Minimally Conscious State Research Articles

Implications of the Consciousness State on Decannulation in Patients With a Prolonged Disorder of Consciousness

ObjectiveTo prospectively investigate the evolution of the consciousness state and the cannula-weaning progression in patients with prolonged disorders of consciousness. DesignNonconcurrent cohort study. SettingA rehabilitation unit. ParticipantsAdult patients (N=144) with prolonged disorders of consciousness after a severe acquired brain injury admitted between June 2020 and September 2022. InterventionsNot applicable. Main Outcome MeasuresConsciousness state was assessed by repeated Coma Recovery Scale-Revised (CRS-R) questionnaire administration at admission and weekly afterward. The dates of the first improvement of consciousness state and the achievement of decannulation were recorded. Decannulation followed an internal protocol of multiprofessional rehabilitation. ResultsOne hundred forty-four patients were included: age, 69 years; 64 (44.4%) with hemorrhagic etiology; time post onset, 40 days, CRS-R score at admission, 9, median length of stay, 90 days. Seventy-three (50.7%) patients were decannulated. They showed a significantly higher CRS-R (P<.001) and states of consciousness (P<.001) at admission, at the first improvement of the consciousness state (P=.003), and at discharge (P<.001); a lower severity in the Cumulative Illness Rating Scale at admission (P=.01); and a lower rate of pulmonary infections with recurrence (P=.021), compared with nondecannulated patients. Almost all decannulated patients (97.3%) improved their consciousness before decannulation. Consciousness states at decannulation were as follows: unresponsive wakefulness syndrome, 0 (0%); minimally conscious state (MCS) minus, 4 (5.5%); MCS plus, 7 (9.6%); and emergence from MCS, 62 (84.9%). Kaplan-Meier analysis showed a significant divergence between the curves with a higher probability of decannulation in patients who improved consciousness (P<.001). ConclusionsThis study showed that the presence of signs of consciousness, even subtle, is a necessary condition for decannulation, suggesting that consciousness may influence some of the components implied in the decannulation process.

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

Objective: Within the continuum of consciousness, patients in a Minimally Conscious State (MCS) may exhibit high-level behavioral responses (MCS+) or may not (MCS−). The evaluation of residual consciousness and related classification is crucial to propose tailored rehabilitation and pharmacological treatments, considering the inherent differences among groups in diagnosis and prognosis. Currently, differential diagnosis relies on behavioral assessments posing a relevant risk of misdiagnosis. In this context, EEG offers a non-invasive approach to model the brain as a complex network. The search for discriminating features could reveal whether behavioral responses in post-comatose patients have a defined physiological background. Additionally, it is essential to determine whether the standard behavioral assessment for quantifying responsiveness holds physiological significance. Methods: In this prospective observational study, we investigated whether low-density EEG-based graph metrics could discriminate MCS+/− patients by enrolling 57 MCS patients (MCS−: 30; males: 28). At admission to intensive rehabilitation, 30 min resting-state closed-eyes EEG recordings were performed together with consciousness diagnosis following international guidelines. After EEG preprocessing, graphs’ metrics were estimated using different connectivity measures, at multiple connection densities and frequency bands (α,θ,δ). Metrics were also provided to cross-validated Machine Learning (ML) models with outcome MCS+/−. Results: A lower level of brain activity integration was found in the MCS− group in the α band. Instead, in the δ band MCS− group presented an higher level of clustering (weighted clustering coefficient) respect to MCS+. The best-performing solution in discriminating MCS+/− through the use of ML was an Elastic-Net regularized logistic regression with a cross-validation accuracy of 79% (sensitivity and specificity of 74% and 85% respectively). Conclusion: Despite tackling the MCS+/− differential diagnosis is highly challenging, a daily-routine low-density EEG might allow to differentiate across these differently responsive brain networks. Significance: Graph-theoretical features are shown to discriminate between these two neurophysiologically similar conditions, and may thus support the clinical diagnosis.

Breakdown of effective information flow in disorders of consciousness: Insights from TMS-EEG

BackgroundThe 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. ObjectivesTo investigate information flow with TMS-EEG and its relationship to different consciousness states. MethodsWe 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. ResultsTMS 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. ConclusionsTMS-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. SignificanceFindings 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.

