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Middle cerebral artery flow velocity and pulse pressure during dynamic exercise in humans

¹Department of Integrative Physiology, University of North Texas Health Science Center, Fort Worth; ²Institute for Exercise and Environmental Medicine, Presbyterian Hospital and The University of Texas Southwestern Medical Center, Dallas, Texas; and ³Copenhagen Muscle Research Center, Department of Anesthesia, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark

Submitted 23 September 2004 ; accepted in final form 1 December 2004

	ABSTRACT

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Exercise challenges cerebral autoregulation (CA) by a large increase in pulse pressure (PP) that may make systolic pressure exceed what is normally considered the upper range of CA. This study examined the relationship between systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) and systolic (V_s), diastolic (V_d). and mean (V_m) middle cerebral artery (MCA) blood flow velocity during mild, moderate, and heavy cycling exercise. Dynamic CA and steady-state changes in MCA V in relation to changes in arterial pressure were evaluated using transfer function analysis. PP increased by 37% and 57% during moderate and heavy exercise, respectively (P < 0.05), and the pulsatility of MCA V increased markedly. Thus exercise increased MCA V_m and V_s (P < 0.05) but tended to decrease MCA V_d (P = 0.06). However, the normalized low-frequency transfer function gain between MAP and MCA V_m and between SBP and MCA V_s remained unchanged from rest to exercise, whereas that between DBP and MCA V_d increased from rest to heavy exercise (P < 0.05). These findings suggest that during exercise, CA is challenged by a rapid decrease rather than by a rapid increase in blood pressure. However, dynamic CA remains able to modulate blood flow around the exercise-induced increase in MCA V_m, even during high-intensity exercise.

cerebral circulation; diastolic velocity; systolic velocity

Dynamic CA is frequency dependent (10, 15, 24, 26, 27), and frequency-domain analyses of CA allows for evaluation of the influence of exercise-induced changes in arterial blood pressure on MCA V. Brys et al. (3) used frequency-domain analysis to evaluate dynamic CA between mean arterial pressure (MAP) and MCA V_m variability in the low-frequency (LF) domain. They found dynamic CA to be unaltered during incremental exercise to 150 W compared with rest. However, each stage of exercise was performed for just 3 min, raising some question as to whether steady-state conditions were reached (22).

During leg cycling exercise, PP may increase threefold with a large increase in systolic blood pressure (SBP) combined with a slight decrease or no change in diastolic blood pressure (DBP) (18). Considering that fluctuations in MCA V encompass both changes in systolic and diastolic blood flow velocities, we considered the potential differences that may be established during these two distinct phases of the MCA V profile, where increases in SBP may exceed the range of pressure counteracted by CA. This investigation examined dynamic CA using transfer function analysis between arterial blood pressure and MCA V_m, systolic blood flow velocity (V_s), and diastolic blood flow velocity (V_d) during three levels of steady-state cycle exercise. We hypothesized that as PP increases with workload, dynamic CA will be affected by the large increases in SBP.

	METHODS

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Six men and one woman with a mean age of 25 ± 1 yr, height of 184 ± 3 cm, and weight of 75 ± 2 kg (means ± SE) participated in this study. All subjects were free of any known 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 (01-386/02). Subjects were requested to abstain from caffeinated beverages for 12 h and strenuous physical activity and alcohol for at least 24 h before any experimental session. Before the experiments were performed, each subject visited the laboratory for familiarization with the techniques and procedures of the protocol.

Protocol. On the experimental day, the subjects arrived at the laboratory at least 2 h after a light meal. The subjects were seated in a semirecumbent position on a bed that was modified to allow seated cycling exercise. Each subject performed three 8-min bouts of exercise at a steady-state heart rate (HR) of 90, 120, and 150 beats/min representing mild (EX90), moderate (EX120), and heavy (EX150) workloads. After a 10-min resting period, the subjects worked at 10 W subsequently adjusted to reach the target HR for each exercise bout performed in random order separated by 30–40 min to enable recovery of HR and MAP from the preceding trial. Once the target HR was achieved, subjects exercised for 5 min to assure steady-state conditions before the spectral and transfer function analysis data were collected. Hemodynamic parameters were obtained by averaging the data segment over 3 min for each trial.

