Dissociations between spontaneous electroencephalographic features and the perturbational complexity index in the minimally conscious state.

The analysis of spontaneous electroencephalogram (EEG) is a cornerstone in the assessment of patients with disorders of consciousness (DoC). Although preserved EEG patterns are highly suggestive of consciousness even in unresponsive patients, moderately or severely abnormal patterns are difficult to interpret. Indeed, growing evidence shows that consciousness can be present despite either large delta or reduced alpha activity in spontaneous EEG. Quantifying the complexity of EEG responses to direct cortical perturbations (perturbational complexity index [PCI]) may complement the observational approach and provide a reliable assessment of consciousness even when spontaneous EEG features are inconclusive. To seek empirical evidence of this hypothesis, we compared PCI with EEG spectral measures in the same population of minimally conscious state (MCS) patients (n = 40) hospitalized in rehabilitation facilities. We found a remarkable variability in spontaneous EEG features across MCS patients as compared with healthy controls: in particular, a pattern of predominant delta and highly reduced alpha power-more often observed in vegetative state/unresponsive wakefulness syndrome (VS/UWS) patients-was found in a non-negligible number of MCS patients. Conversely, PCI values invariably fell above an externally validated empirical cutoff for consciousness in all MCS patients, consistent with the presence of clearly discernible, albeit fleeting, behavioural signs of awareness. These results confirm that, in some MCS patients, spontaneous EEG rhythms may be inconclusive about the actual capacity for consciousness and suggest that a perturbational approach can effectively compensate for this pitfall with practical implications for the individual patient's stratification and tailored rehabilitation.

Minimally conscious state plus versus minus: Likelihood of emergence and long-term functional independence.

OBJECTIVE: Severe brain injuries can result in disorders of consciousness, such as the Minimally Conscious State (MCS), where individuals display intermittent yet discernible signs of conscious awareness. The varied levels of responsiveness and awareness observed in this state have spurred the progressive delineation of two subgroups within MCS, termed "plus" (MCS+) and "minus" (MCS-). However, the clinical validity of these classifications remains uncertain. This study aimed to investigate and compare the likelihood of emergence from MCS, as well as the functional independence after emergence, in individuals categorized as in MCS+ and MCS-. METHODS: Demographic and behavioral data of 80 participants, admitted as either in MCS+ (n = 30) or MCS- (n = 50) to a long-term neurorehabilitation unit, were retrospectively analyzed. The neurobehavioral condition of each participant was evaluated weekly until discharge, demise, or emergence from MCS. The functional independence of those participants who emerged from MCS was assessed 6 months after emergence. RESULTS: While only about half of the individuals classified as in MCS- (n = 24) emerged from the MCS, all those admitted as in MCS+ did, and in a shorter postinjury period. Despite these differences, all individuals who emerged from the MCS demonstrated similar high disability and low functional independence 6 months after emergence, regardless of their state at admission. INTERPRETATION: Individuals classified as MCS+ exhibited a higher likelihood of emergence and a shorter time to emergence compared to those in MCS-. However, the level of functional independence 6 months after emergence was found to be unrelated to the initial state at admission.

Family caregivers improve the diagnostic accuracy of disorders of consciousness: from remote to near-bed auditory stimulation.

BACKGROUND: Family caregivers (FC) contribute to reducing the misdiagnosis rate in patients with disorders of consciousness (DOC). Unfortunately, the recent pandemic of COVID-19 imposed drastic restrictions that limited the access of FC to the sensory/cognitive stimulation protocols. Telemedicine approaches have been implemented to avoid discontinuity in care pathways and to ensure caregivers involvement in rehabilitation programs. AIM: The aim was to investigate whether the presence of FC remotely connected might help clinicians in eliciting higher cortically mediated behavioral responses in patients with DOC. DESIGN: Cross-sectional study. SETTING: Post-acute Unit of Neurorehabilitation. POPULATION: DOC due to severe brain injury. METHODS: Consecutive patients with DOC were assessed by means of the Coma Recovery Scale-Revised (CRS-R) by two expert examiners. Each patient underwent to five assessments in two weeks in three different conditions: 1) by the examiner only (standard); 2) with the verbal stimulation given by the FC remotely connected by PC tablet (caregiver in remote); and 3) with the verbal stimulation given by the FC physically present (caregiver in presence). RESULTS: Thirty patients with DOC (VS/UWS=10; MCS=20; mean age: 51, range: 21-79; vascular: 16; anoxic: 6; TBI=8) and their FC were enrolled. Higher total scores of CRS-R were recorded both in "caregiver in remote" and in "caregiver in presence" than in standard condition (standard vs. remote, Z=2.942, P=0.003; standard vs. presence, Z=3.736, P<0.001). Furthermore, the administration of the CRS-R with a FC, elicited higher levels of behavioral responses in MCS patients, than CRS-R performed in standard condition. In particular, 2 patients out of 30 (6.66%) showed higher scores and better diagnosis when the CRS-R was administered with FC in remote. Similarly, 5 out of 30 patients (16.66%) showed better diagnoses when the CRS-R was administered with FC in presence. Five patients changed diagnosis between standard and presence conditions (3 MCS- were diagnosed as MCS+; 2 MCS+ were diagnosed as conscious). CONCLUSIONS: Our findings add new evidence regarding the beneficial role of family members in the diagnosis of DOC, even mediated by telemedicine approach. CLINICAL REHABILITATION IMPACT: In future guidelines, FC should have an active and supporting role in the diagnostic and rehabilitative process of DOC.

