Changes in information integration and brain networks during propofol-, dexmedetomidine-, and ketamine-induced unresponsiveness

Background: Information integration and network science are important theories for quantifying consciousness. However, whether these theories propose drug- or conscious state -related changes in EEG during anaesthesia -induced unresponsiveness remains unknown. Methods: A total of 72 participants were randomised to receive i.v. infusion of propofol, dexmedetomidine, or ketamine at a constant infusion rate until loss of responsiveness. High -density EEG was recorded during the consciousness transition from the eye -closed baseline to the unresponsiveness state and then to the recovery of the responsiveness state. Permutation cross mutual information (PCMI) and PCMI-based brain networks in broadband (0.1-45 Hz) and subband frequencies were used to analyse drug- and state -related EEG signature changes. Results: PCMI and brain networks exhibited state -related changes in certain brain regions and frequency bands. The within -area PCMI of the frontal, parietal, and occipital regions, and the between -area PCMI of the parietal -occipital region (median [inter -quartile ranges]), baseline vs unresponsive were as follows: 0.54 (0.46-0.58) vs 0.46 (0.40-0.50), 0.58 (0.52-0.60) vs 0.48 (0.44-0.53), 0.54 (0.49-0.59) vs 0.47 (0.42-0.52) decreased during anaesthesia for three drugs (P<0.05). Alpha PCMI in the frontal region, and gamma PCMI in the posterior area significantly decreased in the unresponsive state (P<0.05). The frontal, parietal, and occipital nodal clustering coefficients and parietal nodal efficiency decreased in the unresponsive state (P<0.05). The increased normalised path length in delta, theta, and gamma bands indicated impaired global integration (P<0.05). Conclusions: The three anaesthetics caused changes in information integration patterns and network functions. Thus, it is possible to build a quantifying framework for anaesthesia -induced conscious state changes on the EEG scale using PCMI and network science.