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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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Response of haemoglobin signals to a step up in demand.The response in μM of ΔHbO2 (red),                            ΔHHb (green) and ΔHbt (black) to a step up in demand.                            The stimulus and parameter values are as in Figure 5. In (A)                                    τu = 0.5                            s (the default value). In (B)                            τu = 1                            s. With the slower response time, there is more pronounced transient                            behaviour including a clear initial decrease in ΔHbO2 before it                            starts to increase.
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pcbi-1000212-g006: Response of haemoglobin signals to a step up in demand.The response in μM of ΔHbO2 (red), ΔHHb (green) and ΔHbt (black) to a step up in demand. The stimulus and parameter values are as in Figure 5. In (A) τu = 0.5 s (the default value). In (B) τu = 1 s. With the slower response time, there is more pronounced transient behaviour including a clear initial decrease in ΔHbO2 before it starts to increase.

Mentions: The behaviour of the other NIRS signals—ΔHbO2, ΔHHb and ΔHbt—during functional activation is plotted in Figure 6. Changing the time constant associated with demand (τu) affects the shape of the response, and the magnitude of a slight initial increase in deoxygenated haemoglobin before it starts to drop.


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)

Response of haemoglobin signals to a step up in demand.The response in μM of ΔHbO2 (red),                            ΔHHb (green) and ΔHbt (black) to a step up in demand.                            The stimulus and parameter values are as in Figure 5. In (A)                                    τu = 0.5                            s (the default value). In (B)                            τu = 1                            s. With the slower response time, there is more pronounced transient                            behaviour including a clear initial decrease in ΔHbO2 before it                            starts to increase.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000212-g006: Response of haemoglobin signals to a step up in demand.The response in μM of ΔHbO2 (red), ΔHHb (green) and ΔHbt (black) to a step up in demand. The stimulus and parameter values are as in Figure 5. In (A) τu = 0.5 s (the default value). In (B) τu = 1 s. With the slower response time, there is more pronounced transient behaviour including a clear initial decrease in ΔHbO2 before it starts to increase.
Mentions: The behaviour of the other NIRS signals—ΔHbO2, ΔHHb and ΔHbt—during functional activation is plotted in Figure 6. Changing the time constant associated with demand (τu) affects the shape of the response, and the magnitude of a slight initial increase in deoxygenated haemoglobin before it starts to drop.

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