Superior fitting of arterial resistance and compliance parameters with genetic algorithms in models of dynamic cerebral autoregulation

Time-domain methods for quantifying dynamic cerebral blood flow autoregulation: Review and recommendations. A white paper from the Cerebrovascular Research Network (CARNet)

Cerebral Autoregulation (CA) is an important physiological mechanism stabilizing cerebral blood flow (CBF) in response to changes in cerebral perfusion pressure (CPP). By maintaining an adequate, relatively constant supply of blood flow, CA plays a critical role in brain function. Quantifying CA under different physiological and pathological states is crucial for understanding its implications. This knowledge may serve as a foundation for informed clinical decision-making, particularly in cases where CA may become impaired. The quantification of CA functionality typically involves constructing models that capture the relationship between CPP (or arterial blood pressure) and experimental measures of CBF. Besides describing normal CA function, these models provide a means to detect possible deviations from the latter. In this context, a recent white paper from the Cerebrovascular Research Network focused on Transfer Function Analysis (TFA), which obtains frequency domain estimates of dynamic CA. In the present paper, we consider the use of time-domain techniques as an alternative approach. Due to their increased flexibility, time-domain methods enable the mitigation of measurement/physiological noise and the incorporation of nonlinearities and time variations in CA dynamics. Here, we provide practical recommendations and guidelines to support researchers and clinicians in effectively utilizing these techniques to study CA.

A multi-objective optimisation approach for the linear modelling of cerebral autoregulation system

OBJECTIVE

Dynamic cerebral autoregulation (dCA) has been addressed through different approaches for discriminating between normal and impaired conditions based on spontaneous fluctuations in arterial blood pressure (ABP) and cerebral blood flow (CF). This work presents a novel multi-objective optimisation (MO) approach for finding good configurations of a cerebrovascular resistance-compliance model.

METHODS

Data from twenty-nine subjects under normo and hypercapnic (5% CO2 in air) conditions was used. Cerebrovascular resistance and vessel compliance models with ABP as input and CF velocity as output were fitted using a MO approach, considering fitting Pearson's correlation and error.

RESULTS

MO approach finds better model configurations than the single-objective (SO) approach, especially for hypercapnic conditions. In addition, the Pareto-optimal front from the multi-objective approach enables new information on dCA, reflecting a higher contribution of myogenic mechanism for explaining dCA impairment.