Sleep in the unresponsive wakefulness syndrome and minimally conscious state

Slow-wave brain connectivity predicts executive functioning and group belonging in socially vulnerable individuals

Important efforts have been made to describe the neural and cognitive features of healthy and clinical populations. However, the neural and cognitive features of socially vulnerable individuals remain largely unexplored, despite their proneness to developing neurocognitive disorders. Socially vulnerable individuals can be characterised as socially deprived, having a low socioeconomic status, suffering from chronic social stress, and exhibiting poor social adaptation. While it is known that such individuals are likely to perform worse than their peers on executive function tasks, studies on healthy but socially vulnerable groups are lacking. In the current study, we explore whether neural power and connectivity signatures can characterise executive function performance in healthy but socially vulnerable individuals, shedding light on the impairing effects that chronic stress and social disadvantages have on cognition. We measured resting-state electroencephalography and executive functioning in 38 socially vulnerable participants and 38 matched control participants. Our findings indicate that while neural power was uninformative, lower delta and theta phase synchrony are associated with worse executive function performance in all participants, whereas delta phase synchrony is higher in the socially vulnerable group compared to the control group. Finally, we found that delta phase synchrony and years of schooling are the best predictors for belonging to the socially vulnerable group. Overall, these findings suggest that exposure to chronic stress due to socioeconomic factors and a lack of education are associated with changes in slow-wave neural connectivity and executive functioning.

Strategy and Philosophy for Treating Pain and Sleep in Disorders of Consciousness

Despite the evolving practice of brain injury medicine, consciousness remains enigmatic. Most patients with disorders of consciousness have disordered sleep and return of normal sleep architecture is essential to the emergence of consciousness and the healing brain. In this article we lay a framework for understanding the emergence of consciousness in brain-injured patients. We then explore ways to use that framework to evaluate and tailor treatment of sleep and pain in patients with disorders of consciousness. Although more research is needed to empower better treatment in the future, validated tools now exist for evaluation of emergent consciousness, pain, and sleep.

The visual stimulation in disorders of consciousness

Severe brain damage usually leads to disorders of consciousness (DOC), which include coma, unresponsive wakefulness syndrome (UWS) and a minimally conscious state (MCS). Visual stimulation is widely used, especially in the diagnosis and treatment and treatment of DOC. Researchers have indicated that tests based on visual stimulation including visual pursuit, when used in conjunction with the Coma Recovery Scale-Revised, are able to differentiate between UWS from an MCS. Recently, targeting patients' circadian rhythms has been proposed to be a possible treatment target for DOC. Indeed, light therapy has been applied in some other fields, including treating seasonal affective disorder, sleep problems, and Parkinson's disease. However, at present, although visual stimulation and light therapy are frequently used in DOC, there is still no international unified standard. Therefore, we recommend the development of an international consensus in regard to the definitions, operational criteria and assessment procedures of visual stimulation and light therapy. This review combines visual stimulation, circadian rhythm recovery, and light therapy in DOC patients and presents the mechanisms and current advances in applications related to light therapy and visual stimulation in an attempt to provide additional ideas for future research and treatment of DOC.

Twenty-four-hour rhythmicities in disorders of consciousness are associated with a favourable outcome

Fluctuations of consciousness and their rhythmicities have been rarely studied in patients with a disorder of consciousness after acute brain injuries. 24-h assessment of brain (EEG), behaviour (eye-opening), and circadian (clock-controlled hormones secretion from urine) functions was performed in acute brain-injured patients. The distribution, long-term predictability, and rhythmicity (circadian/ultradian) of various EEG features were compared with the initial clinical status, the functional outcome, and the circadian rhythmicities of behaviour and clock-controlled hormones. Here we show that more physiological and favourable patterns of fluctuations are associated with a higher 24 h predictability and sharp up-and-down shape of EEG switches, reminiscent of the Flip-Flop model of sleep. Multimodal rhythmic analysis shows that patients with simultaneous circadian rhythmicity for brain, behaviour, and hormones had a favourable outcome. Finally, both re-emerging EEG fluctuations and homogeneous 24-h cycles for EEG, eye-opening, and hormones appeared as surrogates for preserved functionality in brainstem and basal forebrain, which are key prognostic factors for later improvement. While the recovery of consciousness has previously been related to a high short-term complexity, we suggest in this exploratory study the importance of the high predictability of the 24 h long-term generation of brain rhythms and highlight the importance of circadian body-brain rhythms in awakening.

Electroencephalography as a Biomarker of Prognosis in Acute Brain Injury

Electroencephalography (EEG) is a noninvasive tool that allows the monitoring of cerebral brain function in critically ill patients, aiding with diagnosis, management, and prognostication. Specific EEG features have shown utility in the prediction of outcomes in critically ill patients with status epilepticus, acute brain injury (ischemic stroke, intracranial hemorrhage, subarachnoid hemorrhage, and traumatic brain injury), anoxic brain injury, and toxic-metabolic encephalopathy. Studies have also found an association between particular EEG patterns and long-term functional and cognitive outcomes as well as prediction of recovery of consciousness following acute brain injury. This review summarizes these findings and demonstrates the value of utilizing EEG findings in the determination of prognosis.

A predictive model for consciousness recovery of comatose patients after acute brain injury

Background

Predicting the consciousness recovery for comatose patients with acute brain injury is an important issue. Although some efforts have been made in the study of prognostic assessment methods, it is still unclear which factors can be used to establish model to directly predict the probability of consciousness recovery.

