Resting State Functional Connectivity Analysis During General Anesthesia: A High-Density EEG Study

The depth of anesthesia monitoring is helpful to guide administrations of general anesthetics during surgical procedures, however, the conventional 2-4 channels electroencephalogram (EEG) derived monitors have their limitations in monitoring conscious states due to low spatial resolution and suboptimal algorithm. In this study, 256-channel high-density EEG signals in 24 subjects receiving three types of general anesthetics (propofol, sevoflurane and ketamine) respectively were recorded both before and after anesthesia. The raw EEG signals were preprocessed by EEGLAB 14.0. Functional connectivity (FC) analysis by traditional coherence analysis (CA) method and a novel sparse representation (SR) method. And the network parameters, average clustering coefficient (ACC) and average shortest path length (ASPL) before and after anesthesia were calculated. The results show SR method find more significant FC differences in frontal and occipital cortices, and whole brain network (p<0.05). In contrast, CA can hardly obtain consistent ASPL in the whole brain network (p>0.05). Further, ASPL calculated by SR for whole brain connections in all of three anesthesia groups increased, which can be a unified EEG biomarker of general anesthetics-induced loss of consciousness (LOC). Therefore FC analysis based on SR analysis has better performance in distinguishing anesthetic-induced LOC from awake state.

Automated summary

Monitoring the depth of anesthesia is important for administering general anesthetics during surgery. However, traditional EEG monitors have limitations in monitoring conscious states. This study used high-density EEG signals to compare two methods for functional connectivity analysis before and after anesthesia-induced loss of consciousness. The results show that the method based on sparse representation performs better in distinguishing loss of consciousness from awake states.