Prognosis and enhancement of recovery in disorders of consciousness.

Disorders of consciousness after severe brain injury encompass conditions of coma, vegetative state/unresponsive wakefulness syndrome, and minimally conscious state. DoC clinical presentation pose perplexing challenges to medical professionals, researchers, and families alike. The outcome is uncertain in the first weeks to months after a brain injury, with families and medical providers often making important decisions that require certainty. Prognostication for individuals with these conditions has been the subject of intense scientific investigation that continues to strive for valid prognostic indicators and algorithms for predicting recovery of consciousness. This manuscript aims to provide an overview of the current clinical landscape surrounding prognosis and optimizing recovery in DoC and the current and future research that could improve prognostic accuracy after severe brain injury. Improved understanding of these factors will aid healthcare professionals in providing optimal care, fostering hope, and advocating for ethical practices in the management of individuals with DoC.

Prevalence of the Minimally Conscious State Among Institutionalized Patients in the Netherlands: A Nationwide Study.

BACKGROUND AND OBJECTIVES: The minimally conscious state (MCS) is a prolonged disorder of consciousness (pDoC) and one of the most severe outcomes of acquired brain injury. Prevalence data are scarce. The aim of this study was to establish the nationwide point prevalence of institutionalized patients in MCS in the Netherlands. METHODS: This was a descriptive cross-sectional study in which all 86 Dutch hospitals, all 5 specialized pDoC rehabilitation facilities, and all 274 nursing homes were asked whether they were treating patients with a pDoC on the point prevalence date of September 15, 2021. Each patient's legal representative provided informed consent for their inclusion. Patient level of consciousness was verified using the Coma Recovery Scale-Revised (CRS-R) in a single assessment session performed in the facility of residence by an experienced physician. Data on patient demographics, etiology, level of consciousness, facility of residence, and clinical status were collected from a questionnaire by the treating physician. The prevalence of institutionalized patients in MCS of per 100,000 members of the Dutch population was calculated, based on actual census data. RESULTS: Seventy patients were reported to have a pDoC, of whom 6 were excluded. The level of consciousness was verified for 49 patients while for 15, it could not be verified. Of the patients verified, 38 had a pDoC, of whom 32 were in MCS (mean age 44.8 years, 68.8% male). The prevalence of institutionalized patients in MCS is 0.2-0.3 per 100,000 Dutch inhabitants. Traumatic brain injury was present in 21 of 32 patients (65.6%). Specialized pDoC rehabilitation was received by 17 of 32 patients (53%), with the rest admitted to nursing homes. The most frequent signs of consciousness on the CRS-R were visual pursuit, reproducible movement to command, and automatic motor response. DISCUSSION: This nationwide study revealed a low prevalence of institutionalized patients in MCS in the Netherlands. These findings are now being used to organize pDoC care in this country.

Electrophysiological characteristics of CM-pf in diagnosis and outcome of patients with disorders of consciousness.