Objectives

We aimed to establish a model using clinical and neuroelectrophysiological indicators to predict consciousness recovery of comatose patients after acute brain injury.

Methods

The clinical data of patients with acute brain injury admitted to the neurosurgical intensive care unit of Xiangya Hospital of Central South University from May 2019 to May 2022, who underwent electroencephalogram (EEG) and auditory mismatch negativity (MMN) examinations within 28 days after coma onset, were collected. The prognosis was assessed by Glasgow Outcome Scale (GOS) at 3 months after coma onset. The least absolute shrinkage and selection operator (LASSO) regression analysis was applied to select the most relevant predictors. We combined Glasgow coma scale (GCS), EEG, and absolute amplitude of MMN at Fz to develop a predictive model using binary logistic regression and then presented by a nomogram. The predictive efficiency of the model was evaluated with AUC and verified by calibration curve. The decision curve analysis (DCA) was used to evaluate the clinical utility of the prediction model.

Results

A total of 116 patients were enrolled for analysis, of which 60 had favorable prognosis (GOS ≥ 3). Five predictors, including GCS (OR = 13.400, P < 0.001), absolute amplitude of MMN at Fz site (FzMMNA, OR = 1.855, P = 0.038), EEG background activity (OR = 4.309, P = 0.023), EEG reactivity (OR = 4.154, P = 0.030), and sleep spindles (OR = 4.316, P = 0.031), were selected in the model by LASSO and binary logistic regression analysis. This model showed favorable predictive power, with an AUC of 0.939 (95% CI: 0.899-0.979), and calibration. The threshold probability of net benefit was between 5% and 92% in the DCA.

Conclusion

This predictive model for consciousness recovery in patients with acute brain injury is based on a nomogram incorporating GCS, EEG background activity, EEG reactivity, sleep spindles, and FzMMNA, which can be conveniently obtained during hospitalization. It provides a basis for care givers to make subsequent medical decisions.

Sleep in Disorders of Consciousness: A Brief Overview on a Still under Investigated Issue

Consciousness is a multifaceted concept, involving both wakefulness, i.e., a condition of being alert that is regulated by the brainstem, and awareness, a subjective experience of any thoughts or perception or emotion. Recently, the European Academy of Neurology has published international guidelines for a better diagnosis of coma and other disorders of consciousness (DOC) through the investigation of sleep patterns, such as slow-wave and REM, and the study of the EEG using machine learning methods and artificial intelligence. The management of sleep disorders in DOC patients is an increasingly hot topic and deserves careful diagnosis, to allow for the most accurate prognosis and the best medical treatment possible. The aim of this review was to investigate the anatomo-physiological basis of the sleep/wake cycle, as well as the main sleep patterns and sleep disorders in patients with DOC. We found that the sleep characteristics in DOC patients are still controversial. DOC patients often present a theta/delta pattern, while epileptiform activity, as well as other sleep elements, have been reported as correlating with outcomes in patients with coma and DOC. The absence of spindles, as well as REM and K-complexes of NREM sleep, have been used as poor predictors for early awakening in DOC patients, especially in UWS patients. Therefore, sleep could be considered a marker of DOC recovery, and effective treatments for sleep disorders may either indirectly or directly favor recovery of consciousness.

A prediction model of clinical outcomes in prolonged disorders of consciousness: A prospective cohort study

Objective

This study aimed to establish and validate a prediction model for clinical outcomes in patients with prolonged disorders of consciousness (pDOC).

Methods

A total of 170 patients with pDOC enrolled in our rehabilitation unit were included and divided into training (n = 119) and validation sets (n = 51). Independent predictors for improved clinical outcomes were identified by univariate and multivariate logistic regression analyses, and a nomogram model was established. The nomogram performance was quantified using receiver operating curve (ROC) and calibration curves in the training and validated sets. A decision curve analysis (DCA) was performed to evaluate the clinical usefulness of this nomogram model.

Results

Univariate and multivariate logistic regression analyses indicated that age, diagnosis at entry, serum albumin (g/L), and pupillary reflex were the independent prognostic factors that were used to construct the nomogram. The area under the curve in the training and validation sets was 0.845 and 0.801, respectively. This nomogram model showed good calibration with good consistency between the actual and predicted probabilities of improved outcomes. The DCA demonstrated a higher net benefit in clinical decision-making compared to treating all or none.

Conclusion

Several feasible, cost-effective prognostic variables that are widely available in hospitals can provide an efficient and accurate prediction model for improved clinical outcomes and support clinicians to offer suitable clinical care and decision-making to patients with pDOC and their family members.

