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Division of Medical Physics, University of Leicester, Leicester Royal Infirmary, Leicester LE1 5WW; and Division of Medicine for the Elderly, University of Leicester, Glenfield Hospital, Leicester LE3 9QP, United Kingdom
The linear dynamic relationship between
systemic arterial blood pressure (ABP) and cerebral blood flow velocity
(CBFV) was studied by time- and frequency-domain analysis methods. A
nonlinear moving-average approach was also implemented using
Volterra-Wiener kernels. In 47 normal subjects, ABP was measured with
Finapres and CBFV was recorded with Doppler ultrasound in both middle
cerebral arteries at rest in the supine position and also during ABP
drops induced by the sudden deflation of thigh cuffs. Impulse response functions estimated by Fourier transfer function analysis, a
second-order mathematical model proposed by Tiecks, and the linear
kernel of the Volterra-Wiener moving-average representation provided
reconstructed velocity model responses, for the same segment of data,
with significant correlations to CBFV recordings corresponding to
r = 0.52 ± 0.19, 0.53 ± 0.16, and 0.67 ± 0.12 (mean ± SD), respectively. The correlation coefficient for the linear plus quadratic kernels was 0.82 ± 0.08, significantly superior to that for the linear models
(P < 10
6). The supine linear
impulse responses were also used to predict the velocity transient of a
different baseline segment of data and of the thigh cuff velocity
response with significant correlations. In both cases, the three linear
methods provided equivalent model performances, but the correlation
coefficient for the nonlinear model dropped to 0.26 ± 0.25 for the
baseline test set of data and to 0.21 ± 0.42 for the thigh cuff
data. Whereas it is possible to model dynamic cerebral autoregulation
in humans with different linear methods, in the supine position a
second-order nonlinear component contributes significantly to improve
model accuracy for the same segment of data used to estimate model
parameters, but it cannot be automatically extended to represent the
nonlinear component of velocity responses of different segments of data or transient changes induced by the thigh cuff test.
cerebral blood flow; mathematical model; thigh cuff test; Volterra-Wiener kernels; cerebral hemodynamics
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