EEG Correlates of Language Function in Traumatic Disorders of Consciousness

Total recall: Detecting autobiographical memory retrieval in the absence of behaviour

Functional neuroimaging has fundamentally changed our understanding of disorders of consciousness (DoC). While many DoC patients exhibit minimal to no behavioural responsiveness, a significant minority show neural evidence of awareness and preserved cognitive functioning. Although several cognitive functions have been explored in DoC patients, autobiographical memory -- the ability to form and retrieve personal memories -- has yet to be investigated. To address this gap, we used functional magnetic resonance imaging (fMRI) to investigate autobiographical memory in one DoC patient. The patient viewed video clips across three conditions: (1) Own - clips recorded from their perspective during a recent mall visit; (2) Other - clips from a healthy control's visit to the same mall; and (3) Bookstore - novel clips from an entirely different store that had not been visited. We trained a linear support vector classifier to associate fMRI activity in canonical autobiographical memory regions with each condition using data from twelve healthy participants. We then applied the trained model to the patient's data to 'decode' which condition their fMRI activity predicted. The model accurately distinguished between Own, Other, and Bookstore conditions in the patient (Balanced Accuracy = 0.448, p = .032), with performance within the control group range (p = .068). Similarly, the model distinguished between the Own and Other conditions above chance (Balanced Accuracy = 0.609, p = .032) and within the control group's distribution (p = .620), suggesting that the patient was still able to differentiate personal experiences from visually similar scenes, despite being behaviourally unable to report that this was the case. These findings provide preliminary evidence that autobiographical memory processes, critical to conscious awareness and identity, remain intact in some DoC patients, shedding further light on their covert capabilities and inner experiences.

Parallel EEG-fNIRS assessments of covert cognition in behaviorally non-responsive ICU patients: A multi-task feasibility study in a case of acute motor sensory axonal neuropathy

BACKGROUND

Repeat neurological assessment is standard in cases of severe acute brain injury. However, conventional measures rely on overt behavior. Unfortunately, behavioral responses may be difficult or impossible for some patients. As a result, patients who recover consciousness before the ability to express so may go undetected. Recent studies have demonstrated the efficacy of incorporating functional neuroimaging into clinical assessment protocols. The objective of the current study is to assess the feasibility of a multi-task, multimodal bedside technique to evaluate sensory and cognitive function in behaviorally non-responsive patients.

METHODS

We deployed a novel assessment paradigm to evaluate sensory and cognitive processing in one 63-year-old unresponsive patient with acute motor sensory axonal neuropathy (AMSAN). We collected parallel bedside EEG-fNIRS activity during hierarchical auditory processing, movie listening, and motor imagery.

RESULTS

We found appropriate hemodynamic activation in the patient's middle and superior temporal gyri to simple sounds and activation in their superior temporal gyrus, left angular and precentral gyri during speech. During movie listening, the patient produced patterns of EEG and fNIRS activity that were statistically indistinguishable from healthy controls. The patient also showed appropriate fNIRS and source-localized EEG activation of motor areas during motor imagery. Upon recovering, the patient correctly recalled multiple aspects of our assessment procedures.

CONCLUSION

In sum, our assessment protocol effectively captures neural markers of sensory and cognitive function in behaviorally non-responsive patients. Crucially, while AMSAN is distinct from brain injury, the patient's assumed dissociation between behavior and awareness provided an ideal test case to validate our protocol.

A scientific approach to diagnosis of disorders of consciousness

Disorder of consciousness (DoC) are the shared clinical manifestation of severe brain injuries resulting from a variety of etiologies. The nosology of DoC, as well as the armamentarium of methods available to diagnose it, has rapidly evolved. As a result, the diagnosis of DoC is complex and dynamic. We offer an evidence-based approach to DoC diagnosis, highlighting the challenges and pitfalls therein. Accordingly, we summarize the contemporary taxonomy of DoC and its development. We discuss the standardized behavioral diagnostic tools that form the foundation of DoC diagnosis, the evidence for their use, and their limitations. We also highlight recent advances in functional MRI (fMRI) and electroencephalography (EEG) techniques to increase the sensitivity and specificity of DoC diagnosis. We discuss the concept of covert consciousness (i.e., cognitive motor dissociation) as a discrete diagnostic category of DoC, as well as its diagnostic implications. Finally, we underscore issues of neuroethics and equity raised by contemporary models of DoC.

