Information sharing in the brain indexes consciousness in noncommunicative patients

Effaced Enigmata

Severe head injury or brain injury presents clinical neuroscientists with a unique challenge. Based on an objective assessment of cognitive and neurological function, it is sometimes hard to recognize our patients as members of our moral community (actually or potentially) but we treat them as if that were is the case, and, therefore, as if they need rescuing. Thus their existences as enigmata-beings who may or may not reveal themselves to us through social and personal function realized in conversations and relationships-are in doubt. However, the objective mode of assessing individuals and their mental functions needs to be bracketed here, as we reconnect with them and offer them our help in the restorative journey that they need to take. The journey has many tortuous paths comprising it, not the least of which is the existential question of whether the damaged human being with whom we are engaged actually can be restored to a meaningful life. A negative answer to that question can bring the whole process to an abrupt end. Neuroscience cannot answer some of these questions, as they are ethical. Is this a life worth living and are our commitments going to go the distance that must be traversed here. Therefore, this is an area where ethics take priority over neuroscience, and it is on our ethical response that everything else hinges. Understanding the light this throws on the nature of a human being takes us to the heart of the value of every human being and the nexus of mutuality that is the moral community.

A Review of Resting-State Electroencephalography Analysis in Disorders of Consciousness

Recently, neuroimaging technologies have been developed as important methods for assessing the brain condition of patients with disorders of consciousness (DOC). Among these technologies, resting-state electroencephalography (EEG) recording and analysis has been widely applied by clinicians due to its relatively low cost and convenience. EEG reflects the electrical activity of the underlying neurons, and it contains information regarding neuronal population oscillations, the information flow pathway, and neural activity networks. Some features derived from EEG signal processing methods have been proposed to describe the electrical features of the brain with DOC. The computation of these features is challenging for clinicians working to comprehend the corresponding physiological meanings and then to put them into clinical applications. This paper reviews studies that analyze spontaneous EEG of DOC, with the purpose of diagnosis, prognosis, and evaluation of brain interventions. It is expected that this review will promote our understanding of the EEG characteristics in DOC.

Directional connectivity in the EEG is able to discriminate wakefulness from NREM sleep

A reliable measure of consciousness is of great interest for various clinical applications including sleep studies and the assessment of depth of anaesthesia. A number of measures of consciousness based on the EEG have been proposed in the literature and tested in studies of dreamless sleep, general anaesthesia and disorders of consciousness. However, reliability has remained a persistent challenge. Despite considerable theoretical and experimental effort, the neural mechanisms underlying consciousness remain unclear, but connectivity between brain regions is thought to be disrupted, impairing information flow.

OBJECTIVE

The objective of the current work was to assess directional connectivity between brain regions using directed coherence and propose and assess an index that robustly reflects changes associated with non-REM sleep.

APPROACH

We tested the performance on polysomnographic recordings from ten healthy subjects and compared directed coherence (and derived features) with more established measures calculated from EEG spectra. We compared the performance of the different indexes to discriminate the level of consciousness at group and individual level.

MAIN RESULTS

At a group level all EEG measures could significantly discriminate NREM sleep from waking, but there was considerable individual variation. Across all individuals, normalized power, the strength of long-range connections and the direction of functional links strongly correlate with NREM sleep stages over the experimental timeline. At an individual level, of the EEG measures considered, the direction of functional links constitutes the most reliable index of the level of consciousness, highly correlating with the individual experimental time-line of sleep in all subjects.

SIGNIFICANCE

Directed coherence provides a promising new means of assessing level of consciousness, firmly based on current physiological understanding of consciousness.

