"Look at my classifier's result": Disentangling unresponsive from (minimally) conscious patients

Given the fact that clinical bedside examinations can have a high rate of misdiagnosis, machine learning techniques based on neuroimaging and electrophysiological measurements are increasingly being considered for comatose patients and patients with unresponsive wakefulness syndrome, a minimally conscious state or locked-in syndrome. Machine learning techniques have the potential to move from group-level statistical results to personalized predictions in a clinical setting. They have been applied for the purpose of (1) detecting changes in brain activation during functional tasks, equivalent to a behavioral command-following test and (2) estimating signs of consciousness by analyzing measurement data obtained from multiple subjects in resting state. In this review, we provide a comprehensive overview of the literature on both approaches and discuss the translation of present findings to clinical practice. We found that most studies struggle with the difficulty of establishing a reliable behavioral assessment and fluctuations in the patient's levels of arousal. Both these factors affect the training and validation of machine learning methods to a considerable degree. In studies involving more than 50 patients, small to moderate evidence was found for the presence of signs of consciousness or good outcome, where one study even showed strong evidence for good outcome.

Detecting and interpreting conscious experiences in behaviorally non-responsive patients

Decoding the contents of consciousness from brain activity is one of the most challenging frontiers of cognitive neuroscience. The ability to interpret mental content without recourse to behavior is most relevant for understanding patients who may be demonstrably conscious, but entirely unable to speak or move willfully in any way, precluding any systematic investigation of their conscious experience. The lack of consistent behavioral responsivity engenders unique challenges to decoding any conscious experiences these patients may have solely based on their brain activity. For this reason, paradigms that have been successful in healthy individuals cannot serve to interpret conscious mental states in this patient group. Until recently, patient studies have used structured instructions to elicit willful modulation of brain activity according to command, in order to decode the presence of willful brain-based responses in this patient group. In recent work, we have used naturalistic paradigms, such as watching a movie or listening to an audio-story, to demonstrate that a common neural code supports conscious experiences in different individuals. Moreover, we have demonstrated that this code can be used to interpret the conscious experiences of a patient who had remained non-responsive for several years. This approach is easy to administer, brief, and does not require compliance with task instructions. Rather, it engages attention naturally through meaningful stimuli that are similar to the real-world sensory information in a patient's environment. Therefore, it may be particularly suited to probing consciousness and revealing residual brain function in highly impaired, acute, patients in a comatose state, thus helping to improve diagnostication and prognostication for this vulnerable patient group from the critical early stages of severe brain-injury.

The psychosis-like effects of Δ(9)-tetrahydrocannabinol are associated with increased cortical noise in healthy humans

BACKGROUND

Drugs that induce psychosis may do so by increasing the level of task-irrelevant random neural activity or neural noise. Increased levels of neural noise have been demonstrated in psychotic disorders. We tested the hypothesis that neural noise could also be involved in the psychotomimetic effects of delta-9-tetrahydrocannabinol (Δ(9)-THC), the principal active constituent of cannabis.

METHODS

Neural noise was indexed by measuring the level of randomness in the electroencephalogram during the prestimulus baseline period of an oddball task using Lempel-Ziv complexity, a nonlinear measure of signal randomness. The acute, dose-related effects of Δ(9)-THC on Lempel-Ziv complexity and signal power were studied in humans (n = 24) who completed 3 test days during which they received intravenous Δ(9)-THC (placebo, .015 and .03 mg/kg) in a double-blind, randomized, crossover, and counterbalanced design.

RESULTS

Δ(9)-THC increased neural noise in a dose-related manner. Furthermore, there was a strong positive relationship between neural noise and the psychosis-like positive and disorganization symptoms induced by Δ(9)-THC, which was independent of total signal power. Instead, there was no relationship between noise and negative-like symptoms. In addition, Δ(9)-THC reduced total signal power during both active drug conditions compared with placebo, but no relationship was detected between signal power and psychosis-like symptoms.

CONCLUSIONS

At doses that produced psychosis-like effects, Δ(9)-THC increased neural noise in humans in a dose-dependent manner. Furthermore, increases in neural noise were related with increases in Δ(9)-THC-induced psychosis-like symptoms but not negative-like symptoms. These findings suggest that increases in neural noise may contribute to the psychotomimetic effects of Δ(9)-THC.

Breakthrough in cardiac arrest: reports from the 4th Paris International Conference

Jean-Luc Diehl The French Intensive Care Society organized on 5th and 6th June 2014 its 4th "Paris International Conference in Intensive Care", whose principle is to bring together the best international experts on a hot topic in critical care medicine. The 2014 theme was "Breakthrough in cardiac arrest", with many high-quality updates on epidemiology, public health data, pre-hospital and in-ICU cares. The present review includes short summaries of the major presentations, classified into six main chapters: Epidemiology of CA Pre-hospital management Post-resuscitation management: targeted temperature management Post-resuscitation management: optimizing organ perfusion and metabolic parameters Neurological assessment of brain damages Public healthcare.