BACKGROUND: Deep brain stimulation (DBS) in the centromedian-parafascicular complex (CM-pf) has been reported as a potential therapeutic option for disorders of consciousness (DoC). However, the lack of understanding of its electrophysiological characteristics limits the improvement of therapeutic effect. OBJECTIVE: To investigate the CM-pf electrophysiological characteristics underlying disorders of consciousness (DoC) and its recovery. METHODS: We collected the CM-pf electrophysiological signals from 23 DoC patients who underwent central thalamus DBS (CT-DBS) surgery. Five typical electrophysiological features were extracted, including neuronal firing properties, multiunit activity (MUA) properties, signal stability, spike-MUA synchronization strength (syncMUA), and the background noise level. Their correlations with the consciousness level, the outcome, and the primary clinical factors of DoC were analyzed. RESULTS: 11 out of 23 patients (0/2 chronic coma, 5/13 unresponsive wakefulness syndrome/vegetative state (UWS/VS), 6/8 minimally conscious state minus (MCS-)) exhibited an improvement in the level of consciousness after CT-DBS. In CM-pf, significantly stronger gamma band syncMUA strength and alpha band normalized MUA power were found in MCS- patients. In addition, higher firing rates, stronger high-gamma band MUA power and alpha band normalized power, and more stable theta oscillation were correlated with better outcomes. Besides, we also identified electrophysiological properties that are correlated with clinical factors, including etiologies, age, and duration of DoC. CONCLUSION: We provide comprehensive analyses of the electrophysiological characteristics of CM-pf in DoC patients. Our results support the 'mesocircuit' hypothesis, one proposed mechanism of DoC recovery, and reveal CM-pf electrophysiological features that are crucial for understanding the pathogenesis of DoC, predicting its recovery, and explaining the effect of clinical factors on DoC.

What is a minimal clinically important difference for clinical trials in patients with disorders of consciousness? a novel probabilistic approach.

Over the last 30 years, there has been a growing trend in clinical trials towards assessing novel interventions not only against the benchmark of statistical significance, but also with respect to whether they lead to clinically meaningful changes for patients. In the context of Disorders of Consciousness (DOC), despite a growing landscape of experimental interventions, there is no agreed standard as to what counts as a minimal clinically important difference (MCID). In part, this issue springs from the fact that, by definition, DOC patients are either unresponsive (i.e., in a Vegetative State; VS) or non-communicative (i.e., in a Minimally Conscious State; MCS), which renders it impossible to assess any subjective perception of benefit, one of the two core aspects of MCIDs. Here, we develop a novel approach that leverages published, international diagnostic guidelines to establish a probability-based minimal clinically important difference (pMCID), and we apply it to the most validated and frequently used scale in DOC: the Coma Recovery Scale-Revised (CRS-R). This novel method is objective (i.e., based on published criteria for patient diagnosis) and easy to recalculate as the field refines its agreed-upon criteria for diagnosis. We believe this new approach can help clinicians determine whether observed changes in patients' behavior are clinically important, even when patients cannot communicate their experiences, and can align the landscape of clinical trials in DOC with the practices in other medical fields.

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.

EEG fractal dimensions predict high-level behavioral responses in minimally conscious patients.

Objective.Brain-injured patients may enter a state of minimal or inconsistent awareness termed minimally conscious state (MCS). Such patient may (MCS+) or may not (MCS-) exhibit high-level behavioral responses, and the two groups retain two inherently different rehabilitative paths and expected outcomes. We hypothesized that brain complexity may be treated as a proxy of high-level cognition and thus could be used as a neural correlate of consciousness.Approach.In this prospective observational study, 68 MCS patients (MCS-: 30; women: 31) were included (median [IQR] age 69 [20]; time post-onset 83 [28]). At admission to intensive rehabilitation, 30 min resting-state closed-eyes recordings were performed together with consciousness diagnosis following international guidelines. The width of the multifractal singularity spectrum (MSS) was computed for each channel time series and entered nested cross-validated interpretable machine learning models targeting the differential diagnosis of MCS±.Main results.Frontal MSS widths (p< 0.05), as well as the ones deriving from the left centro-temporal network (C3:p= 0.018, T3:p= 0.017; T5:p= 0.003) were found to be significantly higher in the MCS+ cohort. The best performing solution was found to be the K-nearest neighbor model with an aggregated test accuracy of 75.5% (median [IQR] AuROC for 100 executions 0.88 [0.02]). Coherently, the electrodes with highest Shapley values were found to be Fz and Cz, with four out the first five ranked features belonging to the fronto-central network.Significance.MCS+ is a frequent condition associated with a notably better prognosis than the MCS-. High fractality in the left centro-temporal network results coherent with neurological networks involved in the language function, proper of MCS+ patients. Using EEG-based interpretable algorithm to complement differential diagnosis of consciousness may improve rehabilitation pathways and communications with caregivers.

Sham-controlled randomized multicentre trial of transcranial direct current stimulation for prolonged disorders of consciousness.

