Perturbations in dynamical models of whole-brain activity dissociate between the level and stability of consciousness.

Consciousness transiently fades away during deep sleep, more stably under anesthesia, and sometimes permanently due to brain injury. The development of an index to quantify the level of consciousness across these different states is regarded as a key problem both in basic and clinical neuroscience. We argue that this problem is ill-defined since such an index would not exhaust all the relevant information about a given state of consciousness. While the level of consciousness can be taken to describe the actual brain state, a complete characterization should also include its potential behavior against external perturbations. We developed and analyzed whole-brain computational models to show that the stability of conscious states provides information complementary to their similarity to conscious wakefulness. Our work leads to a novel methodological framework to sort out different brain states by their stability and reversibility, and illustrates its usefulness to dissociate between physiological (sleep), pathological (brain-injured patients), and pharmacologically-induced (anesthesia) loss of consciousness.

Constructing a Consciousness Meter Based on the Combination of Non-Linear Measurements and Genetic Algorithm-Based Support Vector Machine.

OBJECTIVE: Constructing a framework to evaluate consciousness is an important issue in neuroscience research and clinical practice. However, there is still no systematic framework for quantifying altered consciousness along the dimensions of both level and content. This study builds a framework to differentiate the following states: coma, general anesthesia, minimally conscious state (MCS), and normal wakefulness. METHODS: This study analyzed electroencephalography (EEG) recorded from frontal channels in patients with disorders of consciousness (either coma or MCS), patients under general anesthesia, and healthy participants in normal waking consciousness (NWC). Four non-linear methods-permutation entropy (PE), sample entropy (SampEn), permutation Lempel-Ziv complexity (PLZC), and detrended fluctuation analysis (DFA)-as well as relative power (RP), extracted features from the EEG recordings. A genetic algorithm-based support vector machine (GA-SVM) classified the states of consciousness based on the extracted features. A multivariable linear regression model then built EEG indices for level and content of consciousness. RESULTS: The PE differentiated all four states of consciousness (p<0.001). Altered contents of consciousness for NWC, MCS, coma, and general anesthesia were best differentiated by the SampEn, and PLZC. In contrast, the levels of consciousness for these four states were best differentiated by RP of Gamma and PE. A multi-dimensional index, combined with the GA-SVM, showed that the integration of PE, PLZC, SampEn, and DFA had the highest classification accuracy (92.3%). The GA-SVM was better than random forest and neural networks at differentiating these four states. The 'coordinate value' in the dimensions of level and content were constructed by the multivariable linear regression model and the non-linear measures PE, PLZC, SampEn, and DFA. CONCLUSIONS: Multi-dimensional measurements, especially the PE, SampEn, PLZC, and DFA, when combined with GA-SVM, are promising methods for constructing a framework to quantify consciousness.

Novel Signal Processing Technique for Capture and Isolation of Blink-Related Oscillations Using a Low-Density Electrode Array for Bedside Evaluation of Consciousness.

OBJECTIVE: Blink-related oscillations derived from electroencephalography (EEG) have recently emerged as an important measure of awareness. Combined with portable EEG hardware with low-density electrode arrays, this neural marker may crucially augment the existing bedside assessments of consciousness in unresponsive patients. Nonetheless, the close relationship between signal characteristics of the neural response of interest and blink-induced oculomotor artifacts poses particular challenges when measuring blink-related oscillations using a point-of-care platform. This study presents a novel denoising approach based on time-frequency (TF) filtering that exploits the differential temporal and spectral features to isolate the neural response from ocular artifact in a low-density array. METHODS: We investigated the effectiveness of the TF filtering technique using 64-channel EEG data collected in healthy adults, with focal analysis of the Pz and POz channels. RESULTS: TF filtering showed comparable performance in denoising the signal relative to the established gold-standard independent component analysis approach, with strong similarities in morphological characteristics as measured by intraclass correlations (p < 0.001), extent of artifact rejection based on the ocular contamination index (p < 0.006), as well as time- and frequency-domain signal capture (p < 0.05). Results are robust at the individual and group levels, and are crucially validated using raw data from only four electrodes comprising Pz, POz, Fp2, and T7. CONCLUSION: These results demonstrate for the first time that TF filtering enables the successful capture and isolation of the blink-related oscillations response using a four-electrode array. SIGNIFICANCE: This significantly advances the translation of the blink-related oscillations marker to a point-of-care platform for eventual bedside applications.

