Cognitive Motor Dissociation in Disorders of Consciousness.

BACKGROUND: Patients with brain injury who are unresponsive to commands may perform cognitive tasks that are detected on functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). This phenomenon, known as cognitive motor dissociation, has not been systematically studied in a large cohort of persons with disorders of consciousness. METHODS: In this prospective cohort study conducted at six international centers, we collected clinical, behavioral, and task-based fMRI and EEG data from a convenience sample of 353 adults with disorders of consciousness. We assessed the response to commands on task-based fMRI or EEG in participants without an observable response to verbal commands (i.e., those with a behavioral diagnosis of coma, vegetative state, or minimally conscious state-minus) and in participants with an observable response to verbal commands. The presence or absence of an observable response to commands was assessed with the use of the Coma Recovery Scale-Revised (CRS-R). RESULTS: Data from fMRI only or EEG only were available for 65% of the participants, and data from both fMRI and EEG were available for 35%. The median age of the participants was 37.9 years, the median time between brain injury and assessment with the CRS-R was 7.9 months (25% of the participants were assessed with the CRS-R within 28 days after injury), and brain trauma was an etiologic factor in 50%. We detected cognitive motor dissociation in 60 of the 241 participants (25%) without an observable response to commands, of whom 11 had been assessed with the use of fMRI only, 13 with the use of EEG only, and 36 with the use of both techniques. Cognitive motor dissociation was associated with younger age, longer time since injury, and brain trauma as an etiologic factor. In contrast, responses on task-based fMRI or EEG occurred in 43 of 112 participants (38%) with an observable response to verbal commands. CONCLUSIONS: Approximately one in four participants without an observable response to commands performed a cognitive task on fMRI or EEG as compared with one in three participants with an observable response to commands. (Funded by the James S. McDonnell Foundation and others.).

Neuroimmune activation is associated with neurological outcome in anoxic and traumatic coma.

The pathophysiological underpinnings of critically disrupted brain connectomes resulting in coma are poorly understood. Inflammation is potentially an important but still undervalued factor. Here, we present a first-in-human prospective study using the 18-kDa translocator protein (TSPO) radioligand 18F-DPA714 for PET imaging to allow in vivo neuroimmune activation quantification in patients with coma (n = 17) following either anoxia or traumatic brain injuries in comparison with age- and sex-matched controls. Our findings yielded novel evidence of an early inflammatory component predominantly located within key cortical and subcortical brain structures that are putatively implicated in consciousness emergence and maintenance after severe brain injury (i.e. mesocircuit and frontoparietal networks). We observed that traumatic and anoxic patients with coma have distinct neuroimmune activation profiles, both in terms of intensity and spatial distribution. Finally, we demonstrated that both the total amount and specific distribution of PET-measurable neuroinflammation within the brain mesocircuit were associated with the patient's recovery potential. We suggest that our results can be developed for use both as a new neuroprognostication tool and as a promising biometric to guide future clinical trials targeting glial activity very early after severe brain injury.

Dyspnoea in acutely ill mechanically ventilated adult patients: an ERS/ESICM statement.

This statement outlines a review of the literature and current practice concerning the prevalence, clinical significance, diagnosis and management of dyspnoea in critically ill, mechanically ventilated adult patients. It covers the definition, pathophysiology, epidemiology, short- and middle-term impact, detection and quantification, and prevention and treatment of dyspnoea. It represents a collaboration of the European Respiratory Society and the European Society of Intensive Care Medicine. Dyspnoea ranks among the most distressing experiences that human beings can endure. Approximately 40% of patients undergoing invasive mechanical ventilation in the intensive care unit (ICU) report dyspnoea, with an average intensity of 45 mm on a visual analogue scale from 0 to 100 mm. Although it shares many similarities with pain, dyspnoea can be far worse than pain in that it summons a primal fear response. As such, it merits universal and specific consideration. Dyspnoea must be identified, prevented and relieved in every patient. In the ICU, mechanically ventilated patients are at high risk of experiencing breathing difficulties because of their physiological status and, in some instances, because of mechanical ventilation itself. At the same time, mechanically ventilated patients have barriers to signalling their distress. Addressing this major clinical challenge mandates teaching and training, and involves ICU caregivers and patients. This is even more important because, as opposed to pain which has become a universal healthcare concern, very little attention has been paid to the identification and management of respiratory suffering in mechanically ventilated ICU patients.

