Graph models of brain state in deep anaesthesia reveal sink state dynamics of reduced spatiotemporal complexity

Anaesthetisia is an important surgical and explorative tool in the study of consciousness. Much work has been done to connect the deeply anaesthetised condition with decreased complexity. However, anaesthesia-induced unconsciousness is also a dynamic condition in which functional activity and complexity may fluctuate, being perturbed by internal or external (e.g. noxious) stimuli. We use fMRI data from a cohort undergoing deep propofol anaesthesia to investigate resting state dynamics using dynamic brain state models and spatiotemporal network analysis. We focus our analysis on group-level dynamics of brain state temporal complexity, functional activity, connectivity and spatiotemporal modularization in deep anaesthesia and wakefulness. We find that in contrast to dynamics in the wakeful condition, anaesthesia dynamics are dominated by a handful of sink states that act as low-complexity attractors to which subjects repeatedly return. On a subject-level, our analysis provides tentative evidence that these low complexity attractor states appear to depend on subject-specific age and anaesthesia susceptibility factors. Finally, our spatiotemporal analysis, including a novel spatiotemporal clustering of graphs representing hidden Markov models, suggests that dynamic functional organisation in anaesthesia can be characterised by mostly unchanging, isolated regional subnetworks that share some similarities with the brains underlying structural connectivity, as determined from normative tractography data.