Dissociable network properties of anesthetic state transitions

Abstract

Background: It is still unknown whether anesthetic state transitions are continuous or binary. Mathematical graph theory is one method by which to assess whether brain networks change gradually or abruptly upon anesthetic induction and emergence.

Methods: Twenty healthy males were anesthetized with an induction dose of propofol, with continuous measurement of 21-channel electroencephalogram at baseline, during anesthesia, and during recovery. From these electroencephalographic data a "genuine network" was reconstructed based on the surrogate data method. The effects of topologic structure and connection strength on information transfer through the network were measured independently across different states.

Results: Loss of consciousness was consistently associated with a disruption of network topology. However, recovery of consciousness was associated with complex patterns of altered connection strength after the initial topologic structure had slowly recovered. In one group of subjects, there was a precipitous increase of connection strength that was associated with reduced variability of emergence time. Analysis of regional effects on brain networks demonstrated that the parietal network was significantly disrupted, whereas the frontal network was minimally affected.

Conclusions: By dissociating the effects of network structure and connection strength, both continuous and discrete elements of anesthetic state transitions were identified. The study also supports a critical role of parietal networks as a target of general anesthetics.