Classifying disorders of consciousness using a novel dual-level and dual-modal graph learning model

BACKGROUND

Disorders of consciousness (DoC) are a group of conditions that affect the level of awareness and communication in patients. While neuroimaging techniques can provide useful information about the brain structure and function in these patients, most existing methods rely on a single modality for analysis and rarely account for brain injury. To address these limitations, we propose a novel method that integrates two neuroimaging modalities, functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), to enhance the classification of subjects into different states of consciousness.

METHOD AND RESULTS

The main contributions of our work are threefold: first, after constructing a dual-model individual graph using functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), we introduce a brain injury mask mechanism that consolidates damaged brain regions into a single graph node, enhancing the modeling of brain injuries and reducing deformation effects. Second, to address over-smoothing, we construct a dual-level graph that dynamically construct a population-level graph with node features from individual graphs, to promote the clustering of similar subjects while distinguishing dissimilar ones. Finally, we employ a subgraph exploration model with task-fMRI data to validate the interpretability of our model, confirming that the selected brain regions are task-relevant in cognition. Our experimental results on data from 89 healthy participants and 204 patients with DoC from Huashan Hospital, Fudan University, demonstrate that our method achieves high accuracy in classifying patients into unresponsive wakefulness syndrome (UWS), minimally conscious state (MCS), or normal conscious state, outperforming current state-of-the-art methods. The explainability results of our method identified a subset of brain regions that are important for consciousness, such as the default mode network, the salience network, the dorsal attention network, and the visual network. Our method also revealed the relationship between brain networks and language processing in consciousness, and showed that language-related subgraphs can distinguish MCS from UWS patients.

CONCLUSION

We proposed a novel graph learning method for classifying DoC based on fMRI and DTI data, introducing a brain injury mask mechanism to effectively handle damaged brains. The classification results demonstrate the effectiveness of our method in distinguishing subjects across different states of consciousness, while the explainability results identify key brain regions relevant to this classification. Our study provides new evidence for the role of brain networks and language processing in consciousness, with potential implications for improving the diagnosis and prognosis of patients with DoC.

Alpha coherence is a network signature of cognitive recovery from disorders of consciousness

Alpha (8-12 Hz) frequency band oscillations are among the most informative features in electroencephalographic (EEG) assessment of patients with disorders of consciousness (DoC). Because interareal alpha synchrony is thought to facilitate long-range communication in healthy brains, coherence measures of resting-state alpha oscillations may provide insights into a patient's capacity for higher-order cognition beyond channel-wise estimates of alpha power. In multi-channel EEG, global coherence methods may be used to augment standard spectral analysis methods by both estimating the strength and identifying the structure of coherent oscillatory networks. We performed global coherence analysis in 95 separate clinical EEG recordings (28 healthy controls and 33 patients with acute or chronic DoC, 25 of whom returned for follow-up) collected between two academic medical centers. We found that posterior alpha coherence is associated with recovery of higher-level cognition. We developed a measure of network organization, based on the distance between eigenvectors of the alpha cross-spectral matrix, that detects recovery of posterior alpha networks. In patients who have emerged from a minimally conscious state, we showed that coherence-based alpha networks are reconfigured prior to restoration of alpha power to resemble those seen in healthy controls. This alpha network measure performs well in classifying recovery from DoC (AUC = 0.78) compared to common representations of functional connectivity using the weighted phase lag index (AUC = 0.50 - 0.57). Lastly, we observed that activity within these alpha networks is suppressed during positive responses to task-based EEG command-following paradigms, supporting the potential utility of this biomarker to detect covert cognition. Our findings suggest that restored alpha networks may represent a sensitive early signature of cognitive recovery in patients with DoC. Therefore, network detection methods may augment the utility of EEG assessments for DoC.

