Abstract
We construct a model of brain circulation and energy metabolism. The model is designed to explain experimental data and predict the response of the circulation and metabolism to a variety of stimuli, in particular, changes in arterial blood pressure, CO2 levels, O2 levels, and functional activation. Significant model outputs are predictions about blood flow, metabolic rate, and quantities measurable noninvasively using near-infrared spectroscopy (NIRS), including cerebral blood volume and oxygenation and the redox state of the CuA centre in cytochrome c oxidase. These quantities are now frequently measured in clinical settings; however the relationship between the measurements and the underlying physiological events is in general complex. We anticipate that the model will play an important role in helping to understand the NIRS signals, in particular, the cytochrome signal, which has been hard to interpret. A range of model simulations are presented, and model outputs are compared to published data obtained from both in vivo and in vitro settings. The comparisons are encouraging, showing that the model is able to reproduce observed behaviour in response to various stimuli.
Highlights
In recent years there has been widespread use of near infrared spectroscopy (NIRS) to monitor brain oxygenation, haemodynamics and metabolism [1,2]
There is no theoretical model that can be used to decode simultaneously all of the spectroscopic changes in these proteins, and limited information about the underlying biochemistry and physiology can be extracted from the NIRS signals
Consistent with the analysis in [38], both the size and the direction of DoxCCO change in response to functional activation are sensitive to a number of model parameters including the baseline PMF and values of the standard redox potentials
Summary
In recent years there has been widespread use of near infrared spectroscopy (NIRS) to monitor brain oxygenation, haemodynamics and metabolism [1,2]. In the case of TOS and DoxCCO there are no obvious ‘‘gold standard’’ measurements against which a direct experimental validation can be performed, these NIRS signals undoubtedly encode information of biological and, potentially, clinical importance on tissue oxygen levels, blood flow, metabolic rate (CMRO2), and other underlying state variables in the brain. In order to correctly interpret and maximise the clinical usefulness of the information that can be extracted from NIRS data, a model of the underlying physiology is required. The model is designed to respond to four input stimuli, which have been chosen both because they are physiologically important, and because there is considerable data on the response of NIRS
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