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A Pharmacokinetics-Neural Mass Model (PK-NMM) for the Simulation of EEG Activity during Propofol Anesthesia.

Liang Z, Duan X, Su C, Voss L, Sleigh J, Li X - PLoS ONE (2015)

Bottom Line: The PK model was used to derive propofol effect-site drug concentrations (C(eff)) based on the actual drug infusion regimen.The correlation coefficient of PE was 0.80 ± 0.13 (mean ± standard deviation) between rEEG and sEEG for all subjects.The PK-NMM model could simulate EEG activity and might be a useful tool for understanding the action of propofol on brain activity.

View Article: PubMed Central - PubMed

Affiliation: Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China.

ABSTRACT
Modeling the effects of anesthetic drugs on brain activity is very helpful in understanding anesthesia mechanisms. The aim of this study was to set up a combined model to relate actual drug levels to EEG dynamics and behavioral states during propofol-induced anesthesia. We proposed a new combined theoretical model based on a pharmacokinetics (PK) model and a neural mass model (NMM), which we termed PK-NMM--with the aim of simulating electroencephalogram (EEG) activity during propofol-induced general anesthesia. The PK model was used to derive propofol effect-site drug concentrations (C(eff)) based on the actual drug infusion regimen. The NMM model took C(eff) as the control parameter to produce simulated EEG-like (sEEG) data. For comparison, we used real prefrontal EEG (rEEG) data of nine volunteers undergoing propofol anesthesia from a previous experiment. To see how well the sEEG could describe the dynamic changes of neural activity during anesthesia, the rEEG data and the sEEG data were compared with respect to: power-frequency plots; nonlinear exponent (permutation entropy (PE)); and bispectral SynchFastSlow (SFS) parameters. We found that the PK-NMM model was able to reproduce anesthesia EEG-like signals based on the estimated drug concentration and patients' condition. The frequency spectrum indicated that the frequency power peak of the sEEG moved towards the low frequency band as anesthesia deepened. Different anesthetic states could be differentiated by the PE index. The correlation coefficient of PE was 0.80 ± 0.13 (mean ± standard deviation) between rEEG and sEEG for all subjects. Additionally, SFS could track the depth of anesthesia and the SFS of rEEG and sEEG were highly correlated with a correlation coefficient of 0.77 ± 0.13. The PK-NMM model could simulate EEG activity and might be a useful tool for understanding the action of propofol on brain activity.

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Time course for excitatory (blue curve), inhibitory (red curve), and anesthetic-modified inhibitory (dashed) postsynaptic potential.λ is the dimensionless anesthetic-effect scale factor giving the lengthening of the IPSP duration.
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pone.0145959.g002: Time course for excitatory (blue curve), inhibitory (red curve), and anesthetic-modified inhibitory (dashed) postsynaptic potential.λ is the dimensionless anesthetic-effect scale factor giving the lengthening of the IPSP duration.

Mentions: Assuming that a subject’s effect-site concentration of propofol has been derived using the Schnider PK model, a significant problem is how to apply this data to the neural mass model. To do this we must have some understanding of how anesthetic drugs affect brain function. So far, the most convincing mechanism for how commonly used GABAergic general anesthetic drugs operate at the cellular level is by enhancing the inhibitory effect of the GABA neurotransmitter, by keeping the chloride channels of the postsynaptic neurons open longer, allowing a larger negative charge to accumulate within the cell [45]. Liley et al. modeled the time course for the post synaptic potential (PSP) as a gamma-function impulse of the form γt exp(1−γt), where γ is the neurotransmitter rate constant for post synaptic potential (PSP)—γe for excitatory post synaptic potential (EPSP) and γi for inhibitory post synaptic potential (IPSP) [17]. The effect of the anesthetic drug propofol was incorporated into the model by lengthening the duration of IPSP by a dimensionless factor λ, which is done by replacing the IPSP neurotransmitter rate constant γi with γi/λ. The time course for excitatory, inhibitory, and anesthetic-modified inhibitory postsynaptic potential is shown in Fig 2 [18]. IPSP duration increases with the increase of γ.