EEG lexicality effect predicts clinical outcome in disorders of consciousness.

This study aimed at probing covert language processing in patients with disorders of consciousness. An auditory paradigm contrasting words to pronounceable pseudowords was designed, while recording bedside electroencephalogram and computing the two main correlates of lexicality: N400 and late positive component (LPC). Healthy volunteers and 19 patients, 10 in a minimally conscious state and 9 in a vegetative state (also coined unresponsive wakefulness syndrome), were recorded. N400 was present in all groups, whereas LPC was only present in the healthy volunteers and minimally conscious state groups. At the individual level, an unprecedented detection rate of N400 and LPC was reached, and LPC predicted overt cognitive improvement at 6 months. ANN NEUROL 2023;93:762-767.

Prognostic value of early EEG abnormalities in severe stroke patients requiring mechanical ventilation: a pre-planned analysis of the SPICE prospective multicenter study.

INTRODUCTION: Prognostication of outcome in severe stroke patients necessitating invasive mechanical ventilation poses significant challenges. The objective of this study was to assess the prognostic significance and prevalence of early electroencephalogram (EEG) abnormalities in adult stroke patients receiving mechanical ventilation. METHODS: This study is a pre-planned ancillary investigation within the prospective multicenter SPICE cohort study (2017-2019), conducted in 33 intensive care units (ICUs) in the Paris area, France. We included adult stroke patients requiring invasive mechanical ventilation, who underwent at least one intermittent EEG examination during their ICU stay. The primary endpoint was the functional neurological outcome at one year, determined using the modified Rankin scale (mRS), and dichotomized as unfavorable (mRS 4-6, indicating severe disability or death) or favorable (mRS 0-3). Multivariable regression analyses were employed to identify EEG abnormalities associated with functional outcomes. RESULTS: Of the 364 patients enrolled in the SPICE study, 153 patients (49 ischemic strokes, 52 intracranial hemorrhages, and 52 subarachnoid hemorrhages) underwent at least one EEG at a median time of 4 (interquartile range 2-7) days post-stroke. Rates of diffuse slowing (70% vs. 63%, p = 0.37), focal slowing (38% vs. 32%, p = 0.15), periodic discharges (2.3% vs. 3.7%, p = 0.9), and electrographic seizures (4.5% vs. 3.7%, p = 0.4) were comparable between patients with unfavorable and favorable outcomes. Following adjustment for potential confounders, an unreactive EEG background to auditory and pain stimulations (OR 6.02, 95% CI 2.27-15.99) was independently associated with unfavorable outcomes. An unreactive EEG predicted unfavorable outcome with a specificity of 48% (95% CI 40-56), sensitivity of 79% (95% CI 72-85), and positive predictive value (PPV) of 74% (95% CI 67-81). Conversely, a benign EEG (defined as continuous and reactive background activity without seizure, periodic discharges, triphasic waves, or burst suppression) predicted favorable outcome with a specificity of 89% (95% CI 84-94), and a sensitivity of 37% (95% CI 30-45). CONCLUSION: The absence of EEG reactivity independently predicts unfavorable outcomes at one year in severe stroke patients requiring mechanical ventilation in the ICU, although its prognostic value remains limited. Conversely, a benign EEG pattern was associated with a favorable outcome.

A Novel Approach to Screen for Somatosensory Evoked Potentials in Critical Care.