Predicting outcome in disorders of consciousness: Amega-analysis.

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. A systematic literature search revealed a total of 3290 patients. In this sample, we performed a Cox proportional hazards analysis for interval censored data. 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. 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.

Neurobehavioral Progress and Signs of Transition in Children With Prolonged Disorders of Consciousness: A Retrospective Longitudinal Study With the Coma Recovery Scale-Revised

BackgroundResearch 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). MethodsSystematic weekly assessments were conducted with the Coma Recovery Scale-Revised (CRS-R) until emergence from MCS, discharge, or death. ResultsTwenty-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. ConclusionsAlmost 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.

121 The Effectiveness of Deep Brain Stimulation in Patients With Disorders of Consciousness During Short- and Long-Term

INTRODUCTION: Disorders of consciousness (DoCs) impose heavy medical and economic burdens on families and society. Deep brain stimulation (DBS) has been preliminarily applied to treat patients with disorders of consciousness (DoCs). 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. The CRS-R was used to assess the level of consciousness of each patient. Scores on this scale may vary from 0 to 23, and six subscores are used to quantify auditory, visual, motor and verbal functions as well as communication and arousal. During the surgery, an electrode was implanted into the CM-Pf complex. 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 &lt; 0.001). There was a significant treatment × follow-up interaction (H = 14.99, p &lt; 0.001). An priliminary improvement after DBS treatment could also be seen at 6 months. DBS had significantly better effects in patients with minimally conscious state (MCS) compared with patients with vegetative state/unresponsive wakefulness syndrome (p for interaction &lt;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.

Improving Neuroplasticity through Robotic Verticalization Training in Patients with Minimally Conscious State: A Retrospective Study.

In disorders of consciousness, verticalization is considered an effective type of treatment to improve motor and cognitive recovery. Our purpose is to investigate neurophysiological effects of robotic verticalization training (RVT) in patients with minimally conscious state (MCS). Thirty subjects affected by MCS due to traumatic or vascular brain injury, attending the intensive Neurorehabilitation Unit of the IRCCS Neurolesi (Messina, Italy), were included in this retrospective study. They were equally divided into two groups: the control group (CG) received traditional verticalization with a static bed and the experimental group (EG) received advanced robotic verticalization using the Erigo device. Each patient was evaluated using both clinical scales, including Levels of Cognitive Functioning (LCF) and Functional Independence Measure (FIM), and quantitative EEG pre (T0) and post each treatment (T1). The treatment lasted for eight consecutive weeks, and sessions were held three times a week, in addition to standard neurorehabilitation. In addition to a notable improvement in clinical parameters, such as functional (FIM) (p < 0.01) and cognitive (LCF) (p < 0.01) outcomes, our findings showed a significant modification in alpha and beta bands post-intervention, underscoring the promising effect of the Erigo device to influence neural plasticity and indicating a noteworthy difference between pre-post intervention. This was not observed in the CG. The observed changes in alpha and beta bands underscore the potential of the Erigo device to induce neural plasticity. The device's custom features and programming, tailored to individual patient needs, may contribute to its unique impact on brain responses.

Precise detection of awareness in disorders of consciousness using deep learning framework

Diagnosis of disorders of consciousness (DOC) remains a formidable challenge. Deep learning methods have been widely applied in general neurological and psychiatry disorders, while limited in DOC domain. Considering the successful use of resting-state functional MRI (rs-fMRI) for evaluating patients with DOC, this study seeks to explore the conjunction of deep learning techniques and rs-fMRI in precisely detecting awareness in DOC. We initiated our research with a benchmark dataset comprising 140 participants, including 76 unresponsive wakefulness syndrome (UWS), 25 minimally conscious state (MCS), and 39 Controls, from three independent sites. We developed a cascade 3D EfficientNet-B3-based deep learning framework tailored for discriminating MCS from UWS patients, referred to as “DeepDOC”, and compared its performance against five state-of-the-art machine learning models. We also included an independent dataset consists of 11 DOC patients to test whether our model could identify patients with cognitive motor dissociation (CMD), in which DOC patients were behaviorally diagnosed unconscious but could be detected conscious by brain computer interface (BCI) method. Our results demonstrate that DeepDOC outperforms the five machine learning models, achieving an area under curve (AUC) value of 0.927 and accuracy of 0.861 for distinguishing MCS from UWS patients. More importantly, DeepDOC excels in CMD identification, achieving an AUC of 1 and accuracy of 0.909. Using gradient-weighted class activation mapping algorithm, we found that the posterior cortex, encompassing the visual cortex, posterior middle temporal gyrus, posterior cingulate cortex, precuneus, and cerebellum, as making a more substantial contribution to classification compared to other brain regions. This research offers a convenient and accurate method for detecting covert awareness in patients with MCS and CMD using rs-fMRI data.

