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

License
getmorefigures.php?uid=PMC2573000&req=5

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

Mentions: As a test of the model's behaviour in the context of changes in arterialCO2, NIRS data from healthy subjects monitored while undergoingmoderate hypercapnia, described in [68], was comparedwith model predictions. In this study, the only NIRS signal monitored was TOS.There was wide variation in baseline TOS between subjects, corresponding tonatural variability in blood flow and CMRO2, but more importantly tothe fact that the arterio-venous ratio in the region of tissue queried can havehigh variability. In all cases the modelled and measured data were qualitativelycomparable before any attempt to optimise model parameters. However a good fitto the data could be obtained by varying two parameters: Normal arterio-venousratio AVRn, and RC, the sensitivity of blood flow toPaCO2. Despite the fact that information is often not clearlyvisible in the data (see Figure14A, for example), in all cases but one, optimisation gave positivevalues for RC, in other words, the model was able todetect a positive cerebrovascular reactivity to CO2 in thedata—a fact which is potentially of clinical importance ([69]for example). Two examples of data-sets before and after fitting are presentedin 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 hypercapniachallenge.Measured (red) and modelled (black) responses of TOS: (A) For subject 1without optimisation. (B) For subject 1 following optimisation of AVRnand RC, which gave values ofAVRn = 1.28 andRC = 1.31.(C) For subject 2 without optimisation. (D) For subject 2 followingoptimisation of AVRn and RC, which gavevalues of AVRn = 0.286 andRC = 1.62.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000212-g014: Responses of measured and modelled TOS during a hypercapniachallenge.Measured (red) and modelled (black) responses of TOS: (A) For subject 1without optimisation. (B) For subject 1 following optimisation of AVRnand RC, which gave values ofAVRn = 1.28 andRC = 1.31.(C) For subject 2 without optimisation. (D) For subject 2 followingoptimisation of AVRn and RC, which gavevalues of AVRn = 0.286 andRC = 1.62.
Mentions: As a test of the model's behaviour in the context of changes in arterialCO2, NIRS data from healthy subjects monitored while undergoingmoderate hypercapnia, described in [68], was comparedwith model predictions. In this study, the only NIRS signal monitored was TOS.There was wide variation in baseline TOS between subjects, corresponding tonatural variability in blood flow and CMRO2, but more importantly tothe fact that the arterio-venous ratio in the region of tissue queried can havehigh variability. In all cases the modelled and measured data were qualitativelycomparable before any attempt to optimise model parameters. However a good fitto the data could be obtained by varying two parameters: Normal arterio-venousratio AVRn, and RC, the sensitivity of blood flow toPaCO2. Despite the fact that information is often not clearlyvisible in the data (see Figure14A, for example), in all cases but one, optimisation gave positivevalues for RC, in other words, the model was able todetect a positive cerebrovascular reactivity to CO2 in thedata—a fact which is potentially of clinical importance ([69]for example). Two examples of data-sets before and after fitting are presentedin Figure 14.

Bottom Line: 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.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