Multimodal Prediction of 3- and 12-Month Outcomes in ICU Patients with Acute Disorders of Consciousness

BACKGROUND

In intensive care unit (ICU) patients with coma and other disorders of consciousness (DoC), outcome prediction is key to decision-making regarding prognostication, neurorehabilitation, and management of family expectations. Current prediction algorithms are largely based on chronic DoC, whereas multimodal data from acute DoC are scarce. Therefore, the Consciousness in Neurocritical Care Cohort Study Using Electroencephalography and Functional Magnetic Resonance Imaging (i.e. CONNECT-ME; ClinicalTrials.gov identifier: NCT02644265) investigates ICU patients with acute DoC due to traumatic and nontraumatic brain injuries, using electroencephalography (EEG) (resting-state and passive paradigms), functional magnetic resonance imaging (fMRI) (resting-state) and systematic clinical examinations.

METHODS

We previously presented results for a subset of patients (n = 87) concerning prediction of consciousness levels in the ICU. Now we report 3- and 12-month outcomes in an extended cohort (n = 123). Favorable outcome was defined as a modified Rankin Scale score ≤ 3, a cerebral performance category score ≤ 2, and a Glasgow Outcome Scale Extended score ≥ 4. EEG features included visual grading, automated spectral categorization, and support vector machine consciousness classifier. fMRI features included functional connectivity measures from six resting-state networks. Random forest and support vector machine were applied to EEG and fMRI features to predict outcomes. Here, random forest results are presented as areas under the curve (AUC) of receiver operating characteristic curves or accuracy. Cox proportional regression with in-hospital death as a competing risk was used to assess independent clinical predictors of time to favorable outcome.

RESULTS

Between April 2016 and July 2021, we enrolled 123 patients (mean age 51 years, 42% women). Of 82 (66%) ICU survivors, 3- and 12-month outcomes were available for 79 (96%) and 77 (94%), respectively. EEG features predicted both 3-month (AUC 0.79 [95% confidence interval (CI) 0.77-0.82]) and 12-month (AUC 0.74 [95% CI 0.71-0.77]) outcomes. fMRI features appeared to predict 3-month outcome (accuracy 0.69-0.78) both alone and when combined with some EEG features (accuracies 0.73-0.84) but not 12-month outcome (larger sample sizes needed). Independent clinical predictors of time to favorable outcome were younger age (hazard ratio [HR] 1.04 [95% CI 1.02-1.06]), traumatic brain injury (HR 1.94 [95% CI 1.04-3.61]), command-following abilities at admission (HR 2.70 [95% CI 1.40-5.23]), initial brain imaging without severe pathological findings (HR 2.42 [95% CI 1.12-5.22]), improving consciousness in the ICU (HR 5.76 [95% CI 2.41-15.51]), and favorable visual-graded EEG (HR 2.47 [95% CI 1.46-4.19]).

CONCLUSIONS

Our results indicate that EEG and fMRI features and readily available clinical data predict short-term outcome of patients with acute DoC and that EEG also predicts 12-month outcome after ICU discharge.

Intracortical recordings reveal vision-to-action cortical gradients driving human exogenous attention

Exogenous attention, the process that makes external salient stimuli pop-out of a visual scene, is essential for survival. How attention-capturing events modulate human brain processing remains unclear. Here we show how the psychological construct of exogenous attention gradually emerges over large-scale gradients in the human cortex, by analyzing activity from 1,403 intracortical contacts implanted in 28 individuals, while they performed an exogenous attention task. The timing, location and task-relevance of attentional events defined a spatiotemporal gradient of three neural clusters, which mapped onto cortical gradients and presented a hierarchy of timescales. Visual attributes modulated neural activity at one end of the gradient, while at the other end it reflected the upcoming response timing, with attentional effects occurring at the intersection of visual and response signals. These findings challenge multi-step models of attention, and suggest that frontoparietal networks, which process sequential stimuli as separate events sharing the same location, drive exogenous attention phenomena such as inhibition of return.

Processing of the same narrative stimuli elicits common functional connectivity dynamics between individuals

It has been suggested that conscious experience is linked to the richness of brain state repertories, which change in response to environmental and internal stimuli. High-level sensory stimulation has been shown to alter local brain activity and induce neural synchrony across participants. However, the dynamic interplay of cognitive processes underlying moment-to-moment information processing remains poorly understood. Using naturalistic movies as an ecological laboratory model of the real world, here we investigate how the processing of complex naturalistic stimuli alters the dynamics of brain network interactions and how these in turn support information processing. Participants underwent fMRI recordings during movie watching, scrambled movie watching, and resting. By measuring the phase-synchrony between different brain networks, we analyzed whole-brain connectivity patterns. Our finding revealed distinct connectivity patterns associated with each experimental condition. We found higher synchronization of brain patterns across participants during movie watching compared to rest and scrambled movie conditions. Furthermore, synchronization levels increased during the most engaging parts of the movie. The synchronization dynamics among participants were associated with suspense; scenes with higher levels of suspense induced greater synchronization. These results suggest that processing the same high-level information elicits common neural dynamics across individuals, and that whole-brain functional connectivity tracks variations in processed information and subjective experience.

Pupil dilation response elicited by violations of auditory regularities is a promising but challenging approach to probe consciousness at the bedside

Pupil dilation response (PDR) has been proposed as a physiological marker of conscious access to a stimulus or its attributes, such as novelty. In a previous study on healthy volunteers, we adapted the auditory "local global" paradigm and showed that violations of global regularity elicited a PDR. Notably without instructions, this global effect was present only in participants who could consciously report violations of global regularities. In the present study, we used a similar approach in 24 non-communicating patients affected with a Disorder of Consciousness (DoC) and compared PDR to ERPs regarding diagnostic and prognostic performance. At the group level, global effect could not be detected in DoC patients. At the individual level, the only patient with a PDR global effect was in a MCS and recovered consciousness at 6 months. Contrasting the most regular trials to the most irregular ones improved PDR's diagnostic and prognostic power in DoC patients. Pupillometry is a promising tool but requires several methodological improvements to enhance the signal-to-noise ratio and make it more robust for probing consciousness and cognition in DoC patients.