Neck muscle spasticity in patients with disorder of consciousness: a pilot study.

Disorder of consciousness (DOC) is a state of prolonged altered consciousness due to severe acquired brain injury (ABI). DOC can be differentiated into coma, unresponsive wakefulness syndrome (UWS), or minimally conscious state (MCS) depending on the behavioral features observed and their relationship to the level of consciousness. Spasticity is one of the most frequently reported medical comorbidities in DOC patients. Since there is a critical lack of spasticity-focused studies and, in turn, of target treatment, we designed this pilot prospective study to evaluate cervical spine muscle spasticity and its effect on rehabilitation outcome in a large cohort of patients followed from the post-acute phase to 6 months after severe ABI. To evaluate neck muscle spasticity and investigate its impact on neurological and functional outcome in a large cohort of adult patients with DOC followed from post-acute to 6 months after severe ABI. Single-center prospective pilot study. Highly specialized inpatient neurorehabilitation clinic. Patients with severe ABI admitted within 3 months after the acute event to our Neurorehabilitation Unit between May 21st, 2019 and April 23rd, 2020 for treatment of DOC as a part of their rehabilitation program. In this single-center prospective pilot study demographic data, etiology of ABI (traumatic versus non-traumatic), DOC evaluated with the revised Coma Recovery Scale (CRS-R), and neurological and functional outcome assessed respectively with the Glasgow Coma Scale (GCS) and Functional Independence Measure (FIM) were considered. During cervical examination, we assessed spasticity with the Modified Ashworth Scale (MAS), deviation of head alignment with a goniometer, and pain with the Nociception Coma Scale-Revised (NCS-R). Of the 48 patients, 41.7% were diagnosed with UWS and 58.3% were in a minimally conscious state (MCS). We found spasticity of neck muscles in 91.7% of patients, with no difference in severity (assessed with MAS) between UWV and MCS. The NCS-R score at cervical spine examination was lower in UWS than MCS. Spasticity was severer in patients with traumatic brain injury (TBI) compared to non-traumatic. At multiple linear regression analysis, younger age, hemisyndrome, and tetraparesis were independent predictors of severity of neck muscle spasticity in MCS. More severe spasticity was a predictor of worse neurological and functional outcome at discharge in UWS patients, independently of the other confounding variables at admission (e.g., age, severity of brain injury, functional assessment, and pain). Spasticity of neck muscles frequently develops in patients with DOC and is more severe in those after TBI. UWV and MCS have different spasticity profiles as regards risk factors and neurological and functional outcome. Severity of neck muscle spasticity in UWV patients may represent an early indicator of worse neurological and functional outcome after inpatient rehabilitation. Our findings could prompt clinicians to redefine the rehabilitation aims regarding spasticity and to estimate the functional outcome in patients undergoing intensive rehabilitation after severe ABI.

A Patient With Minimally Conscious Syndrome Due to Cerebrovascular Accident Whose Symptoms Resolved With Zolpidem.

In this report, we discuss the case of a patient with minimally conscious state (MCS) whose clinical condition significantly improved after Zolpidem therapy. We aim to provide supportive evidence for inclusion of zolpidem trials in patients with MCS. Our team used electronic medical records, direct patient care experiences, and literature review to obtain information for this case report. Twice daily zolpidem therapy led to significant clinical improvement in our patient with MCS. In addition, this improvement was maintained throughout an increasingly arduous medical course. Minimally conscious state is a disorder with limited proven therapeutic options. Zolpidem administration has demonstrated immense benefit in a select population of patients, including ours. Given the potential for great improvement with limited downside, zolpidem trial presents an intriguing treatment option. Further clarification of prognostic features to stratify responders and nonresponders to therapy is needed.