BACKGROUND AND PURPOSE: Transcranial direct current stimulation (tDCS) has been shown to improve signs of consciousness in a subset of patients with disorders of consciousness (DoC). However, no multicentre study confirmed its efficacy when applied during rehabilitation. In this randomized controlled double-blind study, the effects of tDCS whilst patients were in rehabilitation were tested at the group level and according to their diagnosis and aetiology to better target DoC patients who might repond to tDCS. METHODS: Patients received 2 mA tDCS or sham applied over the left prefrontal cortex for 4 weeks. Behavioural assessments were performed weekly and up to 3 months' follow-up. Analyses were conducted at the group and subgroup levels based on the diagnosis (minimally conscious state [MCS] and unresponsive wakefulness syndrome) and the aetiology (traumatic or non-traumatic). Interim analyses were planned to continue or stop the trial. RESULTS: The trial was stopped for futility when 62 patients from 10 centres were enrolled (44 ± 14 years, 37 ± 24.5 weeks post-injury, 18 women, 32 MCS, 39 non-traumatic). Whilst, at the group level, no treatment effect was found, the subgroup analyses at 3 months' follow-up revealed a significant improvement for patients in MCS and with traumatic aetiology. CONCLUSIONS: Transcranial direct current stimulation during rehabilitation does not seem to enhance patients' recovery. However, diagnosis and aetiology appear to be important factors leading to a response to the treatment. These findings bring novel insights into possible cortical plasticity changes in DoC patients given these differential results according to the subgroups of patients.

Cortical responses to auditory stimulation predict the prognosis of patients with disorders of consciousness.

OBJECTIVE: This study aimed to assess the prognosis of patients with disorders of consciousness (DoC) using auditory stimulation with electroencephalogram (EEG) recordings. METHODS: We enrolled 72 patients with DoC in the study, which involved subjecting patients to auditory stimulation while EEG responses were recorded. Coma Recovery Scale-Revised (CRS-R) scores and Glasgow Outcome Scale (GOS) were determined for each patient and followed up for three months. A frequency spectrum analysis was performed on the EEG recordings. Finally, the power spectral density (PSD) index was used to predict the prognosis of patients with DoC based on a support vector machine (SVM) model. RESULTS: Power spectral analyses revealed that the cortical response to auditory stimulation showed a decreasing trend with decreasing consciousness levels. Auditory stimulation-induced changes in absolute PSD at the delta and theta bands were positively correlated with the CRS-R and GOS scores. Furthermore, these cortical responses to auditory stimulation had a good ability to discriminate between good and poor prognoses of patients with DoC. CONCLUSIONS: Auditory stimulation-induced changes in the PSD were highly predictive of DoC outcomes. SIGNIFICANCE: Our findings showed that cortical responses to auditory stimulation may be an important electrophysiological indicator of prognosis in patients with DoC.

Multidimensional assessment of heartbeat-evoked responses in disorders of consciousness.

Because consciousness does not necessarily translate into overt behaviour, detecting residual consciousness in noncommunicating patients remains a challenge. Bedside diagnostic methods based on EEG are promising and cost-effective alternatives to detect residual consciousness. Recent evidence showed that the cortical activations triggered by each heartbeat, namely, heartbeat-evoked responses (HERs), can detect through machine learning the presence of minimal consciousness and distinguish between overt and covert minimal consciousness. In this study, we explore different markers to characterize HERs to investigate whether different dimensions of the neural responses to heartbeats provide complementary information that is not typically found under standard event-related potential analyses. We evaluated HERs and EEG average non-locked to heartbeats in six types of participants: healthy state, locked-in syndrome, minimally conscious state, vegetative state/unresponsive wakefulness syndrome, comatose and brain-dead patients. We computed a series of markers from HERs that can generally separate the unconscious from the conscious. Our findings indicate that HER variance and HER frontal segregation tend to be higher in the presence of consciousness. These indices, when combined with heart rate variability, have the potential to enhance the differentiation between different levels of awareness. We propose that a multidimensional evaluation of brain-heart interactions could be included in a battery of tests to characterize disorders of consciousness. Our results may motivate further exploration of markers in brain-heart communication for the detection of consciousness at the bedside. The development of diagnostic methods based on brain-heart interactions may be translated into more feasible methods for clinical practice.

A functional near-infrared spectroscopy study on hemodynamic changes of patients with prolonged disorders of consciousness responding to different auditory stimuli.

