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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

Real EEG and simulated EEG.(A) rEEG time series and (B) sEEG time series for a single subject. (C) and (D) show the EEG frequency spectrum of the two EEG signals. The dark red color denotes higher power and the blue color denotes lower power. (E), (F) and (G) show rEEG series of 10 s during consciousness (rEEG_consciousness), unconsciousness (rEEG_unconsciousness), recovery (rEEG_recovery) and the corresponding power spectra, respectively. (H), (I) and (J) show sEEG of 10 s during consciousness (sEEG_consciousness), unconsciousness (sEEG_unconsciousness), recovery (sEEG_recovery) and the corresponding power spectra, respectively. The 10 s EEG epochs extracted are labeled as I,II, III in the integral signal.
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pone.0145959.g006: Real EEG and simulated EEG.(A) rEEG time series and (B) sEEG time series for a single subject. (C) and (D) show the EEG frequency spectrum of the two EEG signals. The dark red color denotes higher power and the blue color denotes lower power. (E), (F) and (G) show rEEG series of 10 s during consciousness (rEEG_consciousness), unconsciousness (rEEG_unconsciousness), recovery (rEEG_recovery) and the corresponding power spectra, respectively. (H), (I) and (J) show sEEG of 10 s during consciousness (sEEG_consciousness), unconsciousness (sEEG_unconsciousness), recovery (sEEG_recovery) and the corresponding power spectra, respectively. The 10 s EEG epochs extracted are labeled as I,II, III in the integral signal.

Mentions: The simulated anesthesia EEG-like signal is calculated by subtracting the steady-state values of excitatory neurons at each time point from the curve of fluctuations of excitatory neurons along the steady state. Fig 6(A) and 6(B) show rEEG and sEEG for the same subject as a function of time. The upper portion of (A) and (B) are the expanded EEG waveforms of 1s in the conscious and unconscious states. It is shown that the electroencephalographic data during unconsciousness is more regular than during conscious state. To reveal the frequency content changes of the two types of anesthesia EEG signals, the frequency spectra are calculated, as shown in Fig 6(C) and 6(D). It can be seen that the sEEG spectrogram illustrates a change in the dominant EEG frequency pattern with the deepening of anesthesia, from high to low frequency; which is similar to the real EEG signal. However, the rEEG activity shows two prominent rhythmic activities around the delta frequency band (<3Hz) and the alpha frequency band at the start of LoC (the bifurcation between the delta frequency band and the alpha frequency band), lasting to the unconscious state. These two rhythmic peaks are not seen in the sEEG. Three 10 s EEG epochs are extracted, label as I(conscious EEG), II(unconscious EEG), III(recovery EEG) in Fig 6(A) and 6(B). Fig 6(E) shows real EEG of 10 s along with the power spectrum from one subject during consciousness. Fig 6(F) shows real EEG of 10 s along with the power spectrum during unconsciousness. The real EEG series of 10 s during recovery and the corresponding power spectrum are shown in Fig 6(G). Fig 6(H), 6(I) and 6(J) show simulated EEG series of 10 s and their corresponding power spectra during conscious, unconscious and recovery states, respectively. It is seen that during consciousness, for rEEG, the oscillation activity in low frequency bands (<5Hz) is strong, while the sEEG presents strong activity in 0~47 Hz. During unconsciousness the experimentally observed increases in low-frequency (<5Hz) power and more pronounced alpha oscillations are visible in the simulated series as well, but the theta activity still remains strong. During recovery state the increases in beta frequency power is seen in sEEG. The incomplete understanding of the physiological and anatomical structure of the cortex and the simplification of the spatial cortex all could lead to the differences seen with the experimentally observed data. From the variation of Ceff and the oscillation in the EEG, it seems that after drug infusion it takes some time before the EEG gives an obvious change. This is due to the fact that the anesthetic takes time to diffuse from the blood to the brain effect site, where the altered EEG response is generated.


