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Uncovering the neural basis of consciousness is a major challenge to neuroscience. In this Perspective, Tononi and colleagues describe the integrated information theory of consciousness and how it might be used to answer outstanding questions about the nature of consciousness. In this Opinion article, we discuss how integrated information theory accounts for several aspects of the relationship between consciousness and the brain. Integrated information theory starts from the essential properties of phenomenal experience, from which it derives the requirements for the physical substrate of consciousness. It argues that the physical substrate of consciousness must be a maximum of intrinsic cause–effect power and provides a means to determine, in principle, the quality and quantity of experience. The theory leads to some counterintuitive predictions and can be used to develop new tools for assessing consciousness in non-communicative patients.

Despite the absence of responsiveness during anesthesia, conscious experience may persist. However, reliable, easily acquirable and interpretable neurophysiological markers of the presence of consciousness in unresponsive states are still missing. A promising marker is based on the decay-rate of the power spectral density (PSD) of the resting EEG. We acquired resting electroencephalogram (EEG) in three groups of healthy participants (n = 5 each), before and during anesthesia induced by either xenon, propofol or ketamine. Dosage of each anesthetic agent was tailored to yield unresponsiveness (Ramsay score = 6). Delayed subjective reports assessed whether conscious experience was present (‘Conscious report’) or absent/inaccessible to recall (‘No Report’). We estimated the decay of the PSD of the resting EEG—after removing oscillatory peaks—via the spectral exponent β, for a broad band (1–40 Hz) and narrower sub-bands (1–20 Hz, 20–40 Hz). Within-subject anesthetic changes in β were assessed. Furthermore, based on β, ‘Conscious report’ states were discriminated against ‘no report’ states. Finally, we evaluated the correlation of the resting spectral exponent with a recently proposed index of consciousness, the Perturbational Complexity Index (PCI), derived from a previous TMS-EEG study. The spectral exponent of the resting EEG discriminated states in which consciousness was present (wakefulness, ketamine) from states where consciousness was reduced or abolished (xenon, propofol). Loss of consciousness substantially decreased the (negative) broad-band spectral exponent in each subject undergoing xenon or propofol anesthesia—indexing an overall steeper PSD decay. Conversely, ketamine displayed an overall PSD decay similar to that of wakefulness—consistent with the preservation of consciousness—yet it showed a flattening of the decay in the high-frequencies (20–40 Hz)—consistent with its specific mechanism of action. The spectral exponent was highly correlated to PCI, corroborating its interpretation as a marker of the presence of consciousness. A steeper PSD of the resting EEG reliably indexed unconsciousness in anesthesia, beyond sheer unresponsiveness. •Unconsciousness does not imply unresponsiveness.•Consciousness is abolished during xenon and propofol, yet preserved during ketamine.•EEG Spectral exponent indexes the 1/f-like decay of non-oscillatory PSD background.•Xenon and propofol steepen broad-band decay; ketamine flattens high-frequency decay.•Spectral exponent separates un/consciousness in anesthesia-induced unresponsiveness.

Emerging neural theories of consciousness suggest a correlation between a specific type of neural dynamical complexity and the level of consciousness: When awake and aware, causal interactions between brain regions are both integrated (all regions are to a certain extent connected) and differentiated (there is inhomogeneity and variety in the interactions). In support of this, recent work by Casali et al (2013) has shown that Lempel-Ziv complexity correlates strongly with conscious level, when computed on the EEG response to transcranial magnetic stimulation. Here we investigated complexity of spontaneous high-density EEG data during propofol-induced general anaesthesia. We consider three distinct measures: (i) Lempel-Ziv complexity, which is derived from how compressible the data are; (ii) amplitude coalition entropy, which measures the variability in the constitution of the set of active channels; and (iii) the novel synchrony coalition entropy (SCE), which measures the variability in the constitution of the set of synchronous channels. After some simulations on Kuramoto oscillator models which demonstrate that these measures capture distinct 'flavours' of complexity, we show that there is a robustly measurable decrease in the complexity of spontaneous EEG during general anaesthesia.

Sleep can be distinguished from wake by changes in brain electrical activity, typically assessed using electroencephalography (EEG). The hallmark of nonrapid-eye-movement (NREM) sleep is the shift from high-frequency, low-amplitude wake EEG to low-frequency, high-amplitude sleep EEG dominated by spindles and slow waves. Here we identified signatures of sleep in brain hemodynamic activity, using simultaneous functional MRI (fMRI) and EEG. We found that, at the transition from wake to sleep, fMRI blood oxygen level–dependent (BOLD) activity evolved from a mixed-frequency pattern to one dominated by two distinct oscillations: a low-frequency (<0.1 Hz) oscillation prominent in light sleep and correlated with the occurrence of spindles, and a high-frequency oscillation (>0.1 Hz) prominent in deep sleep and correlated with the occurrence of slow waves. The two oscillations were both detectable across the brain but exhibited distinct spatiotemporal patterns. During the falling-asleep process, the low-frequency oscillation first appeared in the thalamus, then the posterior cortex, and lastly the frontal cortex, while the high-frequency oscillation first appeared in the midbrain, then the frontal cortex, and lastly the posterior cortex. During the waking-up process, both oscillations disappeared first from the thalamus, then the frontal cortex, and lastly the posterior cortex. The BOLD oscillations provide local signatures of spindle and slow wave activity. They may be employed to monitor the regional occurrence of sleep or wakefulness, track which regions are the first to fall asleep or wake up at the wake–sleep transitions, and investigate local homeostatic sleep processes.