Detecting awareness after acute brain injury

Advances over the past two decades in functional neuroimaging have provided new diagnostic and prognostic tools for patients with severe brain injury. Some of the most pertinent developments in this area involve the assessment of residual brain function in patients in the intensive care unit during the acute phase of severe injury, when they are at their most vulnerable and prognosis is uncertain. Advanced neuroimaging techniques, such as functional MRI and EEG, have now been used to identify preserved cognitive processing, including covert conscious awareness, and to relate them to outcome in patients who are behaviourally unresponsive. Yet, technical and logistical challenges to clinical integration of these advanced neuroimaging techniques remain, such as the need for specialised expertise to acquire, analyse, and interpret data and to determine the appropriate timing for such assessments. Once these barriers are overcome, advanced functional neuroimaging technologies could improve diagnosis and prognosis for millions of patients worldwide.

Low-density EEG-based Functional Connectivity Discriminates Minimally Conscious State plus from minus

OBJECTIVE

Within the continuum of consciousness, patients in a Minimally Conscious State (MCS) may exhibit high-level behavioral responses (MCS+) or may not (MCS-). The evaluation of residual consciousness and related classification is crucial to propose tailored rehabilitation and pharmacological treatments, considering the inherent differences among groups in diagnosis and prognosis. Currently, differential diagnosis relies on behavioral assessments posing a relevant risk of misdiagnosis. In this context, EEG offers a non-invasive approach to model the brain as a complex network. The search for discriminating features could reveal whether behavioral responses in post-comatose patients have a defined physiological background. Additionally, it is essential to determine whether the standard behavioral assessment for quantifying responsiveness holds physiological significance.

METHODS

In this prospective observational study, we investigated whether low-density EEG-based graph metrics could discriminate MCS+/- patients by enrolling 57 MCS patients (MCS-: 30; males: 28). At admission to intensive rehabilitation, 30 min resting-state closed-eyes EEG recordings were performed together with consciousness diagnosis following international guidelines. After EEG preprocessing, graphs' metrics were estimated using different connectivity measures, at multiple connection densities and frequency bands (α,θ,δ). Metrics were also provided to cross-validated Machine Learning (ML) models with outcome MCS+/-.

RESULTS

A lower level of brain activity integration was found in the MCS- group in the α band. Instead, in the δ band MCS- group presented an higher level of clustering (weighted clustering coefficient) respect to MCS+. The best-performing solution in discriminating MCS+/- through the use of ML was an Elastic-Net regularized logistic regression with a cross-validation accuracy of 79% (sensitivity and specificity of 74% and 85% respectively).

CONCLUSION

Despite tackling the MCS+/- differential diagnosis is highly challenging, a daily-routine low-density EEG might allow to differentiate across these differently responsive brain networks.

SIGNIFICANCE

Graph-theoretical features are shown to discriminate between these two neurophysiologically similar conditions, and may thus support the clinical diagnosis.

Multimodal approaches supporting the diagnosis, prognosis and investigation of neural correlates of disorders of consciousness: A systematic review

The limits of the standard, behaviour-based clinical assessment of patients with disorders of consciousness (DoC) prompted the employment of functional neuroimaging, neurometabolic, neurophysiological and neurostimulation techniques, to detect brain-based covert markers of awareness. However, uni-modal approaches, consisting in employing just one of those techniques, are usually not sufficient to provide an exhaustive exploration of the neural underpinnings of residual awareness. This systematic review aimed at collecting the evidence from studies employing a multimodal approach, that is, combining more instruments to complement DoC diagnosis, prognosis and better investigating their neural correlates. Following the PRISMA guidelines, records from PubMed, EMBASE and Scopus were screened to select peer-review original articles in which a multi-modal approach was used for the assessment of adult patients with a diagnosis of DoC. Ninety-two observational studies and 32 case reports or case series met the inclusion criteria. Results highlighted a diagnostic and prognostic advantage of multi-modal approaches that involve electroencephalography-based (EEG-based) measurements together with neuroimaging or neurometabolic data or with neurostimulation. Multimodal assessment deepened the knowledge on the neural networks underlying consciousness, by showing correlations between the integrity of the default mode network and the different clinical diagnosis of DoC. However, except for studies using transcranial magnetic stimulation combined with electroencephalography, the integration of more than one technique in most of the cases occurs without an a priori-designed multi-modal diagnostic approach. Our review supports the feasibility and underlines the advantages of a multimodal approach for the diagnosis, prognosis and for the investigation of neural correlates of DoCs.

