Linear and nonlinear analysis of human dynamic cerebral autoregulation.

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.