Consciousness and Complexity during Unresponsiveness Induced by Propofol, Xenon, and Ketamine

A common endpoint of general anesthetics is behavioral unresponsiveness, which is commonly associated with loss of consciousness. However, subjects can become disconnected from the environment while still having conscious experiences, as demonstrated by sleep states associated with dreaming. Among anesthetics, ketamine is remarkable in that it induces profound unresponsiveness, but subjects often report "ketamine dreams" upon emergence from anesthesia. Here, we aimed at assessing consciousness during anesthesia with propofol, xenon, and ketamine, independent of behavioral responsiveness. To do so, in 18 healthy volunteers, we measured the complexity of the cortical response to transcranial magnetic stimulation (TMS)--an approach that has proven helpful in assessing objectively the level of consciousness irrespective of sensory processing and motor responses. In addition, upon emergence from anesthesia, we collected reports about conscious experiences during unresponsiveness. Both frontal and parietal TMS elicited a low-amplitude electroencephalographic (EEG) slow wave corresponding to a local pattern of cortical activation with low complexity during propofol anesthesia, a high-amplitude EEG slow wave corresponding to a global, stereotypical pattern of cortical activation with low complexity during xenon anesthesia, and a wakefulness-like, complex spatiotemporal activation pattern during ketamine anesthesia. Crucially, participants reported no conscious experience after emergence from propofol and xenon anesthesia, whereas after ketamine they reported long, vivid dreams unrelated to the external environment. These results are relevant because they suggest that brain complexity may be sensitive to the presence of disconnected consciousness in subjects who are considered unconscious based on behavioral responses.

Critical Changes in Cortical Neuronal Interactions in Anesthetized and Awake Rats

BACKGROUND

Neuronal interactions are fundamental for information processing, cognition, and consciousness. Anesthetics reduce spontaneous cortical activity; however, neuronal reactivity to sensory stimuli is often preserved or augmented. How sensory stimulus-related neuronal interactions change under anesthesia has not been elucidated. In this study, the authors investigated the visual stimulus-related cortical neuronal interactions during stepwise emergence from desflurane anesthesia.

METHODS

Parallel spike trains were recorded with 64-contact extracellular microelectrode arrays from the primary visual cortex of chronically instrumented, unrestrained rats (N = 6) at 8, 6, 4, and 2% desflurane anesthesia and wakefulness. Light flashes were delivered to the retina by transcranial illumination at 5- to 15-s randomized intervals. Information theoretical indices, integration and interaction complexity, were calculated from the probability distribution of coincident spike patterns and used to quantify neuronal interactions before and after flash stimulation.

RESULTS

Integration and complexity showed significant negative associations with desflurane concentration (N = 60). Flash stimulation increased integration and complexity at all anesthetic levels (N = 60); the effect on complexity was reduced in wakefulness. During stepwise withdrawal of desflurane, the largest increase in integration (74%) and poststimulus complexity (35%) occurred before reaching 4% desflurane concentration-a level associated with the recovery of consciousness according to the rats' righting reflex.

CONCLUSIONS

Neuronal interactions in the cerebral cortex are augmented during emergence from anesthesia. Visual flash stimuli enhance neuronal interactions in both wakefulness and anesthesia; the increase in interaction complexity is attenuated as poststimulus complexity reaches plateau. The critical changes in cortical neuronal interactions occur during transition to consciousness.

Exploration of Functional Connectivity During Preferred Music Stimulation in Patients with Disorders of Consciousness

Preferred music is a highly emotional and salient stimulus, which has previously been shown to increase the probability of auditory cognitive event-related responses in patients with disorders of consciousness (DOC). To further investigate whether and how music modifies the functional connectivity of the brain in DOC, five patients were assessed with both a classical functional connectivity scan (control condition), and a scan while they were exposed to their preferred music (music condition). Seed-based functional connectivity (left or right primary auditory cortex), and mean network connectivity of three networks linked to conscious sound perception were assessed. The auditory network showed stronger functional connectivity with the left precentral gyrus and the left dorsolateral prefrontal cortex during music as compared to the control condition. Furthermore, functional connectivity of the external network was enhanced during the music condition in the temporo-parietal junction. Although caution should be taken due to small sample size, these results suggest that preferred music exposure might have effects on patients auditory network (implied in rhythm and music perception) and on cerebral regions linked to autobiographical memory.

Cortical Activation During Levitation and Tentacular Movements of Corticobasal Syndrome

Levitation and tentacular movements (LTM) are considered specific, yet rare (30%), features of Corticobasal Syndrome (CBS), and are erroneously classified as alien hand. Our study focuses on these typical involuntary movements and aims to highlight possible neural correlates.LTM were recognizable during functional magnetic resonance imaging (fMRI) in 4 of 19 CBS patients. FMRI activity was evaluated with an activation recognition program for movements, during LTM, consisting of levitaton and finger writhing, and compared with the absence of movement (rest) and voluntary movements (VM), similar to LTM, of affected and unaffected arm-hand. FMRI acquisition blocks were balanced in order to match LTM blocks with rest and VM conditions. In 1 of the 4 patients, fMRI was acquired only during LTM and with a different equipment.Despite variable intensity and range of involuntary movements, evidenced by videos, fMRI showed, during LTM, a significant (P<0.05-0.001) activation only of the contralateral primary motor cortex (M1). Voluntary movements of the affected and unaffected arm elicited the known network including frontal, supplementary, sensory-motor cortex, and cerebellum. Willed movements of the LTM-affected arm induced higher and wider activation of contralateral M1 compared with the unaffected arm.The isolated activation of M1 suggests that LTM is a cortical disinhibition symptom, not involving a network. Higher activation of M1 during VM confirms that M1 excitability changes occur in CBS. Our study calls, finally, attention to the necessity to separate LTM from other alien hand phenomena.

