Psychedelics have been recognized as model interventions for studying altered states of consciousness. However, few empirical studies of the shamanic state of consciousness, which is anecdotally similar to the psychedelic state, exist. We investigated the neural correlates of shamanic trance using high-density electroencephalography (EEG) in 24 shamanic practitioners and 24 healthy controls during rest, shamanic drumming, and classical music listening, followed by an assessment of altered states of consciousness. EEG data were used to assess changes in absolute power, connectivity, signal diversity, and criticality, which were correlated with assessment measures. We also compared assessment scores to those of individuals in a previous study under the influence of psychedelics. Shamanic practitioners were significantly different from controls in several domains of altered states of consciousness, with scores comparable to or exceeding that of healthy volunteers under the influence of psychedelics. Practitioners also displayed increased gamma power during drumming that positively correlated with elementary visual alterations. Furthermore, shamanic practitioners had decreased low alpha and increased low beta connectivity during drumming and classical music and decreased neural signal diversity in the gamma band during drumming that inversely correlated with insightfulness. Finally, criticality in practitioners was increased during drumming in the low and high beta and gamma bands, with increases in the low beta band correlating with complex imagery and elementary visual alterations. These findings suggest that psychedelic drug-induced and non-pharmacologic alterations in consciousness have overlapping phenomenal traits but are distinct states of consciousness, as reflected by the unique brain-related changes during shamanic trance compared to previous literature investigating the psychedelic state.

Anesthetics are known to disrupt neural interactions in cortical and subcortical brain circuits. While the effect of anesthetic drugs on consciousness is reversible, the neural mechanism mediating induction and recovery may be different. Insight into these distinct mechanisms can be gained from a systematic comparison of neural dynamics during slow induction of and emergence from anesthesia. To this end, we used functional magnetic resonance imaging (fMRI) data obtained in healthy volunteers before, during, and after the administration of propofol at incrementally adjusted target concentrations. We analyzed functional connectivity of corticocortical and subcorticocortical networks and the temporal autocorrelation of fMRI signal as an index of neural processing timescales. We found that en route to unconsciousness, temporal autocorrelation across the entire brain gradually increased, whereas functional connectivity gradually decreased. In contrast, regaining consciousness was associated with an abrupt restoration of cortical but not subcortical temporal autocorrelation and an abrupt boost of subcorticocortical functional connectivity. Pharmacokinetic effects could not account for the difference in neural dynamics between induction and emergence. We conclude that the induction and recovery phases of anesthesia follow asymmetric neural dynamics. A rapid increase in the speed of cortical neural processing and subcorticocortical neural interactions may be a mechanism that reboots consciousness.

Important scientific and clinical questions persist about general anesthesia despite the ubiquitous clinical use of anesthetic drugs in humans since their discovery. For example, it is not known how the brain reconstitutes consciousness and cognition after the profound functional perturbation of the anesthetized state, nor has a specific pattern of functional recovery been characterized. To date, there has been a lack of detailed investigation into rates of recovery and the potential orderly return of attention, sensorimotor function, memory, reasoning and logic, abstract thinking, and processing speed. Moreover, whether such neurobehavioral functions display an invariant sequence of return across individuals is similarly unknown. To address these questions, we designed a study of healthy volunteers undergoing general anesthesia with electroencephalography and serial testing of cognitive functions (NCT01911195). The aims of this study are to characterize the temporal patterns of neurobehavioral recovery over the first several hours following termination of a deep inhaled isoflurane general anesthetic and to identify common patterns of cognitive function recovery. Additionally, we will conduct spectral analysis and reconstruct functional networks from electroencephalographic data to identify any neural correlates (e.g., connectivity patterns, graph-theoretical variables) of cognitive recovery after the perturbation of general anesthesia. To accomplish these objectives, we will enroll a total of 60 consenting adults aged 20-40 across the three participating sites. Half of the study subjects will receive general anesthesia slowly titrated to loss of consciousness (LOC) with an intravenous infusion of propofol and thereafter be maintained for 3 h with 1.3 age adjusted minimum alveolar concentration of isoflurane, while the other half of subjects serves as awake controls to gauge effects of repeated neurobehavioral testing, spontaneous fatigue and endogenous rest-activity patterns.