Attention, in and Out: Scalp-Level and Intracranial EEG Correlates of Interoception and Exteroception

Interoception, the monitoring of visceral signals, is often presumed to engage attentional mechanisms specifically devoted to inner bodily sensing. In fact, most standardized interoceptive tasks require directing attention to internal signals. However, most studies in the field have failed to compare attentional modulations between internally- and externally-driven processes, thus probing blind to the specificity of the former. Here we address this issue through a multidimensional approach combining behavioral measures, analyses of event-related potentials and functional connectivity via high-density electroencephalography, and intracranial recordings. In Study 1, 50 healthy volunteers performed a heartbeat detection task as we recorded modulations of the heartbeat-evoked potential (HEP) in three conditions: exteroception, basal interoception (also termed interoceptive accuracy), and post-feedback interoception (sometimes called interoceptive learning). In Study 2, to evaluate whether key interoceptive areas (posterior insula, inferior frontal gyrus, amygdala, and somatosensory cortex) were differentially modulated by externally- and internally-driven processes, we analyzed human intracranial recordings with depth electrodes in these regions. This unique technique provides a very fine grained spatio-temporal resolution compared to other techniques, such as EEG or fMRI. We found that both interoceptive conditions in Study 1 yielded greater HEP amplitudes than the exteroceptive one. In addition, connectivity analysis showed that post-feedback interoception, relative to basal interoception, involved enhanced long-distance connections linking frontal and posterior regions. Moreover, results from Study 2 showed a differentiation between oscillations during basal interoception (broadband: 35–110 Hz) and exteroception (1–35 Hz) in the insula, the amygdala, the somatosensory cortex, and the inferior frontal gyrus. In sum, this work provides convergent evidence for the specificity and dynamics of attentional mechanisms involved in interoception.

Probing consciousness in a sensory-disconnected paralyzed patient

BACKGROUND

Diagnosis of consciousness can be very challenging in some clinical situations such as severe sensory-motor impairments.

CASE STUDY

We report the case study of a patient who presented a total "locked-in syndrome" associated with and a multi-sensory deafferentation (visual, auditory and tactile modalities) following a protuberantial infarction.

RESULT

In spite of this severe and extreme disconnection from the external world, we could detect reliable evidence of consciousness using a multivariate analysis of his high-density resting state electroencephalogram. This EEG-based diagnosis was eventually confirmed by the clinical evolution of the patient.

CONCLUSION

This approach illustrates the potential importance of functional brain-imaging data to improve diagnosis of consciousness and of cognitive abilities in critical situations in which the behavioral channel is compromised such as deafferented locked-in syndrome.

Towards affordable biomarkers of frontotemporal dementia: A classification study via network's information sharing

Developing effective and affordable biomarkers for dementias is critical given the difficulty to achieve early diagnosis. In this sense, electroencephalographic (EEG) methods offer promising alternatives due to their low cost, portability, and growing robustness. Here, we relied on EEG signals and a novel information-sharing method to study resting-state connectivity in patients with behavioral variant frontotemporal dementia (bvFTD) and controls. To evaluate the specificity of our results, we also tested Alzheimer’s disease (AD) patients. The classification power of the ensuing connectivity patterns was evaluated through a supervised classification algorithm (support vector machine). In addition, we compared the classification power yielded by (i) functional connectivity, (ii) relevant neuropsychological tests, and (iii) a combination of both. BvFTD patients exhibited a specific pattern of hypoconnectivity in mid-range frontotemporal links, which showed no alterations in AD patients. These functional connectivity alterations in bvFTD were replicated with a low-density EEG setting (20 electrodes). Moreover, while neuropsychological tests yielded acceptable discrimination between bvFTD and controls, the addition of connectivity results improved classification power. Finally, classification between bvFTD and AD patients was better when based on connectivity than on neuropsychological measures. Taken together, such findings underscore the relevance of EEG measures as potential biomarker signatures for clinical settings.

