be

countered by CA.

In conclusion, the MCA Vmean-PaCO2 relationship appeared to be linear

throughout the range of PaCO2 that was produced by exercise to exhaustion. However,

the slope of the relationship was markedly less during exercise than at rest and despite the

large reduction in PaCO2 resulting from hyperventilation dynamic CA was impaired.

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ACKOWLEDGEMENTS

The authors appreciate the time and effort expended by the subjects. We thank Peter

Nissen for his expert technical assistance and Lisa Marquez for her assistance in

preparing the document.

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FIGURE LEGENDS Figure 1. Mean arterial pressure (MAP) and middle cerebral artery mean blood flow

velocity (MCA Vmean) during exercise for one subject.

Figure 2. A; The relationship between middle cerebral artery mean blood flow velocity

(MCA Vmean) and arterial CO2 tension (PaCO2), B; The relationship between venous O2

saturation (SvO2) and PaCO2 at rest and during exercise. Values are mean ± SE.

Figure 3. Cross-spectral analysis in the entire spectrum from 0.02 to 0.3 Hz at rest, 6-9

min, 12-15 min and exhaustion. Group-averaged phase (A), gain (B) and normalized

gain (C) between MAP and MCA Vmean are shown. Values are means. N=7.

Figure 4. Group-averaged low frequency (LF; 0.07-0.2 Hz) transfer function phase (A),

gain (B), normalized gain (C) and coherence (D) between MAP and MCA Vmean at rest

and during exercise. *different from rest (P<0.05); †different from 6-9 min (P<0.05).

Values are mean ± SE.

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Table 1. Cardiovascular and blood gas variables at rest and during exercise to exhaustion.

Rest 5 min 15 min Exhaustion MAP (mmHg) 93 ± 2 115 ± 3* 110 ± 2* 110 ± 4*

MAP at MCA (mmHg) 64 ± 2 86 ± 3* 81 ± 3* 81 ± 4* JVP (mmHg) -3.2 ± 0.7 0.3 ± 0.5* -1.5 ± 0.4 -1.0 ± 0.6

MCA Vmean (cm/s) 59 ± 3 70 ± 4* 66 ± 4 57 ± 5† CVRi (mmHg s/cm) 1.16 ± 0.07 1.27 ± 0.10 1.29 ± 0.13 1.51 ± 0.23 HR (bpm) 69 ± 2 166 ± 4* 174 ± 4*† 185 ± 3*†‡ SV (%) 100 ± 0 143 ± 2* 147 ± 3* 138 ± 5* Q (%) 100 ± 0 349 ± 11* 380 ± 13* 377 ± 12*†

pH (a) 7.41 ± 0.01 7.35 ± 0.01* 7.34 ± 0.01* 7.32 ± 0.03* pH (v) 7.35 ± 0.01 7.32 ± 0.01 7.29 ± 0.02* 7.26 ± 0.03*† PaO2 (kPa) 14.3 ± 0.5 12.1 ± 1.3 12.9 ± 0.3 13.0 ± 0.4 PvO2 (kPa) 4.6 ± 0.1 6.4 ± 1.4 4.4 ± 0.1 4.5 ± 0.1

PaCO2 (kPa) 5.2± 0.2 5.5 ± 0.2 4.5 ± 0.2*† 3.9 ± 0.2*†‡ PvCO2 (kPa) 6.7± 0.1 6.4 ± 0.3 6.0 ± 0.1 5.8 ± 0.2* SaO2 (%) 98.4 ± 0.2 97.6 ± 0.2* 97.2 ± 0.1* 97.0 ± 0.2* SvO2 (%) 62.7 ± 1.7 68.1 ± 0.7* 58.3 ± 1.8*† 58.0 ± 2.0†

SO2 a-v diff. (%) 35.7 ± 1.7 29.5 ± 0.8* 38.9 ± 1.7† 39.1 ± 1.8† La (a) (mmol/L) 0.74 ± 0.09 4.71 ± 0.51* 9.27 ± 1.35*† 12.26 ± 1.98*† La (v) (mmol/L) 1.23 ± 0.32 4.88 ± 0.32* 8.80 ± 1.46*† 11.55 ± 2.16*†