Large-Scale Functional Brain Network Reorganization During Taoist Meditation

Meditation induces a distinct and reversible mental state that provides insights into brain correlates of consciousness. We explored brain network changes related to meditation by graph theoretical analysis of resting-state functional magnetic resonance imaging data. Eighteen Taoist meditators with varying levels of expertise were scanned using a within-subjects counterbalanced design during resting and meditation states. State-related differences in network topology were measured globally and at the level of individual nodes and edges. Although measures of global network topology, such as small-worldness, were unchanged, meditation was characterized by an extensive and expertise-dependent reorganization of the hubs (highly connected nodes) and edges (functional connections). Areas of sensory cortex, especially the bilateral primary visual and auditory cortices, and the bilateral temporopolar areas, which had the highest degree (or connectivity) during the resting state, showed the biggest decrease during meditation. Conversely, bilateral thalamus and components of the default mode network, mainly the bilateral precuneus and posterior cingulate cortex, had low degree in the resting state but increased degree during meditation. Additionally, these changes in nodal degree were accompanied by reorganization of anatomical orientation of the edges. During meditation, long-distance longitudinal (antero-posterior) edges increased proportionally, whereas orthogonal long-distance transverse (right-left) edges connecting bilaterally homologous cortices decreased. Our findings suggest that transient changes in consciousness associated with meditation introduce convergent changes in the topological and spatial properties of brain functional networks, and the anatomical pattern of integration might be as important as the global level of integration when considering the network basis for human consciousness.

Are we ever aware of concepts? A critical question for the Global Neuronal Workspace, Integrated Information, and Attended Intermediate-Level Representation theories of consciousness

To locate consciousness in the flow of synaptic activity in the brain, we must first locate it in the flow of information processing in the mind. Two different positions have been debated for centuries. The liberal view maintains that the contents of experience include not only sensory, motor, and affective states, but also concepts and the thoughts they enter into. In contrast, the conservative view maintains that concepts have no intrinsic qualia of their own, and that the contents of experience are therefore restricted to sensory, motor, and affective states. Here I discuss how this long-standing controversy is relevant to several contemporary neuroscientific theories of consciousness. I do so, however, in a manner that is admittedly biased toward the conservative view, since I am among those who believe that it is more consistent than the liberal view with a number of key findings. I focus first on two of the most prominent neuroscientific theories of consciousness—namely, Stanislas Dehaene's Global Neuronal Workspace Theory and Giulio Tononi's Integrated Information Theory. I argue that because both of these approaches assume the liberal view, they are challenged in significant ways by data favoring the competing conservative view. I then turn to a third framework—namely, Jesse Prinz's Attended Intermediate-Level Representation Theory. I contend that because it explicitly endorses the conservative view, it has a unique advantage over the other two approaches. I also point out, however, that it has independent shortcomings that prevent it from achieving adequate explanatory coherence. I conclude by emphasizing that, if the conservative view is in fact correct, a central goal of future research should be to distinguish, at both psychological and neurobiological levels of analysis, between the following two kinds of information processing that often occur simultaneously: first, activation of the modality-specific sensory, motor, and affective representations that constitute the sole ingredients of conscious experiences; and second, activation of the conceptual representations that give those experiences meaning and that may even influence them in a top-down manner, but that never themselves reach awareness.

Percolation Model of Sensory Transmission and Loss of Consciousness Under General Anesthesia

Neurons communicate with each other dynamically; how such communications lead to consciousness remains unclear. Here, we present a theoretical model to understand the dynamic nature of sensory activity and information integration in a hierarchical network, in which edges are stochastically defined by a single parameter p representing the percolation probability of information transmission. We validate the model by comparing the transmitted and original signal distributions, and we show that a basic version of this model can reproduce key spectral features clinically observed in electroencephalographic recordings of transitions from conscious to unconscious brain activities during general anesthesia. As p decreases, a steep divergence of the transmitted signal from the original was observed, along with a loss of signal synchrony and a sharp increase in information entropy in a critical manner; this resembles the precipitous loss of consciousness during anesthesia. The model offers mechanistic insights into the emergence of information integration from a stochastic process, laying the foundation for understanding the origin of cognition.