Understanding, detecting, and stimulating consciousness recovery in the ICU

Coma is a medical and socioeconomic emergency. Although underfunded, research on coma and disorders of consciousness has made impressive progress. Lesion-network-mapping studies have delineated the precise brainstem regions that consistently produce coma when damaged. Functional neuroimaging has revealed how mechanisms like "communication through coherence" and "inhibition by gating" work in synergy to enable cortico-cortical processing and how this information transfer is disrupted in brain injury. On the cellular level, break-down of intracellular communication between the layer 5 pyramidal cell soma and the apical dendritic part impairs dendritic information integration, with up-stream effects on microcircuits in local neuronal populations and on large-scale fronto-parietal networks, which correlates with loss of consciousness. A breakthrough in clinical concepts occurred when fMRI, and later EEG, studies revealed that 15% of clinically unresponsive patients in acute and chronic settings are in fact awake and aware, as shown by their command following abilities revealed by brain activation during motor and locomotion imagery tasks. This condition is now termed "cognitive motor dissociation." Furthermore, epidemiological data on coma were literally non-existent until recently because of difficulties related to case ascertainment with traditional methods, but crowdsourcing of family observations enabled the first estimates of how frequent coma is in the general population (pooled annual incidence of 201 coma cases per 100,000 population in the UK and the USA). Diagnostic guidelines on coma and disorders of consciousness by the American Academy of Neurology and the European Academy of Neurology provide ambitious clinical frameworks to accommodate these achievements. As for therapy, a broad range of medical and non-medical treatment options is now being tested in increasingly larger trials; in particular, amantadine and transcranial direct current stimulation appear promising in this regard. Major international initiatives like the Curing Coma Campaign aim to raise awareness for coma and disorders of consciousness in the public, with the ultimate goal to make more brain-injured patients recover consciousness after a coma. To highlight all these accomplishments, this paper provides a comprehensive overview of recent progress and future challenges related to understanding, detecting, and stimulating consciousness recovery in the ICU.

Assessment of electroencephalography and event-related potentials in unresponsive patients with brain injury

OBJECTIVE

To investigate the predictors of clinical outcomes in unresponsive patients with acquired brain injuries.

METHODS

Patients with coma or disorders of consciousness were enrolled from August 2019 to March 2021. A retrospective analysis of demographics, etiology, clinical score, diagnosis, electroencephalography (EEG), and event-related potential (ERP) data from 1 week to 2 months after coma onset was conducted. Findings were assessed for predicting favorable outcomes at 6 months post-coma, and functional outcomes were determined using the Glasgow Outcome Scale-Extended (GOS-E).

RESULTS

Of 68 patients, 22 patients had a good neurological outcome at 6 months, while 11 died. Univariate analysis showed that motor response (Motor-R; p < 0.001), EEG pattern (p = 0.015), sleep spindles (p = 0.018), EEG reactivity (EEG-R; p < 0.001), mismatch negativity (MMN) amplitude at electrode Fz (FzMMNA; p = 0.001), P3a latency (p = 0.044), and P3a amplitude at electrode Cz (CzP3aA; p < 0.001) were significantly correlated with patient prognosis. Multivariable logistic regression analysis showed that FzMMNA, CzP3aA, EEG-R, and Motor-R were significant independent predictors of a favorable outcome. The sensitivity and specificity of FzMMNA (dichotomized at 1.16 μV) were 86.4% and 58.5%, and of CzP3aA (cut-off value 2.76 μV) were 90.9% and 70.7%, respectively. ERP amplitude (ERP-A), a combination of FzMMNA and CzP3aA, improved prediction accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.884. A model incorporating Motor-R, EEG-R, and ERP-A yielded an outstanding predictive performance (AUC=0.921) for a favorable outcome.

CONCLUSION

ERP-A and the prognostic model resulted in the efficient prediction of a favorable outcome in unresponsive patients.

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.

Sleep Treatments in Disorders of Consciousness: A Systematic Review

Sleep disorders are among the main comorbidities in patients with a Disorder of Consciousness (DOC). Given the key role of sleep in neural and cognitive functioning, detecting and treating sleep disorders in DOCs might be an effective therapeutic strategy to boost consciousness recovery and levels of awareness. To date, no systematic reviews have been conducted that explore the effect of sleep treatments in DOCs; thus, we systematically reviewed the existing studies on both pharmacological and non-pharmacological treatments for sleep disorders in DOCs. Among 2267 assessed articles, only 7 were included in the systematic review. The studies focused on two sleep disorder categories (sleep-related breathing disorders and circadian rhythm dysregulation) treated with both pharmacological (Modafinil and Intrathecal Baclofen) and non-pharmacological (positive airway pressure, bright light stimulation, and central thalamic deep brain stimulation) interventions. Although the limited number of studies and their heterogeneity do not allow generalized conclusions, all the studies highlighted the effectiveness of treatments on both sleep disorders and levels of awareness. For this reason, clinical and diagnostic evaluations able to detect sleep disorders in DOC patients should be adopted in the clinical routine for the purpose of intervening promptly with the most appropriate treatment.

EEG spindles integrity in critical care adults. Analysis of a randomized trial

Objectives

Occurrence of EEG spindles has been recently associated with favorable outcome in ICU patients. Available data mostly rely on relatively small patients' samples, particular etiologies, and limited variables ascertainment. We aimed to expand previous findings on a larger dataset, to identify clinical and EEG patterns correlated with spindle occurrence, and explore its prognostic implications.

Methods

Retrospective observational study of prospectively collected data from a randomized trial (CERTA, NCT03129438) assessing the relationship of continuous (cEEG) versus repeated routine EEG (rEEG) with outcome in adults with acute consciousness impairment. Spindles were prospectively assessed visually as 12‐16Hz activity on fronto‐central midline regions, at any time during EEG interventions. Uni‐ and multivariable analyses explored correlations between spindles occurrence, clinical and EEG variables, and outcome (modified Rankin Scale, mRS; mortality) at 6 months.

Results

Among the analyzed 364 patients, spindles were independently associated with EEG background reactivity (OR 13.2, 95% CI: 3.11–56.26), and cEEG recording (OR 4.35, 95% CI: 2.5 – 7.69). In the cEEG subgroup (n=182), 33.5% had spindles. They had better FOUR scores (p=0.004), fewer seizures or status epilepticus (p=0.02), and lower mRS (p=0.02). Mortality was reduced (p=0.002), and independently inversely associated with spindle occurrence (OR 0.50, CI 95% 0.25–0.99) and increased EEG background continuity (OR 0.16, 95% CI: 0.07 – 0.41).