Measurements. Blood pressure was measured by a catheter (1.1 mm inner diameter, 20 gauge) placed in the brachial artery of the nondominant arm and connected to a transducer (Baxter; Uden, The Netherlands) positioned at the level of the right atrium in the midaxillary line. The HR was monitored using a lead II electrocardiogram, and the signals were connected to Dialogue 2000 monitor (IBC-Danica; Copenhagen, Denmark) interfaced with a personal computer equipped with customized data-acquisition software for the beat-to-beat recordings. Arterial blood samples were obtained at rest and after steady state was reached at each bout of exercise and immediately analyzed for pH, arterial PCO₂ (Pa_CO2), and lactate (ABL725, Radiometer; Copenhagen, Denmark). Beat-to-beat MAP, SBP, and DBP were measured, and PP was calculated from SBP and DBP for each cardiac cycle.

The MCA V was obtained by transcranial Doppler ultrasonography (Multidop X, DWL; Sipplingen, Germany). A 2-MHz Doppler probe was placed over the temporal window and fixed with an adjustable headband and adhesive ultrasonic gel (Tensive, Parker Laboratories; Orange, NJ). Beat-to-beat MCA V_m, V_s, and V_d were measured as well as MCA PP V (MCA PP V = MCA V_s – MCA V_d). The cerebrovascular conductance index (CVCI) was calculated by dividing MCA V_m by MAP and used as an estimate for changes in cerebrovascular conductance (23). MAP at the level of the MCA took into consideration the vertical distance from the fourth intercostal space in the midclavicular line (heart level) to the transcranial Doppler probe (i.e, hydrostatic pressure = vertical distance x 0.77 mmHg/cm).

Data analysis. The relationships between changes in MAP, SBP, and DBP and MCA V_m, V_s, and V_d, respectively, were evaluated using transfer function analysis. From the temporal sequence input [x(t)] and output [y(t)], the frequency-domain transforms x(f) and y(f) were computed with a fast Fourier transform. The transfer function H(f) between the two signals was calculated as H(f) = S_xy(f)/S_xx(f), where S_xx(f) is the input autospectrum of changes in x(t) and S_xy(f) is the cross-spectrum between the two signals. The transfer function magnitude H(f) and phase spectrum (f) obtained from the real part H_R(f) and imaginary part H_I(f) of the complex transfer function becomes:

In addition, H(f) was normalized to the mean values of input (x) and output (y) variables as H'(f) = S_xy(f)·x/S_xx(f)·y, and the normalized gain was calculated as 20 log H'(f) to express values in decibels. A value of 0 indicates that the output varies by the same fraction of the mean value as the input, a negative value indicates that it varies less, and a positive value indicates that it varies greater than the input.

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

where S_yy(f) is the autospectrum of changes in y(t). The squared coherence function reflects the fraction of output power that can be linearly related to the input power at each frequency. Similar to a correlation coefficient, it varies between 0 and 1 and reflects the validity of transfer function analysis between two values.

Analog signals of arterial blood pressure and the spectral envelope of MCA V were sampled at 100 Hz and digitized at 12 bits for off-line analysis. Beat-to-beat MAP, SBP, DBP, MCA V_m, MCA V_s, and MCA V_d were obtained from the analog signals. These beat-to-beat data were then linearly interpolated and resampled at 2 Hz for spectral analysis of dynamic CA (24). For an estimate of dynamic CA, using the transfer function, the cross-spectrum between changes in MAP and MCA V_m, SBP and MCA V_s, and DBP and MCA V_d were calculated and divided by the autospectrum of MAP, SBP, and DBP, respectively. Spectral power, mean value of transfer function gain, phase, and coherence function were calculated in the very-LF (VLF; 0.02–0.07 Hz), LF (0.07–0.20 Hz), and high-frequency (HF; 0.20–0.30 Hz) ranges to reflect patterns of the dynamic pressure-flow relationship (24). These frequency ranges reflect patterns of the dynamic pressure-flow relationship, as identified by transfer function analysis (24, 25). Blood pressure fluctuations in the HF range are induced primarily by respiration, whereas those in the LF range are independent of the respiratory frequency and are dampened by CA (8). Furthermore, the VLF range of both flow and pressure variability appears to reflect multiple physiological mechanisms that confound interpretation. Thus we used the LF range of each variable for the spectral analysis to identify the dynamic CA during exercise (24). The transfer function gain and phase shift reflect the relative amplitude and time relationships, respectively, between the changes in perfusion pressure and blood flow over a specific frequency range. CA decreases the transmission effect of pressure on flow. Thus increased transfer function gain or decreased transfer function phase between pressure and flow can be interpreted as an increased effect of transmission, which suggests that dynamic CA is impaired.