Assessment of Frequency and Predictive Value of Comorbidities in Patients With Disorders of Consciousness in the Acute Setting.

Medical comorbidities are frequent in patients with disorders of consciousness (DoC) and their impact on outcomes is under investigation. The aim of this study was to investigate patients with DoC in the acute stage and the influence of comorbidities. Patients admitted to intensive care units and neurological units with a diagnosis of coma, vegetative state/unresponsive wakefulness syndrome (VS/UWS), and minimally conscious state (MCS) were investigated through the Glasgow Coma Scale (GCS), the Coma Recovery Scale - Revised (CRS-R) and the Comorbidities Coma Scale (CoCos). Forty-three patients (21 men and 22 women; mean age at admission: 60.4 ± 21.0) were included in the study. The most frequent diagnosis at admission was coma (72%) followed by VS/UWS (14%) and MCS (14%). The most frequent brain injury was subarachnoid hemorrhage (46%). At the 6-month follow-up, 19 patients had died (44%), 15 showed a full recovery of consciousness (35%), 7 were in a condition of emergence from MCS (16%), and 2 showed a persistent VS/UWS (5%). Forty-two (98%) patients showed at least one comorbidity: presence of life-support device (92.9%), anemia (76.2%), arterial hypertension (66,7%), hydrocephalus (45.3%), and respiratory infections (45.2%) were those most frequently reported. At the Multivariable Cox regression, the presence of renal disease (hazard ratio [HR] 33.37; p = 0.033) and malnutrition (HR 14.52; p = 0.001) were predictors of missed recovery of full consciousness. Although adverse outcomes are generally predicted by the severity of brain damage, the presence of medical comorbidities in an acute phase could influence outcomes and long-term prognosis.

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.

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-. 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. 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. 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.

Variability of large timescale functional networks in patients with disorders of consciousness.

Most brain function assessments for disorders of consciousness (DOC) utilized quantified characteristics, measured only once, ignoring the variation of patients' brain states. The study aims to investigate the brain activities of patients with DOC from a new perspective: variability of a large timescale functional network. Forty-nine patients were enrolled in this study and performed a 1-week behavioral assessment. Subsequently, each patient received electroencephalography (EEG) recordings five times daily at 2-h intervals. Functional connectivity and networks were measured by weighted phase lag index and complex network parameters (characteristic path length, cluster coefficient, and betweenness centrality). The relative coefficient of variation (CV) of network parameters was measured to evaluate functional network variability. Functional networks of patients with vegetative state/unresponsive wakefulness syndrome (VS/UWS) showed significantly higher segregation (characteristic path length) and lower centrality (betweenness centrality) than emerging from the minimal conscious state (EMCS) and minimal conscious state (MCS), as well as lower integration (cluster coefficient) than MCS. The functional networks of VS/UWS patients consistently presented the highest variability in segregation and integration (i.e., highest CV values of characteristic path length and cluster coefficient) on a larger time scale than MCS and EMCS. Moreover, the CV values of characteristic path length and cluster coefficient showed a significant inverse correlation with the Coma Recovery Scale-Revised scores (CRS-R). The CV values of network betweenness centrality, particularly of the cento-parietal region, showed a positive correlation with the CRS-R. The functional networks of VS/UWS patients present the most invariant segregation and integration but divergent centrality on the large time scale networks than MCS and EMCS. The variations observed within large timescale functional networks significantly correlate with the degree of consciousness impairment. This finding augments our understanding of the neurophysiological mechanisms underpinning disorders of consciousness.

Variational mode decomposition-based EEG analysis for the classification of disorders of consciousness.