Decoupled temporal variability and signal synchronization of spontaneous brain activity in loss of consciousness: An fMRI study in anesthesia

Two aspects of the low frequency fluctuations of spontaneous brain activity have been proposed which reflect the complex and dynamic features of resting-state activity, namely temporal variability and signal synchronization. The relationship between them, especially its role in consciousness, nevertheless remains unclear. Our study examined the temporal variability and signal synchronization of spontaneous brain activity, as well as their relationship during loss of consciousness. We applied an intra-subject design of resting-state functional magnetic resonance imaging (rs-fMRI) in two conditions: during wakefulness, and under anesthesia with clinical unconsciousness. In addition, an independent group of patients with disorders of consciousness (DOC) was included in order to test the reliability of our findings. We observed a global reduction in the temporal variability, local and distant brain signal synchronization for subjects during anesthesia. Importantly, we found a link between temporal variability and both local and distant signal synchronizations during wakefulness: the higher the degree of temporal variability, the higher its intra-regional homogeneity and inter-regional functional connectivity. In contrast, this link was broken down under anesthesia, implying a decoupling between temporal variability and signal synchronization; this decoupling was reproduced in patients with DOC. Our results suggest that there exist some as yet unclear physiological mechanisms of consciousness which "couple" the two mathematically independent measures, temporal variability and signal synchronization of spontaneous brain activity. Our findings not only extend our current knowledge of the neural correlates of anesthetic-induced unconsciousness, but have implications for both computational neural modeling and clinical practice, such as in the diagnosis of loss of consciousness in patients with DOC.

How are different neural networks related to consciousness?

OBJECTIVE

We aimed to investigate the roles of different resting-state networks in predicting both the actual level of consciousness and its recovery in brain injury patients.

METHODS

We investigated resting-state functional connectivity within different networks in patients with varying levels of consciousness: unresponsive wakefulness syndrome (UWS; n = 56), minimally conscious state (MCS; n = 29), and patients with brain lesions but full consciousness (BL; n = 48). Considering the actual level of consciousness, we compared the strength of network connectivity among the patient groups. We then checked the presence of connections between specific regions in individual patients and calculated the frequency of this in the different patient groups. Considering the recovery of consciousness, we split the UWS group into 2 subgroups according to recovery: those who emerged from UWS (UWS-E) and those who remained in UWS (UWS-R). The above analyses were repeated on these 2 subgroups.

RESULTS

Functional connectivity strength in salience network (SN), especially connectivity between the supragenual anterior cingulate cortex (SACC) and left anterior insula (LAI), was reduced in the unconscious state (UWS) compared to the conscious state (MCS and BL). Moreover, at the individual level, SACC-LAI connectivity was more present in MCS than in UWS. Default-mode network (DMN) connectivity strength, especially between the posterior cingulate cortex (PCC) and left lateral parietal cortex (LLPC), was reduced in UWS-R compared with UWS-E. Furthermore, PCC-LLPC connectivity was more present in UWS-E than in UWS-R.

INTERPRETATION

Our findings show that SN (SACC-LAI) connectivity correlates with behavioral signs of consciousness, whereas DMN (PCC-LLPC) connectivity instead predicts recovery of consciousness.

Complexity of Multi-Dimensional Spontaneous EEG Decreases during Propofol Induced General Anaesthesia

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.

Loss of Consciousness Is Associated with Stabilization of Cortical Activity

What aspects of neuronal activity distinguish the conscious from the unconscious brain? This has been a subject of intense interest and debate since the early days of neurophysiology. However, as any practicing anesthesiologist can attest, it is currently not possible to reliably distinguish a conscious state from an unconscious one on the basis of brain activity. Here we approach this problem from the perspective of dynamical systems theory. We argue that the brain, as a dynamical system, is self-regulated at the boundary between stable and unstable regimes, allowing it in particular to maintain high susceptibility to stimuli. To test this hypothesis, we performed stability analysis of high-density electrocorticography recordings covering an entire cerebral hemisphere in monkeys during reversible loss of consciousness. We show that, during loss of consciousness, the number of eigenmodes at the edge of instability decreases smoothly, independently of the type of anesthetic and specific features of brain activity. The eigenmodes drift back toward the unstable line during recovery of consciousness. Furthermore, we show that stability is an emergent phenomenon dependent on the correlations among activity in different cortical regions rather than signals taken in isolation. These findings support the conclusion that dynamics at the edge of instability are essential for maintaining consciousness and provide a novel and principled measure that distinguishes between the conscious and the unconscious brain.

SIGNIFICANCE STATEMENT

What distinguishes brain activity during consciousness from that observed during unconsciousness? Answering this question has proven difficult because neither consciousness nor lack thereof have universal signatures in terms of most specific features of brain activity. For instance, different anesthetics induce different patterns of brain activity. We demonstrate that loss of consciousness is universally and reliably associated with stabilization of cortical dynamics regardless of the specific activity characteristics. To give an analogy, our analysis suggests that loss of consciousness is akin to depressing the damper pedal on the piano, which makes the sounds dissipate quicker regardless of the specific melody being played. This approach may prove useful in detecting consciousness on the basis of brain activity under anesthesia and other settings.