The ongoing stream of human consciousness relies on two distinct cortical systems, the default mode network and the dorsal attention network, which alternate their activity in an anticorrelated manner. We examined how the two systems are regulated in the conscious brain and how they are disrupted when consciousness is diminished. We provide evidence for a "temporal circuit" characterized by a set of trajectories along which dynamic brain activity occurs. We demonstrate that the transitions between default mode and dorsal attention networks are embedded in this temporal circuit, in which a balanced reciprocal accessibility of brain states is characteristic of consciousness. Conversely, isolation of the default mode and dorsal attention networks from the temporal circuit is associated with unresponsiveness of diverse etiologies. These findings advance the foundational understanding of the functional role of anticorrelated systems in consciousness.

Understanding how the brain recovers from unconsciousness can inform neurobiological theories of consciousness and guide clinical investigation. To address this question, we conducted a multicenter study of 60 healthy humans, half of whom received general anesthesia for 3 hr and half of whom served as awake controls. We administered a battery of neurocognitive tests and recorded electroencephalography to assess cortical dynamics. We hypothesized that recovery of consciousness and cognition is an extended process, with differential recovery of cognitive functions that would commence with return of responsiveness and end with return of executive function, mediated by prefrontal cortex. We found that, just prior to the recovery of consciousness, frontal-parietal dynamics returned to baseline. Consistent with our hypothesis, cognitive reconstitution after anesthesia evolved over time. Contrary to our hypothesis, executive function returned first. Early engagement of prefrontal cortex in recovery of consciousness and cognition is consistent with global neuronal workspace theory.

Motifs are patterns of inter-connections between nodes of a network, and have been investigated as building blocks of directed networks. This study explored the re-organization of 3-node motifs during loss and recovery of consciousness. Nine healthy subjects underwent a 3-h anesthetic protocol while 128-channel electroencephalography (EEG) was recorded. In the alpha (8-13 Hz) band, 5-min epochs of EEG were extracted for: Baseline; Induction; Unconscious; 30-, 10- and 5-min pre-recovery of responsiveness; 30- and 180-min post-recovery of responsiveness. We constructed a functional brain network using the weighted and directed phase lag index, on which we calculated the frequency and topology of 3-node motifs. Three motifs (motifs 1, 2 and 5) were significantly present across participants and epochs, when compared to random networks (p < 0.05). The topology of motifs 1 and 5 changed significantly between responsive and unresponsive epochs (p-values < 0.01; Kendall's W = 0.664 (motif 1) and 0.529 (motif 5)). Motif 1 was constituted of long-range chain-like connections, while motif 5 was constituted of short-range, loop-like connections. Our results suggest that anesthetic-induced unconsciousness is associated with a topological re-organization of network motifs. As motif topological re-organization may precede (motif 5) or accompany (motif 1) the return of responsiveness, motifs could contribute to the understanding of the neural correlates of consciousness.

Detecting covert consciousness in behaviorally unresponsive patients by brain imaging is of great interest, but a reproducible model and evidence from independent sources is still lacking. Here we demonstrate the possibility of using general anesthetics in a within-subjects study design to test methods or statistical paradigms of assessing covert consciousness. Using noninvasive neuroimaging in healthy volunteers, we identified a healthy study participant who was able to exhibit the specific fMRI signatures of volitional mental imagery while behaviorally unresponsive due to sedation with propofol. Our findings reveal a novel model that may accelerate the development of new approaches to reproducibly detect covert consciousness, which is difficult to achieve in patients with heterogeneous and sometimes clinically unstable neuropathology.