Conclusions

Besides confirming that spindle activity occurs in up to one third of acutely ill patients and is associated with better outcome, this study shows that cEEG has a higher yield than rEEG in identifying them. Furthermore, it unravels associations with several clinical and EEG features in this clinical setting.

A Systematic Review of Sleep in Patients with Disorders of Consciousness: From Diagnosis to Prognosis

With the development of intensive care technology, the number of patients who survive acute severe brain injury has increased significantly. At present, it is difficult to diagnose the patients with disorders of consciousness (DOCs) because motor responses in these patients may be very limited and inconsistent. Electrophysiological criteria, such as event-related potentials or motor imagery, have also been studied to establish a diagnosis and prognosis based on command-following or active paradigms. However, the use of such task-based techniques in DOC patients is methodologically complex and requires careful analysis and interpretation. The present paper focuses on the analysis of sleep patterns for the evaluation of DOC and its relationships with diagnosis and prognosis outcomes. We discuss the concepts of sleep patterns in patients suffering from DOC, identification of this challenging population, and the prognostic value of sleep. The available literature on individuals in an unresponsive wakefulness syndrome (UWS) or minimally conscious state (MCS) following traumatic or nontraumatic severe brain injury is reviewed. We can distinguish patients with different levels of consciousness by studying sleep patients with DOC. Most MCS patients have sleep and wake alternations, sleep spindles and rapid eye movement (REM) sleep, while UWS patients have few EEG changes. A large number of sleep spindles and organized sleep-wake patterns predict better clinical outcomes. It is expected that this review will promote our understanding of sleep EEG in DOC.

Sleep in disorders of consciousness: diagnostic, prognostic, and therapeutic considerations

PURPOSE OF REVIEW

Sleep is important in the evaluation of patients with disorders of consciousness (DOC). However, it remains unclear whether reconstitution of sleep could enable consciousness or vice versa. Here we synthesize recent evidence on natural recovery of sleep in DOC, and sleep-promoting therapeutic interventions for recovery of consciousness.

RECENT FINDINGS

In subacute DOC, physiological sleep--wake cycles and complex sleep patterns are related to better outcomes. Moreover, structured rapid-eye-movement (REM), non-REM (NREM) stages, and presence of sleep spindles correlate with full or partial recovery. In chronic DOC, sleep organization may reflect both integrity of consciousness-supporting brain networks and engagement of those networks during wakefulness. Therapeutic strategies have integrated improvement of sleep and sleep--wake cycles in DOC patients; use of bright light stimulation or drugs enhancing sleep and/or vigilance, treatment of sleep apneas, and neuromodulatory stimulations are promising tools to promote healthy sleep architecture and wakeful recovery.

SUMMARY

Sleep features and sleep--wake cycles are important prognostic markers in subacute DOC and can provide insight into covert recovery in chronic DOC. Although large-scale studies are needed, preliminary studies in limited patients suggest that therapeutic options restoring sleep and/or sleep--wake cycles may improve cognitive function and outcomes in DOC.

The Timecourse of Electrophysiological Brain-Heart Interaction in DoC Patients

Disorders of Consciousness (DOC) are a spectrum of pathologies affecting one's ability to interact with the external world. Two possible conditions of patients with DOC are Unresponsive Wakefulness Syndrome/Vegetative State (UWS/VS) and Minimally Conscious State (MCS). Analysis of spontaneous EEG activity and the Heart Rate Variability (HRV) are effective techniques in exploring and evaluating patients with DOC. This study aims to observe fluctuations in EEG and HRV parameters in the morning/afternoon resting-state recording. The study enrolled 13 voluntary Healthy Control (HC) subjects and 12 DOC patients (7 MCS, 5 UWS/VS). EEG and EKG were recorded. PSDalpha, PSDtheta powerband, alpha-blocking, alpha/theta of the EEG, Complexity Index (CI) and SDNN of EKG were analyzed. Higher values of PSDalpha, alpha-blocking, alpha/theta and CI values and lower values of PSD theta characterized HC individuals in the morning with respect to DOC patients. In the afternoon, we detected a significant difference between groups in the CI, PSDalpha, PSDtheta, alpha/theta and SDNN, with lower PSDtheta value for HC. CRS-R scores showed a strong correlation with recorded parameters mainly during evaluations in the morning. Our finding put in evidence the importance of the assessment, as the stimulation of DOC patients in research for behavioural response, in the morning.

Narrative Review: Quantitative EEG in Disorders of Consciousness

In this narrative review, we focus on the role of quantitative EEG technology in the diagnosis and prognosis of patients with unresponsive wakefulness syndrome and minimally conscious state. This paper is divided into two main parts, i.e., diagnosis and prognosis, each consisting of three subsections, namely, (i) resting-state EEG, including spectral power, functional connectivity, dynamic functional connectivity, graph theory, microstates and nonlinear measurements, (ii) sleep patterns, including rapid eye movement (REM) sleep, slow-wave sleep and sleep spindles and (iii) evoked potentials, including the P300, mismatch negativity, the N100, the N400 late positive component and others. Finally, we summarize our findings and conclude that QEEG is a useful tool when it comes to defining the diagnosis and prognosis of DOC patients.