BACKGROUND: Somatosensory evoked potentials (SSEPs) help prognostication, particularly in patients with diffuse brain injury. However, use of SSEP is limited in critical care. We propose a novel, low-cost approach allowing acquisition of screening SSEP using widely available intensive care unit (ICU) equipment, specifically a peripheral "train-of-four" stimulator and standard electroencephalograph. METHODS: The median nerve was stimulated using a train-of-four stimulator, and a standard 21-channel electroencephalograph was recorded to generate the screening SSEP. Generation of the SSEP was supported by visual inspection, univariate event-related potentials statistics, and a multivariate support vector machine (SVM) decoding algorithm. This approach was validated in 15 healthy volunteers and validated against standard SSEPs in 10 ICU patients. The ability of this approach to predict poor neurological outcome, defined as death, vegetative state, or severe disability at 6 months, was tested in an additional set of 39 ICU patients. RESULTS: In each of the healthy volunteers, both the univariate and the SVM methods reliably detected SSEP responses. In patients, when compared against the standard SSEP method, the univariate event-related potentials method matched in nine of ten patients (sensitivity = 94%, specificity = 100%), and the SVM had 100% sensitivity and specificity when compared with the standard method. For the 49 ICU patients, we performed both the univariate and the SVM methods: a bilateral absence of short latency responses (n = 8) predicted poor neurological outcome with 0% FPR (sensitivity = 21%, specificity = 100%). CONCLUSIONS: Somatosensory evoked potentials can reliably be recorded using the proposed approach. Given the very good but slightly lower sensitivity of absent SSEPs in the proposed screening approach, confirmation of absent SSEP responses using standard SSEP recordings is advised.

Description and Outcome of Severe Hypoglycemic Encephalopathy in the Intensive Care Unit.

BACKGROUND: Disorders of consciousness due to severe hypoglycemia are rare but challenging to treat. The aim of this retrospective cohort study was to describe our multimodal neurological assessment of patients with hypoglycemic encephalopathy hospitalized in the intensive care unit and their neurological outcomes. METHODS: Consecutive patients with disorders of consciousness related to hypoglycemia admitted for neuroprognostication from 2010 to 2020 were included. Multimodal neurological assessment included electroencephalography, somatosensory and cognitive event-related potentials, and morphological and quantitative magnetic resonance imaging (MRI) with quantification of fractional anisotropy. Neurological outcomes at 28 days, 3 months, 6 months, 1 year, and 2 years after hypoglycemia were retrieved. RESULTS: Twenty patients were included. After 2 years, 75% of patients had died, 5% remained in a permanent vegetative state, 10% were in a minimally conscious state, and 10% were conscious but with severe disabilities (Glasgow Outcome Scale-Extended scores 3 and 4). All patients showed pathologic electroencephalography findings with heterogenous patterns. Morphological brain MRI revealed abnormalities in 95% of patients, with various localizations including cortical atrophy in 65% of patients. When performed, quantitative MRI showed decreased fractional anisotropy affecting widespread white matter tracts in all patients. CONCLUSIONS: The overall prognosis of patients with severe hypoglycemic encephalopathy was poor, with only a small fraction of patients who slowly improved after intensive care unit discharge. Of note, patients who did not improve during the first 6 months did not recover consciousness. This study suggests that a multimodal approach capitalizing on advanced brain imaging and bedside electrophysiology techniques could improve diagnostic and prognostic performance in severe hypoglycemic encephalopathy.

Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group on Behavioral Phenotyping.

BACKGROUND: The recent publication of practice guidelines for management of patients with disorders of consciousness (DoC) in the United States and Europe was a major step forward in improving the accuracy and consistency of terminology, diagnostic criteria, and prognostication in this population. There remains a pressing need for a more precise brain injury classification system that combines clinical semiology with neuroimaging, electrophysiologic, and other biomarker data. To address this need, the National Institute of Neurological Disorders and Stroke launched the Common Data Elements (CDEs) initiative to facilitate systematic collection of high-quality research data in studies involving patients with neurological disease. The Neurocritical Care Society's Curing Coma Campaign expanded this effort in 2018 to develop CDEs for DoC. Herein, we present CDE recommendations for behavioral phenotyping of patients with DoC. METHODS: The Behavioral Phenotyping Workgroup used a preestablished, five-step process to identify and select candidate CDEs that included review of existing National Institute of Neurological Disorders and Stroke CDEs, nomination and systematic vetting of new CDEs, CDE classification, iterative review, and approval of panel recommendations and development of corresponding case review forms. RESULTS: We identified a slate of existing and newly proposed basic, supplemental, and exploratory CDEs that can be used for behavioral phenotyping of adult and pediatric patients with DoC. CONCLUSIONS: The proposed behavioral phenotyping CDEs will assist with international harmonization of DoC studies and allow for more precise characterization of study cohorts, favorably impacting observational studies and clinical trials aimed at improving outcome in this population.

