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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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Relationship between ΔHbO2, ΔoxCCO andCMRO2 during changes in arterial oxygen saturation.(A) The model was run with normal parameter values and an approximatelylinear relationship between ΔHbO2 and ΔoxCCO held. (B)At these same normal parameter values CMRO2 showed anapproximately linear relationship with ΔoxCCO. (C) BaselineCMRO2 was lowered to about 60 percent of the normal modelbaseline, by settingu = 0.1, while normalCBF was also lowered by about the same amount by settingCBFn = 0.007 ml blood per mlbrain tissue per second. A more clearly biphasic relationship betweenΔHbO2 and ΔoxCCO was obtained. (D) Again, at the changedparameter values, CMRO2 had an approximately linearrelationship with ΔoxCCO.
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pcbi-1000212-g012: Relationship between ΔHbO2, ΔoxCCO andCMRO2 during changes in arterial oxygen saturation.(A) The model was run with normal parameter values and an approximatelylinear relationship between ΔHbO2 and ΔoxCCO held. (B)At these same normal parameter values CMRO2 showed anapproximately linear relationship with ΔoxCCO. (C) BaselineCMRO2 was lowered to about 60 percent of the normal modelbaseline, by settingu = 0.1, while normalCBF was also lowered by about the same amount by settingCBFn = 0.007 ml blood per mlbrain tissue per second. A more clearly biphasic relationship betweenΔHbO2 and ΔoxCCO was obtained. (D) Again, at the changedparameter values, CMRO2 had an approximately linearrelationship with ΔoxCCO.

Mentions: In [66] data on the relationship between ΔHbO2and ΔoxCCO during hypoxia is presented. In order to test the modelbehaviour in this situation, a steady state simulation (as in the production ofsteady state curves above) was carried out. The results of this simulation areplotted 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 andCMRO2 during changes in arterial oxygen saturation.(A) The model was run with normal parameter values and an approximatelylinear relationship between ΔHbO2 and ΔoxCCO held. (B)At these same normal parameter values CMRO2 showed anapproximately linear relationship with ΔoxCCO. (C) BaselineCMRO2 was lowered to about 60 percent of the normal modelbaseline, by settingu = 0.1, while normalCBF was also lowered by about the same amount by settingCBFn = 0.007 ml blood per mlbrain tissue per second. A more clearly biphasic relationship betweenΔHbO2 and ΔoxCCO was obtained. (D) Again, at the changedparameter values, CMRO2 had an approximately linearrelationship with ΔoxCCO.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000212-g012: Relationship between ΔHbO2, ΔoxCCO andCMRO2 during changes in arterial oxygen saturation.(A) The model was run with normal parameter values and an approximatelylinear relationship between ΔHbO2 and ΔoxCCO held. (B)At these same normal parameter values CMRO2 showed anapproximately linear relationship with ΔoxCCO. (C) BaselineCMRO2 was lowered to about 60 percent of the normal modelbaseline, by settingu = 0.1, while normalCBF was also lowered by about the same amount by settingCBFn = 0.007 ml blood per mlbrain tissue per second. A more clearly biphasic relationship betweenΔHbO2 and ΔoxCCO was obtained. (D) Again, at the changedparameter values, CMRO2 had an approximately linearrelationship with ΔoxCCO.
Mentions: In [66] data on the relationship between ΔHbO2and ΔoxCCO during hypoxia is presented. In order to test the modelbehaviour in this situation, a steady state simulation (as in the production ofsteady state curves above) was carried out. The results of this simulation areplotted in Figure 12.

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