Measuring states of pathological (un)consciousness: research dimensions, clinical applications, and ethics

Consciousness is a multidimensional construct with no widely accepted definition. Especially in pathological conditions, it is less clear what exactly is meant by (un)consciousness, how it can be reliably observed or measured. Here, we aim at (i) bringing together state of the art approaches to classification of single patients suffering from disorders of consciousness by means of motor-independent assessment of consciousness states with electrophysiology and functional neuroimaging, (ii) showing how each proposed metric translates into clinical practice and (iii) raising a discussion on the ethical aspects of consciousness measurements. We realize that when dealing with patients some issues commonly pertain to each methodology discussed here, such as the overall clinical condition, clinical heterogeneity, and diagnostic uncertainty. When predicting patients' diagnosis, though, each method adopts a different approach to determine (a) a "gold standard" of the benchmark population upon which the metric is computed and (b) the generalization and replicability in the attempt to avoid overfitting. From an applied ethics perspective, the focus is, hence, on knowing what one is measuring and on the validity of measurements. We conclude that, when searching for consciousness in pathological conditions, confident diagnosis can be based on the use of probabilistic predictions as well as on accumulative evidence stemming from multiple non-overlapping assessments with different modalities. A framework which will regulate the application order of these techniques (balancing their availability, sensitivity, and specificity, based on underlying clinical assumptions about a patient's conscious state), is expected to ameliorate clinical management and further inform on the critical patterns of (un)consciousness.

Night and day variations of sleep in patients with disorders of consciousness

Brain injuries substantially change the entire landscape of oscillatory dynamics and render detection of typical sleep patterns difficult. Yet, sleep is characterized not only by specific EEG waveforms, but also by its circadian organization. In the present study we investigated whether brain dynamics of patients with disorders of consciousness systematically change between day and night. We recorded ~24 h EEG at the bedside of 18 patients diagnosed to be vigilant but unaware (Unresponsive Wakefulness Syndrome) and 17 patients revealing signs of fluctuating consciousness (Minimally Conscious State). The day-to-night changes in (i) spectral power, (ii) sleep-specific oscillatory patterns and (iii) signal complexity were analyzed and compared to 26 healthy control subjects. Surprisingly, the prevalence of sleep spindles and slow waves did not systematically vary between day and night in patients, whereas day-night changes in EEG power spectra and signal complexity were revealed in minimally conscious but not unaware patients.

Multidimensional cognitive evaluation of patients with disorders of consciousness using EEG: A proof of concept study

The use of cognitive evoked potentials in EEG is now part of the routine evaluation of non-communicating patients with disorders of consciousness in several specialized medical centers around the world. They typically focus on one or two cognitive markers, such as the mismatch negativity or the P3 to global auditory regularity. However it has become clear that none of these markers in isolation is at the same time sufficiently specific and sufficiently sensitive to be taken as the unique gold standard for diagnosing consciousness. A good way forward would be to combine several cognitive markers within the same test to improve evaluation. Furthermore, given the diversity of lesions leading to disorders of consciousness, it is important not only to probe whether a patient is conscious or not, but also to establish a more general and nuanced profile of the residual cognitive capacities of each patient using a combination of markers.

In the present study we built a unique EEG protocol that probed 8 dimensions of cognitive processing in a single 1.5 h session. This protocol probed variants of classical markers together with new markers of spatial attention, which has not yet been studied in these patients. The eight dimensions were: (1) own name recognition, (2) temporal attention, (3) spatial attention, (4) detection of spatial incongruence (5) motor planning, and (6,7,8) modulations of these effects by the global context, reflecting higher-level functions. This protocol was tested in 15 healthy control subjects and in 17 patients with various etiologies, among which 13 could be included in the analysis. The results in the control group allowed a validation and a specific description of the cognitive levels probed by each marker. At the single-subject level, this combined protocol allowed assessing the presence of both classical and newly introduced markers for each patient and control, and revealed that the combination of several markers increased diagnostic sensitivity. The presence of a high-level effect in any of the three tested domains distinguished between minimally conscious and vegetative patients, while the presence of low-level effects was similar in both groups. In summary, this study constitutes a validated proof of concept in favor of probing multiple cognitive dimensions to improve the evaluation of non-communicating patients. At a more conceptual level, this EEG tool can help achieve a better understanding of disorders of consciousness by exploring consciousness in its multiple cognitive facets.

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This new EEG protocol probes 8 cognitive functions within a single 1.5 h session.

