Abstract
Cerebrovascular control is carried out by multiple nonlinear mechanisms imposing a certain degree of coupling between mean arterial pressure (MAP) and mean cerebral blood flow (MCBF). We explored the ability of two nonlinear tools in the information domain, namely cross-approximate entropy (CApEn) and cross-sample entropy (CSampEn), to assess the degree of asynchrony between the spontaneous fluctuations of MAP and MCBF. CApEn and CSampEn were computed as a function of the translation time. The analysis was carried out in 23 subjects undergoing recordings at rest in supine position (REST) and during active standing (STAND), before and after surgical aortic valve replacement (SAVR). We found that at REST the degree of asynchrony raised, and the rate of increase in asynchrony with the translation time decreased after SAVR. These results are likely the consequence of the limited variability of MAP observed after surgery at REST, more than the consequence of a modified cerebrovascular control, given that the observed differences disappeared during STAND. CApEn and CSampEn can be utilized fruitfully in the context of the evaluation of cerebrovascular control via the noninvasive acquisition of the spontaneous MAP and MCBF variability.
Highlights
Given the importance of the brain and its high susceptibility to hypoxic states [1], the assessment of the dynamical relationship between mean cerebral perfusion pressure, approximated by the mean arterial pressure (MAP), and mean cerebral blood flow (MCBF), usually approximated by the MCBF velocity (MCBFV) assessed via the transcranial Doppler ultrasound device [2], is of paramount relevance
In addition to the multiplicity of mechanisms that have opposite influences on the strength of the MCBFVMAP dynamical link, the listed regulatory mechanisms are highly nonlinear, given that the cerebral autoregulation (CA) characteristic holds solely in a specific MAP range [1], MCBFV responses depend on the sign of MAP variations [22,23], MCBFV-MAP coupling varies with the breathing phase [24], and vagal and sympathetic controls are likely to non-additively interact each other in shaping CA [25,26]
Given the complexity of the cerebrovascular control, we propose to characterize the dynamical relationship between MAP and MCBFV via cross-entropy approaches [27]
Summary
Given the importance of the brain and its high susceptibility to hypoxic states [1], the assessment of the dynamical relationship between mean cerebral perfusion pressure, approximated by the mean arterial pressure (MAP), and mean cerebral blood flow (MCBF), usually approximated by the MCBF velocity (MCBFV) assessed via the transcranial Doppler ultrasound device [2], is of paramount relevance. This evaluation is traditionally carried out by computing the degree of linear association between MAP and MCBFV as a function of the frequency via squared coherence function [3,4]. In addition to the multiplicity of mechanisms that have opposite influences on the strength of the MCBFVMAP dynamical link, the listed regulatory mechanisms are highly nonlinear, given that the CA characteristic holds solely in a specific MAP range [1], MCBFV responses depend on the sign of MAP variations [22,23], MCBFV-MAP coupling varies with the breathing phase [24], and vagal and sympathetic controls are likely to non-additively interact each other in shaping CA [25,26]
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