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)

Real EEG and simulated EEG.(A) rEEG time series and (B) sEEG time series for a single subject. (C) and (D) show the EEG frequency spectrum of the two EEG signals. The dark red color denotes higher power and the blue color denotes lower power. (E), (F) and (G) show rEEG series of 10 s during consciousness (rEEG_consciousness), unconsciousness (rEEG_unconsciousness), recovery (rEEG_recovery) and the corresponding power spectra, respectively. (H), (I) and (J) show sEEG of 10 s during consciousness (sEEG_consciousness), unconsciousness (sEEG_unconsciousness), recovery (sEEG_recovery) and the corresponding power spectra, respectively. The 10 s EEG epochs extracted are labeled as I,II, III in the integral signal.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0145959.g006: Real EEG and simulated EEG.(A) rEEG time series and (B) sEEG time series for a single subject. (C) and (D) show the EEG frequency spectrum of the two EEG signals. The dark red color denotes higher power and the blue color denotes lower power. (E), (F) and (G) show rEEG series of 10 s during consciousness (rEEG_consciousness), unconsciousness (rEEG_unconsciousness), recovery (rEEG_recovery) and the corresponding power spectra, respectively. (H), (I) and (J) show sEEG of 10 s during consciousness (sEEG_consciousness), unconsciousness (sEEG_unconsciousness), recovery (sEEG_recovery) and the corresponding power spectra, respectively. The 10 s EEG epochs extracted are labeled as I,II, III in the integral signal.
Mentions: The simulated anesthesia EEG-like signal is calculated by subtracting the steady-state values of excitatory neurons at each time point from the curve of fluctuations of excitatory neurons along the steady state. Fig 6(A) and 6(B) show rEEG and sEEG for the same subject as a function of time. The upper portion of (A) and (B) are the expanded EEG waveforms of 1s in the conscious and unconscious states. It is shown that the electroencephalographic data during unconsciousness is more regular than during conscious state. To reveal the frequency content changes of the two types of anesthesia EEG signals, the frequency spectra are calculated, as shown in Fig 6(C) and 6(D). It can be seen that the sEEG spectrogram illustrates a change in the dominant EEG frequency pattern with the deepening of anesthesia, from high to low frequency; which is similar to the real EEG signal. However, the rEEG activity shows two prominent rhythmic activities around the delta frequency band (<3Hz) and the alpha frequency band at the start of LoC (the bifurcation between the delta frequency band and the alpha frequency band), lasting to the unconscious state. These two rhythmic peaks are not seen in the sEEG. Three 10 s EEG epochs are extracted, label as I(conscious EEG), II(unconscious EEG), III(recovery EEG) in Fig 6(A) and 6(B). Fig 6(E) shows real EEG of 10 s along with the power spectrum from one subject during consciousness. Fig 6(F) shows real EEG of 10 s along with the power spectrum during unconsciousness. The real EEG series of 10 s during recovery and the corresponding power spectrum are shown in Fig 6(G). Fig 6(H), 6(I) and 6(J) show simulated EEG series of 10 s and their corresponding power spectra during conscious, unconscious and recovery states, respectively. It is seen that during consciousness, for rEEG, the oscillation activity in low frequency bands (<5Hz) is strong, while the sEEG presents strong activity in 0~47 Hz. During unconsciousness the experimentally observed increases in low-frequency (<5Hz) power and more pronounced alpha oscillations are visible in the simulated series as well, but the theta activity still remains strong. During recovery state the increases in beta frequency power is seen in sEEG. The incomplete understanding of the physiological and anatomical structure of the cortex and the simplification of the spatial cortex all could lead to the differences seen with the experimentally observed data. From the variation of Ceff and the oscillation in the EEG, it seems that after drug infusion it takes some time before the EEG gives an obvious change. This is due to the fact that the anesthetic takes time to diffuse from the blood to the brain effect site, where the altered EEG response is generated.

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