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It allows a complete neuropsychological evaluation only based on brain activity.

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It increases sensitivity in detecting both low-level and high-level functions in patients.

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Only the high-level functions distinguish minimally conscious from vegetative states.

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Multidimensional EEG testing is feasible in patients and can improve evaluation.

Unified framework for information integration based on information geometry

Assessment of causal influences is a ubiquitous and important subject across diverse research fields. Drawn from consciousness studies, integrated information is a measure that defines integration as the degree of causal influences among elements. Whereas pairwise causal influences between elements can be quantified with existing methods, quantifying multiple influences among many elements poses two major mathematical difficulties. First, overestimation occurs due to interdependence among influences if each influence is separately quantified in a part-based manner and then simply summed over. Second, it is difficult to isolate causal influences while avoiding noncausal confounding influences. To resolve these difficulties, we propose a theoretical framework based on information geometry for the quantification of multiple causal influences with a holistic approach. We derive a measure of integrated information, which is geometrically interpreted as the divergence between the actual probability distribution of a system and an approximated probability distribution where causal influences among elements are statistically disconnected. This framework provides intuitive geometric interpretations harmonizing various information theoretic measures in a unified manner, including mutual information, transfer entropy, stochastic interaction, and integrated information, each of which is characterized by how causal influences are disconnected. In addition to the mathematical assessment of consciousness, our framework should help to analyze causal relationships in complex systems in a complete and hierarchical manner.

Bedside quantitative electroencephalography improves assessment of consciousness in comatose subarachnoid hemorrhage patients

OBJECTIVE

Accurate behavioral assessments of consciousness carry tremendous significance in guiding management, but are extremely challenging in acutely brain-injured patients. We evaluated whether electroencephalography (EEG) and multimodality monitoring parameters may facilitate assessment of consciousness in patients with subarachnoid hemorrhage.

METHODS

A retrospective analysis was performed of 83 consecutively treated adults with subarachnoid hemorrhage. All patients were initially comatose and had invasive brain monitoring placed. Behavioral assessments were performed during daily interruption of sedation and categorized into 3 groups based on their best examination as (1) comatose, (2) arousable (eye opening or attending toward a stimulus), and (3) aware (command following). EEG features included spectral power and complexity measures. Comparisons were made using bootstrapping methods and partial least squares regression.

RESULTS

We identified 389 artifact-free EEG clips following behavioral assessments. Increasing central gamma, posterior alpha, and diffuse theta-delta oscillations differentiated patients who were arousable from those in coma. Command following was characterized by a further increase in central gamma and posterior alpha, as well as an increase in alpha permutation entropy. These EEG features together with basic neurological examinations (eg, pupillary light reflex) contributed heavily to a linear model predicting behavioral state, whereas brain physiology measures (eg, brain oxygenation), structural injury, and clinical course added less.

INTERPRETATION

EEG measures of behavioral states provide distinctive signatures that complement behavioral assessments of patients with subarachnoid hemorrhage shortly after the injury. Our data support the hypothesis that impaired connectivity of cortex with both central thalamus and basal forebrain underlies decreasing levels of consciousness. Ann Neurol 2016;80:541-553.

Sleep/Wake Modulation of Polysomnographic Patterns has Prognostic Value in Pediatric Unresponsive Wakefulness Syndrome

STUDY OBJECTIVE

Sleep patterns of pediatric patients in unresponsive wakefulness syndrome (UWS) have been poorly investigated, and the prognostic potential of polysomnography (PSG) in these subjects is still uncertain. The goal of the study was to identify quantitative PSG indices to be applied as possible prognostic markers in pediatric UWS.

METHODS

We performed PSG in 27 children and adolescents with UWS due to acquired brain damage in the subacute phase. Patients underwent neurological examination and clinical assessment with standardized scales. Outcome was assessed after 36 mo. PSG tracks were scored for sleep stages and digitally filtered. The spectral difference between sleep and wake was computed, as the percent difference at specific spectral frequencies. We computed (1) the ratio between percent power in the delta and alpha frequency bands, (2) the ratio between alpha and theta frequency bands, and (3) the power ratio index, during wake and sleep, as proposed in previous literature. The predictive role of several clinical and PSG measures was tested by logistic regression.

