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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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Related in: MedlinePlus

The effect-site concentration of propofol for all nine subjects.(A) is the Ceff for all subjects depending on time, (B) is the corresponding rCeff, and (C) represents the rCeff with the time divided by their respective syringe-drop time.
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pone.0145959.g010: The effect-site concentration of propofol for all nine subjects.(A) is the Ceff for all subjects depending on time, (B) is the corresponding rCeff, and (C) represents the rCeff with the time divided by their respective syringe-drop time.

Mentions: On the other hand, the Ceff and rCeff figures for all nine subjects were displayed in Fig 10(A) and 10(B), respectively. Different subjects had different reactions to propofol, with various peak time and peak Ceff (Fig 10(A)). But each of the subjects underwent the same states (awake, unconscious, recovery) during the experiment. Fig 10(C) presented a “normalization” of Fig 10(B) where the time for each figure was divided by their respective syringe-drop time, so that at the start of the unconscious state (at syringe-drop time) in normalized time is 1. It can be seen that the wide variety of figures in (A) fall basically on one "normalized" figure (C), close to normalized time 1, where they all had the value 1.49. And in some sense we were also normalizing the experimental recordings, because we were triggering on the same events (syringe drop, etc) for each subject, and so clearly the time was normalized in the same way, and furthermore dropping the syringe should mean "functionally similar" anaesthesia concentration in the brain. So the experimental and our theoretical procedure both basically eliminated the inter-subject variation dependence.


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)

The effect-site concentration of propofol for all nine subjects.(A) is the Ceff for all subjects depending on time, (B) is the corresponding rCeff, and (C) represents the rCeff with the time divided by their respective syringe-drop time.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0145959.g010: The effect-site concentration of propofol for all nine subjects.(A) is the Ceff for all subjects depending on time, (B) is the corresponding rCeff, and (C) represents the rCeff with the time divided by their respective syringe-drop time.
Mentions: On the other hand, the Ceff and rCeff figures for all nine subjects were displayed in Fig 10(A) and 10(B), respectively. Different subjects had different reactions to propofol, with various peak time and peak Ceff (Fig 10(A)). But each of the subjects underwent the same states (awake, unconscious, recovery) during the experiment. Fig 10(C) presented a “normalization” of Fig 10(B) where the time for each figure was divided by their respective syringe-drop time, so that at the start of the unconscious state (at syringe-drop time) in normalized time is 1. It can be seen that the wide variety of figures in (A) fall basically on one "normalized" figure (C), close to normalized time 1, where they all had the value 1.49. And in some sense we were also normalizing the experimental recordings, because we were triggering on the same events (syringe drop, etc) for each subject, and so clearly the time was normalized in the same way, and furthermore dropping the syringe should mean "functionally similar" anaesthesia concentration in the brain. So the experimental and our theoretical procedure both basically eliminated the inter-subject variation dependence.

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