Aberrant alterations in any of the two dimensions of consciousness, namely awareness and arousal, can lead to the emergence of disorders of consciousness (DOC). The development of DOC may arise from more severe or targeted lesions in the brain, resulting in widespread functional abnormalities. However, when it comes to classifying patients with disorders of consciousness, particularly utilizing resting-state electroencephalogram (EEG) signals through machine learning methods, several challenges surface. The non-stationarity and intricacy of EEG data present obstacles in understanding neuronal activities and achieving precise classification. To address these challenges, this study proposes variational mode decomposition (VMD) of EEG before feature extraction along with machine learning models. By decomposing preprocessed EEG signals into specified modes using VMD, features such as sample entropy, spectral entropy, kurtosis, and skewness are extracted across these modes. The study compares the performance of the features extracted from VMD-based approach with the frequency band-based approach and also the approach with features extracted from raw-EEG. The classification process involves binary classification between unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS), as well as multi-class classification (coma vs. UWS vs. MCS). Kruskal-Wallis test was applied to determine the statistical significance of the features and features with a significance of p < 0.05 were chosen for a second round of classification experiments. Results indicate that the VMD-based features outperform the features of other two approaches, with the ensemble bagged tree (EBT) achieving the highest accuracy of 80.5% for multi-class classification (the best in the literature) and 86.7% for binary classification. This approach underscores the potential of integrating advanced signal processing techniques and machine learning in improving the classification of patients with disorders of consciousness, thereby enhancing patient care and facilitating informed treatment decision-making.

Temporal and spatial variability of dynamic microstate brain network in disorders of consciousness.

Accurately diagnosing patients with the vegetative state (VS) and the minimally conscious state (MCS) reached a misdiagnosis of approximately 40%. A method combined microstate and dynamic functional connectivity (dFC) to study the spatiotemporal variability of the brain in disorders of consciousness (DOC) patients was proposed. Resting-state EEG data were obtained from 16 patients with MCS and 16 patients with VS. Mutual information (MI) was used to assess the EEG connectivity in each microstate. MI-based features with statistical differences were selected as the total feature subset (TFS), then the TFS was utilized to feature selection and fed into the classifier, obtaining the optimal feature subsets (OFS) in each microstate. Subsequently, an OFS-based MI functional connectivity network (MIFCN) was constructed in the cortex. The group-average MI connectivity matrix focused on all channels revealed that all five microstates exhibited stronger information interaction in the MCS when comparing with the VS. While OFS-based MIFCN, which only focused on a few channels, revealed greater MI flow in VS patients than in MCS patients under microstates A, B, C, and E, except for microstate D. Additionally, the average classification accuracy of OFS in the five microstates was 96.2%. Constructing features based on microstates to distinguish between two categories of DOC patients had effectiveness.

Effects of different frequencies music on cortical responses and functional connectivity in patients with minimal conscious state.

The objective of this study was to investigate brain activation and functional network patterns during musical interventions in different frequency bands using functional near-infrared spectroscopy, and to provide a basis for more effective music therapy strategy selection for patients in minimally conscious state (MCS). Twenty six MCS patients and 20 healthy people were given music intervention with low frequency (31-180 Hz), medium frequency (180-4k Hz), and high frequency (4k-22k Hz) audio. In MCS patients, low frequency music intervention induced activation of left prefrontal cortex and left primary sensory cortex (S1), also a left-hemisphere lateralization effect of dorsolateral prefrontal cortex (DLPFC). And the functional connectivity of right DLPFC-right S1 was significantly improved by high frequency music intervention. The low frequency and high frequency music may contribute more than medium frequency music to the recovery of consciousness. This study also validated the effectiveness of fNIRS in studies of brain function in MCS patients.

The Effectiveness of Zolpidem in Improving Consciousness in Patients with Acute Brain Injury