Age-dependent electroencephalogram (EEG) patterns during sevoflurane general anesthesia in infants

Electroencephalogram (EEG) approaches may provide important information about developmental changes in brain-state dynamics during general anesthesia. We used multi-electrode EEG, analyzed with multitaper spectral methods and video recording of body movement to characterize the spatio-temporal dynamics of brain activity in 36 infants 0-6 months old when awake, and during maintenance of and emergence from sevoflurane general anesthesia. During maintenance: (1) slow-delta oscillations were present in all ages; (2) theta and alpha oscillations emerged around 4 months; (3) unlike adults, all infants lacked frontal alpha predominance and coherence. Alpha power was greatest during maintenance, compared to awake and emergence in infants at 4-6 months. During emergence, theta and alpha power decreased with decreasing sevoflurane concentration in infants at 4-6 months. These EEG dynamic differences are likely due to developmental factors including regional differences in synaptogenesis, glucose metabolism, and myelination across the cortex. We demonstrate the need to apply age-adjusted analytic approaches to develop neurophysiologic-based strategies for pediatric anesthetic state monitoring.

Restructuring consciousness -the psychedelic state in light of integrated information theory

The psychological state elicited by the classic psychedelics drugs, such as LSD and psilocybin, is one of the most fascinating and yet least understood states of consciousness. However, with the advent of modern functional neuroimaging techniques, the effect of these drugs on neural activity is now being revealed, although many of the varied phenomenological features of the psychedelic state remain challenging to explain. Integrated information theory (IIT) is one of the foremost contemporary theories of consciousness, providing a mathematical formalization of both the quantity and quality of conscious experience. This theory can be applied to all known states of consciousness, including the psychedelic state. Using the results of functional neuroimaging data on the psychedelic state, the effects of psychedelic drugs on both the level and structure of consciousness can be explained in terms of the conceptual framework of IIT. This new IIT-based model of the psychedelic state provides an explanation for many of its phenomenological features, including unconstrained cognition, alterations in the structure and meaning of concepts and a sense of expanded awareness. This model also suggests that whilst cognitive flexibility, creativity, and imagination are enhanced during the psychedelic state, this occurs at the expense of cause-effect information, as well as degrading the brain's ability to organize, categorize, and differentiate the constituents of conscious experience. Furthermore, the model generates specific predictions that can be tested using a combination of functional imaging techniques, as has been applied to the study of levels of consciousness during anesthesia and following brain injury.

Stimulus set meaningfulness and neurophysiological differentiation: a functional magnetic resonance imaging study

A meaningful set of stimuli, such as a sequence of frames from a movie, triggers a set of different experiences. By contrast, a meaningless set of stimuli, such as a sequence of ‘TV noise’ frames, triggers always the same experience—of seeing ‘TV noise’—even though the stimuli themselves are as different from each other as the movie frames. We reasoned that the differentiation of cortical responses underlying the subject’s experiences, as measured by Lempel-Ziv complexity (incompressibility) of functional MRI images, should reflect the overall meaningfulness of a set of stimuli for the subject, rather than differences among the stimuli. We tested this hypothesis by quantifying the differentiation of brain activity patterns in response to a movie sequence, to the same movie scrambled in time, and to ‘TV noise’, where the pixels from each movie frame were scrambled in space. While overall cortical activation was strong and widespread in all conditions, the differentiation (Lempel-Ziv complexity) of brain activation patterns was correlated with the meaningfulness of the stimulus set, being highest in the movie condition, intermediate in the scrambled movie condition, and minimal for ‘TV noise’. Stimulus set meaningfulness was also associated with higher information integration among cortical regions. These results suggest that the differentiation of neural responses can be used to assess the meaningfulness of a given set of stimuli for a given subject, without the need to identify the features and categories that are relevant to the subject, nor the precise location of selective neural responses.

Electroencephalographic coherence and cortical acetylcholine during ketamine-induced unconsciousness

BACKGROUND

There is limited understanding of cortical neurochemistry and cortical connectivity during ketamine anaesthesia. We conducted a systematic study to investigate the effects of ketamine on cortical acetylcholine (ACh) and electroencephalographic coherence.

METHODS

Male Sprague-Dawley rats (n=11) were implanted with electrodes to record electroencephalogram (EEG) from frontal, parietal, and occipital cortices, and with a microdialysis guide cannula for simultaneous measurement of ACh concentrations in prefrontal cortex before, during, and after ketamine anaesthesia. Coherence and power spectral density computed from the EEG, and ACh concentrations, were compared between conscious and unconscious states. Loss of righting reflex was used as a surrogate for unconsciousness.

RESULTS

Ketamine-induced unconsciousness was associated with a global reduction of power (P=0.02) in higher gamma bandwidths (>65 Hz), a global reduction of coherence (P≤0.01) across a broad frequency range (0.5-250 Hz), and a significant increase in ACh concentrations (P=0.01) in the prefrontal cortex. Compared with the unconscious state, recovery of righting reflex was marked by a further increase in ACh concentrations (P=0.0007), global increases in power in theta (4-10 Hz; P=0.03) and low gamma frequencies (25-55 Hz; P=0.0001), and increase in power (P≤0.01) and coherence (P≤0.002) in higher gamma frequencies (65-250 Hz). Acetylcholine concentrations, coherence, and spectral properties returned to baseline levels after a prolonged recovery period.