Sleep in disorders of consciousness: behavioral and polysomnographic recording

BACKGROUND

Sleep-wakefulness cycles are an essential diagnostic criterion for disorders of consciousness (DOC), differentiating prolonged DOC from coma. Specific sleep features, like the presence of sleep spindles, are an important marker for the prognosis of recovery from DOC. Based on increasing evidence for a link between sleep and neuronal plasticity, understanding sleep in DOC might facilitate the development of novel methods for rehabilitation. Yet, well-controlled studies of sleep in DOC are lacking. Here, we aimed to quantify, on a reliable evaluation basis, the distribution of behavioral and neurophysiological sleep patterns in DOC over a 24-h period while controlling for environmental factors (by recruiting a group of conscious tetraplegic patients who resided in the same hospital).

METHODS

We evaluated the distribution of sleep and wakefulness by means of polysomnography (EEG, EOG, EMG) and video recordings in 32 DOC patients (16 unresponsive wakefulness syndrome [UWS], 16 minimally conscious state [MCS]), and 10 clinical control patients with severe tetraplegia. Three independent raters scored the patients' polysomnographic recordings.

RESULTS

All but one patient (UWS) showed behavioral and electrophysiological signs of sleep. Control and MCS patients spent significantly more time in sleep during the night than during daytime, a pattern that was not evident in UWS. DOC patients (particularly UWS) exhibited less REM sleep than control patients. Forty-four percent of UWS patients and 12% of MCS patients did not have any REM sleep, while all control patients (100%) showed signs of all sleep stages and sleep spindles. Furthermore, no sleep spindles were found in 62% of UWS patients and 21% of MCS patients. In the remaining DOC patients who had spindles, their number and amplitude were significantly lower than in controls.

CONCLUSIONS

The distribution of sleep signs in DOC over 24 h differs significantly from the normal sleep-wakefulness pattern. These abnormalities of sleep in DOC are independent of external factors such as severe immobility and hospital environment.

Managing disorders of consciousness: the role of electroencephalography

Disorders of consciousness (DOC) are an important but still underexplored entity in neurology. Novel electroencephalography (EEG) measures are currently being employed for improving diagnostic classification, estimating prognosis and supporting medicolegal decision-making in DOC patients. However, complex recording protocols, a confusing variety of EEG measures, and complicated analysis algorithms create roadblocks against broad application. We conducted a systematic review based on English-language studies in PubMed, Medline and Web of Science databases. The review structures the available knowledge based on EEG measures and analysis principles, and aims at promoting its translation into clinical management of DOC patients.

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.

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.

Prognostic roles of sleep electroencephalography pattern and circadian rhythm biomarkers in the recovery of consciousness in patients with coma: a prospective cohort study

OBJECTIVE

To investigate the potential prognostic value of sleep electroencephalography (EEG) pattern and serum circadian rhythm biomarkers in the recovery of consciousness in patients at the acute stage of coma.

METHODS

A prospective observational study which included 75 patients with coma was conducted. Twenty-four-hour continuous polysomnography (PSG) was performed to determine the sleep EEG pattern according to the modified Valente's Grade (mVG) that we proposed. Serum levels of melatonin and orexin-A at four consecutive time points during the PSG were examined. Patients were then followed for one month to determine their level of consciousness. Multivariate logistic regression analysis was performed to examine associations between demographics, aetiology, baseline clinical features (pupillary and corneal reflex, and neuron-specific enolase [NSE]), clinical scores (Glasgow Coma Scale-Motor Response [GCS-M], Full Outline of Unresponsiveness [FOUR] scale, Acute Physiology and Chronic Health Evaluation II [APACHE II] scale), mVG, serum circadian biomarkers, and recovery of consciousness within one month.

RESULTS

Within one month of enrolment, 34 patients regained consciousness and 36 patients remained non-conscious. Spearman rank correlation revealed a significant association between mVG and state of consciousness after one month. Significant variation in serum melatonin or orexin-A was not detected in either the conscious or non-conscious groups. Hypoxic aetiology, APACHE II, and mVG were independently associated with recovery of consciousness within one month.

CONCLUSION

Sleep EEG structure, hypoxic aetiology, and APACHE II can independently predict recovery of consciousness in patients with acute coma. Taken together, we encourage neurologists to use sleep elements to assess patients with acute coma.

Toward Improving Diagnostic Strategies in Chronic Disorders of Consciousness: An Overview on the (Re-)Emergent Role of Neurophysiology

The differential diagnosis of patients with Disorder of Consciousness (DoC), in particular in the chronic phase, is significantly difficult. Actually, about 40% of patients with unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS) are misdiagnosed. Indeed, only advanced paraclinical approaches, including advanced EEG analyses, can allow achieving a more reliable diagnosis, that is, discovering residual traces of awareness in patients with UWS (namely, functional Locked-In Syndrome (fLIS)). These approaches aim at capturing the residual brain network models, at rest or that may be activated in response to relevant stimuli, which may be appropriate for awareness to emerge (despite their insufficiency to generate purposeful motor behaviors). For this, different brain network models have been studied in patients with DoC by using sensory stimuli (i.e., passive tasks), probing response to commands (i.e., active tasks), and during resting-state. Since it can be difficult for patients with DoC to perform even simple active tasks, this scoping review aims at summarizing the current, innovative neurophysiological examination methods in resting state/passive modality to differentiate and prognosticate patients with DoC. We conclude that the electrophysiologically-based diagnostic procedures represent an important resource for diagnosis, prognosis, and, therefore, management of patients with DoC, using advance passive and resting state paradigm analyses for the patients who lie in the “greyzones” between MCS, UWS, and fLIS.

