Consciousness can be defined by two components: arousal (wakefulness) and awareness (subjective experience). However, neurophysiological consciousness metrics able to disentangle between these components have not been reported. Here, we propose an explainable consciousness indicator (ECI) using deep learning to disentangle the components of consciousness. We employ electroencephalographic (EEG) responses to transcranial magnetic stimulation under various conditions, including sleep (n = 6), general anesthesia (n = 16), and severe brain injury (n = 34). We also test our framework using resting-state EEG under general anesthesia (n = 15) and severe brain injury (n = 34). ECI simultaneously quantifies arousal and awareness under physiological, pharmacological, and pathological conditions. Particularly, ketamine-induced anesthesia and rapid eye movement sleep with low arousal and high awareness are clearly distinguished from other states. In addition, parietal regions appear most relevant for quantifying arousal and awareness. This indicator provides insights into the neural correlates of altered states of consciousness.

General anesthesia reversibly alters consciousness, without shutting down the brain globally. Depending on the anesthetic agent and dose, it may produce different consciousness states including a complete absence of subjective experience (unconsciousness), a conscious experience without perception of the environment (disconnected consciousness, like during dreaming), or episodes of oriented consciousness with awareness of the environment (connected consciousness). Each consciousness state may potentially be followed by explicit or implicit memories after the procedure. In this respect, anesthesia can be considered as a proxy to explore consciousness. During the recent years, progress in the exploration of brain function has allowed a better understanding of the neural correlates of consciousness, and of their alterations during anesthesia. Several changes in functional and effective between-region brain connectivity, consciousness network topology, and spatio-temporal dynamics of between-region interactions have been evidenced during anesthesia. Despite a set of effects that are common to many anesthetic agents, it is still uneasy to draw a comprehensive picture of the precise cascades during general anesthesia. Several questions remain unsolved, including the exact identification of the neural substrate of consciousness and its components, the detection of specific consciousness states in unresponsive patients and their associated memory processes, the processing of sensory information during anesthesia, the pharmacodynamic interactions between anesthetic agents, the direction-dependent hysteresis phenomenon during the transitions between consciousness states, the mechanisms of cognitive alterations that follow an anesthetic procedure, the identification of an eventual unitary mechanism of anesthesia-induced alteration of consciousness, the relationship between network effects and the biochemical or sleep-wake cycle targets of anesthetic agents, as well as the vast between-studies variations in dose and administration mode, leading to difficulties in between-studies comparisons. In this narrative review, we draw the picture of the current state of knowledge in anesthesia-induced unconsciousness, from insights gathered on propofol, halogenated vapors, ketamine, dexmedetomidine, benzodiazepines and xenon. We also describe how anesthesia can help understanding consciousness, we develop the above-mentioned unresolved questions, and propose tracks for future research.

The Surgical Pleth Index (SPI) is proposed as a means to assess the balance between noxious stimulation and the anti-nociceptive effects of anaesthesia. In this study, we compared SPI, mean arterial pressure (MAP), and heart rate (HR) as a means of assessing this balance. We studied a standard stimulus [head-holder insertion (HHI)] and varying remifentanil concentrations (CeREMI) in a group of patients undergoing neurosurgery. Patients receiving target-controlled infusions were randomly assigned to one of the three CeREMI (2, 4, or 6 ng m⁻¹), whereas propofol target was fixed at 3 µg ml⁻¹. Steady state for both targets was achieved before HHI. Intravascular volume status (IVS) was evaluated using respiratory variations in arterial pressure. Prediction probability (Pk) and ordinal regression were used to assess SPI, MAP, and HR performance at indicating CeREMI, and the influence of IVS and chronic treatment for high arterial pressure, as possible confounding factors. The maximum SPI, MAP, or HR observed after HHI correctly indicated CeREMI in one of the two patients [accurate prediction rate (APR)=0.5]. When IVS and chronic treatment for high arterial pressure were taken into account, the APR was 0.6 for each individual variable and 0.8 when all of them predicted the same CeREMI. That increase in APR paralleled an increase in Pk from 0.63 to 0.89. SPI, HR, and MAP are of comparable value at gauging noxious stimulation-CeREMI balance. Their interpretation is improved by taking account of IVS, treatment for chronic high arterial pressure, and concordance between their predictions.

To understand how pharmacological interventions can exert their powerful effects on brain function, we need to understand how they engage the brain’s rich neurotransmitter landscape. Here, we bridge microscale molecular chemoarchitecture and pharmacologically induced macroscale functional reorganization, by relating the regional distribution of 19 neurotransmitter receptors and transporters obtained from positron emission tomography, and the regional changes in functional magnetic resonance imaging connectivity induced by 10 different mind-altering drugs: propofol, sevoflurane, ketamine, lysergic acid diethylamide (LSD), psilocybin, N,N-Dimethyltryptamine (DMT), ayahuasca, 3,4-methylenedioxymethamphetamine (MDMA), modafinil, and methylphenidate. Our results reveal a many-to-many mapping between psychoactive drugs’ effects on brain function and multiple neurotransmitter systems. The effects of both anesthetics and psychedelics on brain function are organized along hierarchical gradients of brain structure and function. Last, we show that regional co-susceptibility to pharmacological interventions recapitulates co-susceptibility to disorder-induced structural alterations. Collectively, these results highlight rich statistical patterns relating molecular chemoarchitecture and drug-induced reorganization of the brain’s functional architecture.

Consciousness transiently fades away during deep sleep, more stably under anesthesia, and sometimes permanently due to brain injury. The development of an index to quantify the level of consciousness across these different states is regarded as a key problem both in basic and clinical neuroscience. We argue that this problem is ill-defined since such an index would not exhaust all the relevant information about a given state of consciousness. While the level of consciousness can be taken to describe the actual brain state, a complete characterization should also include its potential behavior against external perturbations. We developed and analyzed whole-brain computational models to show that the stability of conscious states provides information complementary to their similarity to conscious wakefulness. Our work leads to a novel methodological framework to sort out different brain states by their stability and reversibility, and illustrates its usefulness to dissociate between physiological (sleep), pathological (brain-injured patients), and pharmacologically-induced (anesthesia) loss of consciousness.

Propofol is one of the most commonly used anesthetics in the world, but much remains unknown about the mechanisms by which it induces loss of consciousness. In this resting state functional magnetic resonance imaging study, we examined qualitative and quantitative changes of resting state networks, ‘total brain connectivity’, and mean oscillation frequencies of the regional blood oxygenation level dependent (BOLD) signal, associated with propofol-induced mild sedation and loss of responsiveness in healthy subjects. We found that detectability of resting state networks diminished significantly with loss of responsiveness, and total brain connectivity decreased strongly in the frontal cortex, which was associated with increased mean oscillation frequencies of the BOLD signal. Our results suggest a pivotal role of the frontal cortex in propofol-induced loss of responsiveness.