Conscious processing of global and local auditory irregularities causes differentiated heartbeat-evoked responses

Recent research suggests that brain-heart interactions are associated with perceptual and self-consciousness. In this line, the neural responses to visceral inputs have been hypothesized to play a leading role in shaping our subjective experience. This study aims to investigate whether the contextual processing of auditory irregularities modulates both direct neuronal responses to the auditory stimuli (ERPs) and the neural responses to heartbeats, as measured with heartbeat-evoked responses (HERs). HERs were computed in patients with disorders of consciousness, diagnosed with a minimally conscious state or unresponsive wakefulness syndrome. We tested whether HERs reflect conscious auditory perception, which can potentially provide additional information for the consciousness diagnosis. EEG recordings were taken during the local-global paradigm, which evaluates the capacity of a patient to detect the appearance of auditory irregularities at local (short-term) and global (long-term) levels. The results show that local and global effects produce distinct ERPs and HERs, which can help distinguish between the minimally conscious state and unresponsive wakefulness syndrome patients. Furthermore, we found that ERP and HER responses were not correlated suggesting that independent neuronal mechanisms are behind them. These findings suggest that HER modulations in response to auditory irregularities, especially local irregularities, may be used as a novel neural marker of consciousness and may aid in the bedside diagnosis of disorders of consciousness with a more cost-effective option than neuroimaging methods.

The arrow of time of brain signals in cognition: Potential intriguing role of parts of the default mode network

A promising idea in human cognitive neuroscience is that the default mode network (DMN) is responsible for coordinating the recruitment and scheduling of networks for computing and solving task-specific cognitive problems. This is supported by evidence showing that the physical and functional distance of DMN regions is maximally removed from sensorimotor regions containing environment-driven neural activity directly linked to perception and action, which would allow the DMN to orchestrate complex cognition from the top of the hierarchy. However, discovering the functional hierarchy of brain dynamics requires finding the best way to measure interactions between brain regions. In contrast to previous methods measuring the hierarchical flow of information using, for example, transfer entropy, here we used a thermodynamics-inspired, deep learning based Temporal Evolution NETwork (TENET) framework to assess the asymmetry in the flow of events, 'arrow of time', in human brain signals. This provides an alternative way of quantifying hierarchy, given that the arrow of time measures the directionality of information flow that leads to a breaking of the balance of the underlying hierarchy. In turn, the arrow of time is a measure of nonreversibility and thus nonequilibrium in brain dynamics. When applied to large-scale Human Connectome Project (HCP) neuroimaging data from close to a thousand participants, the TENET framework suggests that the DMN plays a significant role in orchestrating the hierarchy, that is, levels of nonreversibility, which changes between the resting state and when performing seven different cognitive tasks. Furthermore, this quantification of the hierarchy of the resting state is significantly different in health compared to neuropsychiatric disorders. Overall, the present thermodynamics-based machine-learning framework provides vital new insights into the fundamental tenets of brain dynamics for orchestrating the interactions between cognition and brain in complex environments.

Numerical discrimination in Drosophila melanogaster

Sensitivity to numbers is a crucial cognitive ability. The lack of experimental models amenable to systematic genetic and neural manipulation has precluded discovering neural circuits required for numerical cognition. Here, we demonstrate that Drosophila flies spontaneously prefer sets containing larger numbers of objects. This preference is determined by the ratio between the two numerical quantities tested, a characteristic signature of numerical cognition across species. Individual flies maintained their numerical choice over consecutive days. Using a numerical visual conditioning paradigm, we found that flies are capable of associating sucrose with numerical quantities and can be trained to reverse their spontaneous preference for large quantities. Finally, we show that silencing lobula columnar neurons (LC11) reduces the preference for more objects, thus identifying a neuronal substrate for numerical cognition in invertebrates. This discovery paves the way for the systematic analysis of the behavioral and neural mechanisms underlying the evolutionary conserved sensitivity to numerosity.

Fronto-parietal networks shape human conscious report through attention gain and reorienting

How do attention and consciousness interact in the human brain? Rival theories of consciousness disagree on the role of fronto-parietal attentional networks in conscious perception. We recorded neural activity from 727 intracerebral contacts in 13 epileptic patients, while they detected near-threshold targets preceded by attentional cues. Clustering revealed three neural patterns: first, attention-enhanced conscious report accompanied sustained right-hemisphere fronto-temporal activity in networks connected by the superior longitudinal fasciculus (SLF) II-III, and late accumulation of activity (>300 ms post-target) in bilateral dorso-prefrontal and right-hemisphere orbitofrontal cortex (SLF I-III). Second, attentional reorienting affected conscious report through early, sustained activity in a right-hemisphere network (SLF III). Third, conscious report accompanied left-hemisphere dorsolateral-prefrontal activity. Task modeling with recurrent neural networks revealed multiple clusters matching the identified brain clusters, elucidating the causal relationship between clusters in conscious perception of near-threshold targets. Thus, distinct, hemisphere-asymmetric fronto-parietal networks support attentional gain and reorienting in shaping human conscious experience.