Neural coding of autonomic functions in different states of consciousness

Detecting signs of residual neural activity in patients with altered states of consciousness is a crucial issue for the customization of neurorehabilitation treatments and clinical decision-making. With this large observational prospective study, we propose an innovative approach to detect residual signs of consciousness via the assessment of the amount of autonomic information coded within the brain. The latter was estimated by computing the mutual information (MI) between preprocessed EEG and ECG signals, to be then compared across consciousness groups, together with the absolute power and an international qualitative labeling. One-hundred seventy-four patients (73 females, 42%) were included in the study (median age of 65 years [IQR = 20], MCS +: 29, MCS -: 23, UWS: 29). Electroencephalography (EEG) information content was found to be mostly related to the coding of electrocardiography (ECG) activity, i.e., with higher MI (p < 0.05), in Unresponsive Wakefulness Syndrome and Minimally Consciousness State minus (MCS -). EEG-ECG MI, besides clearly discriminating patients in an MCS - and +, significantly differed between lesioned areas (sides) in a subgroup of unilateral hemorrhagic patients. Crucially, such an accessible and non-invasive measure of residual consciousness signs was robust across electrodes and patient groups. Consequently, exiting from a strictly neuro-centric consciousness detection approach may be the key to provide complementary insights for the objective assessment of patients' consciousness levels and for the patient-specific planning of rehabilitative interventions.

Hyperbaric oxygen therapy promotes consciousness, cognitive function, and prognosis recovery in patients following traumatic brain injury through various pathways

Objective

The aim of this study was to investigate the clinical curative effect of hyperbaric oxygen (HBO) treatment and its mechanism in improving dysfunction following traumatic brain injury (TBI).

Methods

Patients were enrolled into control and HBO groups. Glasgow coma scale (GCS) and coma recovery scale-revised (CRS-R) scores were used to measure consciousness; the Rancho Los Amigos scale-revised (RLAS-R) score was used to assess cognitive impairment; the Stockholm computed tomography (CT) score, quantitative electroencephalography (QEEG), and biomarkers, including neuron-specific enolase (NSE), S100 calcium-binding protein beta (S100β), glial fibrillary acidic protein (GFAP), brain-derived neurotrophic factor (BDNF), nerve growth factor (NGF), and vascular endothelial growth factor (VEGF), were used to assess TBI severity. The patients were followed up 6 months after discharge and assessed with the Glasgow outcome scale-extended (GOSE), functional independence measure (FIM), and the disability rating scale (DRS).

Results

The CRS-R scores were higher in the HBO group than the control group at 10 days after treatment. The RLAS-R scores were higher in the HBO group than the control group at 10 and 20 days after treatment. The Stockholm CT scores were significantly lower in the HBO group than the control group at 10 days after treatment. HBO depressed the (δ + θ)/(α + β) ratio (DTABR) of EEG, with lower δ band relative power and higher α band relative power than those in the control group. At 20 days after treatment, the expression of NSE, S100β, and GFAP in the HBO group was lower than that in controls, whereas the expression of BDNF, NGF, and VEGF in the HBO group was higher than that in controls. Six months after discharge, the HBO group had lower DRS scores and higher FIM and GOSE scores than the control group significantly.

Conclusions

HBO may be an effective treatment for patients with TBI to improve consciousness, cognitive function and prognosis through decreasing TBI-induced hematoma volumes, promoting the recovery of EEG rhythm, and modulating the expression of serum NSE, S100β, GFAP, BDNF, NGF, and VEGF.