CONCLUSIONS

Ketamine-induced unconsciousness is characterized by suppression of high-frequency gamma activity and a breakdown of cortical coherence, despite increased cholinergic tone in the cortex.

Cortical activity is more stable when sensory stimuli are consciously perceived

According to recent evidence, stimulus-tuned neurons in the cerebral cortex exhibit reduced variability in firing rate across trials, after the onset of a stimulus. However, in order for a reduction in variability to be directly relevant to perception and behavior, it must be realized within trial--the pattern of activity must be relatively stable. Stability is characteristic of decision states in recurrent attractor networks, and its possible relevance to conscious perception has been suggested by theorists. However, it is difficult to measure on the within-trial time scales and broadly distributed spatial scales relevant to perception. We recorded simultaneous magneto- and electroencephalography (MEG and EEG) data while subjects observed threshold-level visual stimuli. Pattern-similarity analyses applied to the data from MEG gradiometers uncovered a pronounced decrease in variability across trials after stimulus onset, consistent with previous single-unit data. This was followed by a significant divergence in variability depending upon subjective report (seen/unseen), with seen trials exhibiting less variability. Applying the same analysis across time, within trial, we found that the latter effect coincided in time with a difference in the stability of the pattern of activity. Stability alone could be used to classify data from individual trials as "seen" or "unseen." The same metric applied to EEG data from patients with disorders of consciousness exposed to auditory stimuli diverged parametrically according to clinically diagnosed level of consciousness. Differences in signal strength could not account for these results. Conscious perception may involve the transient stabilization of distributed cortical networks, corresponding to a global brain-scale decision.

Evolutionary aspects of self- and world consciousness in vertebrates

Although most aspects of world and self-consciousness are inherently subjective, neuroscience studies in humans and non-human animals provide correlational and causative indices of specific links between brain activity and representation of the self and the world. In this article we review neuroanatomic, neurophysiological and neuropsychological data supporting the hypothesis that different levels of self and world representation in vertebrates rely upon (i) a “basal” subcortical system that includes brainstem, hypothalamus and central thalamic nuclei and that may underpin the primary (or anoetic) consciousness likely present in all vertebrates; and (ii) a forebrain system that include the medial and lateral structures of the cerebral hemispheres and may sustain the most sophisticated forms of consciousness [e.g., noetic (knowledge based) and autonoetic, reflective knowledge]. We posit a mutual, bidirectional functional influence between these two major brain circuits. We conclude that basic aspects of consciousness like primary self and core self (based on anoetic and noetic consciousness) are present in many species of vertebrates and that, even self-consciousness (autonoetic consciousness) does not seem to be a prerogative of humans and of some non-human primates but may, to a certain extent, be present in some other mammals and birds

Across the consciousness continuum-from unresponsive wakefulness to sleep

Advances in the development of new paradigms as well as in neuroimaging techniques nowadays enable us to make inferences about the level of consciousness patients with disorders of consciousness (DOC) retain. They, moreover, allow to predict their probable development. Today, we know that certain brain responses (e.g., event-related potentials or oscillatory changes) to stimulation, circadian rhythmicity, the presence or absence of sleep patterns as well as measures of resting state brain activity can serve the diagnostic and prognostic evaluation process. Still, the paradigms we are using nowadays do not allow to disentangle VS/UWS and minimally conscious state (MCS) patients with the desired reliability and validity. Furthermore, even rather well-established methods have, unfortunately, not found their way into clinical routine yet. We here review current literature as well as recent findings from our group and discuss how neuroimaging methods (fMRI, PET) and particularly electroencephalography (EEG) can be used to investigate cognition in DOC or even to assess the degree of residual awareness. We, moreover, propose that circadian rhythmicity and sleep in brain-injured patients are promising fields of research in this context.

Bistability breaks-off deterministic responses to intracortical stimulation during non-REM sleep

During non-rapid eye movement (NREM) sleep (stage N3), when consciousness fades, cortico-cortical interactions are impaired while neurons are still active and reactive. Why is this? We compared cortico-cortical evoked-potentials recorded during wakefulness and NREM by means of time-frequency analysis and phase-locking measures in 8 epileptic patients undergoing intra-cerebral stimulations/recordings for clinical evaluation. We observed that, while during wakefulness electrical stimulation triggers a chain of deterministic phase-locked activations in its cortical targets, during NREM the same input induces a slow wave associated with an OFF-period (suppression of power>20Hz), possibly reflecting a neuronal down-state. Crucially, after the OFF-period, cortical activity resumes to wakefulness-like levels, but the deterministic effects of the initial input are lost, as indicated by a sharp drop of phase-locked activity. These findings suggest that the intrinsic tendency of cortical neurons to fall into a down-state after a transient activation (i.e. bistability) prevents the emergence of stable patterns of causal interactions among cortical areas during NREM. Besides sleep, the same basic neurophysiological dynamics may play a role in pathological conditions in which thalamo-cortical information integration and consciousness are impaired in spite of preserved neuronal activity.

