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The progress in neuroimaging and electrophysiological techniques has shown substantial promise in improving the clinical assessment of disorders of consciousness (DOC). Through the examination of both stimulus-induced and spontaneous brain activity, numerous comprehensive investigations have explored variations in brain activity patterns among patients with DOC, yielding valuable insights for clinical diagnosis and prognostic purposes. Nonetheless, reaching a consensus on precise neuroimaging biomarkers for patients with DOC remains a challenge. Therefore, in this review, we begin by summarizing the empirical evidence related to neuroimaging biomarkers for DOC using various paradigms, including active, passive, and resting-state approaches, by employing task-based fMRI, resting-state fMRI (rs-fMRI), electroencephalography (EEG), and positron emission tomography (PET) techniques. Subsequently, we conducted a review of studies examining the neural correlates of consciousness in patients with DOC, with the findings holding potential value for the clinical application of DOC. Notably, previous research indicates that neuroimaging techniques have the potential to unveil covert awareness that conventional behavioral assessments might overlook. Furthermore, when integrated with various task paradigms or analytical approaches, this combination has the potential to significantly enhance the accuracy of both diagnosis and prognosis in DOC patients. Nonetheless, the stability of these neural biomarkers still needs additional validation, and future directions may entail integrating diagnostic and prognostic methods with big data and deep learning approaches., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The Coma Recovery Scale-revised (CRS-r) is the gold standard for the behavioral assessment of patients with Disorders of Consciousness (DoCs). However, the misdiagnosis rate is around 40%. For this reason, recent guidelines suggested enhancing the assessment with neurophysiological measures: among these, surface electromyography (sEMG) represents a convenient bedside solution. This work presents the use of the STRIVEfc system, a wearable device that allows CRS-r administration while recording four sEMG signals. STRIVEfc was employed in 40 sessions on 33 DoCs patients and the sEMG was analyzed to look for voluntary and consistent over threshold (OT) muscular activities. Their duration, amplitude, and number were retained and compared between patients in Unresponsive Wakefulness Syndrome (UWS) and Minimally Conscious State (MCS), revealing more numerous and significantly longer OTs in the latter group. Lastly, the EMG information was exploited to enrich the behavioral assessment by building the instrumented CRS-r score (ICRS-r). In 9/16 UWS sessions, the ICRS-r score suggested a higher-level functioning, not translated into a behavioral response, compatible with MCS diagnosis. Overall, the use of STRIVEfc allows to reveal hidden muscular patterns not detectable by the clinician, thus improving the characterization of DoCs patient's functional capabilities and supporting the diagnostic process., (© 2024. The Author(s).)

Disorders of consciousness (DoC) are characterized by alteration in arousal and/or awareness commonly caused by severe brain injury. There exists a consensus on adopting advanced neuroimaging and electrophysiological procedures to improve diagnosis/prognosis of DoC patients. Currently, these procedures are prevalently applied in a research-oriented context and their translation into clinical practice is yet to come. The aim of the study consisted in the identification of measures derived from routinary electroencephalography (EEG) able to support clinicians in the prediction of DoC patients' outcome. In the present study, a routine EEG was recorded during rest from a sample of 58 DoC patients clinically diagnosed as Unresponsive Wakefulness State (UWS) and Minimally Conscious State (MCS) and followed-up for 3 months. EEG-based features characterizing brain activity in terms of spectral content and resting state networks organization were used in a predictive machine learning model to i) identify which were the most promising features in predicting patients' exit from the DoC, regardless of the clinical diagnosis and ii) verify whether such features would have been the same best discriminating UWS from MCS or specific of the outcome prediction. A predictive machine learning model was built on EEG features related to spectral content and resting state networks which returned up to 85% of performance accuracy in outcome prediction and 76% in DoC state recognition (UWS vs MCS). We provided preliminary evidence for the exploitation of a routine EEG to improve the clinical management of non-communicative patients to be confirmed in a larger DoC population.

Purpose: EEG reactivity (EEG-R) has become widely used in intensive care units for diagnosing and prognosticating patients with disorders of consciousness. Despite efforts toward standardization, including the establishment of terminology for critical care EEG in 2012, the processes of testing and interpreting EEG-R remain inconsistent., Methods: A review was conducted on PubMed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Inclusion criteria consisted of articles published between January 2012, and November 2022, testing EEG-R on adult intensive care unit patients. Exclusion criteria included articles focused on highly specialized stimulation equipment or animal, basic science, or small case report studies. The Quality In Prognostic Studies tool was used to assess risk of bias., Results: One hundred and five articles were identified, with 26 variables collected for each. EEG-R testing varied greatly, including the number of stimuli (range: 1-8; 26 total described), stimulus length (range: 2-30 seconds), length between stimuli (range: 10 seconds-5 minutes), frequency of stimulus application (range: 1-9), frequency of EEG-R testing (range: 1-3 times daily), EEG electrodes (range: 4-64), personnel testing EEG-R (range: neurophysiologists to nonexperts), and sedation protocols (range: discontinuing all sedation to no attempt). EEG-R interpretation widely varied, including EEG-R definitions and grading scales, personnel interpreting EEG-R (range: EEG specialists to nonneurologists), use of quantitative methods, EEG filters, and time to detect EEG-R poststimulation (range: 1-30 seconds)., Conclusions: This study demonstrates the persistent heterogeneity of testing and interpreting EEG-R over the past decade, and contributing components were identified. Further many institutional efforts must be made toward standardization, focusing on the reproducibility and unification of these methods, and detailed documentation in the published literature., Competing Interests: E. K. Ritzl is a medical legal expert witness. The remaining authors have disclosed that they do not have any conflicts of interest., (Copyright © 2024 by the American Clinical Neurophysiology Society.)

Purpose: The 2HELPS2B score is an invaluable tool for assessing seizure risk in critically ill patients with unconsciousness. However, this can be challenging for non-epileptologists to use owing to its reliance on electroencephalogram (EEG) analysis. Thus, identifying clinical manifestations associated with high 2HELPS2B scores is crucial., Methods: We examined patients who underwent EEG for acute impaired consciousness in the emergency department between 2020 and 2022. We evaluated the clinical manifestations immediately prior to the EEG tests and identified those associated with a 2HELPS2B score ≥ 2. Additionally, we investigated clinical outcomes in accordance with these manifestations and the 2HELPS2B score., Results: A total of 78 patients were included in this study. While the median 2HELPS2B score was 1 (range: 0-6), 13 patients (16.6%) showed electrographic/electroclinical seizures or status epilepticus and 16 patients (20.5%) showed ictal-interictal continuum in their EEGs. Abnormal muscle tonus (p = 0.034) and eye deviation (p = 0.021) were Significantly associated with a 2HELPS2B score ≥ 2. The presence of these manifestations (p < 0.001) and a 2HELPS2B score ≥ 2 (p < 0.001) were both significantly associated with a favorable response to anti-seizure medication. Conversely, patients with a 2HELPS2B score ≥ 2 who exhibited these clinical manifestations were more likely to be non-dischargeable (p = 0.053), have prolonged intensive care unit stays (p = 0.002), or require extended ventilator use (p = 0.082)., Conclusion: Abnormal muscle tonus and eye deviation were significant manifestations compatible with a 2HELPS2B score ≥ 2 and may indicate an increased risk of seizures or the severity of the epileptic condition., Competing Interests: Declaration of competing interest On behalf of all authors, the corresponding author states that there are no conflicts of interest to disclose, (Copyright © 2024 Elsevier B.V. All rights reserved.)

We aimed to investigate the electrocardiogram (ECG) features in persons with chronic disorders of consciousness (DOC, ≥ 29 days since injury, DSI) resulted from the most severe brain damages. The ECG data from 30 patients with chronic DOC and 18 healthy controls (HCs) were recorded during resting wakefulness state for about five minutes. The patients were classified into vegetative state (VS) and minimally conscious state (MCS). Eight ECG metrics were extracted for comparisons between the subject subgroups, and regression analysis of the metrics were conducted on the DSI (29-593 days). The DOC patients exhibit a significantly higher heart rate (HR, p = 0.009) and lower values for SDNN (p = 0.001), CVRR (p = 0.009), and T-wave amplitude (p < 0.001) compared to the HCs. However, there're no significant differences in QRS, QT, QTc, or ST amplitude between the two groups (p > 0.05). Three ECG metrics of the DOC patients-HR, SDNN, and CVRR-are significantly correlated with the DSI. The ECG abnormalities persist in chronic DOC patients. The abnormalities are mainly manifested in the rhythm features HR, SDNN and CVRR, but not the waveform features such as QRS width, QT, QTc, ST and T-wave amplitudes., (© 2024. International Federation for Medical and Biological Engineering.)