Monitoring the Depth of Anesthesia Through the Use of Cerebral Hemodynamic Measurements Based on Sample Entropy Algorithm.

OBJECTIVE: The aim of this study is to explore the relationship between the depth of anesthesia and the cerebral hemodynamic variables during the complete anesthesia process. METHODS: In this study, near-infrared spectroscopy signals were used to record eight kinds of cerebral hemodynamic variables, including left, right, proximal, distal deoxygenated (Hb) and oxygenated (HbO2) hemoglobin concentration changes. Then, by measuring the complexity information of cerebral hemodynamic variables, the sample entropy was calculated as a new index of monitoring the depth of anesthesia. RESULTS: By means of receiver operating characteristic curve analysis, the sample entropy approach was proved to effectively discriminate anesthesia maintenance and waking phases. The discriminatory ability of HbO2 signals was stronger than that of Hb signals and the distal signals had weaker discrimination capability when compared with the proximal signals. In addition, there was statistical consistency between the bispectral index and sample entropy of cerebral hemodynamic variables during the complete anesthesia process. Moreover, the cerebral hemodynamic signals could not be interfered by clinical electrical devices. CONCLUSION: The sample entropy of cerebral hemodynamic variables could be suitable as a new index for monitoring the depth of anesthesia. SIGNIFICANCE: This study is very meaningful for developing new modality and decoding methods in perspective of anesthesia surveillance and may result in the anesthesia monitoring system with high performance.

Evaluation of the compliance between EEG monitoring (Bispectral IndexTM) and Ramsey Sedation Scale to measure the depth of sedation in the patients who underwent procedural sedation and analgesia in the emergency department.

BACKGROUND: This study aimed to investigate the compliance between electroencephalogram monitoring (Bispectral Index, BIS) and Ramsay Sedation Scale (RSS) to measure the depth of sedation in patients who underwent procedural sedation and analgesia (PSA) in an emergency department. This study also aimed to investigate the usefulness of this compliance for early diagnosis of complications. METHODS: A total of 54 consecutive patients during PSA in the emergency department were included in this study. The BIS and RSS scores at regular intervals and also all complications and interventions of these patients were evaluated. The compliance between the BIS and the RSS score was evaluated. The BIS scores of cases with complication and without complication were compared. RESULTS: The BIS and RSS scores exhibited a high correlation was detected between the average BIS and RSS scores at each time interval (r=-0.989, p<0.001). The BIS scores of the complicated and uncomplicated cases were different at 15 min after the procedure (p=0.019). The cases were divided into two groups according to the BIS scores <70 and ≥70; complication rates were higher in the BIS score <70 group during the procedure (p=0.037). CONCLUSION: In our study, a high correlation was detected between BIS monitoring and RSS scores. BIS monitoring for PSA can be used as a full-time, objective, and an alternative technique for person-dependent clinical scales and also as an indicator for early diagnosis of complications.

Uncovering Consciousness in Unresponsive ICU Patients: Technical, Medical and Ethical Considerations.

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2019. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2019 . Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901 .

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

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

[Human consciousness in health and disease].

Functional neuroimaging, electroencephalography and clinical examination are essential to understand the many facets of disorders of consciousness. Yet, it is still not widely known that patients exist who are clearly conscious but unable to show it owing to complete loss of motor output, a condition which has been termed cognitive motor dissociation. In this review, the mechanisms of normal and impaired consciousness are discussed, and current theories of consciousness, as well as specific clinical signs, which are essential to discern the state of consciousness in a given patient, are highlighted.

Consciousness assessment: A questionnaire of current neuroscience nursing practice in Europe.

AIMS AND OBJECTIVES: To study practice in consciousness assessment among neuroscience nurses in Europe. BACKGROUND: Over the years, several instruments have been developed to assess the level of consciousness for patients with brain injury. It is unclear which instrument is being used by nurses in Europe and how they are trained to use these tools adequately. DESIGN/METHODS: A cross-sectional questionnaire, created by the European Association of Neuroscience Nurses Research Committee, was sent to neuroscience nurses in 13 European countries. The countries participated in 2016 with a response period of 3 months for each country. RESULTS: A total of 331 questionnaires were completed by nurses in 11 different countries. Assessment of consciousness was part of the daily routine for a majority of bedside nurses (95%), with an estimated median frequency of six times per shift. The majority uses a standardised instrument, and the Glasgow Coma Scale is the most common. Most participants assess consciousness primarily for clinical decision-making and report both total scores and subscores. The majority was formally trained or educated in use of the instrument, but methods of training were divers. Besides the estimated frequency of assessments and training, no significant difference was found between bedside nurses and other nurse positions, educational level or kind of institution. CONCLUSION: Our study shows that consciousness assessment is part of the daily routine for most nurses working in neurology/neurosurgery/neurorehabilitation wards in Europe. The greatest variation existed in training methods for the use of the instruments, and we recommend standardised practice in the use of assessment scales. RELEVANCE TO CLINICAL PRACTICE: In clinical practice, both managers and staff nurses should focus on formalised training in the use of assessment tools, to ensure reliability and reproducibility. This may also increase the professionalism in the neuroscience nurses' role and performance.