RESULTS

Correlation was found between the differential measures of electroencephalographic activity during sleep and wake in several frequency bands and the clinical scales (Glasgow Outcome Score, Level of Cognitive Functioning Assessment Scale, and Disability Rating Scale) at follow-up; the Sleep Patterns for Pediatric Unresponsive Wakefulness Syndrome (SPPUWS) scores correlated with the differential measures, and allowed outcome prediction with 96.3% of accuracy.

CONCLUSIONS

The differential measure of electroencephalographic activity during sleep and wake in the beta band and, more incisively, SPPUWS can help in determining the capability to recover from pediatric UWS well before the confirmation provided by suitable clinical scales.

Preserved consciousness in vegetative and minimal conscious states: systematic review and meta-analysis

Active, passive and resting state paradigms using functional MRI (fMRI) or EEG may reveal consciousness in the vegetative (VS) and the minimal conscious state (MCS). A meta-analysis was performed to assess the prevalence of preserved consciousness in VS and MCS as revealed by fMRI and EEG, including command following (active paradigms), cortical functional connectivity elicited by external stimuli (passive paradigms) and default mode networks (resting state). Studies were selected from multiple indexing databases until February 2015 and evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2. 37 studies were identified, including 1041 patients (mean age 43 years, range 16-89; male/female 2.1:1; 39.5% traumatic brain injuries). MCS patients were more likely than VS patients to follow commands during active paradigms (32% vs 14%; OR 2.85 (95% CI 1.90 to 4.27; p<0.0001)) and to show preserved functional cortical connectivity during passive paradigms (55% vs 26%; OR 3.53 (95% CI 2.49 to 4.99; p<0.0001)). Passive paradigms suggested preserved consciousness more often than active paradigms (38% vs 24%; OR 1.98 (95% CI 1.54 to 2.54; p<0.0001)). Data on resting state paradigms were insufficient for statistical evaluation. In conclusion, active paradigms may underestimate the degree of consciousness as compared to passive paradigms. While MCS patients show signs of preserved consciousness more frequently in both paradigms, roughly 15% of patients with a clinical diagnosis of VS are able to follow commands by modifying their brain activity. However, there remain important limitations at the single-subject level; for example, patients from both categories may show command following despite negative passive paradigms.

Frequency-Dependent Representation of Reinforcement-Related Information in the Human Medial and Lateral Prefrontal Cortex

The feedback-related negativity (FRN) is a commonly observed potential in scalp electroencephalography (EEG) studies related to the valence of feedback about a subject's performance. This potential classically manifests as a negative deflection in medial frontocentral EEG contacts following negative feedback. Recent work has shown prominence of theta power in the spectral composition of the FRN, placing it within the larger class of "frontal midline theta" cognitive control signals. Although the dorsal anterior cingulate cortex (dACC) is thought to be the cortical generator of the FRN, conclusive data regarding its origin and propagation are lacking. Here we examine intracranial electrophysiology from the human medial and lateral prefrontal cortex (PFC) to better understand the anatomical localization and communication patterns of the FRN. We show that the FRN is evident in both low- and high-frequency local field potentials (LFPs) recorded on electrocorticography. The FRN is larger in medial compared with lateral PFC, and coupling between theta band phase and high-frequency LFP power is also greater in medial PFC. Using Granger causality and conditional mutual information analyses, we provide evidence that feedback-related information propagates from medial to lateral PFC, and that this information transfer oscillates with theta-range periodicity. These results provide evidence for the dACC as the cortical source of the FRN, provide insight into the local computation of frontal midline theta, and have implications for reinforcement learning models of cognitive control.

Consciousness: here, there and everywhere?