Background: Psychedelic substances reliably induce marked altered states of consciousness (ASC), which may be important for lasting effects and clinical outcomes of psychedelic intervention. Several instruments are available to measure the acute psychedelic experience, of which the Five Dimensional Altered States of Consciousness Questionnaire (5D-ASC) is commonly used. The questionnaire can be scored and analyzed as having five dimensions or 11 subscales, but the two have not been evaluated with comparable factor analysis methods., Methods: The Danish translation of the 5D-ASC was completed by one sample of healthy volunteers receiving psilocybin in a laboratory setting ( N  = 47) and one sample of recreative users5D-ASC of psychedelics ( N  = 550), who reported retrospectively through an online survey based on their most recent experience with psilocybin. We calculated internal consistency measures of Cronbach's alpha and McDonald's omega, conducted a confirmatory factor analysis of the previously suggested factor structures, and tested for possible associations between the 5D-ASC total scores and dose, setting, and intention. For the 11 subscales, we reported omega-sem (composite reliability) using the parameters of the fitted confirmatory factor analyses model., Results: Confirmatory factor analysis showed that the 11 subscales had a good fit to data and showed a better fit compared to the originally proposed five-dimensional solution and good internal consistencies. We further found that the 5D-ASC total scores correlated positively with the dose in the recreative sample. We found no correlations between 5D-ASC total scores and intention or setting., Discussion: We find the Danish 5D-ASC to be a valid tool for measuring ASC among Danish-speaking individuals., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Clinical management of persons with disorders of consciousness (DoC) is dedicated largely to optimizing recovery. However, selecting a measure to evaluate the extent of recovery is challenging because few measures are designed to precisely assess the full range of potential outcomes, from prolonged DoC to return of preinjury functioning. Measures that are designed specifically to assess persons with DoC are often performance-based and only validated for in-person use. Moreover, there are no published recommendations addressing which outcome measures should be used to evaluate DoC recovery. The resulting inconsistency in the measures selected by individual investigators to assess outcome prevents comparison of results across DoC studies. The National Institute of Neurological Disorders and Stroke (NINDS) common data elements (CDEs) is an amalgamation of standardized variables and tools that are recommended for use in studies of neurologic diseases and injuries. The Neurocritical Care Society Curing Coma Campaign launched an initiative to develop CDEs specifically for DoC and invited our group to recommend CDE outcomes and endpoints for persons with DoCs., Methods: The Curing Coma Campaign Outcomes and Endpoints CDE Workgroup, consisting of experts in adult and pediatric neurocritical care, neurology, and neuroscience, used a previously established five-step process to identify and select candidate CDEs: (1) review of existing NINDS CDEs, (2) nomination and systematic vetting of new CDEs, (3) CDE classification, (4) iterative review and approval of panel recommendations, and (5) development of case report forms., Results: Among hundreds of existing NINDS outcome and endpoint CDE measures, we identified 20 for adults and 18 for children that can be used to assess the full range of recovery from coma. We also proposed 14 new outcome and endpoint CDE measures for adults and 5 for children., Conclusions: The DoC outcome and endpoint CDEs are a starting point in the broader effort to standardize outcome evaluation of persons with DoC. The ultimate goal is to harmonize DoC studies and allow for more precise assessment of outcomes after severe brain injury or illness. An iterative approach is required to modify and adjust these outcome and endpoint CDEs as new evidence emerges., (© 2024. Springer Science+Business Media, LLC, part of Springer Nature and Neurocritical Care Society.)

Functional neuroimaging and electrophysiological assessments can identify evidence of residual consciousness and cognition in patients with prolonged disorders of consciousness (PDOC) who are otherwise behaviourally unresponsive. These functional neurodiagnostics are increasingly available in clinical settings and are recommended by international clinical guidelines to reduce diagnostic and prognostic uncertainty, and thereby assist family caregivers in their best-interests decision-making. Nevertheless, little is known about how family caregivers make sense of the results of these state-of-the-art functional neurodiagnostics. By applying Interpretative Phenomenological Analysis (IPA) to interviews with family caregivers of patients with diagnoses of PDOC who had received a functional neurodiagnostic assessment, we identify three primary themes of sense-making: The special significance of "brain scans"; A dynamic sense-making process; Holding on to hope and holding on to the person. These themes highlight the challenges of helping family caregivers to balance the relative importance of functional neurodiagnostic results with other clinical assessments and identify an ability of family caregivers to hold a contradiction in which they hope for recovery but simultaneously express a rational understanding of evidence to the contrary. We offer several recommendations for the ways in which family caregivers can be better supported to make sense of the results of functional neurodiagnostics.

Objective . Accurately diagnosing patients with disorders of consciousness (DOC) is challenging and prone to errors. Recent studies have demonstrated that EEG (electroencephalography), a non-invasive technique of recording the spontaneous electrical activity of brains, offers valuable insights for DOC diagnosis. However, some challenges remain: (1) the EEG signals have not been fully used; and (2) the data scale in most existing studies is limited. In this study, our goal is to differentiate between minimally conscious state (MCS) and unresponsive wakefulness syndrome (UWS) using resting-state EEG signals, by proposing a new deep learning framework. Approach . We propose DOCTer, an end-to-end framework for DOC diagnosis based on EEG. It extracts multiple pertinent features from the raw EEG signals, including time-frequency features and microstates. Meanwhile, it takes clinical characteristics of patients into account, and then combines all the features together for the diagnosis. To evaluate its effectiveness, we collect a large-scale dataset containing 409 resting-state EEG recordings from 128 UWS and 187 MCS cases. Main results . Evaluated on our dataset, DOCTer achieves the state-of-the-art performance, compared to other methods. The temporal/spectral features contributes the most to the diagnosis task. The cerebral integrity is important for detecting the consciousness level. Meanwhile, we investigate the influence of different EEG collection duration and number of channels, in order to help make the appropriate choices for clinics. Significance . The DOCTer framework significantly improves the accuracy of DOC diagnosis, helpful for developing appropriate treatment programs. Findings derived from the large-scale dataset provide valuable insights for clinics., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Background: Disorders of Consciousness (DoC) caused by severe brain injuries represent a challenging clinical entity, which is easy to misdiagnosis and lacks effective treatment options. Repetitive Transcranial Magnetic Stimulation (rTMS) is a non-invasive neuroelectric stimulation method that shows promise in improving consciousness for DoC, especially in minimally conscious state (MCS). However, there is little evidence of its effectiveness, especially in RCT studies., Methods: Twenty MCS patients participated in a double-blind, randomized, crossover, sham-controlled clinical study to evaluate the safety and efficacy of rTMS for MCS. Subjects were randomized into two groups: one group received rTMS-active for 10 consecutive days (n = 10), and the other group received rTMS-sham for 10 consecutive days (n = 10). After a 10-day washout period, the two groups were crossed over and received the opposite treatment. the rTMS protocol consisted of 2,000 pulses per day in the left dorsolateral prefrontal cortex (L-DLPFC), sent at 10 Hz. The stimulation intensity was 90% of the resting motor threshold. Coma Recovery Scale Revised (CRS-R), the main evaluation index, was evaluated before and after each phase in a double-blind manner. Meanwhile RS-EEG and TMS-EEG data were acquired and relative alpha power (RAP), and perturbational complexity index based on state transitions (PCIst) were caculated., Results: One-way ANOVA revealed significantly higher scores in rTMS-active treatment compared to rTMS-sham across various measures, including CRS-R total score, RAP, PCIst (all P < 0.05). Among the 20 MCS patients, 7 (35%) were identified as responders following rTMS treatment. Compared to rTMS-sham, responder scores for CRS-R, RAP, and PCIst (all P < 0.05) were significantly elevated after rTMS-active treatment. Conversely, there was no significant difference observed in non-responders. Furthermore, post-hoc analysis revealed that baseline PCIst was significantly higher in responders than non-responders. Upon a 6-month follow-up, CRS-R scores significantly increased in all 20 patients (P = 0.026). However, the responder group exhibited a more favorable prognosis compared to the non-responder group (P = 0.031)., Conclusions: Applying 10 Hz rTMS to L-DLPFC significantly increased consciousness level in MCS patients. PCIst is a neurophysiological index that has the potential to evaluate and predict therapeutic efficacy., Trial Registration: www., Clinicaltrials: gov , identifier: NCT05187000., (© 2024. The Author(s).)