Genetic algorithms for feature selection when classifying severe chronic disorders of consciousness

The diagnosis and prognosis of patients with severe chronic disorders of consciousness are still challenging issues and a high rate of misdiagnosis is evident. Hence, new tools are needed for an accurate diagnosis, which will also have an impact on the prognosis. In recent years, functional Magnetic Resonance Imaging (fMRI) has been gaining more and more importance when diagnosing this patient group. Especially resting state scans, i.e., an examination when the patient does not perform any task in particular, seems to be promising for these patient groups. After preprocessing the resting state fMRI data with a standard pipeline, we extracted the correlation matrices of 132 regions of interest. The aim was to find the regions of interest which contributed most to the distinction between the different patient groups and healthy controls. We performed feature selection using a genetic algorithm and a support vector machine. Moreover, we show by using only those regions of interest for classification that are most often selected by our algorithm, we get a much better performance of the classifier.

Parametric Description of EEG Profiles for Assessment of Sleep Architecture in Disorders of Consciousness

We propose a fully parametric approach to the assessment of sleep architecture, based upon the classical electroencephalographic criteria, applicable also to the recordings of patients with disorders of consciousness (DOC). Sleep spindles and slow waves are automatically detected from the matching pursuit decomposition of overnight EEG recordings. Their evolution can be presented in the form of EEG profiles, yielding a continuous description of sleep architecture, compatible with the classical criteria used in sleep staging. We propose assessment of these EEG profiles by five parameters, which can be combined by a linear classifier, assessing the quality of sleep architecture. Proposed methodology is evaluated on 59 overnight EEG recordings from 19 patients from a hospital for children with severe brain damage, in relation to their behavioral diagnosis according to the Coma Recovery Scale-Revised. Presented results indicate robustness of the proposed approach, which may serve as a valuable aid in diagnosis of DOC patients. Complete software environment for computing and presentation of EEG profiles is freely available from http://svarog.pl .

Sleep in patients with disorders of consciousness characterized by means of machine learning

Sleep has been proposed to indicate preserved residual brain functioning in patients suffering from disorders of consciousness (DOC) after awakening from coma. However, a reliable characterization of sleep patterns in this clinical population continues to be challenging given severely altered brain oscillations, frequent and extended artifacts in clinical recordings and the absence of established staging criteria. In the present study, we try to address these issues and investigate the usefulness of a multivariate machine learning technique based on permutation entropy, a complexity measure. Specifically, we used long-term polysomnography (PSG), along with video recordings in day and night periods in a sample of 23 DOC; 12 patients were diagnosed as Unresponsive Wakefulness Syndrome (UWS) and 11 were diagnosed as Minimally Conscious State (MCS). Eight hour PSG recordings of healthy sleepers (N = 26) were additionally used for training and setting parameters of supervised and unsupervised model, respectively. In DOC, the supervised classification (wake, N1, N2, N3 or REM) was validated using simultaneous videos which identified periods with prolonged eye opening or eye closure.The supervised classification revealed that out of the 23 subjects, 11 patients (5 MCS and 6 UWS) yielded highly accurate classification with an average F1-score of 0.87 representing high overlap between the classifier predicting sleep (i.e. one of the 4 sleep stages) and closed eyes. Furthermore, the unsupervised approach revealed a more complex pattern of sleep-wake stages during the night period in the MCS group, as evidenced by the presence of several distinct clusters. In contrast, in UWS patients no such clustering was found. Altogether, we present a novel data-driven method, based on machine learning that can be used to gain new and unambiguous insights into sleep organization and residual brain functioning of patients with DOC.

Sleep patterns associated with the severity of impairment in a large cohort of patients with chronic disorders of consciousness

OBJECTIVE

We assessed sleep patterns in 85 patients with chronic disorders of consciousness (DOC) in order to reveal any relationship with the degree of the impairment.

METHODS

Nocturnal polysomnography (PSG) was scored in patients classified as being in an unresponsive wakefulness syndrome/vegetative state (UWS/VS; n = 49) or a minimally conscious state (MCS; n = 36) in accordance with the rules of the American Academy of Sleep Medicine. The PSG data in the two diagnostic groups were compared, and the PSG parameters associated with the degree of impairment were analysed.

RESULTS

In 19/49 UWS/VS patients, signal attenuation was the only EEG pattern detectable in sleep. Non-REM 2 (NREM2) and slow-wave sleep (SWS) (but not REM) stages were more frequent in the MCS patients. The presence of SWS was the most appropriate factor for classifying patients as UWS/VS or MCS, and the duration of SWS was the main factor that significantly correlated with revised Coma Recovery Scale scores.

CONCLUSION

The presence of NREM sleep (namely SWS) reflects better preservation of the circuitry and structures needed to sustain this stage of sleep in DOC patients.

SIGNIFICANCE

PSG is a simple and effective technique, and sleep patterns may reflect the degree of impairment in chronic DOC patients.

Disordered Consciousness or Disordered Wakefulness? The Importance of Prolonged Polysomnography for the Diagnosis, Drug Therapy, and Rehabilitation of an Unresponsive Patient With Brain Injury

ABSTRACT

Disorders of consciousness may follow brain injury, due to impairments of wakefulness and/or awareness. Polysomnography can identify elements that may be ascribed to impairments of specific neuroanatomical areas. Recognizing which impairments affect each patient is crucial for diagnosis, prognosis, and to select an appropriate therapy. We present a pediatric case of insufficient wakefulness in a patient with severe disability following a pilocytic astrocytoma. Polysomnography was crucial for diagnosis, as it detected a well-structured pattern with daytime sleep initiations in the REM sleep phase. Treatment with modafinil was successful, as confirmed by polysomnography, leading to partial recovery of the patient's consciousness and communication ability. We suggest that polysomnography is a useful diagnostic tool to direct the pharmacotherapy and rehabilitation of states of reduced consciousness.