Electrophysiological correlates of thalamocortical function in acute severe traumatic brain injury

Tools assaying the neural networks that modulate consciousness may facilitate tracking of recovery after acute severe brain injury. The ABCD framework classifies resting-state EEG into categories reflecting levels of thalamocortical network function that correlate with outcome in post-cardiac arrest coma. In this longitudinal cohort study, we applied the ABCD framework to 20 patients with acute severe traumatic brain injury requiring intensive care (12 of whom were also studied at ≥6-months post-injury) and 16 healthy controls. We tested four hypotheses: 1) EEG ABCD classifications are spatially heterogeneous and temporally variable; 2) ABCD classifications improve longitudinally, commensurate with the degree of behavioral recovery; 3) ABCD classifications correlate with behavioral level of consciousness; and 4) the Coma Recovery Scale-Revised arousal facilitation protocol yields improved ABCD classifications. Channel-level EEG power spectra were classified based on spectral peaks within pre-defined frequency bands: 'A' = no peaks above delta (<4 Hz) range (complete thalamocortical disruption); 'B' = theta (4-8 Hz) peak (severe thalamocortical disruption); 'C' = theta and beta (13-24 Hz) peaks (moderate thalamocortical disruption); or 'D' = alpha (8-13 Hz) and beta peaks (normal thalamocortical function). Acutely, 95% of patients demonstrated 'D' signals in at least one channel but exhibited within-session temporal variability and spatial heterogeneity in the proportion of different channel-level ABCD classifications. By contrast, healthy participants and patients at follow-up consistently demonstrated signals corresponding to intact thalamocortical network function. Patients demonstrated longitudinal improvement in ABCD classifications (p < .05) and ABCD classification distinguished patients with and without command-following in the subacute-to-chronic phase of recovery (p < .01). In patients studied acutely, ABCD classifications improved after the Coma Recovery Scale-Revised arousal facilitation protocol (p < .05) but did not correspond with behavioral level of consciousness. These findings support the use of the ABCD framework to characterize channel-level EEG dynamics and track fluctuations in functional thalamocortical network integrity in spatial detail.

Conventional and Investigational Approaches Leveraging Clinical EEG for Prognosis in Acute Disorders of Consciousness

Prediction of recovery of consciousness after severe brain injury is difficult and limited by a lack of reliable, standardized biomarkers. Multiple approaches for analysis of clinical electroencephalography (EEG) that shed light on prognosis in acute severe brain injury have emerged in recent years. These approaches fall into two major categories: conventional characterization of EEG background and quantitative measurement of resting state or stimulus-induced EEG activity. Additionally, a small number of studies have associated the presence of electrophysiologic sleep features with prognosis in the acute phase of severe brain injury. In this review, we focus on approaches for the analysis of clinical EEG that have prognostic significance and that could be readily implemented with minimal additional equipment in clinical settings, such as intensive care and intensive rehabilitation units, for patients with acute disorders of consciousness.

Language Assessment in Patients with Disorders of Consciousness

The assessment of residual language abilities in patients with disorders of consciousness (DoC) after severe brain injury is particularly challenging due to their limited behavioral repertoire. Moreover, associated language impairment such as receptive aphasia may lead to an underestimation of actual consciousness levels. In this review, we examine past research on the assessment of residual language processing in DoC patients, and we discuss currently available tools for identifying language-specific abilities and their prognostic value. We first highlight the need for validated and sensitive bedside behavioral assessment tools for residual language abilities in DoC patients. As regards neuroimaging and electrophysiological methods, the tasks involving higher level linguistic commands appear to be the most informative about level of consciousness and have the best prognostic value. Neuroimaging methods should be combined with the most appropriate behavioral tools in multimodal assessment protocols to assess receptive language abilities in DoC patients in the most complete and sensitive manner.