Objectives: For critically ill patients with acute severe brain injuries, consciousness may reemerge before behavioral responsiveness. The phenomenon of covert consciousness (i.e., cognitive motor dissociation) may be detected by advanced neurotechnologies such as task-based functional MRI (fMRI) and electroencephalography (EEG) in patients who appear unresponsive on the bedside behavioral examination. In this narrative review, we summarize the state-of-the-science in ICU detection of covert consciousness. Further, we consider the prognostic and therapeutic implications of diagnosing covert consciousness in the ICU, as well as its potential to inform discussions about continuation of life-sustaining therapy for patients with severe brain injuries., Data Sources: We reviewed salient medical literature regarding covert consciousness., Study Selection: We included clinical studies investigating the diagnostic performance characteristics and prognostic utility of advanced neurotechnologies such as task-based fMRI and EEG. We focus on clinical guidelines, professional society scientific statements, and neuroethical analyses pertaining to the implementation of advanced neurotechnologies in the ICU to detect covert consciousness., Data Extraction and Data Synthesis: We extracted study results, guideline recommendations, and society scientific statement recommendations regarding the diagnostic, prognostic, and therapeutic relevance of covert consciousness to the clinical care of ICU patients with severe brain injuries., Conclusions: Emerging evidence indicates that covert consciousness is present in approximately 15-20% of ICU patients who appear unresponsive on behavioral examination. Covert consciousness may be detected in patients with traumatic and nontraumatic brain injuries, including patients whose behavioral examination suggests a comatose state. The presence of covert consciousness in the ICU may predict the pace and extent of long-term functional recovery. Professional society guidelines now recommend assessment of covert consciousness using task-based fMRI and EEG. However, the clinical criteria for patient selection for such investigations are uncertain and global access to advanced neurotechnologies is limited., (Copyright © 2024 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

Purpose of Review: As artificial intelligence and machine learning technologies continue to develop, they are being increasingly used to improve the scientific understanding and clinical care of patients with severe disorders of consciousness following acquired brain damage. We here review recent studies that utilized these techniques to reduce the diagnostic and prognostic uncertainty in disorders of consciousness, and to better characterize patients' response to novel therapeutic interventions., Recent Findings: Most papers have focused on differentiating between unresponsive wakefulness syndrome and minimally conscious state, utilizing artificial intelligence to better analyze functional neuroimaging and electroencephalography data. They often proposed new features using conventional machine learning rather than deep learning algorithms. To better predict the outcome of patients with disorders of consciousness, recovery was most often based on the Glasgow Outcome Scale, and traditional machine learning techniques were used in most cases. Machine learning has also been employed to predict the effects of novel therapeutic interventions (e.g., zolpidem and transcranial direct current stimulation)., Summary: Artificial intelligence and machine learning can assist in clinical decision-making, including the diagnosis, prognosis, and therapy for patients with disorders of consciousness. The performance of these models can be expected to be significantly improved by the use of deep learning techniques., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Objectives: The aim of this study was to analyze the Chinese version of the Nociception Coma Scale-Revised in patients with prolonged disorders of consciousness within the framework of Rasch modeling, including investigating the invariance of total scores across different etiologies of disorders of consciousness., Design: Prospective psychometric study., Participants: Patients with prolonged disorders of consciousness from the Rehabilitation and Neurology units in hospital., Interventions: None., Main Outcome Measure: The Nociception Coma Scale-Revised was undertaken by trained raters and the Coma Recovery Scale-Revised was used to assess patients' consciousness. The psychometric properties within the Rasch model including item-person targeting, reliability and separation, item fit, unidimensionality, and differential item functioning were assessed., Results: 84 patients with prolonged disorders of consciousness (mean age 53 years; mean injury 5 months; 42 with Minimally Conscious State and 42 with Unresponsive Wakefulness Syndrome) of 252 observations were enrolled in the study. Through the procedure of repeated assessment and differential item function, a lower item bias Rasch set was purified. The Rasch model assumptions were examined and met, with item reliability and validity meeting the recommended threshold., Conclusions: The Chinese version of the Nociception Coma Scale-Revised demonstrated unidimensionality, good reliability and separation, and good item fit, but dissatisfied person fit and item-person targeting. The verbal subscale showed a notable discrepancy between person responses and the difficulty of the items, suggesting limited clinical significance., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Disorders of consciousness represent altered mental status at its most severe, comprising a continuum between coma, the vegetative state/unresponsive wakefulness syndrome, the minimally conscious state, and emergence from the minimally conscious state. Patients often transition between these levels throughout their recovery, and determining a patient's current level can be challenging, particularly in the acute care setting. Although healthcare providers have classically relied on a bedside neurological exam or the Glasgow Coma Scale to aid with assessment of consciousness, studies have identified multiple limitations of doing so. Neurobehavioral assessment measures, such as the Coma Recovery Scale-Revised, have been developed to address these shortcomings. Each behavioral metric has strengths as well as weaknesses when applied in the acute care setting. In this review, we appraise common assessment approaches, outline alternative measures for fine-tuning these assessments in the acute care setting, and highlight strategies for implementing these practices in an interdisciplinary manner., Competing Interests: None declared., (Thieme. All rights reserved.)

Accurate prediction of the recovery of Disorders of Consciousness (DoC) is of paramount significance for clinicians and families. Serum total bilirubin (TBIL) formed by activation of heme oxygenase 2, is associated with incidence and prognosis of cardiovascular and cerebrovascular diseases. However, studies that based TBIL and DoC are limited. The study attempted to examine the association between serum TBIL levels and prognosis in patients with DoC. One hundred and sixty-eight patients with DoC in the Second hospital of Shandong University from June 2021 to June 2023 were recruited. The clinical characteristics and venous blood samples were collected within 24 h after admission. The diagnosis of DoC was determined by two skilled investigators employing various behavioral evaluations along the coma recovery scale-revised (CRS-R) and the investigators conducted follow-up assessments of diagnosis at 1, 3, and 6 months after admission. For statistical analysis, we categorized patients with an improvement in clinical diagnosis from study entry as having a "good outcome". In total, 139 individuals enrolled in the study. The median TBIL level was 8.2 μmol/L. Good recovery of DoC at 1, 3, and 6 months occurred in 25 (18.0%), 41 (29.5%), and 56 (40.3%) patients, respectively. After full adjustment, a significant association was found between TBIL levels and the prognosis of DoC at 1, 3, and 6 months. When TBIL levels were analyzed as categorical variables, an increasing trend in the tertiles of TBIL levels demonstrated a significant positive association with the recovery of DoC at 1, 3, and 6 months. Stratified analysis revealed that the association between serum TBIL levels and the recovery of DoC remained consistent across different sub-populations. A high serum TBIL level is associated with an improved likelihood of recovery of DoC. Additional research is required to elucidate the underlying pathophysiological causal association between TBIL levels and DoC., (© 2024. The Author(s).)

Prolonged disorders of consciousness (pDOC) are pathological conditions of alterations in consciousness caused by various severe brain injuries, profoundly affecting patients' life ability and leading to a huge burden for both the family and society. Exploring the mechanisms underlying pDOC and accurately assessing the level of consciousness in the patients with pDOC provide the basis of developing therapeutic strategies. Research of non-invasive functional neuroimaging technologies, such as functional magnetic resonance (fMRI) and scalp electroencephalography (EEG), have demonstrated that the generation, maintenance and disorders of consciousness involve functions of multiple cortical and subcortical brain regions, and their networks. Invasive intracranial neuroelectrophysiological technique can directly record the electrical activity of subcortical or cortical neurons with high signal-to-noise ratio and spatial resolution, which has unique advantages and important significance for further revealing the brain function and disease mechanism of pDOC. Here we reviewed the current progress of pDOC research based on two intracranial electrophysiological signals, spikes reflecting single-unit activity and field potential reflecting multi-unit activities, and then discussed the current challenges and gave an outlook on future development, hoping to promote the study of pathophysiological mechanisms related to pDOC and provide guides for the future clinical diagnosis and therapy of pDOC.