Consciousness is determined both by level (e.g., being awake versus being anesthetized) and content (i.e., the qualitative aspects of experience). Subcortical areas are known to play a causal role in regulating the level of consciousness [1-9], but the role of the cortex is less well understood. Clinical and correlative data have been used both to support and refute a role for prefrontal and posterior cortices in the level of consciousness [10-22]. The prefrontal cortex has extensive reciprocal connections to wake-promoting centers in the brainstem and diencephalon [23, 24], and hence is in a unique position to modulate level of consciousness. Furthermore, a recent study suggested that the prefrontal cortex might be important in regulating level of consciousness [25] but causal evidence, and a comparison with more posterior cortical sites, is lacking. Therefore, to test the hypothesis that prefrontal cortex plays a role in regulating level of consciousness, we attempted to reverse sevoflurane anesthesia by cholinergic or noradrenergic stimulation of the prefrontal prelimbic cortex and two areas of parietal cortex in rat. General anesthesia was defined by loss of the righting reflex, a widely used surrogate measure in rodents. We demonstrate that cholinergic stimulation of prefrontal cortex, but not parietal cortex, restored wake-like behavior, despite continuous exposure to clinically relevant concentrations of sevoflurane anesthesia. Noradrenergic stimulation of the prefrontal and parietal areas resulted in electroencephalographic activation but failed to produce any signs of wake-like behavior. We conclude that cholinergic mechanisms in prefrontal cortex can regulate the level of consciousness.

General anesthesia significantly alters brain network connectivity. Graph-theoretical analysis has been used extensively to study static brain networks but may be limited in the study of rapidly changing brain connectivity during induction of or recovery from general anesthesia. Here we introduce a novel method to study the temporal evolution of network modules in the brain. We recorded multichannel electroencephalograms (EEG) from 18 surgical patients who underwent general anesthesia with either propofol (n = 9) or sevoflurane (n = 9). Time series data were used to reconstruct networks; each electroencephalographic channel was defined as a node and correlated activity between the channels was defined as a link. We analyzed the frequency of subgraphs in the network with a defined number of links; subgraphs with a high probability of occurrence were deemed network "backbones." We analyzed the behavior of network backbones across consciousness, anesthetic induction, anesthetic maintenance, and two points of recovery. Constitutive, variable and state-specific backbones were identified across anesthetic state transitions. Brain networks derived from neurophysiologic data can be deconstructed into network backbones that change rapidly across states of consciousness. This technique enabled a granular description of network evolution over time. The concept of network backbones may facilitate graph-theoretical analysis of dynamically changing networks.

The development of sophisticated computational tools to quantify changes in the brain's oscillatory dynamics across states of consciousness have included both envelope- and phase-based measures of functional connectivity (FC), but there are very few direct comparisons of these techniques using the same dataset. The goal of this study was to compare an envelope-based (i.e. Amplitude Envelope Correlation, AEC) and a phase-based (i.e. weighted Phase Lag Index, wPLI) measure of FC in their classification of states of consciousness. Nine healthy participants underwent a three-hour experimental anesthetic protocol with propofol induction and isoflurane maintenance, in which five minutes of 128-channel electroencephalography were recorded before, during, and after anesthetic-induced unconsciousness, at the following time points: Baseline; light sedation with propofol (Light Sedation); deep unconsciousness following three hours of surgical levels of anesthesia with isoflurane (Unconscious); five minutes prior to the recovery of consciousness (Pre-ROC); and three hours following the recovery of consciousness (Recovery). Support vector machine classification was applied to the source-localized EEG in the alpha (8-13 Hz) frequency band in order to investigate the ability of AEC and wPLI (separately and together) to discriminate i) the four states from Baseline; ii) Unconscious ("deep" unconsciousness) vs. Pre-ROC ("light" unconsciousness); and iii) responsiveness (Baseline, Light Sedation, Recovery) vs. unresponsiveness (Unconscious, Pre-ROC). AEC and wPLI yielded different patterns of global connectivity across states of consciousness, with AEC showing the strongest network connectivity during the Unconscious epoch, and wPLI showing the strongest connectivity during full consciousness (i.e., Baseline and Recovery). Both measures also demonstrated differential predictive contributions across participants and used different brain regions for classification. AEC showed higher classification accuracy overall, particularly for distinguishing anesthetic-induced unconsciousness from Baseline (83.7 ± 0.8%). AEC also showed stronger classification accuracy than wPLI when distinguishing Unconscious from Pre-ROC (i.e., "deep" from "light" unconsciousness) (AEC: 66.3 ± 1.2%; wPLI: 56.2 ± 1.3%), and when distinguishing between responsiveness and unresponsiveness (AEC: 76.0 ± 1.3%; wPLI: 63.6 ± 1.8%). Classification accuracy was not improved compared to AEC when both AEC and wPLI were combined. This analysis of source-localized EEG data demonstrates that envelope- and phase-based FC provide different information about states of consciousness but that, on a group level, AEC is better able to detect relative alterations in brain FC across levels of anesthetic-induced unconsciousness compared to wPLI.