La a-v diff. (mmol/L) -0.45 ± 0.25 0.10 ± 0.28 1.06 ± 0.23*† 1.00 ± 0.24*† Glu (a) (mmol/L) 5.99 ± 0.40 5.83 ± 0.41 5.57 ± 0.38 5.53 ± 0.40 Glu (v) (mmol/L) 5.33 ± 0.42 5.42 ± 0.47 4.70 ± 0.48*† 4.83 ± 0.43*†

a-v diff.[O2/(Glu +1/2 La)] 5.6 ± 0.7 4.2 ± 0.5* 3.0 ± 0.4* 3.8 ± 0.7*

Values are mean ± SE. MAP, mean arterial pressure; MCA, middle cerebral artery; JVP, juglular venous pressure; CVRi, cerebral vascular resistance index; HR, heart rate; SV, stroke volume; Q, cardiac output; pH (a) and pH (v), arterial and venous hydrogen ion concentration; PaO2 and PvO2, arterial and venous O2 tension; SaO2 and SvO2, arterial and venous O2 saturation; SO2 a-v diif., arterial-venous difference of O2 saturation; La (a) and La (v), arterial and venous lactate concentration; La a-v diif., arterial-venous difference of lactate concentration; Glu (a) and Glu (v), arterial and venous glucose concentration; a-v diif. [O2/(Glu + ½ La)], cerebral metabolic ratio of O2/(glucose + ½ lactate). *different from rest (P<0.05); †different from 5 min (P<0.05); ‡different from 15 min (P<0.05).

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Table 2. Power spectra of beat-to-beat variability of mean arterial pressure (MAP) and middle cerebral artery mean blood flow velocity (MCA Vmean) at rest, 6-9 min, 12-15min and 3min prior to exhaustion during exercise.

Rest 6-9 min 12-15 min Exhaustion (n=7) (n=7) (n=5) (n=7)

MAP VL (mmHg)2 5.57 ± 1.30 3.44 ± 0.38 2.25 ± 0.33 3.35 ± 0.88 L (mmHg)2 5.96 ± 1.96 2.85 ± 0.86 2.98 ± 0.79 4.09 ± 0.66

H (mmHg)2 0.60 ± 0.14 0.42 ± 0.11 0.52 ± 0.13 1.07 ± 0.29 MCA Vmean VL (cm/s)2 3.79 ± 0.46 5.80 ± 1.38 7.98 ± 1.52 6.84 ± 1.28

L (cm/s)2 5.81 ± 1.66 3.09 ± 0.59 5.35 ± 0.77 8.02 ± 1.90 H (cm/s)2 0.69 ± 0.13 0.52 ± 0.09 0.97 ± 0.24 2.70 ± 0.75*†‡

Values are mean ± SE. MAP, mean arterial pressure; MCA Vmean, middle cerebral artery mean blood flow velocity; VLF, very low frequency range; LF, low frequency range; HF, high frequency range. *different from rest (P<0.05); †different from 5 min (P<0.05); ‡different from 15 min (P<0.05).

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Figure 1.

Time (sec.)0 200 400 600 800 1000 1200 1400

MA

P(m

mH

g)

40

60

80

100

120

MC

AV m

ean

(cm

/s)

40

60

80

100

120

MAPMCA Vmean

exhaustion (21:30)start exercise

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Figure 2.

A

3 4 5 6

MC

AV m

ean

(cm

/s)

40

50

60

70

80

RestExerciseRegression line

MCA Vmean = 33 + PaCO2 * 6.8 (r = 0.47, P=0.04)

5 min

15 min

Exhaustion

B

PaCO2 (kPa)3 4 5 6

SvO

2(%

)

50

55

60

65

70

75SvO2 = 34 + PaCO2 *5 (r = 0.74, P<0.001)

5 min

15 min

Exhaustion

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Figure 3.

A

Phas

e(r

adia

n)

-2

-1

0

1

2

Rest6-9 min12-15 minexhaustion

B

Gai

n(c

m/s

/mm

Hg)

0

1

2

3

C

Frequency (Hz)0.05 0.10 0.15 0.20 0.25 0.30

Nor

mal

ized

Gai

n(d

b)

-4-202468

101214

LF range

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Figure 4.

A

LFPh

ase

(rad

ian)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

B

LFG

ain

(cm

/s/m

mH

g)

0

1

2

C

Nor

mal

ized

LFG

ain

(db)

0

2

4

6

8

10

D

Rest 6-9 min 12-15 min exhaustion

LFC

oher

anec

(U)

0.500.550.600.650.700.750.800.85

*†*†

* *†

**

*