For patients with disorders of consciousness (DoC), accurate assessment of residual consciousness levels and cognitive abilities is critical for developing appropriate rehabilitation interventions. In this study, we investigated the potential of electrooculography (EOG) in assessing language processing abilities and consciousness levels. Patients' EOG data and related electrophysiological data were analysed before and after explicit language learning. The results showed distinct differences in vocabulary learning patterns among patients with varying levels of consciousness. While minimally conscious patients showed significant neural tracking of artificial words and notable learning effects similar to those observed in healthy controls, whereas patients with unresponsive wakefulness syndrome did not show such effects. Correlation analysis further indicated that EOG detected vocabulary learning effects with comparable validity to electroencephalography, reinforcing the credibility of EOG indicator as a diagnostic tool. Critically, EOG also revealed significant correlations between individual patients' linguistic learning performance and their Oromotor/verbal function as assessed through behavioural scales. In conclusion, this study explored the differences in language processing abilities among patients with varying consciousness levels. By demonstrating the utility of EOG in evaluating consciousness and detecting vocabulary learning effects, as well as its potential to guide personalised rehabilitation, our findings indicate that EOG indicators show promise as a rapid, accurate and effective additional tool for diagnosing and managing patients with DoC., Competing Interests: Declaration of competing interest The authors report no competing interests., (Copyright © 2024. Published by Elsevier Inc.)

The purpose of this study was to differentiate clinically meaningful improvement or deterioration from normal fluctuations in patients with disorders of consciousness (DoC) following severe brain injury. We computed indices of responsiveness for the Coma Recovery Scale-Revised (CRS-R) using data from a clinical trial of 180 participants with DoC. We used CRS-R scores from baseline (enrollment in a clinical trial) and a 4-week follow-up assessment period for these calculations. To improve precision, we transformed ordinal CRS-R total scores (0-23 points) to equal-interval measures on a 0-100 unit scale using Rasch Measurement theory. Using the 0-100 unit total Rasch measures, we calculated distribution-based 0.5 standard deviation (SD) minimal clinically important difference, minimal detectable change using 95% confidence intervals, and conditional minimal detectable change using 95% confidence intervals. The distribution-based minimal clinically important difference evaluates group-level changes, whereas the minimal detectable change values evaluate individual-level changes. The minimal clinically important difference and minimal detectable change are derived using the overall variability across total measures at baseline and 4 weeks. The conditional minimal detectable change is generated for each possible pair of CRS-R Rasch person measures and accounts for variation in standard error across the scale. We applied these indices to determine the proportions of participants who made a change beyond measurement error within each of the two subgroups, based on treatment arm (amantadine hydrochloride or placebo) or categorization of baseline Rasch person measure to states of consciousness (i.e., unresponsive wakefulness syndrome and minimally conscious state). We compared the proportion of participants in each treatment arm who made a change according to the minimal detectable change and determined whether they also changed to another state of consciousness. CRS-R indices of responsiveness (using the 0-100 transformed scale) were as follows: 0.5SD minimal clinically important difference = 9 units, minimal detectable change = 11 units, and the conditional minimal detectable change ranged from 11 to 42 units. For the amantadine and placebo groups, 70% and 58% of participants showed change beyond measurement error using the minimal detectable change, respectively. For the unresponsive wakefulness syndrome and minimally conscious state groups, 54% and 69% of participants changed beyond measurement error using the minimal detectable change, respectively. Among 115 participants (64% of the total sample) who made a change beyond measurement error, 29 participants (25%) did not change state of consciousness. CRS-R indices of responsiveness can support clinicians and researchers in discerning when behavioral changes in patients with DoC exceed measurement error. Notably, the minimal detectable change can support the detection of patients who make a "true" change within or across states of consciousness. Our findings highlight that the continued use of ordinal scores may result in incorrect inferences about the degree and relevance of a change score.

Background: Identifying covert consciousness in intensive care unit (ICU) patients with coma and other disorders of consciousness (DoC) is crucial for treatment decisions, but sensitive low-cost bedside markers are missing. We investigated whether automated pupillometry combined with passive and active cognitive paradigms can detect residual consciousness in ICU patients with DoC., Methods: We prospectively enrolled clinically low-response or unresponsive patients with traumatic or nontraumatic DoC from ICUs of a tertiary referral center. Age-matched and sex-matched healthy volunteers served as controls. Patients were categorized into clinically unresponsive (coma or unresponsive wakefulness syndrome) or clinically low-responsive (minimally conscious state or better). Using automated pupillometry, we recorded pupillary dilation to passive (visual and auditory stimuli) and active (mental arithmetic) cognitive paradigms, with task-specific success criteria (e.g., ≥ 3 of 5 pupillary dilations on five consecutive mental arithmetic tasks)., Results: We obtained 699 pupillometry recordings at 178 time points from 91 ICU patients with brain injury (mean age 60 ± 13.8 years, 31% women, and 49.5% nontraumatic brain injuries). Recordings were also obtained from 26 matched controls (59 ± 14.8 years, 38% women). Passive paradigms yielded limited distinctions between patients and controls. However, active paradigms enabled discrimination between different states of consciousness. With mental arithmetic of moderate complexity, ≥ 3 pupillary dilations were seen in 17.8% of clinically unresponsive patients and 50.0% of clinically low-responsive patients (odds ratio 4.56, 95% confidence interval 2.09-10.10; p < 0.001). In comparison, 76.9% healthy controls responded with ≥ 3 pupillary dilations (p = 0.028). Results remained consistent across sensitivity analyses using different thresholds for success. Spearman's rank analysis underscored the robust association between pupillary dilations during mental arithmetic and consciousness levels (rho = 1, p = 0.017). Notably, one behaviorally unresponsive patient demonstrated persistent command-following behavior 2 weeks before overt signs of awareness, suggesting prolonged cognitive motor dissociation., Conclusions: Automated pupillometry combined with mental arithmetic can identify cognitive efforts, and hence covert consciousness, in ICU patients with acute DoC., (© 2024. The Author(s).)

The potential influence of pituitary-related hormones (including both pituitary gland and target gland hormones) on functional recovery after traumatic brain injury has been observed. However, the relationship between these hormones and the recovery of consciousness in patients with disorders of consciousness (DOC) remains unclear. In this retrospective and observational study, 208 patients with DOC were recruited. According to the Glasgow Outcome Scale (GOS) scores after 6 months, patients with DOC were categorized into two subgroups: a favorable prognosis subgroup ( n  = 38) comprising those who regained consciousness (GOS score ≥3), and a poor prognosis subgroup ( n  = 156) comprising those who remained in DOC (GOS score <3). Comparative analyses of pituitary-related hormone levels between the two subgroups were conducted. Further, a binary logistic regression analysis was conducted to assess the predictive value of pituitary-related hormones for the patients' prognosis. The favorable prognosis subgroup showed a significant increase in adrenocorticotropic hormone (ACTH) levels ( p  = 0.036). Moreover, higher ACTH levels and shorter days since injury were significantly associated with a better prognosis, with odds ratios (ORs) of 0.928 (95% confidence interval [CI] = 0.873-0.985, p  = 0.014) and 1.015 (95% CI = 1.005-1.026, p  = 0.005), respectively. A subsequent receiver operating characteristic (ROC) analysis demonstrated the potential to predict patients' prognosis with an area under the curve value of 0.78, an overall accuracy of 75.5%, a sensitivity of 77.5%, and a specificity of 66.7%. Our findings indicate that ACTH levels could serve as a clinically valuable and convenient predictor for patients' prognosis.