The lack of temporal brain dynamics asymmetry as a signature of impaired consciousness states

Life is a constant battle against equilibrium. From the cellular level to the macroscopic scale, living organisms as dissipative systems require the violation of their detailed balance, i.e. metabolic enzymatic reactions, in order to survive. We present a framework based on temporal asymmetry as a measure of non-equilibrium. By means of statistical physics, it was discovered that temporal asymmetries establish an arrow of time useful for assessing the reversibility in human brain time series. Previous studies in human and non-human primates have shown that decreased consciousness states such as sleep and anaesthesia result in brain dynamics closer to the equilibrium. Furthermore, there is growing interest in the analysis of brain symmetry based on neuroimaging recordings and since it is a non-invasive technique, it can be extended to different brain imaging modalities and applied at different temporo-spatial scales. In the present study, we provide a detailed description of our methodological approach, paying special attention to the theories that motivated this work. We test, for the first time, the reversibility analysis in human functional magnetic resonance imaging data in patients suffering from disorder of consciousness. We verify that the tendency of a decrease in the asymmetry of the brain signal together with the decrease in non-stationarity are key characteristics of impaired consciousness states. We expect that this work will open the way for assessing biomarkers for patients' improvement and classification, as well as motivating further research on the mechanistic understanding underlying states of impaired consciousness.

Low-dimensional organization of global brain states of reduced consciousness

Brain states are frequently represented using a unidimensional scale measuring the richness of subjective experience (level of consciousness). This description assumes a mapping between the high-dimensional space of whole-brain configurations and the trajectories of brain states associated with changes in consciousness, yet this mapping and its properties remain unclear. We combine whole-brain modeling, data augmentation, and deep learning for dimensionality reduction to determine a mapping representing states of consciousness in a low-dimensional space, where distances parallel similarities between states. An orderly trajectory from wakefulness to patients with brain injury is revealed in a latent space whose coordinates represent metrics related to functional modularity and structure-function coupling, increasing alongside loss of consciousness. Finally, we investigate the effects of model perturbations, providing geometrical interpretation for the stability and reversibility of states. We conclude that conscious awareness depends on functional patterns encoded as a low-dimensional trajectory within the vast space of brain configurations.

Sensory Target Detection at Local and Global Timescales Reveals a Hierarchy of Supramodal Dynamics in the Human Cortex

To ensure survival in a dynamic environment, the human neocortex monitors input streams from different sensory organs for important sensory events. Which principles govern whether different senses share common or modality-specific brain networks for sensory target detection? We examined whether complex targets evoke sustained supramodal activity while simple targets rely on modality-specific networks with short-lived supramodal contributions. In a series of hierarchical multisensory target detection studies (n = 77, of either sex) using EEG, we applied a temporal cross-decoding approach to dissociate supramodal and modality-specific cortical dynamics elicited by rule-based global and feature-based local sensory deviations within and between the visual, somatosensory, and auditory modality. Our data show that each sense implements a cortical hierarchy orchestrating supramodal target detection responses, which operate at local and global timescales in successive processing stages. Across different sensory modalities, simple feature-based sensory deviations presented in temporal vicinity to a monotonous input stream triggered a mismatch negativity-like local signal which decayed quickly and early, whereas complex rule-based targets tracked across time evoked a P3b-like global neural response which generalized across a late time window. Converging results from temporal cross-modality decoding analyses across different datasets, we reveal that global neural responses are sustained in a supramodal higher-order network, whereas local neural responses canonically thought to rely on modality-specific regions evolve into short-lived supramodal activity. Together, our findings demonstrate that cortical organization largely follows a gradient in which short-lived modality-specific as well as supramodal processes dominate local responses, whereas higher-order processes encode temporally extended abstract supramodal information fed forward from modality-specific cortices.SIGNIFICANCE STATEMENT Each sense supports a cortical hierarchy of processes tracking deviant sensory events at multiple timescales. Conflicting evidence produced a lively debate around which of these processes are supramodal. Here, we manipulated the temporal complexity of auditory, tactile, and visual targets to determine whether cortical local and global ERP responses to sensory targets share cortical dynamics between the senses. Using temporal cross-decoding, we found that temporally complex targets elicit a supramodal sustained response. Conversely, local responses to temporally confined targets typically considered modality-specific rely on early short-lived supramodal activation. Our finding provides evidence for a supramodal gradient supporting sensory target detection in the cortex, with implications for multiple fields in which these responses are studied (e.g., predictive coding, consciousness, and attention).

Multimodal prediction of residual consciousness in the intensive care unit: the CONNECT-ME study

Functional MRI (fMRI) and EEG may reveal residual consciousness in patients with disorders of consciousness (DoC), as reflected by a rapidly expanding literature on chronic DoC. However, acute DoC is rarely investigated, although identifying residual consciousness is key to clinical decision-making in the intensive care unit (ICU). Therefore, the objective of the prospective, observational, tertiary centre cohort, diagnostic phase IIb study 'Consciousness in neurocritical care cohort study using EEG and fMRI' (CONNECT-ME, NCT02644265) was to assess the accuracy of fMRI and EEG to identify residual consciousness in acute DoC in the ICU. Between April 2016 and November 2020, 87 acute DoC patients with traumatic or non-traumatic brain injury were examined with repeated clinical assessments, fMRI and EEG. Resting-state EEG and EEG with external stimulations were evaluated by visual analysis, spectral band analysis and a Support Vector Machine (SVM) consciousness classifier. In addition, within- and between-network resting-state connectivity for canonical resting-state fMRI networks was assessed. Next, we used EEG and fMRI data at study enrolment in two different machine-learning algorithms (Random Forest and SVM with a linear kernel) to distinguish patients in a minimally conscious state or better (≥MCS) from those in coma or unresponsive wakefulness state (≤UWS) at time of study enrolment and at ICU discharge (or before death). Prediction performances were assessed with area under the curve (AUC). Of 87 DoC patients (mean age, 50.0 ± 18 years, 43% female), 51 (59%) were ≤UWS and 36 (41%) were ≥ MCS at study enrolment. Thirty-one (36%) patients died in the ICU, including 28 who had life-sustaining therapy withdrawn. EEG and fMRI predicted consciousness levels at study enrolment and ICU discharge, with maximum AUCs of 0.79 (95% CI 0.77-0.80) and 0.71 (95% CI 0.77-0.80), respectively. Models based on combined EEG and fMRI features predicted consciousness levels at study enrolment and ICU discharge with maximum AUCs of 0.78 (95% CI 0.71-0.86) and 0.83 (95% CI 0.75-0.89), respectively, with improved positive predictive value and sensitivity. Overall, both machine-learning algorithms (SVM and Random Forest) performed equally well. In conclusion, we suggest that acute DoC prediction models in the ICU be based on a combination of fMRI and EEG features, regardless of the machine-learning algorithm used.