Residual implicit and explicit language abilities in patients with disorders of consciousness: A systematic review

Language assessment in post-comatose patients is difficult due to their limited behavioral repertoire; yet associated language deficits might lead to an underestimation of consciousness levels in unresponsive wakefulness syndrome (UWS) or minimally conscious state (MCS; -/+) diagnoses. We present a systematic review of studies from 2002 assessing residual language abilities with neuroimaging, electrophysiological or behavioral measures in patients with severe brain injury. Eighty-five articles including a total of 2278 patients were assessed for quality. The median percentages of patients showing residual implicit language abilities (i.e., cortical responses to specific words/sentences) were 33 % for UWS, 50 % for MCS- and 78 % for MCS + patients, whereas explicit language abilities (i.e., command-following using brain-computer interfaces) were reported in 20 % of UWS, 33 % of MCS- and 50 % of MCS + patients. Cortical responses to verbal stimuli increased along with consciousness levels and the progressive recovery of consciousness after a coma was paralleled by the reappearance of both implicit and explicit language processing. This review highlights the importance of language assessment in patients with disorders of consciousness.

Seizures, Status Epilepticus, and Continuous EEG in the Intensive Care Unit

PURPOSE OF REVIEW

This article discusses the evolving definitions of seizures and status epilepticus in the critical care environment and the role of critical care EEG in both diagnosing seizure activity and serving as a predictive biomarker of clinical trajectory.

RECENT FINDINGS

Initial screening EEG has been validated as a tool to predict which patients are at risk of future seizures. However, accepted definitions of seizures and nonconvulsive status epilepticus encourage a treatment trial when the diagnosis on EEG is indeterminate because of periodic or rhythmic patterns or uncertain clinical correlation. Similarly, recent data have demonstrated the diagnostic utility of intracranial EEG in increasing the yield of seizure detection. EEG has additionally been validated as a diagnostic biomarker of covert consciousness, a predictive biomarker of cerebral ischemia and impending neurologic deterioration, and a prognostic biomarker of coma recovery and status epilepticus resolution. A recent randomized trial concluded that patients allocated to continuous EEG had no difference in mortality than those undergoing intermittent EEG but could not demonstrate whether this lack of difference was because of studying heterogeneous conditions, examining a monitoring tool rather than a therapeutic approach, or examining an outcome measure (mortality) perhaps more strongly associated with early withdrawal of life-sustaining therapy than to a sustained response to pharmacotherapy.

SUMMARY

Seizures and status epilepticus are events of synchronous hypermetabolic activity that are either discrete and intermittent or, alternatively, continuous. Seizures and status epilepticus represent the far end of a continuum of ictal-interictal patterns that include lateralized rhythmic delta activity and periodic discharges, which not only predict future seizures but may be further classified as status epilepticus on the basis of intracranial EEG monitoring or a diagnostic trial of antiseizure medication therapy. In particularly challenging cases, neuroimaging or multimodality neuromonitoring may be a useful adjunct documenting metabolic crisis. Specialized uses of EEG as a prognostic biomarker have emerged in traumatic brain injury for predicting language function and covert consciousness, cardiac arrest for predicting coma recovery, and subarachnoid hemorrhage for predicting neurologic deterioration due to delayed cerebral ischemia.

Update on neuroimaging in disorders of consciousness

PURPOSE OF REVIEW

Neuroimaging has acquired a prominent place in the assessment of disorders of consciousness (DoC). Rapidly evolving technologies combined with state-of-the-art data analyses open new horizons to probe brain activity, but selecting appropriate imaging modalities from the plethora of available techniques can be challenging for clinicians. This update reviews selected advances in neuroimaging that demonstrate clinical relevance and translational potential in the assessment of severely brain-injured patients with DoC.

RECENT FINDINGS

Magnetic resonance imaging and high-density electroencephalography provide measurements of brain connectivity between functional networks, assessments of language function, detection of covert consciousness, and prognostic markers of recovery. Positron emission tomography can identify patients with preserved brain metabolism despite clinical unresponsiveness and can measure glucose consumption rates in targeted brain regions. Transcranial magnetic stimulation and near-infrared spectroscopy are noninvasive and practical tools with promising clinical applications.