Although auditory stimuli benefit patients with disorders of consciousness (DOC), the optimal stimulus remains unclear. We explored the most effective electroencephalography (EEG)-tracking method for eliciting brain responses to auditory stimuli and assessed its potential as a neural marker to improve DOC diagnosis. We collected 58 EEG recordings from patients with DOC to evaluate the classification model's performance and optimal auditory stimulus. Using non-linear dynamic analysis (approximate entropy [ApEn]), we assessed EEG responses to various auditory stimuli (resting state, preferred music, subject's own name [SON], and familiar music) in 40 patients. The diagnostic performance of the optimal stimulus-induced EEG classification for vegetative state (VS)/unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) was compared with the Coma Recovery Scale-Revision in 18 patients using the machine learning cascade forward backpropagation neural network model. Regardless of patient status, preferred music significantly activated the cerebral cortex. Patients in MCS showed increased activity in the prefrontal pole and central, occipital, and temporal cortices, whereas those in VS/UWS showed activity in the prefrontal and anterior temporal lobes. Patients in VS/UWS exhibited the lowest preferred music-induced ApEn differences in the central, middle, and posterior temporal lobes compared with those in MCS. The resting state ApEn value of the prefrontal pole (0.77) distinguished VS/UWS from MCS with 61.11% accuracy. The cascade forward backpropagation neural network tested for ApEn values in the resting state and preferred music-induced ApEn differences achieved an average of 83.33% accuracy in distinguishing VS/UWS from MCS (based on K-fold cross-validation). EEG non-linear analysis quantifies cortical responses in patients with DOC, with preferred music inducing more intense EEG responses than SON and familiar music. Machine learning algorithms combined with auditory stimuli showed strong potential for improving DOC diagnosis. Future studies should explore the optimal multimodal sensory stimuli tailored for individual patients.Trial registration: The study is registered in the Chinese Registry of Clinical Trials (Approval no: KYLL-2023-414, Registration code: ChiCTR2300079310)., (© 2024. The Author(s).)

Objective. To improve the understanding and diagnostic accuracy of disorders of consciousness (DOC) by quantifying transcranial magnetic stimulation (TMS) evoked electroencephalography connectivity using permutation conditional mutual information (PCMI). Approach. PCMI can characterize the functional connectivity between different brain regions. This study employed PCMI to analyze TMS-evoked cortical connectivity (TEC) in 154 DOC patients and 16 normal controls, focusing on optimizing parameter selection for PCMI (Data length, Order length, Time delay). We compared short-range and long-range PCMI values across different consciousness states-unresponsive wakefulness syndrome (UWS), minimally conscious state (MCS), and normal (NOR)-and assessed various feature selection and classification techniques to distinguish these states. Main results. (1) PCMI can quantify TEC. We found optimal parameters to be Data length: 500 ms; Order: 3; Time delay: 6 ms. (2) TMS evoked potentials (TEPs) for NOR showed a rich response, while MCS patients showed only a few components, and UWS patients had almost no significant components. The values of PCMI connectivity metrics demonstrated its usefulness for measuring cortical connectivity evoked by TMS. From NOR to MCS to UWS, the number and strength of TEC decreased. Quantitative analysis revealed significant differences in the strength and number of TEC in the entire brain, local regions and inter-regions among different consciousness states. (3) A decision tree with feature selection by mutual information performed the best (balanced accuracy: 87.0% and accuracy: 83.5%). This model could accurately identify NOR (100.0%), but had lower identification accuracy for UWS (86.5%) and MCS (74.1%). Significance. The application of PCMI in measuring TMS-evoked connectivity provides a robust metric that enhances our ability to differentiate between various states of consciousness in DOC patients. This approach not only aids in clinical diagnosis but also contributes to the broader understanding of cortical connectivity and consciousness., (© 2024 IOP Publishing Ltd.)

Background: Mortality in patients with acute onset of impaired consciousness is high: as many as 10% do not survive. The spectrum of differential diagnoses is wide, and more than one underlying condition is found in one-third of all cases. In this article, we describe a structured approach to patients with acute onset of impaired consciousness in the emergency department., Methods: This review is based on pertinent articles retrieved by a selective search of PubMed and on the AWMF guidelines on the most common causes of impairment of consciousness., Results: Impairments of consciousness are classified as quantitative (reduced wakefulness) or qualitative (abnormal content of consciousness). Of all such cases, 45-50% have a primary neurological cause, and approximately 20% are of metabolic or infectious origin. Some cases are due to intoxications, cardiovas - cular disorders, or psychiatric disorders. Important warning signs ("red flags") in acute onset of impaired consciousness are a hyperacute onset, pupillomotor disturbances, focal neurologic deficits, meningismus, headache, tachycardia and tachypnea (with or without fever), muscle contractions, and skin abnormalities. Patients with severely impaired consciousness should be initially treated in the shock room according to the ABCDE scheme., Conclusion: Acute onset of impaired consciousness is a medical emergency. Red flags must be rapidly recognized and treatment initiated immediately. Patients with severely impaired consciousness of new onset and uncertain cause, status epilepticus, lack of protective reflexes, or a new, acute neuro - logic deficit should be admitted via the resuscitation room.

Objectives: Accurate classification of disorders of consciousness (DoC) is key in developing rehabilitation plans after brain injury. The Coma Recovery Scale-Revised (CRS-R) is a sensitive measure of consciousness validated in the rehabilitation phase of care. We tested the feasibility, safety, and impact of CRS-R-guided rehabilitation in the ICU for patients with DoC after acute hemorrhagic stroke., Design: Retrospective cohort study., Setting: This single-center study was conducted in the neurocritical care unit at the University of Maryland Medical Center., Patients: We analyzed records from consecutive patients with subarachnoid hemorrhage (SAH) or intracerebral hemorrhage (ICH), who underwent serial CRS-R assessments during ICU admission from April 1, 2018, to December 31, 2021, where CRS-R less than 8 is vegetative state/unresponsive wakefulness syndrome (VS/UWS); CRS-R greater than or equal to 8 is a minimally conscious state (MCS)., Interventions: None., Measurements and Main Results: Outcomes included adverse events during CRS-R evaluations and associations between CRS-R and discharge disposition, therapy-based function, and mobility. We examined the utility of CRS-R compared with other therapist clinical assessment tools in predicting discharge disposition. Seventy-six patients (22 SAH, 54 ICH, median age = 59, 50% female) underwent 276 CRS-R sessions without adverse events. Discharge to acute rehabilitation occurred in 4.4% versus 41.9% of patients with a final CRS-R less than 8 and CRS-R greater than or equal to 8, respectively (odds ratio [OR] 13.4; 95% CI, 2.7-66.1; p < 0.001). Patients with MCS on final CRS-R completed more therapy sessions during hospitalization and had improved mobility and functional performance. Compared with other therapy assessment tools, the CRS-R had the best performance in predicting discharge disposition (area under the curve: 0.83; 95% CI, 0.72-0.94; p < 0.0001)., Conclusions: Early neurorehabilitation guided by CRS-R appears to be feasible and safe in the ICU following hemorrhagic stroke complicated by DoC and may enhance access to inpatient rehabilitation, with the potential for lasting benefit on recovery. Further research is needed to assess generalizability and understand the impact on long-term outcomes., Competing Interests: Drs. Woodward, Ahmed, Edlow, Braun, and Parikh received support for article research from the National Institutes of Health. Dr. Woodward’s institution received funding from the Institute for Clinical and Translational Research (ICTR) (5TL1TR003100-04). Dr. Morris received funding from the National Institute of Neurological Disorders and Stroke (1R01NS131606); he disclosed he serves on the editorial board of Neurology. Dr. Ciryam’s institution received funding from GEn1E Lifesciences, the Henry M. Jackson Foundation, and the Passano Foundation. Dr. Braun’s institution received funding from the National Center for Medical Rehabilitation Research (R03) and the National Institutes on Aging; they disclosed they are a University of Maryland Rehabilitation Institute–Medical Executive Committee member. Dr. Parihk received funding from the University of Maryland Baltimore, the ICTR, the National Center for Advancing Translational Sciences (1UL1TR003098), and the Korean Neurocritical Care Society. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

Cognitive motor dissociation (CMD) is characterized by a dissociation between volitional brain responses and motor control, detectable only through techniques such as electroencephalography (EEG) and functional magnetic resonance imaging. Hence, it has recently emerged as a major challenge in the assessment of patients with disorders of consciousness. Specifically, this review focuses on the prognostic implications of CMD detection during the acute stage of brain injury. CMD patients were identified in each diagnostic category (coma, unresponsive wakefulness syndrome/vegetative state, minimally conscious state minus) with a relatively similar prevalence of around 20%. Current knowledge tends to indicate that the diagnosis of CMD in the acute phase often predicts a more favorable clinical outcome compared with other unresponsive non-CMD patients. Nevertheless, the review underscores the limited research in this domain, probably at least partially explained by its nascent nature and the lack of uniformity in the nomenclature for CMD-related disorders, hindering the impact of the literature in the field., Competing Interests: None declared., (Thieme. All rights reserved.)