Deep brain stimulation of the thalamus restores signatures of consciousness in a nonhuman primate model

Loss of consciousness is associated with the disruption of long-range thalamocortical and corticocortical brain communication. We tested the hypothesis that deep brain stimulation (DBS) of central thalamus might restore both arousal and awareness following consciousness loss. We applied anesthesia to suppress consciousness in nonhuman primates. During anesthesia, central thalamic stimulation induced arousal in an on-off manner and increased functional magnetic resonance imaging activity in prefrontal, parietal, and cingulate cortices. Moreover, DBS restored a broad dynamic repertoire of spontaneous resting-state activity, previously described as a signature of consciousness. None of these effects were obtained during the stimulation of a control site in the ventrolateral thalamus. Last, DBS restored a broad hierarchical response to auditory violations that was disrupted under anesthesia. Thus, DBS restored the two dimensions of consciousness, arousal and conscious access, following consciousness loss, paving the way to its therapeutical translation in patients with disorders of consciousness.

Predicting the loss of responsiveness when falling asleep in humans

Falling asleep is a dynamical process that is poorly defined. The period preceding sleep, characterized by the progressive alteration of behavioral responses to the environment, which may last several minutes, has no electrophysiological definition, and is embedded in the first stage of sleep (N1). We aimed at better characterizing this drowsiness period looking for neurophysiological predictors of responsiveness using electro and magnetoencephalography. Healthy participants were recorded when falling asleep, while they were presented with continuous auditory stimulations and asked to respond to deviant sounds. We analysed brain responses to sounds and markers of ongoing activity, such as information and connectivity measures, in relation to rapid fluctuations of brain rhythms observed at brain onset and participants' capabilities to respond. Results reveal a drowsiness period distinct from wakefulness and sleep, from alpha rhythms to the first sleep spindles, characterized by diverse and transient brain states that come on and off at the scale of a few seconds and closely reflects, mainly through neural processes in alpha and theta bands, decreasing probabilities to be responsive to external stimuli. Results also show that the global P300 was only present in responsive trials, regardless of vigilance states. A better consideration of the drowsiness period through a formalized classification and its specific brain markers such as described here should lead to significant advances in vigilance assessment in the future, in medicine and ecological environments.

Toward a coherent structuration of disorders of consciousness expertise at a country scale: A proposal for France

Probing consciousness and cognitive abilities in non-communicating patients is one of the most challenging diagnostic issues. A fast growing medical and scientific literature explores the various facets of this challenge, often coined under the generic expression of 'Disorders of Consciousness' (DoC). Crucially, a set of independent converging results demonstrated both (1) the diagnostic and prognostic importance of this expertise, and (2) the need to combine behavioural measures with brain structure and activity data to improve diagnostic and prognostication accuracy as well as potential therapeutic intervention. Thus, probing consciousness in DoC patients appears as a crucial activity rich of human, medical, economic and ethical consequences, but this activity needs to be organized in order to offer this expertise to each concerned patient. More precisely, diagnosis of consciousness differs in difficulty across patients: while a minimal set of data can be sufficient to reach a confident result, some patients need a higher level of expertise that relies on additional behavioural and brain activity and brain structure measures. In order to enable this service on a systematic mode, we present two complementary proposals in the present article. First, we sketch a structuration of DoC expertise at a country-scale, namely France. More precisely, we suggest that a 2-tiers network composed of local (Tier-1) and regional (Tier-2) centers backed by distant electronic databases and algorithmic centers could optimally enable the systematic implementation of DoC expertise in France. Second, we propose to create a national common register of DoC patients in order to better monitor this activity, to improve its performance on the basis of nation-wide collected evidence, and to promote rational decision-making.

Conscious processing of narrative stimuli synchronizes heart rate between individuals

Heart rate has natural fluctuations that are typically ascribed to autonomic function. Recent evidence suggests that conscious processing can affect the timing of the heartbeat. We hypothesized that heart rate is modulated by conscious processing and therefore dependent on attentional focus. To test this, we leverage the observation that neural processes synchronize between subjects by presenting an identical narrative stimulus. As predicted, we find significant inter-subject correlation of heart rate (ISC-HR) when subjects are presented with an auditory or audiovisual narrative. Consistent with our hypothesis, we find that ISC-HR is reduced when subjects are distracted from the narrative, and higher ISC-HR predicts better recall of the narrative. Finally, patients with disorders of consciousness have lower ISC-HR, as compared to healthy individuals. We conclude that heart rate fluctuations are partially driven by conscious processing, depend on attentional state, and may represent a simple metric to assess conscious state in unresponsive patients.