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 magneto-encephalography. 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 sleep 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.

Preservation of Brain Activity in Unresponsive Patients Identifies MCS Star.

OBJECTIVE: Brain-injured patients who are unresponsive at the bedside (ie, vegetative state/unresponsive wakefulness syndrome - VS/UWS) may present brain activity similar to patients in minimally conscious state (MCS). This peculiar condition has been termed "non-behavioural MCS" or "MCS*". In the present study we aimed to investigate the proportion and underlying brain characteristics of patients in MCS*. METHODS: Brain 18 F-fluorodeoxyglucose Positron Emission Tomography (FDG-PET) was acquired on 135 brain-injured patients diagnosed in prolonged VS/UWS (n = 48) or MCS (n = 87). From an existing database, relative metabolic preservation in the fronto-parietal network (measured with standardized uptake value) was visually inspected by three experts. Patients with hypometabolism of the fronto-parietal network were labelled "VS/UWS", while its (partial) preservation either confirmed the behavioural diagnosis of "MCS" or, in absence of behavioural signs of consciousness, suggested a diagnosis of "MCS*". Clinical outcome at 1-year follow-up, functional connectivity, grey matter atrophy, and regional brain metabolic patterns were investigated in the three groups (VS/UWS, MCS* and MCS). RESULTS: 67% of behavioural VS/UWS presented a partial preservation of brain metabolism (ie, MCS*). Compared to VS/UWS patients, MCS* patients demonstrated a better outcome, global functional connectivity and grey matter preservation more compatible with the diagnosis of MCS. MCS* patients presented lower brain metabolism mostly in the posterior brain regions compared to MCS patients. INTERPRETATION: MCS* is a frequent phenomenon that is associated with better outcome and better brain preservation than the diagnosis of VS/UWS. Complementary exams should be provided to all unresponsive patients before taking medical decisions. ANN NEUROL 2021;90:89-100.

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.

A Precision Medicine Framework for Classifying Patients with Disorders of Consciousness: Advanced Classification of Consciousness Endotypes (ACCESS).

BACKGROUND: Consciousness in patients with brain injury is traditionally assessed based on semiological evaluation at the bedside. This classification is limited because of low granularity, ill-defined and rigid nomenclatures incompatible with the highly fluctuating nature of consciousness, failure to identify specific brain states like cognitive motor dissociation, and neglect for underlying biological mechanisms. Here, the authors present a pragmatic framework based on consciousness endotypes that combines clinical phenomenology with all essential physiological and biological data, emphasizing recovery trajectories, therapeutic potentials and clinical feasibility. METHODS: The Neurocritical Care Society's Curing Coma Campaign identified an international group of experts who convened in a series of online meetings between May and November 2020 to discuss and propose a novel framework for classifying consciousness. RESULTS: The expert group proposes Advanced Classification of Consciousness Endotypes (ACCESS), a tiered multidimensional framework reflecting increasing complexity and an aspiration to consider emerging and future approaches. Tier 1 is based on clinical phenotypes and structural imaging. Tier 2 adds functional measures including EEG, PET and functional MRI, that can be summarized using the Arousal, Volition, Cognition and Mechanisms (AVCM) score (where "Volition" signifies volitional motor responses). Finally, Tier 3 reflects dynamic changes over time with a (theoretically infinite) number of physiologically distinct states to outline consciousness recovery and identify opportunities for therapeutic interventions. CONCLUSIONS: Whereas Tiers 1 and 2 propose an approach for low-resource settings and state-of-the-art expertise at leading academic centers, respectively, Tier 3 is a visionary multidimensional consciousness paradigm driven by continuous incorporation of new knowledge while addressing the Curing Coma Campaign's aspirational goals.

Functional and Structural Integrity of Frontoparietal Connectivity in Traumatic and Anoxic Coma.