Background: Disorders of consciousness, including the vegetative state (VS) and the minimally conscious state (MCS) following brain damage and various complications for the patient, also have economic and social consequences. However, there is still no definitive or effective treatment for this condition. However, there is still no definitive or effective treatment for this condition. Therefore, this study aims to investigate the effectiveness of zolpidem in improving consciousness in patients with acute brain injury.&#x0D; Methods: The present quasi-experimental study was performed from 2020 to 2021 after obtaining the necessary permissions from Zahedan University of Medical Sciences, Iran. Eighty patients with acute brain injury who met the study inclusion criteria were recruited and randomized into zolpidem and placebo groups. In the zolpidem group, 10 mg zolpidem tablets were gavage twice daily. In the placebo group, a placebo tablet with the same appearance as zolpidem was gavage twice daily for 14 days. The consciousness level of patients was measured daily until the outcome (ICU discharge or expiration) was established. Eventually, a comparative data analysis was conducted to determine zolpidem's efficacy in enhancing consciousness, reducing mechanical ventilation duration, and improving patient outcomes.&#x0D; Results: The mean GCS score in the zolpidem group was 6.1±2.4 on admission and 11.6±3.8 at the end of the study, compared to 5.9±1.7 on admission and 11.3±2.8 at the end of the study, for the placebo group (p=0.154 and p=0.211, respectively). The mean duration of mechanical ventilation was 24.41±9.14 days in the zolpidem group and 23.16±10.72 days in the placebo group (P=0.529). Twenty-eight patients in the zolpidem group were discharged from ICU, and 12 expired. For the placebo group, 26 patients were discharged from ICU, while 14 were expired (p=0.87). No statistically significant difference was found in any of the measured variables between the two groups.&#x0D; Conclusion: The results have shown that zolpidem administration had no statistically significant effect on improving the level of consciousness and reducing mechanical ventilation duration and clinical outcomes in acute brain injury patients.

Lateral frontoparietal effective connectivity differentiates and predicts state of consciousness in a cohort of patients with traumatic disorders of consciousness.

Neuroimaging studies have suggested an important role for the default mode network (DMN) in disorders of consciousness (DoC). However, the extent to which DMN connectivity can discriminate DoC states-unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS)-is less evident. Particularly, it is unclear whether effective DMN connectivity, as measured indirectly with dynamic causal modelling (DCM) of resting EEG can disentangle UWS from healthy controls and from patients considered conscious (MCS+). Crucially, this extends to UWS patients with potentially "covert" awareness (minimally conscious star, MCS*) indexed by voluntary brain activity in conjunction with partially preserved frontoparietal metabolism as measured with positron emission tomography (PET+ diagnosis; in contrast to PET- diagnosis with complete frontoparietal hypometabolism). Here, we address this gap by using DCM of EEG data acquired from patients with traumatic brain injury in 11 UWS (6 PET- and 5 PET+) and in 12 MCS+ (11 PET+ and 1 PET-), alongside with 11 healthy controls. We provide evidence for a key difference in left frontoparietal connectivity when contrasting UWS PET- with MCS+ patients and healthy controls. Next, in a leave-one-subject-out cross-validation, we tested the classification performance of the DCM models demonstrating that connectivity between medial prefrontal and left parietal sources reliably discriminates UWS PET- from MCS+ patients and controls. Finally, we illustrate that these models generalize to an unseen dataset: models trained to discriminate UWS PET- from MCS+ and controls, classify MCS* patients as conscious subjects with high posterior probability (pp > .92). These results identify specific alterations in the DMN after severe brain injury and highlight the clinical utility of EEG-based effective connectivity for identifying patients with potential covert awareness.

Clinical and ethical challenges in decision-making for patients with disorders of consciousness and locked-in syndrome from Chinese neurologists' perspectives.

The diagnosis of and life-sustaining treatment (LST) for patients with disorders of consciousness (DoC) and locked-in syndrome (LIS) have been the subject of intense debate. We aim to investigate the application of diagnostic knowledge, opinions about the administration of LST, and ethical challenges related to DoC and LIS. A cross-sectional study. A survey was conducted among Chinese neurologists. Questionnaires included three vignettes (unresponsive wakefulness syndrome (UWS); minimally conscious state (MCS), and LIS). They were randomly distributed among neurologists from August 2018 to December 2019. A sample of 360 questionnaires was included (response rate: 78%). Overall, 63% of the participants chose the correct diagnostic category. The neurologists who received the MCS case chose the category more accurately than the neurologists with the UWS (p < 0.001) and LIS case (p = 0.002). Most neurologists preferred never to limit LST for their patients (47%, 63%, and 67% in UWS, MCS, and LIS groups, p = 0.052). A large group of neurologists believed UWS patients could feel pain (73%), with no difference from MCS and LIS patients (p > 0.05). Deciding for patients in the absence of surrogates was rated extremely challenging. A large proportion of Chinese neurologists in our study didn't apply the accurate diagnostic categories to the description of DoC and LIS patients. This calls for more education and training. Most Chinese neurologists were reluctant to limit LST for patients. This may indicate that there may be a need to emphasize the allocation of more resources toward long-term care in China.