Autoregulation refers to the automatic adjustment of blood flow to supply the required oxygen and glucose and remove waste, in proportion to the tissue’s requirement at any instant of time. For the brain, cerebral autoregulation is an active process by which cerebral blood flow is controlled at an approximately steady level despite changes in the arterial blood pressure. Robust assessment of the cerebral autoregulation by a model that characterizes this system has been the goal of many studies, searching for techniques that can be used in clinical scenarios to detect potentially dangerous impairment of control.
Autoregulation refers to the automatic adjustment of blood flow to supply the required oxygen and glucose and remove waste, in proportion to the tissue’s requirement at any instant of time. For the brain, cerebral autoregulation is an active process by which cerebral blood flow is controlled at an approximately steady level despite changes in the arterial blood pressure. Robust assessment of the cerebral autoregulation by a model that characterizes this system has been the goal of many studies, searching for techniques that can be used in clinical scenarios to detect potentially dangerous impairment of control. The physiological control system is highly complex and is not fully understood in spite of extensive research that has been carried out in this area. Through technological advances over the last two decades cerebral autoregulation has been evaluated by measuring relative cerebral blood flow (CBF) response to a steady-state (static) change in the ABP or the response to a sudden and rapid change in the blood pressure (dynamic response). Most recent work on cerebral autoregulation has focused on the transient response, known as dynamic cerebral autoregulation (dCAR). It has been shown that dynamic and static autoregulation have significant correlation for healthy human subjects (Aaslid, 1989). dCA can be quantified from the relationship between ABP and cerebral blood flow (CBF) through the use of appropriate signal processing methods(Aaslid, 1989). This can even be carried out in the presence of only spontaneous variations of blood pressure which is clearly ideal as it avoids major interference with the patient. However, none of the methods of estimating dCA have been found to be sufficiently robust to be considered a ‘gold standard’ nor have they been used routinely in clinical practice.
The aim of this study is to propose innovative experimental and signal analysis techniques for the robust assessment of cerebral blood flow control. This is aimed to be achieved by developing and evaluating advanced signal analysis procedures that allow the dynamic interaction between arterial blood pressure (ABP) and breath-by-breath end-tidal carbon dioxide (PETCO2) as inputs, and cerebral blood flow velocity (CBFV) as the output in order to recommend sensitive and robust procedures for the non-invasive measurement of the blood flow control system in vulnerable patients. Our final aim in this study is to increase our understanding of the dynamic interaction between CBFV, ABP and pCO2 based on modelling of experimental data.
Collaborations: A A Birch [Medical Physics, Southampton General Hospital]; R Panerai [University of Leicester]; J Potter [University of East Anglia]