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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: 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.

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The response of model steady state CBF to blood pressure and PaCO2changes.(A) Response to arterial blood pressure changes with data from [44] (red squares) and [45] (greentriangles) for comparison. (B) Response to PaCO2 changes with data from[48] (with normal resting blood flow takenas 40 ml/min/100 g) for comparison.
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pcbi-1000212-g004: The response of model steady state CBF to blood pressure and PaCO2changes.(A) Response to arterial blood pressure changes with data from [44] (red squares) and [45] (greentriangles) for comparison. (B) Response to PaCO2 changes with data from[48] (with normal resting blood flow takenas 40 ml/min/100 g) for comparison.

Mentions: The steady state response of cerebral blood flow to changes in blood pressuregives rise to “autoregulation” curves with blood flow beinginsensitive to changes in blood pressure around the physiological value [44]–[47]. This isobviously key behaviour that our model must be able to reproduce. Steady stateresponses of cerebral blood flow to other stimuli, in particular PaCO2, are alsowell characterised experimentally [48]. The model steadystate blood flow responses to changes in blood pressure and CO2levels are plotted in Figure4.


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)

The response of model steady state CBF to blood pressure and PaCO2changes.(A) Response to arterial blood pressure changes with data from [44] (red squares) and [45] (greentriangles) for comparison. (B) Response to PaCO2 changes with data from[48] (with normal resting blood flow takenas 40 ml/min/100 g) for comparison.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000212-g004: The response of model steady state CBF to blood pressure and PaCO2changes.(A) Response to arterial blood pressure changes with data from [44] (red squares) and [45] (greentriangles) for comparison. (B) Response to PaCO2 changes with data from[48] (with normal resting blood flow takenas 40 ml/min/100 g) for comparison.
Mentions: The steady state response of cerebral blood flow to changes in blood pressuregives rise to “autoregulation” curves with blood flow beinginsensitive to changes in blood pressure around the physiological value [44]–[47]. This isobviously key behaviour that our model must be able to reproduce. Steady stateresponses of cerebral blood flow to other stimuli, in particular PaCO2, are alsowell characterised experimentally [48]. The model steadystate blood flow responses to changes in blood pressure and CO2levels are plotted in Figure4.

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