Hysteresis, the discrepancy in forward and reverse pathways of state transitions, is observed during changing levels of consciousness. Identifying the underlying mechanism of hysteresis phenomena in the brain will enhance the ability to understand, monitor, and control state transitions related to consciousness. We hypothesized that hysteresis in brain networks shares the same underlying mechanism of hysteresis as other biological and non-biological networks. In particular, we hypothesized that the principle of explosive synchronization, which can mediate abrupt state transitions, would be critical to explaining hysteresis in the brain during conscious state transitions. We analyzed high-density electroencephalogram (EEG) that was acquired in healthy human volunteers during conscious state transitions induced by the general anesthetics sevoflurane or ketamine. We developed a novel method to monitor the temporal evolution of EEG networks in a parameter space, which consists of the strength and topography of EEG-based networks. Furthermore, we studied conditions of explosive synchronization in anatomically informed human brain network models. We identified hysteresis in the trajectory of functional brain networks during state transitions. The model study and empirical data analysis explained various hysteresis phenomena during the loss and recovery of consciousness in a principled way: (1) more potent anesthetics induce a larger hysteresis; (2) a larger range of EEG frequencies facilitates transitions into unconsciousness and impedes the return of consciousness; (3) hysteresis of connectivity is larger than that of EEG power; and (4) the structure and strength of functional brain networks reconfigure differently during the loss vs. recovery of consciousness. We conclude that the hysteresis phenomena observed during the loss and recovery of consciousness are generic network features. Furthermore, the state transitions are grounded in the same principle as state transitions in complex non-biological networks, especially during perturbation. These findings suggest the possibility of predicting and modulating hysteresis of conscious state transitions in large-scale brain networks.

The integrated information theory (IIT) proposes a quantitative measure, denoted as Φ, of the amount of integrated information in a physical system, which is postulated to have an identity relationship with consciousness. IIT predicts that the value of Φ estimated from brain activities represents the level of consciousness across phylogeny and functional states. Practical limitations, such as the explosive computational demands required to estimate Φ for real systems, have hindered its application to the brain and raised questions about the utility of IIT in general. To achieve practical relevance for studying the human brain, it will be beneficial to establish the reliable estimation of Φ from multichannel electroencephalogram (EEG) and define the relationship of Φ to EEG properties conventionally used to define states of consciousness. In this study, we introduce a practical method to estimate Φ from high-density (128-channel) EEG and determine the contribution of each channel to Φ. We examine the correlation of power, frequency, functional connectivity, and modularity of EEG with regional Φ in various states of consciousness as modulated by diverse anesthetics. We find that our approximation of Φ alone is insufficient to discriminate certain states of anesthesia. However, a multi-dimensional parameter space extended by four parameters related to Φ and EEG connectivity is able to differentiate all states of consciousness. The association of Φ with EEG connectivity during clinically defined anesthetic states represents a new practical approach to the application of IIT, which may be used to characterize various physiological (sleep), pharmacological (anesthesia), and pathological (coma) states of consciousness in the human brain.

Background Previous studies have demonstrated inconsistent neurophysiologic effects of ketamine, although discrepant findings might relate to differences in doses studied, brain regions analyzed, coadministration of other anesthetic medications, and resolution of the electroencephalograph. The objective of this study was to characterize the dose-dependent effects of ketamine on cortical oscillations and functional connectivity. Methods Ten healthy human volunteers were recruited for study participation. The data were recorded using a 128-channel electroencephalograph during baseline consciousness, subanesthetic dosing (0.5 mg/kg over 40 min), anesthetic dosing (1.5 mg/kg bolus), and recovery. No other sedative or anesthetic medications were administered. Spectrograms, topomaps, and functional connectivity (weighted and directed phase lag index) were computed and analyzed. Results Frontal theta bandwidth power increased most dramatically during ketamine anesthesia (mean power ± SD, 4.25 ± 1.90 dB) compared to the baseline (0.64 ± 0.28 dB), subanesthetic (0.60 ± 0.30 dB), and recovery (0.68 ± 0.41 dB) states; P < 0.001. Gamma power also increased during ketamine anesthesia. Weighted phase lag index demonstrated theta phase locking within anterior regions (0.2349 ± 0.1170, P < 0.001) and between anterior and posterior regions (0.2159 ± 0.1538, P < 0.01) during ketamine anesthesia. Alpha power gradually decreased with subanesthetic ketamine, and anterior-to-posterior directed connectivity was maximally reduced (0.0282 ± 0.0772) during ketamine anesthesia compared to all other states (P < 0.05). Conclusions Ketamine anesthesia correlates most clearly with distinct changes in the theta bandwidth, including increased power and functional connectivity. Anterior-to-posterior connectivity in the alpha bandwidth becomes maximally depressed with anesthetic ketamine administration, suggesting a dose-dependent effect.

Integrated information theory (IIT) describes consciousness as information integrated across highly differentiated but irreducible constituent parts in a system. However, in a complex dynamic system such as the brain, the optimal conditions for large integrated information systems have not been elucidated. In this study, we hypothesized that network criticality, a balanced state between a large variation in functional network configuration and a large constraint on structural network configuration, may be the basis of the emergence of a large Φ, a surrogate of integrated information. We also hypothesized that as consciousness diminishes, the brain loses network criticality and Φ decreases. We tested these hypotheses with a large-scale brain network model and high-density electroencephalography (EEG) acquired during various levels of human consciousness under general anesthesia. In the modeling study, maximal criticality coincided with maximal Φ. The EEG study demonstrated an explicit relationship between Φ, criticality, and level of consciousness. The conscious resting state showed the largest Φ and criticality, whereas the balance between variation and constraint in the brain network broke down as the response rate dwindled. The results suggest network criticality as a necessary condition of a large Φ in the human brain.