A Pharmacokinetics-Neural Mass Model (PK-NMM) for the Simulation of EEG Activity during Propofol Anesthesia.

Liang Z, Duan X, Su C, Voss L, Sleigh J, Li X - PLoS ONE (2015)

Time course for excitatory (blue curve), inhibitory (red curve), and anesthetic-modified inhibitory (dashed) postsynaptic potential.λ is the dimensionless anesthetic-effect scale factor giving the lengthening of the IPSP duration.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4697853&req=5

pone.0145959.g002: Time course for excitatory (blue curve), inhibitory (red curve), and anesthetic-modified inhibitory (dashed) postsynaptic potential.λ is the dimensionless anesthetic-effect scale factor giving the lengthening of the IPSP duration.
Mentions: Assuming that a subject’s effect-site concentration of propofol has been derived using the Schnider PK model, a significant problem is how to apply this data to the neural mass model. To do this we must have some understanding of how anesthetic drugs affect brain function. So far, the most convincing mechanism for how commonly used GABAergic general anesthetic drugs operate at the cellular level is by enhancing the inhibitory effect of the GABA neurotransmitter, by keeping the chloride channels of the postsynaptic neurons open longer, allowing a larger negative charge to accumulate within the cell [45]. Liley et al. modeled the time course for the post synaptic potential (PSP) as a gamma-function impulse of the form γt exp(1−γt), where γ is the neurotransmitter rate constant for post synaptic potential (PSP)—γe for excitatory post synaptic potential (EPSP) and γi for inhibitory post synaptic potential (IPSP) [17]. The effect of the anesthetic drug propofol was incorporated into the model by lengthening the duration of IPSP by a dimensionless factor λ, which is done by replacing the IPSP neurotransmitter rate constant γi with γi/λ. The time course for excitatory, inhibitory, and anesthetic-modified inhibitory postsynaptic potential is shown in Fig 2 [18]. IPSP duration increases with the increase of γ.

Bottom Line: The PK model was used to derive propofol effect-site drug concentrations (C(eff)) based on the actual drug infusion regimen.The correlation coefficient of PE was 0.80 ± 0.13 (mean ± standard deviation) between rEEG and sEEG for all subjects.The PK-NMM model could simulate EEG activity and might be a useful tool for understanding the action of propofol on brain activity.

View Article: PubMed Central - PubMed

Affiliation: Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China.

ABSTRACT
Modeling the effects of anesthetic drugs on brain activity is very helpful in understanding anesthesia mechanisms. The aim of this study was to set up a combined model to relate actual drug levels to EEG dynamics and behavioral states during propofol-induced anesthesia. We proposed a new combined theoretical model based on a pharmacokinetics (PK) model and a neural mass model (NMM), which we termed PK-NMM--with the aim of simulating electroencephalogram (EEG) activity during propofol-induced general anesthesia. The PK model was used to derive propofol effect-site drug concentrations (C(eff)) based on the actual drug infusion regimen. The NMM model took C(eff) as the control parameter to produce simulated EEG-like (sEEG) data. For comparison, we used real prefrontal EEG (rEEG) data of nine volunteers undergoing propofol anesthesia from a previous experiment. To see how well the sEEG could describe the dynamic changes of neural activity during anesthesia, the rEEG data and the sEEG data were compared with respect to: power-frequency plots; nonlinear exponent (permutation entropy (PE)); and bispectral SynchFastSlow (SFS) parameters. We found that the PK-NMM model was able to reproduce anesthesia EEG-like signals based on the estimated drug concentration and patients' condition. The frequency spectrum indicated that the frequency power peak of the sEEG moved towards the low frequency band as anesthesia deepened. Different anesthetic states could be differentiated by the PE index. The correlation coefficient of PE was 0.80 ± 0.13 (mean ± standard deviation) between rEEG and sEEG for all subjects. Additionally, SFS could track the depth of anesthesia and the SFS of rEEG and sEEG were highly correlated with a correlation coefficient of 0.77 ± 0.13. The PK-NMM model could simulate EEG activity and might be a useful tool for understanding the action of propofol on brain activity.

Show MeSH
Related in: MedlinePlus