Neuroprognostication of Consciousness Recovery in a Patient with COVID-19 Related Encephalitis: Preliminary Findings from a Multimodal Approach

Predicting the functional recovery of patients with severe neurological condition due to coronavirus disease 2019 (COVID-19) is a challenging task. Only limited outcome data are available, the pathophysiology is poorly understood, and the time-course of recovery is still largely unknown. Here, we report the case of a patient with COVID-19 associated encephalitis presenting as a prolonged state of unresponsiveness for two months, who finally fully recovered consciousness, functional communication, and autonomy after immunotherapy. In a multimodal approach, a high-density resting state EEG revealed a rich brain activity in spite of a severe clinical presentation. Using our previously validated algorithms, we could predict a possible improvement of consciousness in this patient. This case report illustrates the value of a multimodal approach capitalizing on advanced brain-imaging and bedside electrophysiology techniques to improve prognosis accuracy in this complex and new aetiology.

Clinical and advanced neurophysiology in the prognostic and diagnostic evaluation of disorders of consciousness: review of an IFCN-endorsed expert group

The analysis of spontaneous EEG activity and evoked potentialsis a cornerstone of the instrumental evaluation of patients with disorders of consciousness (DoC). Thepast few years have witnessed an unprecedented surge in EEG-related research applied to the prediction and detection of recovery of consciousness after severe brain injury,opening up the prospect that new concepts and tools may be available at the bedside. This paper provides a comprehensive, critical overview of bothconsolidated and investigational electrophysiological techniquesfor the prognostic and diagnostic assessment of DoC.We describe conventional clinical EEG approaches, then focus on evoked and event-related potentials, and finally we analyze the potential of novel research findings. In doing so, we (i) draw a distinction between acute, prolonged and chronic phases of DoC, (ii) attempt to relate both clinical and research findings to the underlying neuronal processes and (iii) discuss technical and conceptual caveats.The primary aim of this narrative review is to bridge the gap between standard and emerging electrophysiological measures for the detection and prediction of recovery of consciousness. The ultimate scope is to provide a reference and common ground for academic researchers active in the field of neurophysiology and clinicians engaged in intensive care unit and rehabilitation.

Habituation of auditory startle reflex is a new sign of minimally conscious state

Neurological examination of non-communicating patients relies on a few decisive items that enable the crucial distinction between vegetative state (VS)-also coined unresponsive wakefulness syndrome (UWS)-and minimally conscious state. Over the past 10 years, this distinction has proven its diagnostic value as well as its important prognostic value on consciousness recovery. However, clinicians are currently limited by three factors: (i) the current behavioural repertoire of minimally conscious state items is limited and restricted to a few cognitive domains in the goldstandard revised version of the Coma Recovery Scale; (ii) a proportion of ∼15-20% clinically VS/UWS patients are actually in a richer state than VS/UWS as evidenced by functional brain imaging; and (iii) the neurophysiological and cognitive interpretation of each minimally conscious state item is still unclear and debated. In the current study we demonstrate that habituation of the auditory startle reflex (hASR) tested at bedside constitutes a novel, simple and powerful behavioural sign that can accurately distinguish minimally conscious state from VS/UWS. In addition to enlarging the minimally conscious state items repertoire, and therefore decreasing the low sensitivity of current behavioural measures, we also provide an original and rigorous description of the neurophysiological basis of hASR through a combination of functional (high density EEG and 18F-fluorodeoxyglucose PET imaging) and structural (diffusion tensor imaging MRI) measures. We show that preservation of hASR is associated with the functional and structural integrity of a brain-scale fronto-parietal network, including prefrontal regions related to control of action and inhibition, and meso-parietal areas associated with minimally conscious and conscious states. Lastly, we show that hASR predicts 6-month improvement of consciousness. Taken together, our results show that hASR is a cortically-mediated behaviour, and suggest that it could be a new clinical item to clearly and accurately identify non-communicating patients who are in the minimally conscious state.

Ultrafast ultrasound imaging pattern analysis reveals distinctive dynamic brain states and potent sub-network alterations in arthritic animals

Chronic pain pathologies, which are due to maladaptive changes in the peripheral and/or central nervous systems, are debilitating diseases that affect 20% of the European adult population. A better understanding of the mechanisms underlying this pathogenesis would facilitate the identification of novel therapeutic targets. Functional connectivity (FC) extracted from coherent low-frequency hemodynamic fluctuations among cerebral networks has recently brought light on a powerful approach to study large scale brain networks and their disruptions in neurological/psychiatric disorders. Analysis of FC is classically performed on averaged signals over time, but recently, the analysis of the dynamics of FC has also provided new promising information. Keeping in mind the limitations of animal models of persistent pain but also the powerful tool they represent to improve our understanding of the neurobiological basis of chronic pain pathogenicity, this study aimed at defining the alterations in functional connectivity, in a clinically relevant animal model of sustained inflammatory pain (Adjuvant-induced Arthritis) in rats by using functional ultrasound imaging, a neuroimaging technique with a unique spatiotemporal resolution (100 μm and 2 ms) and sensitivity. Our results show profound alterations of FC in arthritic animals, such as a subpart of the somatomotor (SM) network, occurring several weeks after the beginning of the disease. Also, we demonstrate for the first time that dynamic functional connectivity assessed by ultrasound can provide quantitative and robust information on the dynamic pattern that we define as brain states. While the main state consists of an overall synchrony of hemodynamic fluctuations in the SM network, arthritic animal spend statistically more time in two other states, where the fluctuations of the primary sensory cortex of the inflamed hind paws show asynchrony with the rest of the SM network. Finally, correlating FC changes with pain behavior in individual animals suggest links between FC alterations and either the cognitive or the emotional aspects of pain. Our study introduces fUS as a new translational tool for the enhanced understanding of the dynamic pain connectome and brain plasticity in a major preclinical model of chronic pain.

