Sleep, anesthesia, and coma share a number of neural features but the recovery profiles are radically different. To understand the mechanisms of reversibility of unconsciousness at the network level, we studied the conditions for gradual and abrupt transitions in conscious and anesthetized states. We hypothesized that the conditions for explosive synchronization (ES) in human brain networks would be present in the anesthetized brain just over the threshold of unconsciousness. To test this hypothesis, functional brain networks were constructed from multi-channel electroencephalogram (EEG) recordings in seven healthy subjects across conscious, unconscious, and recovery states. We analyzed four variables that are involved in facilitating ES in generic, non-biological networks: (1) correlation between node degree and frequency, (2) disassortativity (i.e., the tendency of highly-connected nodes to link with less-connected nodes, or vice versa), (3) frequency difference of coupled nodes, and (4) an inequality relationship between local and global network properties, which is referred to as the suppressive rule. We observed that the four network conditions for ES were satisfied in the unconscious state. Conditions for ES in the human brain suggest a potential mechanism for rapid recovery from the lightly-anesthetized state. This study demonstrates for the first time that the network conditions for ES, formerly shown in generic networks only, are present in empirically-derived functional brain networks. Further investigations with deep anesthesia, sleep, and coma could provide insight into the underlying causes of variability in recovery profiles of these unconscious states.

Background Recent studies of anesthetic-induced unconsciousness in humans have focused predominantly on the intravenous drug propofol and have identified anterior dominance of alpha rhythms and frontal phase-amplitude coupling patterns as neurophysiological markers. However, it is unclear whether the correlates of propofol-induced unconsciousness are generalizable to inhaled anesthetics, which have distinct molecular targets and which are used more commonly in clinical practice. Methods We recorded 64-channel electroencephalogram in healthy human participants during consciousness, sevoflurane-induced unconsciousness, and recovery (n=10; n=7 suitable for analysis). Spectrograms and scalp distributions of low-frequency (1 Hz) and alpha (10 Hz) power were analyzed, and phase-amplitude modulation between these two frequencies was calculated in frontal and parietal regions. Phase lag index was used to assess phase relationships across the cortex. Results At concentrations sufficient for unconsciousness, sevoflurane did not result in a consistent anteriorization of alpha power; the relationship between low-frequency phase and alpha amplitude in the frontal cortex did not undergo characteristic transitions. By contrast, there was significant cross-frequency coupling in the parietal region during consciousness that was not observed after loss of consciousness. Furthermore, a reversible disruption of anterior-posterior phase relationships in the alpha bandwidth was identified as a correlate of sevoflurane-induced unconsciousness. Conclusion In humans, sevoflurane-induced unconsciousness is not correlated with anteriorization of alpha and related cross-frequency patterns, but rather by a disruption of phase-amplitude coupling in the parietal region and phase-phase relationships across the cortex.

Ketamine induces altered states of consciousness during periods of reduced alpha power in the precuneus and temporal-parietal junction. Modulation of these temporal-parietal loci are candidate mechanisms of the psychoactive effects of ketamine, given that this region is involved in multisensory integration, body representation, and consciousness.

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Recent studies have investigated local oscillations, long-range connectivity, and global network patterns to identify neural changes associated with anesthetic-induced unconsciousness. These studies typically employ anesthetic protocols that either just cross the threshold of unconsciousness, or induce deep unconsciousness for a brief period of time—neither of which models general anesthesia for major surgery. To study neural patterns of unconsciousness and recovery in a clinically-relevant context, we used a realistic anesthetic regimen to induce and maintain unconsciousness in eight healthy participants for 3 h. High-density electroencephalogram (EEG) was acquired throughout and for another 3 h after emergence. Seven epochs of 5-min eyes-closed resting states were extracted from the data at baseline as well as 30, 60, 90, 120, 150, and 180-min post-emergence. Additionally, 5-min epochs were extracted during induction, unconsciousness, and immediately prior to recovery of consciousness, for a total of 10 analysis epochs. The EEG data in each epoch were analyzed using source-localized spectral analysis, phase-lag index, and graph theoretical techniques. Posterior alpha power was significantly depressed during unconsciousness, and gradually approached baseline levels over the 3 h recovery period. Phase-lag index did not distinguish between states of consciousness or stages of recovery. Network efficiency was significantly depressed and network clustering coefficient was significantly increased during unconsciousness; these graph theoretical measures returned to baseline during the 3 h recovery period. Posterior alpha power may be a potential biomarker for normal recovery of functional brain networks after general anesthesia.