SUMMARY

Each neuroimaging technique conveys advantages and pitfalls to assess consciousness. We recommend a multimodal approach in which complementary techniques provide diagnostic and prognostic information about brain function. Patients demonstrating neuroimaging evidence of covert consciousness may benefit from early adapted rehabilitation. Translating methodological advances to clinical care will require the implementation of recently published international guidelines and the integration of neuroimaging techniques into patient-centered decision-making algorithms.

Treating Traumatic Brain Injuries with Electroceuticals: Implications for the Neuroanatomy of Consciousness

According to the Centers for Disease Control and Prevention (CDC), traumatic brain injury (TBI) is the leading cause of loss of consciousness, long-term disability, and death in children and young adults (age 1 to 44). Currently, there are no United States Food and Drug Administration (FDA) approved pharmacological treatments for post-TBI regeneration and recovery, particularly related to permanent disability and level of consciousness. In some cases, long-term disorders of consciousness (DoC) exist, including the vegetative state/unresponsive wakefulness syndrome (VS/UWS) characterized by the exhibition of reflexive behaviors only or a minimally conscious state (MCS) with few purposeful movements and reflexive behaviors. Electroceuticals, including non-invasive brain stimulation (NIBS), vagus nerve stimulation (VNS), and deep brain stimulation (DBS) have proved efficacious in some patients with TBI and DoC. In this review, we examine how electroceuticals have improved our understanding of the neuroanatomy of consciousness. However, the level of improvements in general arousal or basic bodily and visual pursuit that constitute clinically meaningful recovery on the Coma Recovery Scale-Revised (CRS-R) remain undefined. Nevertheless, these advancements demonstrate the importance of the vagal nerve, thalamus, reticular activating system, and cortico-striatal-thalamic-cortical loop in the process of consciousness recovery.

Proceedings of the First Curing Coma Campaign NIH Symposium: Challenging the Future of Research for Coma and Disorders of Consciousness

Coma and disorders of consciousness (DoC) are highly prevalent and constitute a burden for patients, families, and society worldwide. As part of the Curing Coma Campaign, the Neurocritical Care Society partnered with the National Institutes of Health to organize a symposium bringing together experts from all over the world to develop research targets for DoC. The conference was structured along six domains: (1) defining endotype/phenotypes, (2) biomarkers, (3) proof-of-concept clinical trials, (4) neuroprognostication, (5) long-term recovery, and (6) large datasets. This proceedings paper presents actionable research targets based on the presentations and discussions that occurred at the conference. We summarize the background, main research gaps, overall goals, the panel discussion of the approach, limitations and challenges, and deliverables that were identified.

Simplified frontal EEG in adults under veno-arterial extracorporeal membrane oxygenation

Background

EEG-based prognostication studies in intensive care units often rely on a standard 21-electrode montage (_stdEEG) requiring substantial human, technical, and financial resources. We here evaluate whether a simplified 4-frontal electrode montage (_4-frontEEG) can detect EEG patterns associated with poor outcomes in adult patients under veno-arterial extracorporeal membrane oxygenation (VA-ECMO).

Methods

We conducted a reanalysis of EEG data from a prospective cohort on 118 adult patients under VA-ECMO, in whom EEG was performed on admission to intensive care. EEG patterns of interest included background rhythm, discontinuity, reactivity, and the Synek’s score. They were all reassessed by an intensivist on a _4-frontEEG montage, whose analysis was then compared to an expert’s interpretation made on _stdEEG recordings. The main outcome measure was the degree of correlation between _4-frontEEG and _stdEEG montages to identify EEG patterns of interest. The performance of the Synek scores calculated on _4-frontEEG and _stdEEG montage to predict outcomes (i.e., 28-day mortality and 90-day Rankin score \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\ge {4}}$$\end{document}≥4) was investigated in a secondary exploratory analysis.