The New Classification of Seizures by the International League Against Epilepsy 2017.

PURPOSE OF REVIEW: This review presents the newly developed International League Against Epilepsy (ILAE) 2017 classification of seizure types. RECENT FINDINGS: The fundamental distinction is between seizures that begin focally in one hemisphere of the brain, generalized onset seizures that apparently originate in both hemispheres, and seizures of unknown onset. Focal seizures optionally can be subclassified according to whether awareness (a surrogate marker for consciousness) is intact or impaired. The next level of classification for focal seizures is motor (with subgroups automatisms, atonic, clonic, epileptic spasms, hyperkinetic, myoclonic, tonic), non-motor (with subgroups autonomic, behavior arrest, cognitive, emotional, sensory), and focal to bilateral tonic-clonic. Generalized seizures are categorized as motor (tonic-clonic, clonic, tonic, myoclonic, myoclonic-tonic-clonic, myoclonic-atonic, atonic, epileptic spasms) and non-motor/absence (typical, atypical, myoclonic, eyelid myoclonia). The classification allows new types of focal seizures and a few new generalized seizures, and clarifies terms used to name seizures.

Awake Laryngoscopy in the Emergency Department.

BACKGROUND: Many emergency physicians gain familiarity with the laryngeal anatomy only during the brief view achieved during rapid sequence induction and intubation. Awake laryngoscopy in the emergency department (ED) is an important and clinically underutilized procedure. DISCUSSION: Providing benefit to the emergency physician through a slow, controlled, and deliberate examination of the airway, awake laryngoscopy facilitates confidence in the high-risk airway and eases the evolution to intubation, should it be required. Emergency physicians possess all the tools and skills required to effectively perform this procedure, through either the flexible endoscopic or rigid approaches. The procedure can be conducted utilizing local anesthesia with or without mild sedation, such that patients protect their airway. CONCLUSION: We discuss two clinical scenarios, indications/contraindications, patient selection, and steps to performing two approaches to awake laryngoscopy in the ED.

EEG-detected olfactory imagery to reveal covert consciousness in minimally conscious state.

PRIMARY OBJECTIVE: To reveal covert abilities in a minimally conscious state (MCS) through an innovative activation paradigm based on olfactory imagery. RESEARCH DESIGN: Case study. METHODS AND PROCEDURES: A patient in MCS was asked to 'imagine an unpleasant odour' or to 'relax' in response to the appearance on a screen of a downward pointing arrow or a cross, respectively. Electrophysiological responses to stimuli were investigated by means of an 8-channel EEG equipment and analysed using a specific threshold algorithm. The protocol was repeated for 10 sessions separated from each other by 2 weeks. Accuracy, defined as the number of successes with respect to the total number of trials, was used to evaluate the number of times in which the classification strategy was successful. MAIN OUTCOMES AND RESULTS: Analyses of accuracy showed that the patient was able to activate and to relax himself purposefully and that he optimized his performances with the number of sessions, probably as a result of training-related improvements. CONCLUSIONS: Subtle signs of consciousness may be under-estimated and need to be revealed through specific activation tasks. This paradigm may be useful to detect covert signs of consciousness, especially when patients are precluded from carrying out more complex cognitive tasks.

One-year follow-up of neurological status of patients after cardiac arrest seen at the emergency room of a teaching hospital.

