title: A Pharmacokinetics-Neural Mass Model (PK-NMM) for the Simulation of EEG Activity during Propofol Anesthesia.
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.
authors.affiliation: Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China.
authors.forename: Zhenhu
authors.initials: Z
authors.lastname: Liang
authors.affiliation: Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China.
authors.forename: Xuejing
authors.initials: X
authors.lastname: Duan
authors.affiliation: Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China.
authors.forename: Cui
authors.initials: C
authors.lastname: Su
authors.affiliation: Department of Anesthesia, Waikato Hospital, Hamilton, New Zealand.
authors.forename: Logan
authors.initials: L
authors.lastname: Voss
authors.affiliation: Department of Anesthesia, Waikato Hospital, Hamilton, New Zealand.
authors.forename: Jamie
authors.initials: J
authors.lastname: Sleigh
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.|Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China.
authors.forename: Xiaoli
authors.initials: X
authors.lastname: Li
mesh_terms: D000328:Adult; D000465:Algorithms; D000768:Anesthesia, General / Q000379:methods; D018686:Anesthetics, Intravenous / Q000493:pharmacokinetics*; D004569:Electroencephalography / Q000379:methods; D019277:Entropy; D005260:Female; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D015742:Propofol / Q000493:pharmacokinetics*
chemical_list: D018686:Anesthetics, Intravenous; D015742:Propofol