Nonstationarity of dynamic cerebral autoregulation

Abstract Dynamic cerebral autoregulation (dCA), the transient response of cerebral blood flow (CBF) to rapid changes in arterial blood pressure (BP), is usually quantified by parameters extracted from time- or frequency-domain analysis. Reproducibility studies of dCA parameters and consideration of the physiological determinants of the dynamic BP-CBF relationship provide strong indications that dCA is a nonstationary process. As a consequence, new analytical approaches are needed to estimate dCA parameters with greater temporal resolution thus allowing its longitudinal patterns of variability to be assessed in health and disease states. Techniques proposed for this task include ARMA models with moving windows, recursive least-squares, Laguerre–Volterra networks, wavelet phase synchronisation, and multimodal pressure-flow analysis. Initial results with these techniques have revealed the influence of some key determinants of dCA nonstationarity, such as PaCO2 , as well as their ability to reflect dCA impairment in different clinical conditions. One key priority for future work is the development and validation of multivariate time-varying techniques to minimise the influence to the many co-variates which contribute to dCA nonstationarity.