Functional Networks in Disorders of Consciousness

Severe brain injury may cause disruption of neural networks that sustain arousal and awareness, the two essential components of consciousness. Despite the potentially devastating immediate and long-term consequences, disorders of consciousness (DoC) are poorly understood in terms of their underlying neurobiology, the relationship between pathophysiology and recovery, and the predictors of treatment efficacy. Recent advances in neuroimaging techniques have enabled the study of network connectivity, providing great potential to improve the clinical care of patients with DoC. Initial discoveries in this field were made using positron emission tomography (PET). More recently, functional magnetic resonance (fMRI) techniques have added to our understanding of functional network dynamics in this population. Both methods have shown that whether at rest or performing a goal-oriented task, functional networks essential for processing intrinsic thoughts and extrinsic stimuli are disrupted in patients with DoC compared with healthy subjects. Atypical connectivity has been well established in the default mode network as well as in other cortical and subcortical networks that may be required for consciousness. Moreover, the degree of altered connectivity may be related to the severity of impaired consciousness, and recovery of consciousness has been shown to be associated with restoration of connectivity. In this review, we discuss PET and fMRI studies of functional and effective connectivity in patients with DoC and suggest how this field can move toward clinical application of functional network mapping in the future.

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

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

Sleep-Wake Disturbances After Traumatic Brain Injury: Synthesis of Human and Animal Studies

Sleep-wake disturbances following traumatic brain injury (TBI) are increasingly recognized as a serious consequence following injury and as a barrier to recovery. Injury-induced sleep-wake disturbances can persist for years, often impairing quality of life. Recently, there has been a nearly exponential increase in the number of primary research articles published on the pathophysiology and mechanisms underlying sleep-wake disturbances after TBI, both in animal models and in humans, including in the pediatric population. In this review, we summarize over 200 articles on the topic, most of which were identified objectively using reproducible online search terms in PubMed. Although these studies differ in terms of methodology and detailed outcomes; overall, recent research describes a common phenotype of excessive daytime sleepiness, nighttime sleep fragmentation, insomnia, and electroencephalography spectral changes after TBI. Given the heterogeneity of the human disease phenotype, rigorous translation of animal models to the human condition is critical to our understanding of the mechanisms and of the temporal course of sleep-wake disturbances after injury. Arguably, this is most effectively accomplished when animal and human studies are performed by the same or collaborating research programs. Given the number of symptoms associated with TBI that are intimately related to, or directly stem from sleep dysfunction, sleep-wake disorders represent an important area in which mechanistic-based therapies may substantially impact recovery after TBI.

Night sleep in patients with vegetative state

Polysomnographic recording of night sleep was carried out in 15 patients with the diagnosis vegetative state (syn. unresponsive wakefulness syndrome). Sleep scoring was performed by three raters, and confirmed by means of a spectral power analysis of the electroencephalogram, electrooculogram and electromyogram. All patients but one exhibited at least some signs of sleep. In particular, sleep stage N1 was found in 13 patients, N2 in 14 patients, N3 in nine patients, and rapid eye movement sleep in 10 patients. Three patients exhibited all phenomena characteristic for normal sleep, including spindles and rapid eye movements. However, in all but one patient, sleep patterns were severely disturbed as compared with normative data. All patients had frequent and long periods of wakefulness during the night. In some apparent rapid eye movement sleep episodes, no eye movements were recorded. Sleep spindles were detected in five patients only, and their density was very low. We conclude that the majority of vegetative state patients retain some important circadian changes. Further studies are necessary to disentangle multiple factors potentially affecting sleep pattern of vegetative state patients.

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

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

Sleep Features on Continuous Electroencephalography Predict Rehabilitation Outcomes After Severe Traumatic Brain Injury

OBJECTIVE

Sleep characteristics detected by electroencephalography (EEG) may be predictive of neurological recovery and rehabilitation outcomes after traumatic brain injury (TBI). We sought to determine whether sleep features were associated with greater access to rehabilitation therapies and better functional outcomes after severe TBI.

METHODS

We retrospectively reviewed records of patients admitted with severe TBI who underwent 24 or more hours of continuous EEG (cEEG) monitoring within 14 days of injury for sleep elements and ictal activity. Patient outcomes included discharge disposition and modified Rankin Scale (mRS).

RESULTS

A total of 64 patients underwent cEEG monitoring for a mean of 50.6 hours. Status epilepticus or electrographic seizures detected by cEEG were associated with poor outcomes (death or discharge to skilled nursing facility). Sleep characteristics were present in 19 (30%) and associated with better outcome (89% discharged to home/acute rehabilitation; P = .0002). Lack of sleep elements on cEEG correlated with a poor outcome or mRS > 4 at hospital discharge (P = .012). Of those patients who were transferred to skilled nursing/acute rehabilitation, sleep architecture on cEEG associated with a shorter inpatient hospital stay (20 days vs 27 days) and earlier participation in therapy (9.8 days vs 13.2 days postinjury). Multivariable analyses indicated that sleep features on cEEG predicted functional outcomes independent of admission Glasgow Coma Scale and ictal-interictal activity.

CONCLUSION

The presence of sleep features in the acute period after TBI indicates earlier participation in rehabilitative therapies and a better functional recovery. By contrast, status epilepticus, other ictal activity, or absent sleep architecture may portend a worse prognosis. Whether sleep elements detected by EEG predict long-term prognosis remains to be determined.