European Academy of Neurology guideline on the diagnosis of coma and other disorders of consciousness

BACKGROUND AND PURPOSE

Patients with acquired brain injury and acute or prolonged disorders of consciousness (DoC) are challenging. Evidence to support diagnostic decisions on coma and other DoC is limited but accumulating. This guideline provides the state-of-the-art evidence regarding the diagnosis of DoC, summarizing data from bedside examination techniques, functional neuroimaging and electroencephalography (EEG).

METHODS

Sixteen members of the European Academy of Neurology (EAN) Scientific Panel on Coma and Chronic Disorders of Consciousness, representing 10 European countries, reviewed the scientific evidence for the evaluation of coma and other DoC using standard bibliographic measures. Recommendations followed the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. The guideline was endorsed by the EAN.

RESULTS

Besides a comprehensive neurological examination, the following suggestions are made: probe for voluntary eye movements using a mirror; repeat clinical assessments in the subacute and chronic setting, using the Coma Recovery Scale - Revised; use the Full Outline of Unresponsiveness score instead of the Glasgow Coma Scale in the acute setting; obtain clinical standard EEG; search for sleep patterns on EEG, particularly rapid eye movement sleep and slow-wave sleep; and, whenever feasible, consider positron emission tomography, resting state functional magnetic resonance imaging (fMRI), active fMRI or EEG paradigms and quantitative analysis of high-density EEG to complement behavioral assessment in patients without command following at the bedside.

CONCLUSIONS

Standardized clinical evaluation, EEG-based techniques and functional neuroimaging should be integrated for multimodal evaluation of patients with DoC. The state of consciousness should be classified according to the highest level revealed by any of these three approaches.

Robust EEG-based cross-site and cross-protocol classification of states of consciousness

Determining the state of consciousness in patients with disorders of consciousness is a challenging practical and theoretical problem. Recent findings suggest that multiple markers of brain activity extracted from the EEG may index the state of consciousness in the human brain. Furthermore, machine learning has been found to optimize their capacity to discriminate different states of consciousness in clinical practice. However, it is unknown how dependable these EEG markers are in the face of signal variability because of different EEG configurations, EEG protocols and subpopulations from different centres encountered in practice. In this study we analysed 327 recordings of patients with disorders of consciousness (148 unresponsive wakefulness syndrome and 179 minimally conscious state) and 66 healthy controls obtained in two independent research centres (Paris Pitié-Salpêtrière and Liège). We first show that a non-parametric classifier based on ensembles of decision trees provides robust out-of-sample performance on unseen data with a predictive area under the curve (AUC) of ~0.77 that was only marginally affected when using alternative EEG configurations (different numbers and positions of sensors, numbers of epochs, average AUC = 0.750 ± 0.014). In a second step, we observed that classifiers based on multiple as well as single EEG features generalize to recordings obtained from different patient cohorts, EEG protocols and different centres. However, the multivariate model always performed best with a predictive AUC of 0.73 for generalization from Paris 1 to Paris 2 datasets, and an AUC of 0.78 from Paris to Liège datasets. Using simulations, we subsequently demonstrate that multivariate pattern classification has a decisive performance advantage over univariate classification as the stability of EEG features decreases, as different EEG configurations are used for feature-extraction or as noise is added. Moreover, we show that the generalization performance from Paris to Liège remains stable even if up to 20% of the diagnostic labels are randomly flipped. Finally, consistent with recent literature, analysis of the learned decision rules of our classifier suggested that markers related to dynamic fluctuations in theta and alpha frequency bands carried independent information and were most influential. Our findings demonstrate that EEG markers of consciousness can be reliably, economically and automatically identified with machine learning in various clinical and acquisition contexts.

Electromagnetic Brain Stimulation in Patients With Disorders of Consciousness

Severe brain injury is a common cause of coma. In some cases, despite vigilance improvement, disorders of consciousness (DoC) persist. Several states of impaired consciousness have been defined, according to whether the patient exhibits only reflexive behaviors as in the vegetative state/unresponsive wakefulness syndrome (VS/UWS) or purposeful behaviors distinct from reflexes as in the minimally conscious state (MCS). Recently, this clinical distinction has been enriched by electrophysiological and neuroimaging data resulting from a better understanding of the physiopathology of DoC. However, therapeutic options, especially pharmacological ones, remain very limited. In this context, electroceuticals, a new category of therapeutic agents which act by targeting the neural circuits with electromagnetic stimulations, started to develop in the field of DoC. We performed a systematic review of the studies evaluating therapeutics relying on the direct or indirect electro-magnetic stimulation of the brain in DoC patients. Current evidence seems to support the efficacy of deep brain stimulation (DBS) and non-invasive brain stimulation (NIBS) on consciousness in some of these patients. However, while the latter is non-invasive and well tolerated, the former is associated with potential major side effects. We propose that all chronic DoC patients should be given the possibility to benefit from NIBS, and that transcranial direct current stimulation (tDCS) should be preferred over repetitive transcranial magnetic stimulation (rTMS), based on the literature and its simple use. Surgical techniques less invasive than DBS, such as vagus nerve stimulation (VNS) might represent a good compromise between efficacy and invasiveness but still need to be further investigated.