The science of consciousness has made great strides by focusing on the behavioural and neuronal correlates of experience. However, while such correlates are important for progress to occur, they are not enough if we are to understand even basic facts, for example, why the cerebral cortex gives rise to consciousness but the cerebellum does not, though it has even more neurons and appears to be just as complicated. Moreover, correlates are of little help in many instances where we would like to know if consciousness is present: patients with a few remaining islands of functioning cortex, preterm infants, non-mammalian species and machines that are rapidly outperforming people at driving, recognizing faces and objects, and answering difficult questions. To address these issues, we need not only more data but also a theory of consciousness—one that says what experience is and what type of physical systems can have it. Integrated information theory (IIT) does so by starting from experience itself via five phenomenological axioms: intrinsic existence, composition, information, integration and exclusion. From these it derives five postulates about the properties required of physical mechanisms to support consciousness. The theory provides a principled account of both the quantity and the quality of an individual experience (a quale), and a calculus to evaluate whether or not a particular physical system is conscious and of what. Moreover, IIT can explain a range of clinical and laboratory findings, makes a number of testable predictions and extrapolates to a number of problematic conditions. The theory holds that consciousness is a fundamental property possessed by physical systems having specific causal properties. It predicts that consciousness is graded, is common among biological organisms and can occur in some very simple systems. Conversely, it predicts that feed-forward networks, even complex ones, are not conscious, nor are aggregates such as groups of individuals or heaps of sand. Also, in sharp contrast to widespread functionalist beliefs, IIT implies that digital computers, even if their behaviour were to be functionally equivalent to ours, and even if they were to run faithful simulations of the human brain, would experience next to nothing.

"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.

Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding

Brain-wide interactions generating complex neural dynamics are considered crucial for emergent cognitive functions. However, the irreducible nature of nonlinear and high-dimensional dynamical interactions challenges conventional reductionist approaches. We introduce a model-free method, based on embedding theorems in nonlinear state-space reconstruction, that permits a simultaneous characterization of complexity in local dynamics, directed interactions between brain areas, and how the complexity is produced by the interactions. We demonstrate this method in large-scale electrophysiological recordings from awake and anesthetized monkeys. The cross-embedding method captures structured interaction underlying cortex-wide dynamics that may be missed by conventional correlation-based analysis, demonstrating a critical role of time-series analysis in characterizing brain state. The method reveals a consciousness-related hierarchy of cortical areas, where dynamical complexity increases along with cross-area information flow. These findings demonstrate the advantages of the cross-embedding method in deciphering large-scale and heterogeneous neuronal systems, suggesting a crucial contribution by sensory-frontoparietal interactions to the emergence of complex brain dynamics during consciousness.

Prognostic and diagnostic value of EEG signal coupling measures in coma

OBJECTIVE

Our aim was to assess the diagnostic and predictive value of several quantitative EEG (qEEG) analysis methods in comatose patients.

METHODS

In 79 patients, coupling between EEG signals on the left-right (inter-hemispheric) axis and on the anterior-posterior (intra-hemispheric) axis was measured with four synchronization measures: relative delta power asymmetry, cross-correlation, symbolic mutual information and transfer entropy directionality. Results were compared with etiology of coma and clinical outcome. Using cross-validation, the predictive value of measure combinations was assessed with a Bayes classifier with mixture of Gaussians.

RESULTS

Five of eight measures showed a statistically significant difference between patients grouped according to outcome; one measure revealed differences in patients grouped according to the etiology. Interestingly, a high level of synchrony between the left and right hemisphere was associated with mortality on intensive care unit, whereas higher synchrony between anterior and posterior brain regions was associated with survival. The combination with the best predictive value reached an area-under the curve of 0.875 (for patients with post anoxic encephalopathy: 0.946).

CONCLUSIONS

EEG synchronization measures can contribute to clinical assessment, and provide new approaches for understanding the pathophysiology of coma.

SIGNIFICANCE

Prognostication in coma remains a challenging task. qEEG could improve current multi-modal approaches.