Statistical analysis. One-way ANOVA with repeated measures was used to assess the differences in the steady-state hemodynamic variables, spectral power of arterial pressure and MCA V, and transfer function gain, phase, and coherence function in each frequency range between rest and the three exercise bouts (SigmaStat, Jandel Scientific Software, SPSS; Chicago, IL). A Student-Newman-Keuls test was employed post hoc when main effects were significant, i.e., P < 0.05, and data are expressed as means ± SE.

	RESULTS

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During EX90, MAP and MCA V_m did not change significantly compared with rest, but PP increased (Table 1). However, the changes in MCA V_m were associated with an increased MAP from rest to EX120 and EX150. During EX120 and EX150, MAP increased by 8% and 21% and MCA V_m increased by 12% and 16%, respectively (P < 0.05). Thus CVCI did not increase during increasing exercise workload (P = 0.224). Arterial lactate increased and pH and Pa_CO2 decreased at EX150.

View larger version (32K): [in this window] [in a new window]   Fig. 1. Representative waveforms (solid line) and means (shaded line) of arterial blood pressure (ABP; top) and middle cerebral artery (MCA) blood flow velocity (V; bottom) from a subject at rest (left) and during heavy exercise (EX150; right).

View larger version (30K): [in this window] [in a new window]   Fig. 2. A: mean arterial pressure (MAP) and MCA mean V (MCA Vm). B: change in MCA Vm to change in MAP (MCA Vm/MAP). C: systolic blood pressure (SBP) and MCA systolic V (MCA Vs). D: change in MCA Vs to change in SBP (MCA Vs/SBP). E: diastolic blood pressure (DBP) and MCA diastolic V (MCA Vd). F: change in MCA Vd to change in DBP (MCA Vd/DBP) at rest and during mild exercise (EX90), moderate exercise (EX120), and EX150. Values are means ± SE. *Significantly different from rest (P < 0.05); significantly different from EX90 (P < 0.05); significantly different from EX120 (P < 0.05).

Transfer function analysis. During exercise, the transfer function phase shift between arterial blood pressure and MCA V remained stable and did not differ from resting values (Figs. 3 and 4). Likewise, the normalized LF transfer function gain between MAP and MCA V_m as well as the normalized LF gain between SBP and MCA V_s remained unchanged from rest to exercise. However, there was a tendency for an increase in the MAP/MCA V_m normalized LF gain during EX150 (P = 0.08). Moreover, the normalized LF gain between DBP and MCA V_d increased from rest during EX90, EX120, and EX150 (P < 0.05). Importantly, the coherence between change in arterial blood pressure and MCA V remained above 0.5 during exercise, indicating that statistical significance was maintained.

View larger version (55K): [in this window] [in a new window]   Fig. 3. Cross-spectral analysis in the spectrum from 0 to 0.3 Hz at rest (shaded line) and during EX150 (solid line). Group-averaged phase (top), normalized gain (middle), and coherence (bottom) between MAP and MCA Vm (left), SBP and MCA Vs (middle), and DBP and MCA Vd (right) are shown. Values are means; n = 7.

View larger version (32K): [in this window] [in a new window]   Fig. 4. Group-averaged low-frequency (LF; 0.07–0.2 Hz) phase (A), LF gain (B), normalized LF gain (C), and LF coherence (D) between MAP and MCA Vm (left), SBP and MCA Vs (middle), and DBP and MCA Vd (right) at rest and during EX90, EX120, and EX150.Values are means ± SE. *Significantly different from rest (P < 0.05); significantly different from EX90 (P < 0.05); significantly different from EX120 (P < 0.05).

	DISCUSSION

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MCA V varies in response to fluctuations in arterial pressure (15). During exercise, the change in MCA V between systolic and diastolic flow velocity increases as PP increases. An increase in MCA V_m is in direct relation to workload and is likely a consequence of the increase in cerebral metabolism (5, 6) provided that there is a sufficient increase in cardiac output (13). The increase in MCA V_m from rest throughout exercise suggests that with increasing workload, the cerebral vasculature accommodates an increase in flow. More importantly, the unchanged transfer function gain between SBP and MCA V_s indicates that dynamic CA was functioning in maintaining the systolic phase of the MCA V around this higher flow velocity. However, the progressive decrease in MCA V_d from rest to exercise combined with the increasing normalized transfer function gain between DBP and MCA V_d suggests that dynamic CA was impaired in regulating the diastolic phase of the MCA V profile. This was true particularly at higher workloads when MCA V_d was reduced even in relation to DBP. Although the normalized LF transfer function gain between MAP and MCA V_m and the MCA V_m/MAP relationship remained stable during exercise, there was a tendency for the normalized LF gain of MAP/MCA V_m to increase at the highest workload (P = 0.08).