Consistent Gradient of Performance and Decoding of Stimulus Type and Valence From Local and Network Activity

The individual differences approach focuses on the variation of behavioral and neural signatures across subjects. In this context, we searched for intracranial neural markers of performance in three individuals with distinct behavioral patterns (efficient, borderline, and inefficient) in a dual-valence task assessing facial and lexical emotion recognition. First, we performed a preliminary study to replicate well-established evoked responses in relevant brain regions. Then, we examined time series data and network connectivity, combined with multivariate pattern analyses and machine learning, to explore electrophysiological differences in resting-state versus task-related activity across subjects. Next, using the same methodological approach, we assessed the neural decoding of performance for different dimensions of the task. The classification of time series data mirrored the behavioral gradient across subjects for stimulus type but not for valence. However, network-based measures reflected the subjects' hierarchical profiles for both stimulus types and valence. Therefore, this measure serves as a sensitive marker for capturing distributed processes such as emotional valence discrimination, which relies on an extended set of regions. Network measures combined with classification methods may offer useful insights to study single subjects and understand inter-individual performance variability. Promisingly, this approach could eventually be extrapolated to other neuroscientific techniques.

Wisdom of the caregivers: pooling individual subjective reports to diagnose states of consciousness in brain-injured patients, a monocentric prospective study

OBJECTIVES

The clinical distinction between vegetative state/unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) is a key step to elaborate a prognosis and formulate an appropriate medical plan for any patient suffering from disorders of consciousness (DoC). However, this assessment is often challenging and may require specialised expertise. In this study, we hypothesised that pooling subjective reports of the level of consciousness of a given patient across several nursing staff members can be used to clinically detect MCS.

SETTING AND PARTICIPANTS

Patients referred to consciousness assessment were prospectively screened. MCS (target condition) was defined according to the best Coma Recovery Scale-Revised score (CRS-R) obtained from expert physicians (reference standard). 'DoC-feeling' score was defined as the median of individual subjective reports pooled from multiple staff members during a week of hospitalisation (index test). Individual ratings were collected at the end of each shift using a 100 mm Visual Analogue Scale, blinded from the reference standard. Diagnostic accuracy was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity and specificity metrics.

RESULTS

692 ratings performed by 83 nursing staff members were collected from 47 patients. Twenty patients were diagnosed with UWS and 27 with MCS. DoC-feeling scores obtained by pooling all individual ratings obtained for a given patient were significantly greater in patients with MCS than with UWS (59.2 mm (IQR: 27.3-77.3) vs 7.2 mm (IQR: 2.4-11.4); p<0.001) yielding an AUC of 0.92 (95% CI 0.84 to 0.99).

CONCLUSIONS

DoC-feeling capitalises on the expertise of nursing staff to evaluate patients' consciousness. Together with the CRS-R as well as with brain imaging, DoC-feeling might improve diagnostic and prognostic accuracy of patients with DoC.

Mismatch negativity to predict subsequent awakening in deeply sedated critically ill patients

BACKGROUND

Mismatch negativity (MMN) is the neurophysiological correlate of cognitive integration of novel stimuli. Although MMN is a well-established predictor of awakening in non-sedated comatose patients, its prognostic value in deeply sedated critically ill patients remains unknown. The aim of this prospective, observational pilot study was to investigate the prognostic value of MMN for subsequent awakening in deeply sedated critically ill patients.

METHODS

MMN was recorded in 43 deeply sedated critically ill patients on Day 3 of ICU admission using a classical 'odd-ball' paradigm that delivers rare deviant sounds in a train of frequent standard sounds. Individual visual analyses and a group level analysis of recordings were performed. MMN amplitudes were then analysed according to the neurological status (awake vs not awake) at Day 28.

RESULTS

Median (inter-quartile range) Richmond Assessment Sedation Scale (RASS) at the time of recording was -5 (range, from -5 to -4.5). Visual detection of MMN revealed a poor inter-rater agreement [kappa=0.17, 95% confidence interval (0.07-0.26)]. On Day 28, 30 (70%) patients had regained consciousness while 13 (30%) had not. Quantitative group level analysis revealed a significantly greater MMN amplitude for patients who awakened compared with those who had not [mean (standard deviation) = -0.65 (1.4) vs 0.08 (0.17) μV, respectively; P=0.003).

CONCLUSIONS

MMN can be observed in deeply sedated critically ill patients and could help predict subsequent awakening. However, visual analysis alone is unreliable and should be systematically completed with individual level statistics.

The audiovisual structure of onomatopoeias: An intrusion of real-world physics in lexical creation

Sound-symbolic word classes are found in different cultures and languages worldwide. These words are continuously produced to code complex information about events. Here we explore the capacity of creative language to transport complex multisensory information in a controlled experiment, where our participants improvised onomatopoeias from noisy moving objects in audio, visual and audiovisual formats. We found that consonants communicate movement types (slide, hit or ring) mainly through the manner of articulation in the vocal tract. Vowels communicate shapes in visual stimuli (spiky or rounded) and sound frequencies in auditory stimuli through the configuration of the lips and tongue. A machine learning model was trained to classify movement types and used to validate generalizations of our results across formats. We implemented the classifier with a list of cross-linguistic onomatopoeias simple actions were correctly classified, while different aspects were selected to build onomatopoeias of complex actions. These results show how the different aspects of complex sensory information are coded and how they interact in the creation of novel onomatopoeias.

Brain-heart interactions reveal consciousness in noncommunicating patients

OBJECTIVE

We here aimed at characterizing heart-brain interactions in patients with disorders of consciousness. We tested how this information impacts data-driven classification between unresponsive and minimally conscious patients.

METHODS

A cohort of 127 patients in vegetative state/unresponsive wakefulness syndrome (VS/UWS; n = 70) and minimally conscious state (MCS; n = 57) were presented with the local-global auditory oddball paradigm, which distinguishes 2 levels of processing: short-term deviation of local auditory regularities and global long-term rule violations. In addition to previously validated markers of consciousness extracted from electroencephalograms (EEG), we computed autonomic cardiac markers, such as heart rate (HR) and HR variability (HRV), and cardiac cycle phase shifts triggered by the processing of the auditory stimuli.