Leading neuroscientific theories posit a central role for the functional integration of cortical areas in conscious states. Considerable evidence supporting this hypothesis is based on network changes during anesthesia, but it is unclear whether these changes represent state-related (conscious vs unconscious) or drug-related (anesthetic vs no anesthetic) effects. We recently demonstrated that carbachol delivery to prefrontal cortex (PFC) restored wakefulness despite continuous administration of the general anesthetic sevoflurane. By contrast, carbachol delivery to parietal cortex, or noradrenaline delivery to either prefrontal or parietal cortices, failed to restore wakefulness. Thus, carbachol-induced reversal of sevoflurane anesthesia represents a unique state that combines wakefulness with clinically relevant anesthetic concentrations in the brain. To differentiate the state-related and drug-related associations of cortical connectivity and dynamics, we analyzed the electroencephalographic data gathered from adult male Sprague Dawley rats during the aforementioned experiments for changes in functional cortical gamma connectivity (25-155 Hz), slow oscillations (0.5-1 Hz), and complexity (<175 Hz). We show that higher gamma (85-155 Hz) connectivity is decreased (p ≤ 0.02) during sevoflurane anesthesia, an expected finding, but was not restored during wakefulness induced by carbachol delivery to PFC. Conversely, for rats in which wakefulness was not restored, the functional gamma connectivity remained reduced, but there was a significant decrease (p < 0.001) in the power of slow oscillations and increase (p < 0.001) in cortical complexity, which was similar to that observed during wakefulness induced after carbachol delivery to PFC. We conclude that the level of consciousness can be dissociated from cortical connectivity, oscillations, and dynamics.SIGNIFICANCE STATEMENT Numerous theories of consciousness suggest that functional connectivity across the cortex is characteristic of the conscious state and is reduced during anesthesia. However, it is unknown whether the observed changes are state-related (conscious vs unconscious) or drug-related (drug vs no drug). We used a novel rat model in which cholinergic stimulation of PFC produced wakefulness despite continuous exposure to a general anesthetic. We demonstrate that, as expected, general anesthesia reduces connectivity. Surprisingly, the connectivity remains suppressed despite pharmacologically induced wakefulness in the presence of anesthetic, with restoration occurring only after the anesthetic is discontinued. Thus, whether an animal exhibits wakefulness or not can be dissociated from cortical connectivity, prompting a reevaluation of the role of connectivity in level of consciousness.

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.

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.