It is clear that the rapid changes in systemic blood pressure that occur during exercise present a considerable challenge to CA mechanisms. This situation is important especially in regards to the large increase in systolic pressure that must be countered by CA. Thus CA must work effectively to protect the brain from the high systolic pressures and flows during exercise. Our findings indicate that this is indeed the case in regards to the increases in pressure during systole; however, the effectiveness of CA appears to be impaired with the rapid decreases in pressure during diastole. This apparent selective control mechanism may lead to unusually low cerebral perfusion during the diastolic phase of velocity. Although these alterations had no apparent effect on our subjects, these findings may prove important in understanding the decreases in cerebral perfusion that occur during recovery from dynamic exercise and especially from resistance exercise where grayouts or blackouts develop (4, 9).

A potential factor that may contribute to the impaired control of MCA V_d during exercise is a dynamic shift in the CA curve to the right. CA is a dynamic process with temporal heterogeneity (10) and is subject to modulation (2, 26, 27). Levine et al. (14), in attempting to explain syncope during lower body negative pressure, speculated that sympathetic activation shifts the CA curve to the right. If a rightward shift in the CA curve occurs in response to sympathoexcitation, then, during exercise, it would serve as a protective mechanism during the exercise induced systolic hypertension. However, a rightward shift of CA may be detrimental in the regulation of MCA V during the diastolic phase and lead to impairment in MCA V_d regulation. Clearly, the interaction between arterial barorelfex control of systemic blood pressure and CA becomes increasingly important during dynamic exercise.

A potential limitation of estimating MCA V using transcranial Doppler ultrasonography is that vasoconstriction of the insonated vessel increases MCA V at any given volume of flow. However, in humans, the MCA diameter appears to remain relatively constant under a variety of conditions (19, 20). Furthermore, even though Pott et al. (16) reported that the 50% increase in MCA V_m that occurred during heavy exercise at >80% of maximal O₂ consumption may be confounded by MCA constriction, this was not the case for lower exercise workloads, which elicited a 30% increase in MCA V_m, and the finding during maximal exercise has not been reproduced (11). In the present study, during EX150, the increase in MCA V_m approximated 17%. Thus we would contend that beat-to-beat changes in MCA V_m reflected changes in flow. Another limitation is that heavy exercise may cause signal noise in the data acquisition of MCAV, but that appeared not to be the case from visual inspection of the MCA V waveforms. In addition, the coherence remained above 0.5 during all conditions suggesting that there was little effect of signal noise on the validity of transfer function analysis during exercise. Finally, it is possible that the diastolic phase of the velocity profile may not be faithfully represented by the Doppler monitoring system. However, to insure that flow-velocity profile of the MCA V was faithfully measured, we used transcranial Doppler equipment to measure the flow-velocity wave with a resonance frequency (RF) range of 12–25 Hz. This RF range has been identified to adequately encompass systolic and diastolic pressure wave monitoring using an arterial catheter and pressure transducer (12).

Pott et al. (17) reported that during the catch phase of the rowing stroke, MCA V_m increased in parallel with MAP but during the return phase of the stroke MCA V_m declined to a nadir even though MAP was stable. Together with the present data, these findings indicate that as PP increases and the pulse interval decreases with an increase in workload, dynamic CA becomes less able to regulate transient decreases in cerebral blood flow. These findings may prove important in understanding the decreases in cerebral perfusion that occur during the recovery from dynamic exercise and especially from resistance exercise where grayouts or blackouts develop (4, 9).

	GRANTS

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This study was supported in part by National Heart, Lung, and Blood Institute Grant HL-045547 and by a grant from the Copenhagen Muscle Research Centre, Copenhagen, Denmark.

	ACKNOWLEDGMENTS

 
The authors appreciate the time and effort expended by all the volunteer subjects. We thank Peter Nissen for the expert technical assistance.

Present address of P. J. Fadel: Dept. of Internal Medicine, Univ. of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390-8586.

	FOOTNOTES

 

Address for reprint requests and other correspondence: S. Ogoh, Dept. of Integrative Physiology, Univ. of North Texas Health Science Center, 3500 Camp Bowie Blvd., Fort Worth, TX 76107 (E-mail: sogoh{at}hsc.unt.edu)

The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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