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A model of brain circulation and metabolism: NIRS signal changes during physiological challenges.

Banaji M, Mallet A, Elwell CE, Nicholls P, Cooper CE - PLoS Comput. Biol. (2008)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences, University of Essex, Colchester, United Kingdom. m.banaji@ucl.ac.uk

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, CO(2) levels, O(2) 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 Cu(A) 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.

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Related in: MedlinePlus

Responses of measured and modelled TOS during a hypercapnia                            challenge.Measured (red) and modelled (black) responses of TOS: (A) For subject 1                            without optimisation. (B) For subject 1 following optimisation of AVRn                            and RC, which gave values of                            AVRn = 1.28 and                            RC = 1.31.                            (C) For subject 2 without optimisation. (D) For subject 2 following                            optimisation of AVRn and RC, which gave                            values of AVRn = 0.286 and                                    RC = 1.62.
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pcbi-1000212-g014: Responses of measured and modelled TOS during a hypercapnia challenge.Measured (red) and modelled (black) responses of TOS: (A) For subject 1 without optimisation. (B) For subject 1 following optimisation of AVRn and RC, which gave values of AVRn = 1.28 and RC = 1.31. (C) For subject 2 without optimisation. (D) For subject 2 following optimisation of AVRn and RC, which gave values of AVRn = 0.286 and RC = 1.62.

Mentions: As a test of the model's behaviour in the context of changes in arterial CO2, NIRS data from healthy subjects monitored while undergoing moderate hypercapnia, described in [68], was compared with model predictions. In this study, the only NIRS signal monitored was TOS. There was wide variation in baseline TOS between subjects, corresponding to natural variability in blood flow and CMRO2, but more importantly to the fact that the arterio-venous ratio in the region of tissue queried can have high variability. In all cases the modelled and measured data were qualitatively comparable before any attempt to optimise model parameters. However a good fit to the data could be obtained by varying two parameters: Normal arterio-venous ratio AVRn, and RC, the sensitivity of blood flow to PaCO2. Despite the fact that information is often not clearly visible in the data (see Figure 14A, for example), in all cases but one, optimisation gave positive values for RC, in other words, the model was able to detect a positive cerebrovascular reactivity to CO2 in the data—a fact which is potentially of clinical importance ([69] for example). Two examples of data-sets before and after fitting are presented in Figure 14.


A model of brain circulation and metabolism: NIRS signal changes during physiological challenges.

Banaji M, Mallet A, Elwell CE, Nicholls P, Cooper CE - PLoS Comput. Biol. (2008)

Responses of measured and modelled TOS during a hypercapnia                            challenge.Measured (red) and modelled (black) responses of TOS: (A) For subject 1                            without optimisation. (B) For subject 1 following optimisation of AVRn                            and RC, which gave values of                            AVRn = 1.28 and                            RC = 1.31.                            (C) For subject 2 without optimisation. (D) For subject 2 following                            optimisation of AVRn and RC, which gave                            values of AVRn = 0.286 and                                    RC = 1.62.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2573000&req=5

pcbi-1000212-g014: Responses of measured and modelled TOS during a hypercapnia challenge.Measured (red) and modelled (black) responses of TOS: (A) For subject 1 without optimisation. (B) For subject 1 following optimisation of AVRn and RC, which gave values of AVRn = 1.28 and RC = 1.31. (C) For subject 2 without optimisation. (D) For subject 2 following optimisation of AVRn and RC, which gave values of AVRn = 0.286 and RC = 1.62.
Mentions: As a test of the model's behaviour in the context of changes in arterial CO2, NIRS data from healthy subjects monitored while undergoing moderate hypercapnia, described in [68], was compared with model predictions. In this study, the only NIRS signal monitored was TOS. There was wide variation in baseline TOS between subjects, corresponding to natural variability in blood flow and CMRO2, but more importantly to the fact that the arterio-venous ratio in the region of tissue queried can have high variability. In all cases the modelled and measured data were qualitatively comparable before any attempt to optimise model parameters. However a good fit to the data could be obtained by varying two parameters: Normal arterio-venous ratio AVRn, and RC, the sensitivity of blood flow to PaCO2. Despite the fact that information is often not clearly visible in the data (see Figure 14A, for example), in all cases but one, optimisation gave positive values for RC, in other words, the model was able to detect a positive cerebrovascular reactivity to CO2 in the data—a fact which is potentially of clinical importance ([69] for example). Two examples of data-sets before and after fitting are presented in Figure 14.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences, University of Essex, Colchester, United Kingdom. m.banaji@ucl.ac.uk

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, CO(2) levels, O(2) 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 Cu(A) 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.

Show MeSH
Related in: MedlinePlus