Recent data from our laboratory demonstrate that high-frequency gamma connectivity across the cortex is present during consciousness and depressed during unconsciousness. However, these data were derived from static and well-defined states of arousal rather than during transitions that would suggest functional relevance. We also recently found that subanesthetic ketamine administered during isoflurane anesthesia accelerates recovery upon discontinuation of the primary anesthetic and increases gamma power during emergence. In the current study we re-analyzed electroencephalogram (EEG) data to test the hypothesis that functional cortical connectivity between anterior and posterior cortical regions would be increased during accelerated recovery induced by ketamine when compared to saline-treated controls. Rodents were instrumented with intracranial EEG electrodes and general anesthesia was induced with isoflurane anesthesia. After 37.5 min of continuous isoflurane anesthesia, a subanesthetic dose of ketamine (25 mg/kg intraperitoneal) was administered, with evidence of a 44% reduction in emergence time. In this study, we analyzed gamma and theta coherence (measure of undirected functional connectivity) and normalized symbolic transfer entropy (measure of directed functional connectivity) between frontal and parietal cortices during various levels of consciousness, with a focus on emergence from isoflurane anesthesia. During accelerated emergence in the ketamine-treated group, there was increased frontal-parietal coherence {p = 0.005, 0.05-0.23 [95% confidence interval (CI)]} and normalized symbolic transfer entropy [frontal to parietal: p < 0.001, 0.010-0.026 (95% CI); parietal to frontal: p < 0.001, 0.009-0.025 (95% CI)] in high-frequency gamma bandwidth as compared with the saline-treated group. Surrogates of cortical information exchange in high-frequency gamma are increased in association with accelerated recovery from anesthesia. This finding adds evidence suggesting a functional significance of high-gamma information transfer in consciousness.

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.

Recent neuroimaging studies have demonstrated that spontaneous brain activity exhibits rich spatiotemporal structure that can be characterized as the exploration of a repertoire of spatially distributed patterns that recur over time. The repertoire of brain states may reflect the capacity for consciousness, since general anesthetics suppress and psychedelic drugs enhance such dynamics. However, the modulation of brain activity repertoire across varying states of consciousness has not yet been studied in a systematic and unified framework. As a unique drug that has both psychedelic and anesthetic properties depending on the dose, ketamine offers an opportunity to examine brain reconfiguration dynamics along a continuum of consciousness. Here we investigated the dynamic organization of cortical activity during wakefulness and during altered states of consciousness induced by different doses of ketamine. Through k-means clustering analysis of the envelope data of source-localized electroencephalographic (EEG) signals, we identified a set of recurring states that represent frequency-specific spatial coactivation patterns. We quantified the effect of ketamine on individual brain states in terms of fractional occupancy and transition probabilities and found that ketamine anesthesia tends to shift the configuration toward brain states with low spatial variability. Furthermore, by assessing the temporal dynamics of the occurrence and transitions of brain states, we showed that subanesthetic ketamine is associated with a richer repertoire, while anesthetic ketamine induces dynamic changes in brain state organization, with the repertoire richness evolving from a reduced level to one comparable to that of normal wakefulness before recovery of consciousness. These results provide a novel description of ketamine's modulation of the dynamic configuration of cortical activity and advance understanding of the neurophysiological mechanism of ketamine in terms of the spatial, temporal, and spectral structures of underlying whole-brain dynamics.

Conscious access to sensory information is likely gated at an intermediate site between primary sensory and transmodal association cortices, but the structure responsible remains unknown. We perform functional neuroimaging to determine the neural correlates of conscious access using a volitional mental imagery task, a report paradigm not confounded by motor behavior. Titrating propofol to loss of behavioral responsiveness in healthy volunteers creates dysfunction of the anterior insular cortex (AIC) in association with an impairment of dynamic transitions of default-mode and dorsal attention networks. Candidate subcortical regions mediating sensory gating or arousal (thalamus, basal forebrain) fail to show this association. The gating role of the AIC is consistent with findings in awake participants, whose conscious access is predicted by pre-stimulus AIC activity near perceptual threshold. These data support the hypothesis that AIC, situated at an intermediate position of the cortical hierarchy, regulates brain network transitions that gate conscious access.

Recent studies of propofol-induced unconsciousness have identified characteristic properties of electroencephalographic alpha rhythms that may be mediated by drug activity at γ-aminobutyric acid (GABA) receptors in the thalamus. However, the effect of ketamine (a primarily non-GABAergic anesthetic drug) on alpha oscillations has not been systematically evaluated. We analyzed the electroencephalogram of 28 surgical patients during consciousness and ketamine-induced unconsciousness with a focus on frontal power, frontal cross-frequency coupling, frontal-parietal functional connectivity (measured by coherence and phase lag index), and frontal-to-parietal directional connectivity (measured by directed phase lag index) in the alpha bandwidth. Unlike past studies of propofol, ketamine-induced unconsciousness was not associated with increases in the power of frontal alpha rhythms, characteristic cross-frequency coupling patterns of frontal alpha power and slow-oscillation phase, or decreases in coherence in the alpha bandwidth. Like past studies of propofol using undirected and directed phase lag index, ketamine reduced frontal-parietal (functional) and frontal-to-parietal (directional) connectivity in the alpha bandwidth. These results suggest that directional connectivity changes in the alpha bandwidth may be state-related markers of unconsciousness induced by both GABAergic and non-GABAergic anesthetics.