EEG entropy measures indicate decrease of cortical information processing in Disorders of Consciousness

OBJECTIVE

Clinical assessments that rely on behavioral responses to differentiate Disorders of Consciousness are at times inapt because of some patients' motor disabilities. To objectify patients' conditions of reduced consciousness the present study evaluated the use of electroencephalography to measure residual brain activity.

METHODS

We analyzed entropy values of 18 scalp EEG channels of 15 severely brain-damaged patients with clinically diagnosed Minimally-Conscious-State (MCS) or Unresponsive-Wakefulness-Syndrome (UWS) and compared the results to a sample of 24 control subjects. Permutation entropy (PeEn) and symbolic transfer entropy (STEn), reflecting information processes in the EEG, were calculated for all subjects. Participants were tested on a modified active own-name paradigm to identify correlates of active instruction following.

RESULTS

PeEn showed reduced local information content in the EEG in patients, that was most pronounced in UWS. STEn analysis revealed altered directed information flow in the EEG of patients, indicating impaired feed-backward connectivity. Responses to auditory stimulation yielded differences in entropy measures, indicating reduced information processing in MCS and UWS.

CONCLUSIONS

Local EEG information content and information flow are affected in Disorders of Consciousness. This suggests local cortical information capacity and feedback information transfer as neural correlates of consciousness.

SIGNIFICANCE

The utilized EEG entropy analyses were able to relate to patient groups with different Disorders 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.

Cortical dynamics and subcortical signatures of motor-language coupling in Parkinson's disease

Impairments of action language have been documented in early stage Parkinson’s disease (EPD). The action-sentence compatibility effect (ACE) paradigm has revealed that EPD involves deficits to integrate action-verb processing and ongoing motor actions. Recent studies suggest that an abolished ACE in EPD reflects a cortico-subcortical disruption, and recent neurocognitive models highlight the role of the basal ganglia (BG) in motor-language coupling. Building on such breakthroughs, we report the first exploration of convergent cortical and subcortical signatures of ACE in EPD patients and matched controls. Specifically, we combined cortical recordings of the motor potential, functional connectivity measures, and structural analysis of the BG through voxel-based morphometry. Relative to controls, EPD patients exhibited an impaired ACE, a reduced motor potential, and aberrant frontotemporal connectivity. Furthermore, motor potential abnormalities during the ACE task were predicted by overall BG volume and atrophy. These results corroborate that motor-language coupling is mainly subserved by a cortico-subcortical network including the BG as a key hub. They also evince that action-verb processing may constitute a neurocognitive marker of EPD. Our findings suggest that research on the relationship between language and motor domains is crucial to develop models of motor cognition as well as diagnostic and intervention strategies.

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.

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.

Breakdown of the brain's functional network modularity with awareness

Neurobiological theories of awareness propose divergent accounts of the spatial extent of brain changes that support conscious perception. Whereas focal theories posit mostly local regional changes, global theories propose that awareness emerges from the propagation of neural signals across a broad extent of sensory and association cortex. Here we tested the scalar extent of brain changes associated with awareness using graph theoretical analysis applied to functional connectivity data acquired at ultra-high field while subjects performed a simple masked target detection task. We found that awareness of a visual target is associated with a degradation of the modularity of the brain's functional networks brought about by an increase in intermodular functional connectivity. These results provide compelling evidence that awareness is associated with truly global changes in the brain's functional connectivity.

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.

Quantitative rates of brain glucose metabolism distinguish minimally conscious from vegetative state patients