Lempel-Ziv complexity of cortical activity during sleep and waking in rats

Understanding the dynamics of brain activity manifested in the EEG, local field potentials (LFP), and neuronal spiking is essential for explaining their underlying mechanisms and physiological significance. Much has been learned about sleep regulation using conventional EEG power spectrum, coherence, and period-amplitude analyses, which focus primarily on frequency and amplitude characteristics of the signals and on their spatio-temporal synchronicity. However, little is known about the effects of ongoing brain state or preceding sleep-wake history on the nonlinear dynamics of brain activity. Recent advances in developing novel mathematical approaches for investigating temporal structure of brain activity based on such measures, as Lempel-Ziv complexity (LZC) can provide insights that go beyond those obtained with conventional techniques of signal analysis. Here, we used extensive data sets obtained in spontaneously awake and sleeping adult male laboratory rats, as well as during and after sleep deprivation, to perform a detailed analysis of cortical LFP and neuronal activity with LZC approach. We found that activated brain states—waking and rapid eye movement (REM) sleep are characterized by higher LZC compared with non-rapid eye movement (NREM) sleep. Notably, LZC values derived from the LFP were especially low during early NREM sleep after sleep deprivation and toward the middle of individual NREM sleep episodes. We conclude that LZC is an important and yet largely unexplored measure with a high potential for investigating neurophysiological mechanisms of brain activity in health and disease.

Assessing levels of consciousness with symbolic analysis

'Covert consciousness' is a state in which consciousness is present without the capacity for behavioural response, and it can occur in patients with intraoperative awareness or unresponsive wakefulness syndrome. To detect and prevent this undesirable state, it is critical to develop a reliable neurobiological assessment of an individual's level of consciousness that is independent of behaviour. One such approach that shows potential is measuring surrogates of cortical communication in the brain using electroencephalography (EEG). EEG is practicable in clinical application, but involves many fundamental signal processing problems, including signal-to-noise ratio and high dimensional complexity. Symbolic analysis of EEG can mitigate these problems, improving the measurement of brain connectivity and the ability to successfully assess levels of consciousness. In this article, we review the problem of covert consciousness, basic neurobiological principles of consciousness, current methods of measuring brain connectivity and the advantages of symbolic processing, with a focus on symbolic transfer entropy (STE). Finally, we discuss recent advances and clinical applications of STE and other symbolic analyses to assess levels of consciousness.

Signature of consciousness in the dynamics of resting-state brain activity

At rest, the brain is traversed by spontaneous functional connectivity patterns. Two hypotheses have been proposed for their origins: they may reflect a continuous stream of ongoing cognitive processes as well as random fluctuations shaped by a fixed anatomical connectivity matrix. Here we show that both sources contribute to the shaping of resting-state networks, yet with distinct contributions during consciousness and anesthesia. We measured dynamical functional connectivity with functional MRI during the resting state in awake and anesthetized monkeys. Under anesthesia, the more frequent functional connectivity patterns inherit the structure of anatomical connectivity, exhibit fewer small-world properties, and lack negative correlations. Conversely, wakefulness is characterized by the sequential exploration of a richer repertoire of functional configurations, often dissimilar to anatomical structure, and comprising positive and negative correlations among brain regions. These results reconcile theories of consciousness with observations of long-range correlation in the anesthetized brain and show that a rich functional dynamics might constitute a signature of consciousness, with potential clinical implications for the detection of awareness in anesthesia and brain-lesioned patients.

TMS and drugs revisited 2014

The combination of pharmacology and transcranial magnetic stimulation to study the effects of drugs on TMS-evoked EMG responses (pharmaco-TMS-EMG) has considerably improved our understanding of the effects of TMS on the human brain. Ten years have elapsed since an influential review on this topic has been published in this journal (Ziemann, 2004). Since then, several major developments have taken place: TMS has been combined with EEG to measure TMS evoked responses directly from brain activity rather than by motor evoked potentials in a muscle, and pharmacological characterization of the TMS-evoked EEG potentials, although still in its infancy, has started (pharmaco-TMS-EEG). Furthermore, the knowledge from pharmaco-TMS-EMG that has been primarily obtained in healthy subjects is now applied to clinical settings, for instance, to monitor or even predict clinical drug responses in neurological or psychiatric patients. Finally, pharmaco-TMS-EMG has been applied to understand the effects of CNS active drugs on non-invasive brain stimulation induced long-term potentiation-like and long-term depression-like plasticity. This is a new field that may help to develop rationales of pharmacological treatment for enhancement of recovery and re-learning after CNS lesions. This up-dated review will highlight important knowledge and recent advances in the contribution of pharmaco-TMS-EMG and pharmaco-TMS-EEG to our understanding of normal and dysfunctional excitability, connectivity and plasticity of the human brain.

