Dynamic parameter estimation in thalamo-cortical computational models: a novel approach for tracking anesthetic brain states

Abstract Accurate tracking of brain states during general anesthesia remains challenging due to the complex neurophysiological dynamics involved. This study developed a thalamo-cortical neural mass model (TC-NMM) and a mean-field model (MFM) incorporating shared thalamic nuclei, both integrated with a particle filtering (PF) algorithm, to characterize consciousness transitions during sevoflurane- and propofol-induced anesthesia. The PF algorithm was employed to dynamically estimate model parameters, including excitatory/inhibitory postsynaptic potential (EPSP/IPSP), and the time constant rate of EPSP/IPSP, along with the coupling coefficients of the thalamic and cortical modules. The PF-based TC-NMM and MFM accurately tracked frontal data obtained during sevoflurane anesthesia and thalamo-cortical data acquired during propofol-induced anesthesia, respectively. Parameter estimation results revealed that both sevoflurane and propofol anesthesia reduced thalamo-cortical connectivity, with the thalamo-cortical coupling coefficients reliably distinguishing between distinct consciousness states. Notably, the EPSP parameters and coupling coefficients from the TC-NMM hold potential as clinically viable indicators for monitoring anesthesia depth. These findings not only advance our understanding of anesthetic mechanisms from a model perspective, but also suggest novel, physiologically interpretable indicators for assessing anesthesia depth.