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

Journal article

Anesthetisia is an important surgical and explorative tool in the study of consciousness. Much work has been done to connect the deeply anesthetized condition with decreased complexity. However, anesthesia-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 anesthesia 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 anesthesia and wakefulness. We find that in contrast to dynamics in the wakeful condition, anesthesia 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 anesthesia susceptibility factors. Finally, our spatiotemporal analysis, including a novel spatiotemporal clustering of graphs representing hidden Markov models, suggests that dynamic functional organization in anesthesia can be characterized by mostly unchanging, isolated regional subnetworks that share some similarities with the brain's underlying structural connectivity, as determined from normative tractography data.

Authors: Wilsenach, JB; Deane, CM; Reinert, G; Warnaby, K

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Massachusetts Institute of Technology Press
• Journal: Network Neuroscience
• Volume: 9
• Issue: 4
• Pages: 1176-1198
• Publication date: 2025-10-30
• Acceptance date: 2025-06-23
• DOI: 10.1162/netn.a.27
• EISSN: 2472-1751
• ISSN: 2472-1751
• Pmid: 41209085
• Language: English
• Keywords: Anesthesia; Dynamic Brain State Models; Hidden Markov models; Consciousness; Complexity; Spatiotemporal Community Detection