The linear dynamic relationship between systemic arterial blood pressure (A
BP) and cerebral blood flow velocity (CBFV) was studied by time- and freque
ncy-domain analysis methods. A nonlinear moving-average approach was also i
mplemented using Volterra-Wiener kernels. In 47 normal subjects, ABP was me
asured 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 fun
ctions estimated by Fourier transfer function analysis, a second-order math
ematical model proposed by Tiecks, and the linear kernel of the Volterra-Wi
ener moving-average representation provided reconstructed velocity model re
sponses, for the same segment of data, with significant correlations to CBF
V 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 lin
ear plus quadratic kernels was 0.82 +/- 0.08, significantly superior to tha
t for the linear models (P < 10(-6)). The supine linear impulse responses w
ere also used to predict the velocity transient of a different baseline seg
ment of data and of the thigh cuff velocity response with significant corre
lations. In both cases, the three linear methods provided equivalent model
performances, but the correlation coefficient for the nonlinear model dropp
ed 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 a
utoregulation in humans with different linear methods, in the supine positi
on a second-order nonlinear component contributes significantly to improve
model accuracy for the same segment of data used to estimate model paramete
rs, but it cannot be automatically extended to represent the nonlinear comp
onent of velocity responses of different segments of data or transient chan
ges induced by the thigh cuff test.