Results

The detection of EEG patterns using _4-frontEEG was statistically similar to that of _stdEEG for background rhythm (Spearman rank test, ρ = 0.66, p < 0.001), discontinuity (Cohen’s kappa, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\kappa$$\end{document}κ = 0.955), reactivity (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\kappa$$\end{document}κ = 0.739) and the Synek’s score (ρ = 0.794, p < 0.001). Using the Synek classification, we found similar performances between _4-frontEEG and _stdEEG montages in predicting 28-day mortality (AUC _4-frontEEG 0.71, AUC _stdEEG 0.68) and for 90-day poor neurologic outcome (AUC _4-frontEEG 0.71, AUC _stdEEG 0.66). An exploratory analysis confirmed that the Synek scores determined by 4 or 21 electrodes were independently associated with 28-day mortality and poor 90-day functional outcome.

Conclusion

In adult patients under VA-ECMO, a simplified 4-frontal electrode EEG montage interpreted by an intensivist, detected common EEG patterns associated with poor outcomes, with a performance similar to that of a standard EEG montage interpreted by expert neurophysiologists. This simplified montage could be implemented as part of a multimodal evaluation for bedside prognostication.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13613-021-00854-0.

Recovery from disorders of consciousness: mechanisms, prognosis and emerging therapies

Substantial progress has been made over the past two decades in detecting, predicting and promoting recovery of consciousness in patients with disorders of consciousness (DoC) caused by severe brain injuries. Advanced neuroimaging and electrophysiological techniques have revealed new insights into the biological mechanisms underlying recovery of consciousness and have enabled the identification of preserved brain networks in patients who seem unresponsive, thus raising hope for more accurate diagnosis and prognosis. Emerging evidence suggests that covert consciousness, or cognitive motor dissociation (CMD), is present in up to 15-20% of patients with DoC and that detection of CMD in the intensive care unit can predict functional recovery at 1 year post injury. Although fundamental questions remain about which patients with DoC have the potential for recovery, novel pharmacological and electrophysiological therapies have shown the potential to reactivate injured neural networks and promote re-emergence of consciousness. In this Review, we focus on mechanisms of recovery from DoC in the acute and subacute-to-chronic stages, and we discuss recent progress in detecting and predicting recovery of consciousness. We also describe the developments in pharmacological and electrophysiological therapies that are creating new opportunities to improve the lives of patients with DoC.

Covert Speech Comprehension Predicts Recovery From Acute Unresponsive States

OBJECTIVE

Patients with traumatic brain injury who fail to obey commands after sedation-washout pose one of the most significant challenges for neurological prognostication. Reducing prognostic uncertainty will lead to more appropriate care decisions and ensure provision of limited rehabilitation resources to those most likely to benefit. Bedside markers of covert residual cognition, including speech comprehension, may reduce this uncertainty.

METHODS

We recruited 28 patients with acute traumatic brain injury who were 2 to 7 days sedation-free and failed to obey commands. Patients heard streams of isochronous monosyllabic words that built meaningful phrases and sentences while their brain activity via electroencephalography (EEG) was recorded. In healthy individuals, EEG activity only synchronizes with the rhythm of phrases and sentences when listeners consciously comprehend the speech. This approach therefore provides a measure of residual speech comprehension in unresponsive patients.

RESULTS

Seventeen and 16 patients were available for assessment with the Glasgow Outcome Scale Extended (GOSE) at 3 months and 6 months, respectively. Outcome significantly correlated with the strength of patients' acute cortical tracking of phrases and sentences (r > 0.6, p < 0.007), quantified by inter-trial phase coherence. Linear regressions revealed that the strength of this comprehension response (beta = 0.603, p = 0.006) significantly improved the accuracy of prognoses relative to clinical characteristics alone (eg, Glasgow Coma Scale [GCS], computed tomography [CT] grade).

INTERPRETATION

A simple, passive, auditory EEG protocol improves prognostic accuracy in a critical period of clinical decision making. Unlike other approaches to probing covert cognition for prognostication, this approach is entirely passive and therefore less susceptible to cognitive deficits, increasing the number of patients who may benefit. ANN NEUROL 2021;89:646-656.