Treating prolonged disorders of consciousness (pDoC) is challenging. Thus, accurate assessment of residual consciousness in patients with pDoC is important for the management and recovery of patients. Functional near-infrared spectroscopy (fNIRS) can be used to detect brain activity through changes of oxygenated hemoglobin/deoxygenated hemoglobin (HbO/HbR) concentrations changes and has recently gained increasing attention for its potential applications in assessing residual consciousness. However, the number of fNIRS studies assessing residual awareness in patients with pDoC is still limited. In this study, fNIRS was used to evaluate the brain function in 18 patients with pDoC, including 14 vegetative states (VS) and 4 minimally conscious states (MCS), and 15 healthy controls (HC). All participants accepted two types of external stimuli, i.e., active stimulation (motor imagery, MI) and passive stimulation (subject's own name, SON). The results showed that the mean concentrations of HbO/HbR in the prefrontal cortex of the HC during the passive stimulation were significantly lower than those of the active stimulation, and the fitting slope was high. However, the hemodynamic responses of the patients with pDoC were opposite to those of the HC. Additionally, the mean concentrations of HbO/HbR increased as the level of consciousness decreased during passive stimulation. Our findings suggest that the residual level of consciousness in pDoC patients can be assessed by measuring brain responses to different stimulations using fNIRS. The present study further demonstrates the feasibility and reliability of fNIRS in assessing residual consciousness in patients with pDoC, providing a basis for its expanded clinical application.

EEG complexity correlates with residual consciousness level of disorders of consciousness.

BACKGROUND AND OBJECTIVE: Electroencephalography (EEG) and neuroimaging measurements have been highly encouraged to be applied in clinics of disorders of consciousness (DOC) to improve consciousness detection. We tested the relationships between neural complexity measured on EEG and residual consciousness levels in DOC patients. METHODS: Resting-state EEG was recorded from twenty-five patients with DOC. Lempel-Ziv complexity (LZC) and permutation Lempel-Ziv complexity (PLZC) were measured on the EEG, and their relationships were analyzed with the consciousness levels of the patients. RESULTS: PLZC and LZC values significantly distinguished patients with a minimally conscious state (MCS), vegetative state/unresponsive wakefulness syndrome (VS/UWS), and healthy controls. PLZC was significantly correlated with the Coma Recovery Scale-Revised (CRS-R) scores of DOC patients in the global brain, particularly in electrodes locating in the anterior and posterior brain regions. Patients with higher CRS-R scores showed higher PLZC values. The significant difference in PLZC values between MCS and VS/UWS was mainly located in the bilateral frontal and right hemisphere regions. CONCLUSION: Neural complexity measured on EEG correlates with residual consciousness levels of DOC patients. PLZC showed higher sensitivity than LZC in the classification of consciousness levels.

Behavioral scales variability in patients with prolonged disorders of consciousness.

BACKGROUND: The principal conditions differentiating disorders of consciousness (DOC) patients are the unresponsive wakefulness syndrome/vegetative state (UWS/VS) and the minimally conscious state (MCS). Many individuals who suffer from sudden-onset severe brain injury move through stages of UWS/VS and MCS before regaining full awareness. In some patients, the DOC condition is protracted for years (PDOC). In this study, we observed PDOC patients for 6 months to assess possible changes in their level of consciousness. METHODS: We enrolled 40 PDOC patients, 23 UWS/VS and 17 MCS hosted in a dedicated unit for long-term brain injury care. The time from injury was 472 ± 533 days for UWS/VS and 1090 ± 1079 days for MCS. The Wessex Head Injury Matrix (WHIM), Coma Recovery Scale-R (CRS-R), and Nociception Coma Scale were administered monthly for 6 months. RESULTS: During the period of assessment, the percentage of UWS/VS shifted from 58 to 45%, while for the MCS, from 42 to 55%. A positive correlation was found for the UWS/VS patients between the months of observation with the CRS-R total score and WHIM total numbers of behaviors (TNB). In the UWS/VS group, the CRS-R auditive and visual subscales correlated positively with the observation time. During the whole period of observation, 8 patients had constant CRS-R total scores while the WHIM TNB changed in 7 of them. CONCLUSION: Our findings demonstrated that the monthly assessment of PDOC by means of the CRS-R and WHIM was able to detect also subtle changes in consciousness level.

Consformer: Consciousness Detection Using Transformer Networks With Correntropy-Based Measures.

