Macroscale Neural Computational Modeling Simulation of Propofol Incorporating Pharmacological Effects
Wang Dihuan1, Liang Zhenhu1*, Li Xiaoali2
1(Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, Hebei, China) 2(State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China)
Abstract：To investigate the neural action mechanism of propofol at the macroscale,an unscented Kalman filter framework based on the incorporating pharmacological effects neural mass model (PENMM) was proposed in this paper. The framework regulated the neural effect parameters (G, b, and g) with the optimal weights of effector compartment concentrations, while tracking the real electroencephalogram (rEEG) and estimating the neuromodulation parameters (A, B, and a) of the model for fitting the simulated electroencephalogram (sEEG). The cumulative errors, correlations, and coefficients of determination of rEEG and sEEG were calculated, which indicated that rEEG and sEEG were well correlated, proving that the framework could effectively estimate NMM model parameters and generate sEEG. The results showed that the framework was able to effectively track EEG characteristics of nine volunteers in the effect of propofol. The highest correlation between rEEG and sEEG was 0.91. Parameter B and a curves were significantly different (p<0.001) in different anesthesia states and can be used to differentiate between different anesthesia states. In conclusion, this method holds a potential to be developed as a depth of anesthesia monitoring tool.
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