OBJECTIVES: Recovery from coma might critically depend on the structural and functional integrity of frontoparietal networks. We aimed to measure this integrity in traumatic brain injury and anoxo-ischemic (cardiac arrest) coma patients by using an original multimodal MRI protocol. DESIGN: Prospective cohort study. SETTING: Three Intensive Critical Care Units affiliated to the University in Toulouse (France). PATIENTS: We longitudinally recruited 43 coma patients (Glasgow Coma Scale at the admission < 8; 29 cardiac arrest and 14 traumatic brain injury) and 34 age-matched healthy volunteers. Exclusion criteria were disorders of consciousness lasting more than 30 days and focal brain damage within the explored brain regions. Patient assessments were conducted at least 2 days (5 ± 2 d) after complete withdrawal of sedation. All patients were followed up (Coma Recovery Scale-Revised) 3 months after acute brain injury. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Functional and structural MRI data were recorded, and the analysis was targeted on the posteromedial cortex, the medial prefrontal cortex, and the cingulum. Univariate analyses and machine learning techniques were used to assess diagnostic and predictive values. Coma patients displayed significantly lower medial prefrontal cortex-posteromedial cortex functional connectivity (area under the curve, 0.94; 95% CI, 0.93-0.95). Cardiac arrest patients showed specific structural disturbances within posteromedial cortex. Significant cingulum architectural disturbances were observed in traumatic brain injury patients. The machine learning medial prefrontal cortex-posteromedial cortex multimodal classifier had a significant predictive value (area under the curve, 0.96; 95% CI, 0.95-0.97), best combination of subregions that discriminates a binary outcome based on Coma Recovery Scale-Revised). CONCLUSIONS: This exploratory study suggests that frontoparietal functional disconnections are specifically observed in coma and their structural counterpart provides information about brain injury mechanisms. Multimodal MRI biomarkers of frontoparietal disconnection predict 3-month outcome in our sample. These findings suggest that fronto-parietal disconnection might be particularly relevant for coma outcome prediction and could inspire innovative precision medicine approaches.

EEG evidence of compensatory mechanisms in preclinical Alzheimer's disease.

Early biomarkers are needed to identify individuals at high risk of preclinical Alzheimer's disease and to better understand the pathophysiological processes of disease progression. Preclinical Alzheimer's disease EEG changes would be non-invasive and cheap screening tools and could also help to predict future progression to clinical Alzheimer's disease. However, the impact of amyloid-ß deposition and neurodegeneration on EEG biomarkers needs to be elucidated. We included participants from the INSIGHT-preAD cohort, which is an ongoing single-centre multimodal observational study that was designed to identify risk factors and markers of progression to clinical Alzheimer's disease in 318 cognitively normal individuals aged 70-85 years with a subjective memory complaint. We divided the subjects into four groups, according to their amyloid status (based on 18F-florbetapir PET) and neurodegeneration status (evidenced by 18F-fluorodeoxyglucose PET brain metabolism in Alzheimer's disease signature regions). The first group was amyloid-positive and neurodegeneration-positive, which corresponds to stage 2 of preclinical Alzheimer's disease. The second group was amyloid-positive and neurodegeneration-negative, which corresponds to stage 1 of preclinical Alzheimer's disease. The third group was amyloid-negative and neurodegeneration-positive, which corresponds to 'suspected non-Alzheimer's pathophysiology'. The last group was the control group, defined by amyloid-negative and neurodegeneration-negative subjects. We analysed 314 baseline 256-channel high-density eyes closed 1-min resting state EEG recordings. EEG biomarkers included spectral measures, algorithmic complexity and functional connectivity assessed with a novel information-theoretic measure, weighted symbolic mutual information. The most prominent effects of neurodegeneration on EEG metrics were localized in frontocentral regions with an increase in high frequency oscillations (higher beta and gamma power) and a decrease in low frequency oscillations (lower delta power), higher spectral entropy, higher complexity and increased functional connectivity measured by weighted symbolic mutual information in theta band. Neurodegeneration was associated with a widespread increase of median spectral frequency. We found a non-linear relationship between amyloid burden and EEG metrics in neurodegeneration-positive subjects, either following a U-shape curve for delta power or an inverted U-shape curve for the other metrics, meaning that EEG patterns are modulated differently depending on the degree of amyloid burden. This finding suggests initial compensatory mechanisms that are overwhelmed for the highest amyloid load. Together, these results indicate that EEG metrics are useful biomarkers for the preclinical stage of Alzheimer's disease.