The differentiation of the vegetative or unresponsive wakefulness syndrome (VS/UWS) from the minimally conscious state (MCS) is an important clinical issue. The cerebral metabolic rate of glucose (CMRglc) declines when consciousness is lost, and may reveal the residual cognitive function of these patients. However, no quantitative comparisons of cerebral glucose metabolism in VS/UWS and MCS have yet been reported. We calculated the regional and whole-brain CMRglc of 41 patients in the states of VS/UWS (n=14), MCS (n=21) or emergence from MCS (EMCS, n=6), and healthy volunteers (n=29). Global cortical CMRglc in VS/UWS and MCS averaged 42% and 55% of normal, respectively. Differences between VS/UWS and MCS were most pronounced in the frontoparietal cortex, at 42% and 60% of normal. In brainstem and thalamus, metabolism declined equally in the two conditions. In EMCS, metabolic rates were indistinguishable from those of MCS. Ordinal logistic regression predicted that patients are likely to emerge into MCS at CMRglc above 45% of normal. Receiver-operating characteristics showed that patients in MCS and VS/UWS can be differentiated with 82% accuracy, based on cortical metabolism. Together these results reveal a significant correlation between whole-brain energy metabolism and level of consciousness, suggesting that quantitative values of CMRglc reveal consciousness in severely brain-injured patients.

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.

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.

Large scale screening of neural signatures of consciousness in patients in a vegetative or minimally conscious state

In recent years, numerous electrophysiological signatures of consciousness have been proposed. Here, we perform a systematic analysis of these electroencephalography markers by quantifying their efficiency in differentiating patients in a vegetative state from those in a minimally conscious or conscious state. Capitalizing on a review of previous experiments and current theories, we identify a series of measures that can be organized into four dimensions: (i) event-related potentials versus ongoing electroencephalography activity; (ii) local dynamics versus inter-electrode information exchange; (iii) spectral patterns versus information complexity; and (iv) average versus fluctuations over the recording session. We analysed a large set of 181 high-density electroencephalography recordings acquired in a 30 minutes protocol. We show that low-frequency power, electroencephalography complexity, and information exchange constitute the most reliable signatures of the conscious state. When combined, these measures synergize to allow an automatic classification of patients' state of consciousness.

From the phenomenology to the mechanisms of consciousness: Integrated Information Theory 3.0

This paper presents Integrated Information Theory (IIT) of consciousness 3.0, which incorporates several advances over previous formulations. IIT starts from phenomenological axioms: information says that each experience is specific--it is what it is by how it differs from alternative experiences; integration says that it is unified--irreducible to non-interdependent components; exclusion says that it has unique borders and a particular spatio-temporal grain. These axioms are formalized into postulates that prescribe how physical mechanisms, such as neurons or logic gates, must be configured to generate experience (phenomenology). The postulates are used to define intrinsic information as "differences that make a difference" within a system, and integrated information as information specified by a whole that cannot be reduced to that specified by its parts. By applying the postulates both at the level of individual mechanisms and at the level of systems of mechanisms, IIT arrives at an identity: an experience is a maximally irreducible conceptual structure (MICS, a constellation of concepts in qualia space), and the set of elements that generates it constitutes a complex. According to IIT, a MICS specifies the quality of an experience and integrated information ΦMax its quantity. From the theory follow several results, including: a system of mechanisms may condense into a major complex and non-overlapping minor complexes; the concepts that specify the quality of an experience are always about the complex itself and relate only indirectly to the external environment; anatomical connectivity influences complexes and associated MICS; a complex can generate a MICS even if its elements are inactive; simple systems can be minimally conscious; complicated systems can be unconscious; there can be true "zombies"--unconscious feed-forward systems that are functionally equivalent to conscious complexes.

Toward a computational theory of conscious processing

The study of the mechanisms of conscious processing has become a productive area of cognitive neuroscience. Here we review some of the recent behavioral and neuroscience data, with the specific goal of constraining present and future theories of the computations underlying conscious processing. Experimental findings imply that most of the brain's computations can be performed in a non-conscious mode, but that conscious perception is characterized by an amplification, global propagation and integration of brain signals. A comparison of these data with major theoretical proposals suggests that firstly, conscious access must be carefully distinguished from selective attention; secondly, conscious perception may be likened to a non-linear decision that 'ignites' a network of distributed areas; thirdly, information which is selected for conscious perception gains access to additional computations, including temporary maintenance, global sharing, and flexible routing; and finally, measures of the complexity, long-distance correlation and integration of brain signals provide reliable indices of conscious processing, clinically relevant to patients recovering from coma.