Pilot Study of Propofol-induced Slow Waves as a Pharmacologic Test for Brain Dysfunction after Brain Injury

Background

Slow waves (less than 1 Hz) are the most important electroencephalogram signatures of nonrapid eye movement sleep. While considered to have a substantial importance in, for example, providing conditions for single-cell rest and preventing long-term neural damage, a disturbance in this neurophysiologic phenomenon is a potential indicator of brain dysfunction.

Methods

Since, in healthy individuals, slow waves can be induced with anesthetics, the authors tested the possible association between hypoxic brain injury and slow-wave activity in comatose postcardiac arrest patients (n = 10) using controlled propofol exposure. The slow-wave activity was determined by calculating the low-frequency (less than 1 Hz) power of the electroencephalograms recorded approximately 48 h after cardiac arrest. To define the association between the slow waves and the potential brain injury, the patients’ neurologic recovery was then followed up for 6 months.

Results

In the patients with good neurologic outcome (n = 6), the low-frequency power of electroencephalogram representing the slow-wave activity was found to substantially increase (mean ± SD, 190 ± 83%) due to the administration of propofol. By contrast, the patients with poor neurologic outcome (n = 4) were unable to generate propofol-induced slow waves.

Conclusions

In this experimental pilot study, the comatose postcardiac arrest patients with poor neurologic outcome were unable to generate normal propofol-induced electroencephalographic slow-wave activity 48 h after cardiac arrest. The finding might offer potential for developing a pharmacologic test for prognostication of brain injury by measuring the electroencephalographic response to propofol.

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

STUDY OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Polysomnographic Sleep Patterns in Children and Adolescents in Unresponsive Wakefulness Syndrome

OBJECTIVES

We aimed (i) to search for qualitative sleep patterns for pediatric unresponsive wakefulness syndrome (SPPUWS) in prolonged polysomnographic (PSG) recordings in children and adolescents with subacute severe disorders of consciousness due to an acquired brain damage; (ii) to investigate the clinical relevance of SPPUWS and of possible neurophysiological markers (rapid eye movement sleep and sleep spindles) in PSG recordings of pediatric patients with unresponsive wakefulness syndrome (UWS).

METHODS

We performed a PSG study in 27 children with UWS due to acquired brain damage in the subacute phase. Patients received a full neurological examination and a clinical assessment with standardized scales. In addition, outcome was assessed after 36 months.

RESULTS

We identified 6 PSG patterns (SPPUWS) corresponding to increasing neuroelectrical complexity. The presence of an organized sleep pattern, as well as rapid eye movement sleep and sleep spindles, in the subacute stage appeared highly predictive of a more favorable outcome. Correlation was found between SPPUWS and recovery, as assessed by several clinical and rehabilitation scales.

CONCLUSIONS

Polysomnography can be used as a prognostic tool, as it can help determine the capability to recover from a pediatric UWS and predict outcome well before the confirmation provided by suitable clinical scales.

The Role of Neuroimaging Techniques in Establishing Diagnosis, Prognosis and Therapy in Disorders of Consciousness

Non-communicative brain damaged patients raise important clinical and scientific issues. Here, we review three major pathological disorders of consciousness: coma, the unresponsive wakefulness syndrome and the minimally conscious state. A number of clinical studies highlight the difficulty in making a correct diagnosis in patients with disorders of consciousness based only on behavioral examinations. The increasing use of neuroimaging techniques allows improving clinical characterization of these patients. Recent neuroimaging studies using positron emission tomography, functional magnetic resonance imaging, electroencephalography and transcranial magnetic stimulation can help assess diagnosis, prognosis, and therapeutic treatment. These techniques, using resting state, passive and active paradigms, also highlight possible dissociations between consciousness and responsiveness, and are facilitating a more accurate understanding of brain function in this challenging population.

High-density electroencephalographic recordings during sleep in children with disorders of consciousness

Introduction

A large number of studies have investigated neural correlates of consciousness in adults. However, knowledge about brain function in children with disorders of consciousness (DOC) is very limited. We suggest that EEG recordings during sleep are a promising approach. In healthy adults as well as in children, it has been shown that the activity of sleep slow waves (EEG spectral power 1–4.5 Hz), the primary characteristic of deep sleep, is dependent on use during previous wakefulness. Thus the regulation of slow wave activity (SWA) provides indirect insights into brain function during wakefulness.

Methods

In the present study, we investigated high-density EEG recordings during sleep in ten healthy children and in ten children with acquired brain injury, including five children with DOC and five children with acquired brain injury without DOC. We used the build-up of SWA to quantify SWA regulation.

Results

Children with DOC showed a global reduction in the SWA build-up when compared to both, healthy children and children with acquired brain injury without DOC. This reduction was most pronounced over parietal brain areas. Comparisons within the group of children with DOC revealed that the parietal SWA build-up was the lowest in patients showing poor outcome. Longitudinal measurements during the recovery period showed an increase in parietal SWA build-up from the first to the second sleep recording.

Conclusions

Our results suggest that the reduced parietal SWA regulation may represent a characteristic topographical marker for brain network dysfunction in children with DOC. In the future, the regulation of SWA might be used as a complementary assessment in adult and paediatric patients with DOC.

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Longitudinal high-density EEG recording in children with disorders of consciousness

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Sleep electrophysiology provides a marker for brain network dysfunction.

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The sleep EEG might be used as a complementary assessment in paediatric patients.