RESULTS

HR and HRV were similar in patients across groups. The cardiac cycle was not sensitive to the processing of local regularities in either the VS/UWS or MCS patients. In contrast, global regularities induced a phase shift of the cardiac cycle exclusively in the MCS group. The interval between the auditory stimulation and the following R peak was significantly shortened in MCS when the auditory rule was violated. When the information for the cardiac cycle modulations and other consciousness-related EEG markers were combined, single patient classification performance was enhanced compared to classification with solely EEG markers.

INTERPRETATION

Our work shows a link between residual cognitive processing and the modulation of autonomic somatic markers. These results open a new window to evaluate patients with disorders of consciousness via the embodied paradigm, according to which body-brain functions contribute to a holistic approach to conscious processing. Ann Neurol 2017;82:578-591.

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.

Information sharing in the brain indexes consciousness in noncommunicative patients

Neuronal theories of conscious access tentatively relate conscious perception to the integration and global broadcasting of information across distant cortical and thalamic areas. Experiments contrasting visible and invisible stimuli support this view and suggest that global neuronal communication may be detectable using scalp electroencephalography (EEG). However, whether global information sharing across brain areas also provides a specific signature of conscious state in awake but noncommunicating patients remains an active topic of research. We designed a novel measure termed "weighted symbolic mutual information" (wSMI) and applied it to 181 high-density EEG recordings of awake patients recovering from coma and diagnosed in various states of consciousness. The results demonstrate that this measure of information sharing systematically increases with consciousness state, particularly across distant sites. This effect sharply distinguishes patients in vegetative state (VS), minimally conscious state (MCS), and conscious state (CS) and is observed regardless of etiology and delay since insult. The present findings support distributed theories of conscious processing and open up the possibility of an automatic detection of conscious states, which may be particularly important for the diagnosis of awake but noncommunicating patients.

Single-trial decoding of auditory novelty responses facilitates the detection of residual consciousness

Detecting residual consciousness in unresponsive patients is a major clinical concern and a challenge for theoretical neuroscience. To tackle this issue, we recently designed a paradigm that dissociates two electro-encephalographic (EEG) responses to auditory novelty. Whereas a local change in pitch automatically elicits a mismatch negativity (MMN), a change in global sound sequence leads to a late P300b response. The latter component is thought to be present only when subjects consciously perceive the global novelty. Unfortunately, it can be difficult to detect because individual variability is high, especially in clinical recordings. Here, we show that multivariate pattern classifiers can extract subject-specific EEG patterns and predict single-trial local or global novelty responses. We first validate our method with 38 high-density EEG, MEG and intracranial EEG recordings. We empirically demonstrate that our approach circumvents the issues associated with multiple comparisons and individual variability while improving the statistics. Moreover, we confirm in control subjects that local responses are robust to distraction whereas global responses depend on attention. We then investigate 104 vegetative state (VS), minimally conscious state (MCS) and conscious state (CS) patients recorded with high-density EEG. For the local response, the proportion of significant decoding scores (M=60%) does not vary with the state of consciousness. By contrast, for the global response, only 14% of the VS patients' EEG recordings presented a significant effect, compared to 31% in MCS patients' and 52% in CS patients'. In conclusion, single-trial multivariate decoding of novelty responses provides valuable information in non-communicating patients and paves the way towards real-time monitoring of the state of consciousness.

Physiologically driven avian vocal synthesizer

In this work, we build an electronic syrinx, i.e., a programmable electronic device capable of integrating biomechanical model equations for the avian vocal organ in order to synthesize song. This vocal prosthesis is controlled by the bird's neural instructions to respiratory and the syringeal motor systems, thus opening great potential for studying motor control and its modification by sensory feedback mechanisms. Furthermore, a well-functioning subject-controlled vocal prosthesis can lay the foundation for similar devices in humans and thus provide directly health-related data and procedures.

Dynamical origin of spectrally rich vocalizations in birdsong

Birdsong is a model system for learned vocal behavior with remarkable parallels to human vocal development and sound production mechanisms. Upper vocal tract filtering plays an important role in human speech, and its importance has recently also been recognized in birdsong. However, the mechanisms of how the avian sound source might contribute to spectral richness are largely unknown. Here we show in the most widely studied songbird, the zebra finch (Taeniopygia guttata), that the broad range of upper harmonic content in different low-frequency song elements is the fingerprint of the dynamics displayed by its vocal apparatus, which can be captured by a two-dimensional dynamical model. As in human speech and singing, the varying harmonic content of birdsong is not only the result of vocal tract filtering but of a varying degree of tonality emerging from the sound source. The spectral content carries a strong signature of the intrinsic dynamics of the sound source.

Versatile biologically inspired electronic neuron

We present a biologically inspired electronic neuron based on a conductance model. The channels are constructed using linearly voltage controlled field effect transistors. A two channel and a three channel circuit is developed. The dynamical behavior of this system is studied, showing for the two channel circuit either class-I or class-II excitability and for the three channel circuit bursting and spike frequency adaptation. Voltage-clamp-type measurements, similar to the ones frequently used in neuroscience, are employed in order to determine the conductance characteristics of the electronic channels. We develop an empirical model based on these measurements that reproduces the different dynamical behaviors of the electronic neuron. We found that post-inhibitory rebound is present in the two channel circuit. Reliability and precision of spike timing is induced in the three channel circuit by injecting noise in the control variable of the slow channel that provides a negative feedback. The circuit is appropriate for the design of large scale electronic neural devices that can be used in mixed electronic-biological systems.