Background Previously published studies have reported that up to 43% of patients with disorders of consciousness are erroneously assigned a diagnosis of vegetative state (VS). However, no recent studies have investigated the accuracy of this grave clinical diagnosis. In this study, we compared consensus-based diagnoses of VS and MCS to those based on a well-established standardized neurobehavioral rating scale, the JFK Coma Recovery Scale-Revised (CRS-R). Methods We prospectively followed 103 patients (55 ± 19 years) with mixed etiologies and compared the clinical consensus diagnosis provided by the physician on the basis of the medical staff's daily observations to diagnoses derived from CRS-R assessments performed by research staff. All patients were assigned a diagnosis of 'VS', 'MCS' or 'uncertain diagnosis.' Results Of the 44 patients diagnosed with VS based on the clinical consensus of the medical team, 18 (41%) were found to be in MCS following standardized assessment with the CRS-R. In the 41 patients with a consensus diagnosis of MCS, 4 (10%) had emerged from MCS, according to the CRS-R. We also found that the majority of patients assigned an uncertain diagnosis by clinical consensus (89%) were in MCS based on CRS-R findings. Conclusion Despite the importance of diagnostic accuracy, the rate of misdiagnosis of VS has not substantially changed in the past 15 years. Standardized neurobehavioral assessment is a more sensitive means of establishing differential diagnosis in patients with disorders of consciousness when compared to diagnoses determined by clinical consensus.

In this study involving 54 patients in a vegetative or minimally conscious state, the use of functional magnetic resonance imaging (MRI) to assess responses during mental-imagery tasks showed that 5 patients were able to willfully modulate their brain activation. These findings suggest that functional MRI can be used to demonstrate evidence of awareness and cognition that cannot be detected by means of clinical assessment. In patients in a vegetative or minimally conscious state, the use of functional MRI to assess responses during mental-imagery tasks showed that 5 patients were able to willfully modulate their brain activation. In recent years, improvements in intensive care have led to an increase in the number of patients who survive severe brain injury. Although some of these patients go on to have a good recovery, others awaken from the acute comatose state but do not show any signs of awareness. If repeated examinations yield no evidence of a sustained, reproducible, purposeful, or voluntary behavioral response to visual, auditory, tactile, or noxious stimuli, a diagnosis of a vegetative state — or “wakefulness without awareness” — is made. 1 – 5 Some patients remain in a vegetative state permanently. Others eventually show inconsistent but reproducible . . .

This paper presents Integrated Information Theory (IIT) 4.0. IIT aims to account for the properties of experience in physical (operational) terms. It identifies the essential properties of experience (axioms), infers the necessary and sufficient properties that its substrate must satisfy (postulates), and expresses them in mathematical terms. In principle, the postulates can be applied to any system of units in a state to determine whether it is conscious, to what degree, and in what way. IIT offers a parsimonious explanation of empirical evidence, makes testable predictions concerning both the presence and the quality of experience, and permits inferences and extrapolations. IIT 4.0 incorporates several developments of the past ten years, including a more accurate formulation of the axioms as postulates and mathematical expressions, the introduction of a unique measure of intrinsic information that is consistent with the postulates, and an explicit assessment of causal relations. By fully unfolding a system’s irreducible cause–effect power, the distinctions and relations specified by a substrate can account for the quality of experience.

Frontoparietal cortex is involved in the explicit processing (awareness) of stimuli. Frontoparietal activation has also been found in studies of subliminal stimulus processing. We hypothesized that an impairment of top-down processes, involved in recurrent neuronal message-passing and the generation of long-latency electrophysiological responses, might provide a more reliable correlate of consciousness in severely brain-damaged patients, than frontoparietal responses. We measured effective connectivity during a mismatch negativity paradigm and found that the only significant difference between patients in a vegetative state and controls was an impairment of backward connectivity from frontal to temporal cortices. This result emphasizes the importance of top-down projections in recurrent processing that involve high-order associative cortices for conscious perception.

We used functional magnetic resonance imaging to demonstrate preserved conscious awareness in a patient fulfilling the criteria for a diagnosis of vegetative state. When asked to imagine playing tennis or moving around her home, the patient activated predicted cortical areas in a manner indistinguishable from that of healthy volunteers.

The neuronal connectivity patterns that differentiate consciousness from unconsciousness remain unclear. Previous studies have demonstrated that effective connectivity, as assessed by transcranial magnetic stimulation combined with electroencephalography (TMS–EEG), breaks down during the loss of consciousness. This study investigated changes in EEG connectivity associated with consciousness during non-rapid eye movement (NREM) sleep following parietal TMS. Compared with unconsciousness, conscious experiences during NREM sleep were associated with reduced phase-locking at low frequencies (<4 Hz). Transitivity and clustering coefficient in the delta and theta bands were also significantly lower during consciousness compared to unconsciousness, with differences in the clustering coefficient observed in scalp electrodes over parietal–occipital regions. There were no significant differences in Granger-causality patterns in frontal-to-parietal or parietal-to-frontal connectivity between reported unconsciousness and reported consciousness. Together these results suggest that alterations in spectral and spatial characteristics of network properties in posterior brain areas, in particular decreased local (segregated) connectivity at low frequencies, is a potential indicator of consciousness during sleep.