OBJECTIVE: To describe neurological status and associated factors of survivors after cardiac arrest, upon discharge, and at 6 and 12 month follow-up. METHODS: A cohort, prospective, descriptive study conducted in an emergency room. Patients who suffered cardiac arrest and survived were included. A one-year consecutive sample, comprising 285 patients and survivors (n=16) followed up for one year after discharge. Neurological status was assessed by the Cerebral Performance Category before the cardiac arrest, upon discharge, and at 6 and 12 months after discharge. The following factors were investigated: comorbidities, presence of consciousness upon admission, previous cardiac arrest, witnessed cardiac arrest, location, cause and initial rhythm of cardiac arrest, number of cardiac arrests, interval between collapse and start of cardiopulmonary resuscitation, and between collapse and end of cardiopulmonary resuscitation, and duration of cardiopulmonary resuscitation. RESULTS: Of the patients treated, 4.5% (n=13) survived after 6 and 12 months follow-up. Upon discharge, 50% of patients remained with previous Cerebral Performance Category of the cardiac arrest and 50% had worsening of Cerebral Performance Category. After 6 months, 53.8% remained in the same Cerebral Performance Category and 46.2% improved as compared to discharge. After 12 months, all patients remained in the same Cerebral Performance Category of the previous 6 months. There was no statistically significant association between neurological outcome during follow-up and the variables assessed. CONCLUSION: There was neurological worsening at discharge but improvement or stabilization in the course of a year. There was no association between Cerebral Performance Category and the variables assessed.

One-year follow-up of neurological status of patients after cardiac arrest seen at the emergency room of a teaching hospital / Um ano de seguimento da condição neurológica de pacientes pós-parada cardiorrespiratória atendidos no pronto-socorro de um hospital universitário

ABSTRACT Objective: To describe neurological status and associated factors of survivors after cardiac arrest, upon discharge, and at 6 and 12 month follow-up. Methods: A cohort, prospective, descriptive study conducted in an emergency room. Patients who suffered cardiac arrest and survived were included. A one-year consecutive sample, comprising 285 patients and survivors (n=16) followed up for one year after discharge. Neurological status was assessed by the Cerebral Performance Category before the cardiac arrest, upon discharge, and at 6 and 12 months after discharge. The following factors were investigated: comorbidities, presence of consciousness upon admission, previous cardiac arrest, witnessed cardiac arrest, location, cause and initial rhythm of cardiac arrest, number of cardiac arrests, interval between collapse and start of cardiopulmonary resuscitation, and between collapse and end of cardiopulmonary resuscitation, and duration of cardiopulmonary resuscitation. Results: Of the patients treated, 4.5% (n=13) survived after 6 and 12 months follow-up. Upon discharge, 50% of patients remained with previous Cerebral Performance Category of the cardiac arrest and 50% had worsening of Cerebral Performance Category. After 6 months, 53.8% remained in the same Cerebral Performance Category and 46.2% improved as compared to discharge. After 12 months, all patients remained in the same Cerebral Performance Category of the previous 6 months. There was no statistically significant association between neurological outcome during follow-up and the variables assessed. Conclusion: There was neurological worsening at discharge but improvement or stabilization in the course of a year. There was no association between Cerebral Performance Category and the variables assessed. .

RESUMO Objetivo: Identificar a condição neurológica e os fatores associados de sobreviventes pós-parada cardiorrespiratória na alta hospitalar, após 6 e 12 meses de seguimento. Métodos: Estudo de coorte, prospectivo e descritivo, realizado em um pronto-socorro. Foram incluídos pacientes em parada cardiorrespiratória que sobreviveram à alta. A amostra foi consecutiva por um ano, sendo composta por 285 pacientes, e os sobreviventes (n=16) foram acompanhados por um ano após alta. O estado neurológico foi avaliado pela Categoria de Performance Cerebral antes da parada, na alta, 6 e 12 meses após alta. Foram investigados os seguintes fatores: comorbidades, presença de consciência na admissão, parada cardiorrespiratória prévia, parada cardiorrespiratória testemunhada, local, causa e ritmo inicial da parada, número de paradas, intervalo entre colapso e início da ressuscitação cardiopulmonar, e entre colapso e término da ressuscitação, e duração da ressuscitação. Resultados: Dos pacientes atendidos, 4,5% (n=13) sobreviveram após 6 e 12 meses de seguimento. Na alta, 50% dos pacientes permaneceram com Categoria de Performance Cerebral prévia à parada, e 50% tiveram piora da mesma. Após 6 meses, 53,8% permaneceram com mesma Categoria de Performance Cerebral, e 46,2% tiveram melhora em relação à alta. Após 12 meses, a totalidade dos pacientes permaneceu com mesma Categoria de Performance Cerebral em relação aos 6 meses anteriores. Não houve associação estatisticamente significativa entre evolução neurológica durante o seguimento e variáveis de interesse. Conclusão: Observou-se piora neurológica na alta, mas houve melhora ou estabilização no decorrer de 1 ano. Não foi encontrada associação entre Categoria de Performance Cerebral e variáveis de interesse. .