Neurophysiological dynamics of phrase-structure building during sentence processing.

Although sentences unfold sequentially, one word at a time, most linguistic theories propose that their underlying syntactic structure involves a tree of nested phrases rather than a linear sequence of words. Whether and how the brain builds such structures, however, remains largely unknown. Here, we used human intracranial recordings and visual word-by-word presentation of sentences and word lists to investigate how left-hemispheric brain activity varies during the formation of phrase structures. In a broad set of language-related areas, comprising multiple superior temporal and inferior frontal sites, high-gamma power increased with each successive word in a sentence but decreased suddenly whenever words could be merged into a phrase. Regression analyses showed that each additional word or multiword phrase contributed a similar amount of additional brain activity, providing evidence for a merge operation that applies equally to linguistic objects of arbitrary complexity. More superficial models of language, based solely on sequential transition probability over lexical and syntactic categories, only captured activity in the posterior middle temporal gyrus. Formal model comparison indicated that the model of multiword phrase construction provided a better fit than probability-based models at most sites in superior temporal and inferior frontal cortices. Activity in those regions was consistent with a neural implementation of a bottom-up or left-corner parser of the incoming language stream. Our results provide initial intracranial evidence for the neurophysiological reality of the merge operation postulated by linguists and suggest that the brain compresses syntactically well-formed sequences of words into a hierarchy of nested phrases.

Regional brain volumetry and brain function in severely brain-injured patients.

OBJECTIVE: The relationship between residual brain tissue in patients with disorders of consciousness (DOC) and the clinical condition is unclear. This observational study aimed to quantify gray (GM) and white matter (WM) atrophy in states of (altered) consciousness. METHODS: Structural T1-weighted magnetic resonance images were processed for 102 severely brain-injured and 52 healthy subjects. Regional brain volume was quantified for 158 (sub)cortical regions using Freesurfer. The relationship between regional brain volume and clinical characteristics of patients with DOC and conscious brain-injured patients was assessed using a linear mixed-effects model. Classification of patients with unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) using regional volumetric information was performed and compared to classification using cerebral glucose uptake from fluorodeoxyglucose positron emission tomography. For validation, the T1-based classifier was tested on independent datasets. RESULTS: Patients were characterized by smaller regional brain volumes than healthy subjects. Atrophy occurred faster in UWS compared to MCS (GM) and conscious (GM and WM) patients. Classification was successful (misclassification with leave-one-out cross-validation between 2% and 13%) and generalized to the independent data set with an area under the receiver operator curve of 79% (95% confidence interval [CI; 67-91.5]) for GM and 70% (95% CI [55.6-85.4]) for WM. INTERPRETATION: Brain volumetry at the single-subject level reveals that regions in the default mode network and subcortical gray matter regions, as well as white matter regions involved in long range connectivity, are most important to distinguish levels of consciousness. Our findings suggest that changes of brain structure provide information in addition to the assessment of functional neuroimaging and thus should be evaluated as well. Ann Neurol 2018;83:842-853.

Minimally conscious state or cortically mediated state?

Durable impairments of consciousness are currently classified in three main neurological categories: comatose state, vegetative state (also recently coined unresponsive wakefulness syndrome) and minimally conscious state. While the introduction of minimally conscious state, in 2002, was a major progress to help clinicians recognize complex non-reflexive behaviours in the absence of functional communication, it raises several problems. The most important issue related to minimally conscious state lies in its criteria: while behavioural definition of minimally conscious state lacks any direct evidence of patient's conscious content or conscious state, it includes the adjective 'conscious'. I discuss this major problem in this review and propose a novel interpretation of minimally conscious state: its criteria do not inform us about the potential residual consciousness of patients, but they do inform us with certainty about the presence of a cortically mediated state. Based on this constructive criticism review, I suggest three proposals aiming at improving the way we describe the subjective and cognitive state of non-communicating patients. In particular, I present a tentative new classification of impairments of consciousness that combines behavioural evidence with functional brain imaging data, in order to probe directly and univocally residual conscious processes.

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.