Questioning the dichotomy between vegetative state and minimally conscious state: a review of the statistical evidence

Given the enormous consequences that the diagnosis of vegetative state (VS) vs. minimally conscious state (MCS) may have for the treatment of patients with disorders of consciousness, it is particularly important to empirically legitimate the distinction between these two discrete levels of consciousness. Therefore, the aim of this contribution is to review all the articles reporting statistical evidence concerning the performance of patients in VS vs. patients in MCS, on behavioral or neurophysiological measures. Twenty-three articles matched these inclusion criteria, and comprised behavioral, electroencephalographic (EEG), positron emission tomography (PET) and magnetic resonance imaging (MRI) measures. The analysis of these articles yielded 47 different statistical findings. More than half of these findings (n = 24) did not reveal any statistically significant difference between VS and MCS. Overall, there was no combination of variables that allowed reliably discriminating between VS and MCS. This pattern of results casts doubt on the empirical validity of the distinction between VS and MCS.

Preservation of electroencephalographic organization in patients with impaired consciousness and imaging-based evidence of command-following

OBJECTIVE

Standard clinical characterization of patients with disorders of consciousness (DOC) relies on observation of motor output and may therefore lead to the misdiagnosis of vegetative state or minimally conscious state in patients with preserved cognition. We used conventional electroencephalographic (EEG) measures to assess a cohort of DOC patients with and without functional magnetic resonance imaging (fMRI)-based evidence of command-following, and correlated the findings with standard clinical behavioral evaluation and brain metabolic activity.

METHODS

We enrolled 44 patients with severe brain injury. Behavioral diagnosis was established using standardized clinical assessments. Long-term EEG recordings were analyzed to determine wakeful background organization and presence of elements of sleep architecture. A subset of patients had fMRI testing of command-following using motor imagery paradigms (26 patients) and resting brain metabolism measurement using (18) fluorodeoxyglucose positron emission tomography (31 patients).

RESULTS

All 4 patients with fMRI evidence of covert command-following consistently demonstrated well-organized EEG background during wakefulness, spindling activity during sleep, and relative preservation of cortical metabolic activity. In the entire cohort, EEG organization and overall brain metabolism showed no significant association with bedside behavioral testing, except in a few cases when EEG was severely abnormal.

INTERPRETATION

These findings suggest that conventional EEG is a simple strategy that complements behavioral and imaging characterization of DOC patients. Preservation of specific EEG features may be used to assess the likelihood of unrecognized cognitive abilities in severely brain-injured patients with very limited or no motor responses.

Spectral signatures of reorganised brain networks in disorders of consciousness

Theoretical advances in the science of consciousness have proposed that it is concomitant with balanced cortical integration and differentiation, enabled by efficient networks of information transfer across multiple scales. Here, we apply graph theory to compare key signatures of such networks in high-density electroencephalographic data from 32 patients with chronic disorders of consciousness, against normative data from healthy controls. Based on connectivity within canonical frequency bands, we found that patient networks had reduced local and global efficiency, and fewer hubs in the alpha band. We devised a novel topographical metric, termed modular span, which showed that the alpha network modules in patients were also spatially circumscribed, lacking the structured long-distance interactions commonly observed in the healthy controls. Importantly however, these differences between graph-theoretic metrics were partially reversed in delta and theta band networks, which were also significantly more similar to each other in patients than controls. Going further, we found that metrics of alpha network efficiency also correlated with the degree of behavioural awareness. Intriguingly, some patients in behaviourally unresponsive vegetative states who demonstrated evidence of covert awareness with functional neuroimaging stood out from this trend: they had alpha networks that were remarkably well preserved and similar to those observed in the controls. Taken together, our findings inform current understanding of disorders of consciousness by highlighting the distinctive brain networks that characterise them. In the significant minority of vegetative patients who follow commands in neuroimaging tests, they point to putative network mechanisms that could support cognitive function and consciousness despite profound behavioural impairment.

A common neural code for similar conscious experiences in different individuals

The interpretation of human consciousness from brain activity, without recourse to speech or action, is one of the most provoking and challenging frontiers of modern neuroscience. We asked whether there is a common neural code that underpins similar conscious experiences, which could be used to decode these experiences in the absence of behavior. To this end, we used richly evocative stimulation (an engaging movie) portraying real-world events to elicit a similar conscious experience in different people. Common neural correlates of conscious experience were quantified and related to measurable, quantitative and qualitative, executive components of the movie through two additional behavioral investigations. The movie's executive demands drove synchronized brain activity across healthy participants' frontal and parietal cortices in regions known to support executive function. Moreover, the timing of activity in these regions was predicted by participants' highly similar qualitative experience of the movie's moment-to-moment executive demands, suggesting that synchronization of activity across participants underpinned their similar experience. Thus we demonstrate, for the first time to our knowledge, that a neural index based on executive function reliably predicted every healthy individual's similar conscious experience in response to real-world events unfolding over time. This approach provided strong evidence for the conscious experience of a brain-injured patient, who had remained entirely behaviorally nonresponsive for 16 y. The patient's executive engagement and moment-to-moment perception of the movie content were highly similar to that of every healthy participant. These findings shed light on the common basis of human consciousness and enable the interpretation of conscious experience in the absence of behavior.