[On the concept of lucidity]. / Sobre el concepto de lucidez.

Kraepelin's concept of clarity of awareness (lucidity) had a stable signification during more than hundred years. It is the basis of most of current theories of disturbed consciousness. However, two alternative definitions that arise in Buenos Aires produce confusion due to their conceptual overlap. We discuss these definitions and propose a classification of disorders of consciousness that rely on traditional point of view.

Assessment of patients with disorder of consciousness: do different Coma Recovery Scale scoring correlate with different settings?

Differential diagnosis between Vegetative State and Minimally Conscious State is a challenging task that requires specific assessment scales, involvement of expert neuropsychologists or physicians and use of tailored stimuli for eliciting behavioural responses. Although misdiagnosis rate as high as 40% has been reported, no clear guidelines are available in literature on the optimal setting for assessment. The present study aims to analyse score differences in behavioural assessments of persons with disorders of consciousness (DOC) with or without family members and to determine whether the presence of caregivers could improve clinical accuracy in diagnostic evaluation. The research was conducted on 92 adults with DOC among 153 consecutive patients enrolled in the Coma Research Centre of the Neurological Institute C. Besta of Milan between January 2011 and May 2013. The results indicate that in almost half of the sample the scoring, thus the performance, observed with caregivers was better than without them. Furthermore, in 16% of the sample, when assessment was performed with caregivers there was a change in diagnosis, from Vegetative to Minimally Conscious State or from that to Severe Disability. Finally, statistical differences were found in relation to diagnosis between mean scores in the "visual function" Coma Recovery Scale revised's subscale obtained by raters plus caregiver and rates only assessment. This study demonstrates how the presence of caregivers can positively affect behavioural assessments of persons with DOC, thus contributing to the definition of the optimal setting for behavioural evaluation of patients, to decrease misdiagnosis rates.

What kind of consciousness is minimal?

PRIMARY OBJECTIVE: A comparison between unitary and non-unitary views on minimal consciousness. RESEARCH DESIGN: First, unitary (all-or-none) and non-unitary (gradual or continuous) models of consciousness are defined as they have been developed in both philosophy and neurophysiology. Then, the implications of these ideas to the notion the minimally conscious state (MCS) are discussed. METHODS AND PROCEDURES: Review and analysis of theoretical conceptions and empirical data. MAIN OUTCOME AND RESULTS: Both kinds of models are compatible with the actual definitions of MCS. Although unitary views may seem to contradict the description of the MCS in 'Neurology' 2002, the apparent contradiction can easily be solved. Most recent data, particularly those obtained using fMRI and concerning learning, emotional responsiveness and pain and suffering, speak for non-unitary models. CONCLUSIONS: Most evidence speaks for non-unitary models of minimal consciousness. If these models are correct, patients with MCS may have, in addition to temporal fluctuations, a lower level of consciousness compared with fully conscious individuals. A still lower level could characterize patients diagnosed as unresponsive wakefulness syndrome (UWS). From this point of view, therefore, the difference between UWS and MCS is gradual rather than qualitative. However, due to methodological limitations of the available studies, the evidence for non-unitary models cannot be regarded as definite.

Connectivity biomarkers can differentiate patients with different levels of consciousness.

OBJECTIVE: In the present study, we searched for resting-EEG biomarkers that distinguish different levels of consciousness on a single subject level with an accuracy that is significantly above chance. METHODS: We assessed 44 biomarkers extracted from the resting EEG with respect to their discriminative value between groups of minimally conscious (MCS, N=22) patients, vegetative state patients (VS, N=27), and - for a proof of concept - healthy participants (N=23). We applied classification with support vector machines. RESULTS: Partial coherence, directed transfer function, and generalized partial directed coherence yielded accuracies that were significantly above chance for the group distinction of MCS vs. VS (.88, .80, and .78, respectively), as well as healthy participants vs. MCS (.96, .87, and .93, respectively) and VS (.98, .84, and .96, respectively) patients. CONCLUSIONS: The concept of connectivity is crucial for determining the level of consciousness, supporting the view that assessing brain networks in the resting state is the golden way to examine brain functions such as consciousness. SIGNIFICANCE: The present results directly show that it is possible to distinguish patients with different levels of consciousness on the basis of resting-state EEG.