Background: Research on disorders of consciousness in children is scarce and includes disparate and barely comparable participants and assessment instruments and therefore provides inconclusive information on the clinical progress and recovery in this population. This study retrospectively investigated the neurobehavioral progress and the signs of transition between states of consciousness in a group of children admitted to a rehabilitation program either with an unresponsive wakefulness syndrome (UWS) or in a minimally conscious state (MCS)., Methods: Systematic weekly assessments were conducted with the Coma Recovery Scale-Revised (CRS-R) until emergence from MCS, discharge, or death., Results: Twenty-one children, nine admitted with a UWS and 12 admitted in an MCS, were included in the study. Four children with a UWS transitioned to an MCS with a CRS-R of 10 (9.2 to 12.2) by showing visual pursuit, visual fixation, or localization to noxious stimulation. Twelve children emerged from the MCS with a CRS-R of 20.5 (19 to 21.7). Children who emerged from the MCS had had a shorter time postinjury and higher CRS-R at admission, compared with those who did not emerge., Conclusions: Almost half of the children who were admitted with a UWS transitioned to an MCS, and almost all who were admitted in an MCS emerged from this state. Children who emerged had shorter times since injury and higher scores on the CRS-R at admission, compared with those who did not emerge., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objective: Assessing recovery potential in patients with disorders of consciousness (DoC) is pivotal for guiding clinical and ethical decisions. We conducted a mega-analysis of individual patient data to understand (1) if a time threshold exists, beyond which regaining consciousness is almost impossible, and (2) how recovery varies based on factors such as diagnosis, etiology, age, sex, and neuropsychological status., Methods: A systematic literature search revealed a total of 3290 patients. In this sample, we performed a Cox proportional hazards analysis for interval censored data., Results: We observed a late saturation of probability to regain consciousness in Kaplan-Meier curves, and the annual rate of recovery was remarkably stable, in that approximately 35% of patients regained consciousness per year. Patients in minimally conscious state (MCS) recovered more frequently than patients in unresponsive wakefulness syndrome (UWS). No significant difference was observed between the recovery dynamics of MCS subgroups: MCS+ and MCS-. Patients with hypoxic brain lesions showed worse recovery rate than patients with traumatic brain injury and patients with vascular brain lesions, while the latter two categories did not differ from each other. Male patients had moderately better chance to regain consciousness. While younger UWS patients recovered more frequently than older patients, it was not the case in MCS., Interpretation: Our findings highlight the necessity for neurologists to exercise caution when making negative predictions in individual cases, challenge traditional beliefs regarding recovery timelines, and underscore the importance of conducting detailed and prolonged assessments to better understand recovery prospects in DoC., (© 2024 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Behavioural diagnosis of patients with disorders of consciousness (DOC) is challenging and prone to inaccuracies. Consequently, there have been increased efforts to develop bedside assessment based on EEG and event-related potentials (ERPs) that are more sensitive to the neural factors supporting conscious awareness. However, individual detection of residual consciousness using these techniques is less established. Here, we hypothesize that the cross-state similarity (defined as the similarity between healthy and impaired conscious states) of passive brain responses to auditory stimuli can index the level of awareness in individual DOC patients. To this end, we introduce the global field time-frequency representation-based discriminative similarity analysis (GFTFR-DSA). This method quantifies the average cross-state similarity index between an individual patient and our constructed healthy templates using the GFTFR as an EEG feature. We demonstrate that the proposed GFTFR feature exhibits superior within-group consistency in 34 healthy controls over traditional EEG features such as temporal waveforms. Second, we observed the GFTFR-based similarity index was significantly higher in patients with a minimally conscious state (MCS, 40 patients) than those with unresponsive wakefulness syndrome (UWS, 54 patients), supporting our hypothesis. Finally, applying a linear support vector machine classifier for individual MCS/UWS classification, the model achieved a balanced accuracy and F1 score of 0.77. Overall, our findings indicate that combining discriminative and interpretable markers, along with automatic machine learning algorithms, is effective for the differential diagnosis in patients with DOC. Importantly, this approach can, in principle, be transferred into any ERP of interest to better inform DOC diagnoses.

Background: The recent publication of practice guidelines for management of patients with disorders of consciousness (DoC) in the United States and Europe was a major step forward in improving the accuracy and consistency of terminology, diagnostic criteria, and prognostication in this population. There remains a pressing need for a more precise brain injury classification system that combines clinical semiology with neuroimaging, electrophysiologic, and other biomarker data. To address this need, the National Institute of Neurological Disorders and Stroke launched the Common Data Elements (CDEs) initiative to facilitate systematic collection of high-quality research data in studies involving patients with neurological disease. The Neurocritical Care Society's Curing Coma Campaign expanded this effort in 2018 to develop CDEs for DoC. Herein, we present CDE recommendations for behavioral phenotyping of patients with DoC., Methods: The Behavioral Phenotyping Workgroup used a preestablished, five-step process to identify and select candidate CDEs that included review of existing National Institute of Neurological Disorders and Stroke CDEs, nomination and systematic vetting of new CDEs, CDE classification, iterative review, and approval of panel recommendations and development of corresponding case review forms., Results: We identified a slate of existing and newly proposed basic, supplemental, and exploratory CDEs that can be used for behavioral phenotyping of adult and pediatric patients with DoC., Conclusions: The proposed behavioral phenotyping CDEs will assist with international harmonization of DoC studies and allow for more precise characterization of study cohorts, favorably impacting observational studies and clinical trials aimed at improving outcome in this population., (© 2023. Springer Science+Business Media, LLC, part of Springer Nature and Neurocritical Care Society.)

The Curing Coma Campaign (CCC) and its contributing collaborators identified multiple key areas of knowledge and research gaps in coma and disorders of consciousness (DoC). This step was a crucial effort and essential to prioritize future educational and research efforts. These key areas include defining categories of DoC, assessing DoC using multimodal approach (e.g., behavioral assessment tools, advanced neuroimaging studies), discussing optimal clinical trials' design and exploring computational models to conduct clinical trials in patients with DoC, and establishing common data elements to standardize data collection. Other key areas focused on creating coma care registry and educating clinicians and patients and promoting awareness of DoC to improve care in patients with DoC. The ongoing efforts in these key areas are discussed., Competing Interests: None declared., (Thieme. All rights reserved.)

The advent of neurotechnologies including advanced functional magnetic resonance imaging and electroencephalography to detect states of awareness not detectable by traditional bedside neurobehavioral techniques (i.e., covert consciousness) promises to transform neuroscience research and clinical practice for patients with brain injury. As these interventions progress from research tools into actionable, guideline-endorsed clinical tests, ethical guidance for clinicians on how to responsibly communicate the sensitive results they yield is crucial yet remains underdeveloped. Drawing on insights from empirical and theoretical neuroethics research and our clinical experience with advanced neurotechnologies to detect consciousness in behaviorally unresponsive patients, we critically evaluate ethical promises and perils associated with disclosing the results of clinical covert consciousness assessments and describe a semistructured approach to responsible data sharing to mitigate potential risks., (© 2024. Springer Science+Business Media, LLC, part of Springer Nature and Neurocritical Care Society.)

Disorders of consciousness (DoC) are generally diagnosed by clinical assessment, which is a predominantly motor-driven process and accounts for up to 40 % of non-communication being misdiagnosed as unresponsive wakefulness syndrome (UWS) (previously known as prolonged/persistent vegetative state). Given the consequences of misdiagnosis, a more reliable and objective multimodal protocol to diagnosing DoC is needed, but has not been produced due to concerns regarding their interpretation and reliability. Of the techniques commonly used to detect consciousness in DoC, task-based paradigms (active paradigms) produce the most unequivocal result when findings are positive. It is well-established that command following (CF) reliably reflects preserved consciousness. Task-based electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) can detect motor-independent CF and reveal preserved covert consciousness in up to 14 % of UWS patients. Accordingly, to improve the diagnostic accuracy of DoC, we propose a practical multimodal clinical decision framework centered on task-based EEG and fMRI, and complemented by measures like transcranial magnetic stimulation (TMS-EEG)., (© 2024 the author(s), published by De Gruyter, Berlin/Boston.)