Consciousness detection is important in diagnosis and treatment of disorders of consciousness (DOC). Recent studies have demonstrated that electroencephalography (EEG) signals contain effective information for consciousness state evaluation. We propose two novel EEG measures: the spatiotemporal correntropy and the neuromodulation intensity, to reflect the temporal-spatial complexity in brain signals for consciousness detection. Then, we build a pool of EEG measures with different spectral, complexity and connectivity features, and propose Consformer, a transformer network to learn an adaptive optimization of features for different subjects with the attention mechanism. Experiments are carried out using a large dataset of 280 resting-state EEG recordings of DOC patients. Consformer discriminates minimally conscious state (MCS) from vegetative state (VS) with an accuracy of 85.73% and an F1-score of 86.95%, which achieves the state-of-the-art performance.

Long-term functional outcomes improved with deep brain stimulation in patients with disorders of consciousness.

BACKGROUND: Deep brain stimulation (DBS) has been preliminarily applied to treat patients with disorders of consciousness (DoCs). The study aimed to determine whether DBS was effective for treating patients with DoC and identify factors related to patients' outcomes. METHODS: Data from 365 patients with DoCs who were consecutively admitted from 15 July 2011 to 31 December 2021 were retrospectively analysed. Multivariate regression and subgroup analysis were performed to adjust for potential confounders. The primary outcome was improvement in consciousness at 1 year. RESULTS: An overall improvement in consciousness at 1 year was achieved in 32.4% (12/37) of the DBS group compared with 4.3% (14/328) of the conservative group. After full adjustment, DBS significantly improved consciousness at 1 year (adjusted OR 11.90, 95% CI 3.65-38.46, p<0.001). There was a significant treatment×follow up interaction (H=14.99, p<0.001). DBS had significantly better effects in patients with minimally conscious state (MCS) compared with patients with vegetative state/unresponsive wakefulness syndrome (p for interaction <0.001). A nomogram based on age, state of consciousness, pathogeny and duration of DoCs indicated excellent predictive performance (c-index=0.882). CONCLUSIONS: DBS was associated with better outcomes in patients with DoC, and the effect was likely to be significantly greater in patients with MCS. DBS should be cautiously evaluated by nomogram preoperatively, and randomised controlled trials are still needed.

EEG lexicality effect predicts clinical outcome in disorders of consciousness.

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

Classification of Level of Consciousness in a Neurological ICU Using Physiological Data.

BACKGROUND: Impaired consciousness is common in intensive care unit (ICU) patients, and an individual's degree of consciousness is crucial to determining their care and prognosis. However, there are no methods that continuously monitor consciousness and alert clinicians to changes. We investigated the use of physiological signals collected in the ICU to classify levels of consciousness in critically ill patients. METHODS: We studied 61 patients with subarachnoid hemorrhage (SAH) and 178 patients with intracerebral hemorrhage (ICH) from the neurological ICU at Columbia University Medical Center in a retrospective observational study of prospectively collected data. The level of consciousness was determined on the basis of neurological examination and mapped to comatose, vegetative state or unresponsive wakefulness syndrome (VS/UWS), minimally conscious minus state (MCS-), and command following. For each physiological signal, we extracted time-series features and performed classification using extreme gradient boosting on multiple clinically relevant tasks across subsets of physiological signals. We applied this approach independently on both SAH and ICH patient groups for three sets of variables: (1) a minimal set common to most hospital patients (e.g., heart rate), (2) variables available in most ICUs (e.g., body temperature), and (3) an extended set recorded mainly in neurological ICUs (absent for the ICH patient group; e.g., brain temperature). RESULTS: On the commonly performed classification task of VS/UWS versus MCS-, we achieved an area under the receiver operating characteristic curve (AUROC) in the SAH patient group of 0.72 (sensitivity 82%, specificity 57%; 95% confidence interval [CI] 0.63-0.81) using the extended set, 0.69 (sensitivity 83%, specificity 51%; 95% CI 0.59-0.78) on the variable set available in most ICUs, and 0.69 (sensitivity 56%, specificity 78%; 95% CI 0.60-0.78) on the minimal set. In the ICH patient group, AUROC was 0.64 (sensitivity 56%, specificity 65%; 95% CI 0.55-0.74) using the minimal set and 0.61 (sensitivity 50%, specificity 80%; 95% CI 0.51-0.71) using the variables available in most ICUs. CONCLUSIONS: We find that physiological signals can be used to classify states of consciousness for patients in the ICU. Building on this with intraday assessments and increasing sensitivity and specificity may enable alarm systems that alert physicians to changes in consciousness and frequent monitoring of consciousness throughout the day, both of which may improve patient care and outcomes.

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

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