An extended model of intracranial latency facilitates non-invasive detection of cerebrovascular changes

A method has been recently developed to reduce the confounding factors of extracranial origins on the intracranial latency (the time interval between the electrocardiogram QRS component and the initial inflection of the resulting pulse). Although, the proposed model was shown to portray a better characterization of cerebral vasculature, the parameters of the model and their physiological interpretations have not been fully explored. The present work improves the physiological understanding of these parameters, refines the model and extends its ability to monitor real-time changes in overall cerebrovascular resistance. We show that the slope of the linear model which relates the latency of arterial blood pressure to that of the cerebral blood flow velocity, could be a measure of resistance, and that the intercept is a function of slope and pre-ejection period. A dataset of cerebral blood flow velocity and arterial blood pressure signals from 18 normal subjects at rest was used to validate the derived parameters of the model. Also, the results of further data processing verified our hypothesis that the slope of the model would significantly increase during a period of CO2 rebreathing, due to dilation of the vessels and reduction of cerebrovascular resistance (p ≤ 0.02). Finally as the slope of the proposed model is shown to be highly correlated with other conventional measures of cerebrovascular resistance, (resistance area product and critical closing pressure), we conclude that the derived slope metric is a measure of overall cerebrovascular resistance and therefore could be useful in guiding the non-invasive cerebrovascular management of patients.

Research highlights► A recent intracranial latency model is extended for cerebral pulse wave velocity analysis. ► Slope of the model is shown to be a valid measure of cerebrovascular resistance. ► Intercept of the model is a linear function of slope and pre-ejection period. ► The model has been validated using data from 18 subjects undergoing re-breathing test. ► This finding could facilitate applications of cerebral pulse wave propagation analysis.