Background: The complexity of the neurophysiological mechanisms underlying human consciousness is widely acknowledged, with information processing and flow originating in cortex conceived as a core mechanism of consciousness emergence. Combination of transcranial magnetic stimulation and electroencephalography (TMS-EEG) is considered as a promising technique to understand the effective information flow associated with consciousness., Objectives: To investigate information flow with TMS-EEG and its relationship to different consciousness states., Methods: We applied an effective information flow analysis by combining time-varying multivariate adaptive autoregressive model and adaptive directed transfer function on TMS-EEG data of frontal, motor and parietal cortex in patients with disorder of consciousness (DOC), including 14 vegetative state/unresponsive wakefulness syndrome (VS/UWS) patients, 21 minimally conscious state (MCS) patients, and 22 healthy subjects., Results: TMS in DOC patients, particularly VS/UWS, induced a significantly weaker effective information flow compared to healthy subjects. The bidirectional directed information flow was lost in DOC patients with TMS of frontal, motor and parietal cortex. The interactive ROI rate of the information flow network induced by TMS of frontal and parietal cortex was significantly lower in VS/UWS than in MCS. The interactive ROI rate correlated with DOC clinical scales., Conclusions: TMS-EEG revealed a physiologically relevant correlation between TMS-induced information flow and levels of consciousness. This suggests that breakdown of effective cortical information flow serves as a viable marker of human consciousness., Significance: Findings offer a unique perspective on the relevance of information flow in DOC, thus providing a novel way of understanding the physiological basis of human consciousness., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Disorders of Consciousness are divided into two major categories such as vegetative and minimally conscious states. Objective measures that allow correct identification of patients with vegetative and minimally conscious state are needed. EEG microstate analysis is a promising approach that we believe has the potential to be effective in examining the resting state activities of the brain in different stages of consciousness by allowing the proper identification of vegetative and minimally conscious patients. As a result, we try to identify clinical evaluation scales and microstate characteristics with resting state EEGs from individuals with disorders of consciousness. Our prospective observational study included 28 individuals with a disorder of consciousness. Control group included 18 healthy subjects with proper EEG data. We made clinical evaluations using patient behavior scales. We also analyzed the EEGs using microstate analysis. In our study, microstate D coverage differed substantially between vegetative and minimally conscious state patients. Also, there was a strong connection between microstate D characteristics and clinical scale scores. Consequently, we have demonstrated that the most accurate parameter for representing consciousness level is microstate D. Microstate analysis appears to be a strong option for future use in the diagnosis, follow-up, and treatment response of patients with Disorders of Consciousness., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

This brief report discusses the relationship between verbal function, disorders of consciousness, and neurological follow-up after acute brain injury. It provides valuable insights for improving the accuracy and reliability of Verbal Glasgow Coma Scale scoring in clinical practice. The report addresses the need for standardized training and underlines the importance of physiological stabilization before assessment. Clarity in communication, recognition of non-verbal cues, and serial assessments are emphasized as critical factors to reduce the Verbal Glasgow Coma Scale inconsistencies. It also promotes interdisciplinary collaboration and cultural sensitivity to refine the Verbal Glasgow Coma Scale evaluation, improving the prediction of long-term neurological outcomes after acute brain injury and optimizing effective rehabilitation programs. Possible strategies to implement in the routine clinical practice the provided tips are discussed., Competing Interests: Conflict of Interest The authors have no personal, financial, or institutional conflict of interest, (Copyright © 2024 Elsevier B.V. All rights reserved.)

The Neurological Intensive Care Unit (ICU) at Pitié-Salpêtrière Hospital cares for patients with severe brain injuries, which can lead to acute or chronic disorders of consciousness. To assess the patient's state of consciousness, the team relies on precise clinical examination. This article presents the assessment tools used to establish the patient's prognosis., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

Background: The Coma Recovery Scale-Revised (CRS-R) is the most recommended clinical tool to examine the neurobehavioral condition of individuals with disorders of consciousness (DOCs). Different studies have investigated the prognostic value of the information provided by the conventional administration of the scale, while other measures derived from the scale have been proposed to improve the prognosis of DOCs. However, the heterogeneity of the data used in the different studies prevents a reliable comparison of the identified predictors and measures., Aim: This study investigates which information derived from the CRS-R provides the most reliable prediction of both the clinical diagnosis and recovery of consciousness at the discharge of a long-term neurorehabilitation program., Design: Retrospective observational multisite study., Setting: The enrollment was performed in three neurorehabilitation facilities of the same hospital network., Population: A total of 171 individuals with DOCs admitted to an inpatient neurorehabilitation program for a minimum of 3 months were enrolled., Methods: Machine learning classifiers were trained to predict the clinical diagnosis and recovery of consciousness at discharge using clinical confounders and different metrics extracted from the CRS-R scale., Results: Results showed that the neurobehavioral state at discharge was predicted with acceptable and comparable predictive value with all the indices and measures derived from the CRS-R, but for the clinical diagnosis and the Consciousness Domain Index, and the recovery of consciousness was predicted with higher accuracy and similarly by all the investigated measures, with the exception of initial clinical diagnosis., Conclusions: Interestingly, the total score in the CRS-R and, especially, the total score in its subscales provided the best overall results, in contrast to the clinical diagnosis, which could indicate that a comprehensive measure of the clinical diagnosis rather than the condition of the individuals could provide a more reliable prediction of the neurobehavioral progress of individuals with prolonged DOC., Clinical Rehabilitation Impact: The results of this work have important implications in clinical practice, offering a more accurate prognosis of patients and thus giving the possibility to personalize and optimize the rehabilitation plan of patients with DoC using low-cost and easily collectable information.

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.

This narrative review aims to elucidate an emerging state of consciousness termed "cognitivemotor dissociation", characterized by brain activity patterns closely resembling those observed in healthy individuals. This distinctive phenomenon is notably evident in patients diagnosed with comatose states, unresponsive wakefulness states, or minimally conscious states. Employing functional magnetic resonance imaging or electroencephalography holds promise for identifying cognitive-motor dissociation. The emergence of cognitive-motor dissociation not only introduces a fresh conceptual framework but also provides valuable predictive insights into outcomes and prognoses for individuals with disorders of consciousness. However, the recognition of cognitive-motor dissociation raises ethical considerations, particularly in evaluating the decision-making capacity regarding life-or-death choices for individuals in this state. Therefore, it is crucial to advocate for further scientific and comprehensive research to enhance our understanding of cognitive-motor dissociation and navigate its nuanced complexities. Keywords: cognitive-motor dissociation, coma, unresponsive wakefulness syndrome, minimally conscious state, functional MRI, EEG.

Primary Objective: This study aimed to verify the reliability and validity of the Japanese version of the Coma Recovery Scale-Revised (CRS-R)., Methods: Subjects included 59 patients with disorders of consciousness (DOC) due to acquired brain injury. To validate test-retest reliability, Evaluator A assessed the CRS-R twice on the same day (A1, A2). To examine inter-rater reliability, Evaluators A (A2) and B (B) assessed the CRS-R without a time interval. To test concurrent validity, Evaluator A (A1) assessed the CRS-R, Japan Coma Scale (JCS), and the Glasgow Coma Scale (GCS) consecutively. To validate diagnostic accuracy, we evaluated the degree of agreement between A1 and A2 and between A2 and B in their diagnosis of DOC by CRS-R., Results: The test-retest (ρ = 0.92) and inter- (ρ = 0.98) reliability of CRS-R were excellent" and Concurrent validity of CRS-R with JCS (ρ =  -0.82) and GCS (ρ = 0.92) were high. Results of DOC diagnosis were consistent for 48/59 cases (κ = 0.82) for A1 and A2 and for 54/59 cases (κ = 0.92) for A2 and B., Conlcusion: The Japanese version of the CRS-R may be as reliable and valid as the original English and other language versions.

