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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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Relationship between ΔHbO2, ΔoxCCO and                            CMRO2 during changes in arterial oxygen saturation.(A) The model was run with normal parameter values and an approximately                            linear relationship between ΔHbO2 and ΔoxCCO held. (B)                            At these same normal parameter values CMRO2 showed an                            approximately linear relationship with ΔoxCCO. (C) Baseline                                CMRO2 was lowered to about 60 percent of the normal model                            baseline, by setting                            u = 0.1, while normal                            CBF was also lowered by about the same amount by setting                            CBFn = 0.007 ml blood per ml                            brain tissue per second. A more clearly biphasic relationship between                            ΔHbO2 and ΔoxCCO was obtained. (D) Again, at the changed                            parameter values, CMRO2 had an approximately linear                            relationship with ΔoxCCO.
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pcbi-1000212-g012: Relationship between ΔHbO2, ΔoxCCO and CMRO2 during changes in arterial oxygen saturation.(A) The model was run with normal parameter values and an approximately linear relationship between ΔHbO2 and ΔoxCCO held. (B) At these same normal parameter values CMRO2 showed an approximately linear relationship with ΔoxCCO. (C) Baseline CMRO2 was lowered to about 60 percent of the normal model baseline, by setting u = 0.1, while normal CBF was also lowered by about the same amount by setting CBFn = 0.007 ml blood per ml brain tissue per second. A more clearly biphasic relationship between ΔHbO2 and ΔoxCCO was obtained. (D) Again, at the changed parameter values, CMRO2 had an approximately linear relationship with ΔoxCCO.

Mentions: In [66] data on the relationship between ΔHbO2 and ΔoxCCO during hypoxia is presented. In order to test the model behaviour in this situation, a steady state simulation (as in the production of steady state curves above) was carried out. The results of this simulation are plotted in Figure 12.


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)

Relationship between ΔHbO2, ΔoxCCO and                            CMRO2 during changes in arterial oxygen saturation.(A) The model was run with normal parameter values and an approximately                            linear relationship between ΔHbO2 and ΔoxCCO held. (B)                            At these same normal parameter values CMRO2 showed an                            approximately linear relationship with ΔoxCCO. (C) Baseline                                CMRO2 was lowered to about 60 percent of the normal model                            baseline, by setting                            u = 0.1, while normal                            CBF was also lowered by about the same amount by setting                            CBFn = 0.007 ml blood per ml                            brain tissue per second. A more clearly biphasic relationship between                            ΔHbO2 and ΔoxCCO was obtained. (D) Again, at the changed                            parameter values, CMRO2 had an approximately linear                            relationship with ΔoxCCO.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2573000&req=5

pcbi-1000212-g012: Relationship between ΔHbO2, ΔoxCCO and CMRO2 during changes in arterial oxygen saturation.(A) The model was run with normal parameter values and an approximately linear relationship between ΔHbO2 and ΔoxCCO held. (B) At these same normal parameter values CMRO2 showed an approximately linear relationship with ΔoxCCO. (C) Baseline CMRO2 was lowered to about 60 percent of the normal model baseline, by setting u = 0.1, while normal CBF was also lowered by about the same amount by setting CBFn = 0.007 ml blood per ml brain tissue per second. A more clearly biphasic relationship between ΔHbO2 and ΔoxCCO was obtained. (D) Again, at the changed parameter values, CMRO2 had an approximately linear relationship with ΔoxCCO.
Mentions: In [66] data on the relationship between ΔHbO2 and ΔoxCCO during hypoxia is presented. In order to test the model behaviour in this situation, a steady state simulation (as in the production of steady state curves above) was carried out. The results of this simulation are plotted in Figure 12.

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