Determining the level of consciousness in patients with disorders of consciousness (DOC) remains challenging. To address this challenge, resting-state fMRI (rs-fMRI) has been widely used for detecting the local, regional, and network activity differences between DOC patients and healthy controls. Although substantial progress has been made towards this endeavor, the identification of robust rs-fMRI-based biomarkers for level of consciousness is still lacking. Recent developments in machine learning show promise as a tool to augment the discrimination between different states of consciousness in clinical practice. Here, we investigated whether machine learning models trained to make a binary distinction between conscious wakefulness and anesthetic-induced unconsciousness would then be capable of reliably identifying pathologically induced unconsciousness. We did so by extracting rs-fMRI-based features associated with local activity, regional homogeneity, and interregional functional activity in 44 subjects during wakefulness, light sedation, and unresponsiveness (deep sedation and general anesthesia), and subsequently using those features to train three distinct candidate machine learning classifiers: support vector machine, Extra Trees, artificial neural network. First, we show that all three classifiers achieve reliable performance within-dataset (via nested cross-validation), with a mean area under the receiver operating characteristic curve (AUC) of 0.95, 0.92, and 0.94, respectively. Additionally, we observed comparable cross-dataset performance (making predictions on the DOC data) as the anesthesia-trained classifiers demonstrated a consistent ability to discriminate between unresponsive wakefulness syndrome (UWS/VS) patients and healthy controls with mean AUC's of 0.99, 0.94, 0.98, respectively. Lastly, we explored the potential of applying the aforementioned classifiers towards discriminating intermediate states of consciousness, specifically, subjects under light anesthetic sedation and patients diagnosed as having a minimally conscious state (MCS). Our findings demonstrate that machine learning classifiers trained on rs-fMRI features derived from participants under anesthesia have potential to aid the discrimination between degrees of pathological unconsciousness in clinical patients.

Ketamine is a unique drug that has psychedelic and anesthetic properties in a dose-dependent manner. Recent studies have shown that ketamine anesthesia appears to maintain the spatiotemporal complexity of cortical activation evoked by transcranial magnetic stimulation, while a psychedelic dose of ketamine is associated with increased spontaneous magnetoencephalographic signal diversity. However, a systematic investigation of the dose-dependent effects of ketamine on cortical complexity using the same modality is required. Furthermore, it is unknown whether the complexity level stabilizes or fluctuates over time for the duration of ketamine exposure. Here we investigated the spatiotemporal complexity of spontaneous high-density scalp electroencephalography (EEG) signals in healthy volunteers during alterations of consciousness induced by both subanesthetic and anesthetic doses of ketamine. Given the fast transient spectral dynamics, especially during the gamma-burst pattern after loss of consciousness, we employed a method based on Hidden Markov modeling to classify the EEG signals into a discrete set of brain states that correlated with different behavioral states. We characterized the spatiotemporal complexity specific for each brain state as measured through the Lempel-Ziv complexity algorithm. After controlling for signal diversity due to spectral changes, we found that the subanesthetic dose of ketamine is associated with an elevated complexity level relative to baseline, while the brain activity following an anesthetic dose of ketamine is characterized by alternating low and high complexity levels until stabilizing at a high level comparable to that during baseline. Thus, spatiotemporal complexity associated with ketamine-induced state transitions has features of general anesthesia, normal consciousness, and altered states of consciousness. These results improve our understanding of the complex pharmacological, neurophysiological, and phenomenological properties of ketamine.