Aims: The electroencephalography (EEG) microstates are indicative of fundamental information processing mechanisms, which are severely damaged in patients with prolonged disorders of consciousness (pDoC). We aimed to improve the topographic analysis of EEG microstates and explore indicators available for diagnosis and prognosis prediction of patients with pDoC, which were still lacking., Methods: We conducted EEG recordings on 59 patients with pDoC and 32 healthy controls. We refined the microstate method to accurately estimate topographical differences, and then classify and forecast the prognosis of patients with pDoC. An independent dataset was used to validate the conclusion., Results: Through optimized topographic analysis, the global explained variance (GEV) of microstate E increased significantly in groups with reduced levels of consciousness. However, its ability to classify the VS/UWS group was poor. In addition, the optimized GEV of microstate E exhibited a statistically significant decrease in the good prognosis group as opposed to the group with a poor prognosis. Furthermore, the optimized GEV of microstate E strongly predicted a patient's prognosis., Conclusion: This technique harmonizes with the existing microstate analysis and exhibits precise and comprehensive differences in microstate topography between groups. Furthermore, this method has significant potential for evaluating the clinical prognosis of pDoC patients., (© 2023 The Authors. CNS Neuroscience & Therapeutics Published by John Wiley & Sons Ltd.)

Introduction: Combining transcranial magnetic stimulation with electroencephalography (TMS-EEG), oscillatory reactivity can be measured, allowing us to investigate the interaction between local and distant cortical oscillations. However, the extent to which human consciousness is related to these oscillatory effective networks has yet to be explored., Aims: We tend to investigate the link between oscillatory effective networks and brain consciousness, by monitoring the global transmission of TMS-induced oscillations in disorders of consciousness (DOC)., Results: A cohort of DOC patients was included in this study, which included 28 patients with a minimally conscious state (MCS) and 20 patients with vegetative state/unresponsive wakefulness syndrome (VS/UWS). Additionally, 25 healthy controls were enrolled. The oscillatory reactivity to single-pulse TMS of the frontal, sensorimotor and parietal cortex was measured using event-related spectral perturbation of TMS-EEG. The temporal-spatial properties of the oscillatory reactivity were illustrated through life time, decay gradients and accumulative power. In DOC patients, an oscillatory reactivity was observed to be temporally and spatially suppressed. TMS-EEG of DOC patients showed that the oscillations did not travel as far in healthy controls, in terms of both temporal and spatial dimensions. Moreover, cortical theta reactivity was found to be a reliable indicator in distinguishing DOC versus healthy controls when TMS of the parietal region and in distinguishing MCS versus VS/UWS when TMS of the frontal region. Additionally, a positive correlation was observed between the Coma Recovery Scale-Revised scores of the DOC patients and the cortical theta reactivity., Conclusions: The findings revealed a breakdown of oscillatory effective networks in DOC patients, which has implications for the use of TMS-EEG in DOC evaluation and offers a neural oscillation viewpoint on the neurological basis of human consciousness., (© 2023 The Authors. CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.)

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., (© 2024 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

The limits of the standard, behaviour-based clinical assessment of patients with disorders of consciousness (DoC) prompted the employment of functional neuroimaging, neurometabolic, neurophysiological and neurostimulation techniques, to detect brain-based covert markers of awareness. However, uni-modal approaches, consisting in employing just one of those techniques, are usually not sufficient to provide an exhaustive exploration of the neural underpinnings of residual awareness. This systematic review aimed at collecting the evidence from studies employing a multimodal approach, that is, combining more instruments to complement DoC diagnosis, prognosis and better investigating their neural correlates. Following the PRISMA guidelines, records from PubMed, EMBASE and Scopus were screened to select peer-review original articles in which a multi-modal approach was used for the assessment of adult patients with a diagnosis of DoC. Ninety-two observational studies and 32 case reports or case series met the inclusion criteria. Results highlighted a diagnostic and prognostic advantage of multi-modal approaches that involve electroencephalography-based (EEG-based) measurements together with neuroimaging or neurometabolic data or with neurostimulation. Multimodal assessment deepened the knowledge on the neural networks underlying consciousness, by showing correlations between the integrity of the default mode network and the different clinical diagnosis of DoC. However, except for studies using transcranial magnetic stimulation combined with electroencephalography, the integration of more than one technique in most of the cases occurs without an a priori-designed multi-modal diagnostic approach. Our review supports the feasibility and underlines the advantages of a multimodal approach for the diagnosis, prognosis and for the investigation of neural correlates of DoCs., (© 2023 IRCCS ISMETT. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

Rapid advancements in the critical care management of acute brain injuries have facilitated the survival of numerous patients who may have otherwise succumbed to their injuries. The probability of conscious recovery hinges on the extent of structural brain damage and the level of metabolic and functional cerebral impairment, which remain challenging to assess via laboratory, clinical, or functional tests. Current research settings and guidelines highlight the potential value of fluorodeoxyglucose-PET (FDG-PET) for diagnostic and prognostic purposes, emphasizing its capacity to consistently illustrate a metabolic reduction in cerebral glucose uptake across various disorders of consciousness. Crucially, FDG-PET might be a pivotal tool for differentiating between patients in the minimally conscious state and those in the unresponsive wakefulness syndrome, a persistent clinical challenge. In patients with disorders of consciousness, PET offers utility in evaluating the degree and spread of functional disruption, as well as identifying irreversible neural damage. Further, studies that capture responses to external stimuli can shed light on residual or revived brain functioning. Nevertheless, the validity of these findings in predicting clinical outcomes calls for additional long-term studies with larger patient cohorts suffering from consciousness impairment. Misdiagnosis of conscious illnesses during bedside clinical assessments remains a significant concern. Based on the clinical research settings, current clinical guidelines recommend PET for diagnostic and/or prognostic purposes. This review article discusses the clinical categories of conscious disorders and the diagnostic and prognostic value of PET imaging in clinically unresponsive patients, considering the known limitations of PET imaging in such contexts., (© The Author(s) 2023. Published by Oxford University Press on behalf of the British Institute of Radiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

We report the case of an older female patient suffering from a subacute qualitative and quantitative alteration in the state of consciousness secondary to a major depressive disorder with catatonic features. We discuss and review the clinical presentation, diagnosis, etiology, and treatment of catatonia in older adults from the internist's point of view.

Background: The fundamental gap obstructing forward progress of evidenced-based care in pediatric and neonatal disorders of consciousness (DoC) is the lack of defining consensus-based terminology to perform comparative research. This lack of shared nomenclature in pediatric DoC stems from the inherently recursive dilemma of the inability to reliably measure consciousness in the very young. However, recent advancements in validated clinical examinations and technologically sophisticated biomarkers of brain activity linked to future abilities are unlocking this previously formidable challenge to understanding the DoC in the developing brain., Methods: To address this need, the first of its kind international convergence of an interdisciplinary team of pediatric DoC experts was organized by the Neurocritical Care Society's Curing Coma Campaign. The multidisciplinary panel of pediatric DoC experts proposed pediatric-tailored common data elements (CDEs) covering each of the CDE working groups including behavioral phenotyping, biospecimens, electrophysiology, family and goals of care, neuroimaging, outcome and endpoints, physiology and big Data, therapies, and pediatrics., Results: We report the working groups' pediatric-focused DoC CDE recommendations and disseminate CDEs to be used in studies of pediatric patients with DoC., Conclusions: The CDEs recommended support the vision of progressing collaborative and successful internationally collaborative pediatric coma research., (© 2023. Springer Science+Business Media, LLC, part of Springer Nature and Neurocritical Care Society.)

Historically, there has been a pessimistic view regarding outcomes for patients with disorders of consciousness (DoC). There is a paucity of clinical diagnostic tools and prognostic protocols. Guidelines for the care of patients with DoC require behavioral observation, time, resources, and knowledge of the population. Many nonclinical factors such as patient wishes, family perception, and personal finances can indirectly influence long-term outcomes. Prognostic expectations need to be considered but we health-care professional cannot fully appreciate the decisions and influence of those decisions on the person served or on the care providers involved., Competing Interests: Disclosure The authors have nothing to disclose., (Copyright © 2023 Elsevier Inc. All rights reserved.)