BACKGROUND: The vegetative state (VS) after severe acute brain injury (SABI) is associated with significant prognostic uncertainty and poor long-term functional outcomes. However, it is generally distinguished from imminent death and is exempt from the Life-Sustaining Treatment (LST) Decisions Act in Korea. Here, we aimed to examine the perspectives of the general population (GP) and clinicians regarding decisions on mechanical ventilator withdrawal in patients in a VS after SABI.
METHODS: A cross-sectional survey was undertaken, utilizing a self-reported online questionnaire based on a case vignette. Nationally selected by quota sampling, the GP comprised 500 individuals aged 20 to 69 years. There were 200 doctors from a tertiary university hospital in the clinician sample. Participants were asked what they thought about mechanical ventilator withdrawal in patients in VS 2 months and 3 years after SABI.
RESULTS: Two months after SABI in the case, 79% of the GP and 83.5% of clinicians had positive attitudes toward mechanical ventilator withdrawal. In the GP, attitudes were associated with spirituality, household income, religion, the number of household members. On the other hand, clinicians\' attitudes were related to their experience of completing advance directives (AD) and making decisions about LST. In this case, 3 years after SABI, 92% of the GP and 94% of clinicians were more accepting of ventilator withdrawal compared to previous responses, based on the assumption that the patient had written AD. However, it appeared that the proportion of positive responses to ventilator withdrawal decreased when the patients had only verbal expressions (82% of the GP; 75.5% of clinicians) or had not previously expressed an opinion regarding LST (58% of the GP; 39.5% of clinicians).
CONCLUSIONS: More than three quarters of both the GP and clinicians had positive opinions regarding ventilator withdrawal in patients in a VS after SABI, which was reinforced with time and the presence of AD. Legislative adjustments are needed to ensure that previous wishes for those patients are more respected and reflected in treatment decisions.

BACKGROUND: Patients with brain injury who are unresponsive to commands may perform cognitive tasks that are detected on functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). This phenomenon, known as cognitive motor dissociation, has not been systematically studied in a large cohort of persons with disorders of consciousness.
METHODS: In this prospective cohort study conducted at six international centers, we collected clinical, behavioral, and task-based fMRI and EEG data from a convenience sample of 353 adults with disorders of consciousness. We assessed the response to commands on task-based fMRI or EEG in participants without an observable response to verbal commands (i.e., those with a behavioral diagnosis of coma, vegetative state, or minimally conscious state-minus) and in participants with an observable response to verbal commands. The presence or absence of an observable response to commands was assessed with the use of the Coma Recovery Scale-Revised (CRS-R).
RESULTS: Data from fMRI only or EEG only were available for 65% of the participants, and data from both fMRI and EEG were available for 35%. The median age of the participants was 37.9 years, the median time between brain injury and assessment with the CRS-R was 7.9 months (25% of the participants were assessed with the CRS-R within 28 days after injury), and brain trauma was an etiologic factor in 50%. We detected cognitive motor dissociation in 60 of the 241 participants (25%) without an observable response to commands, of whom 11 had been assessed with the use of fMRI only, 13 with the use of EEG only, and 36 with the use of both techniques. Cognitive motor dissociation was associated with younger age, longer time since injury, and brain trauma as an etiologic factor. In contrast, responses on task-based fMRI or EEG occurred in 43 of 112 participants (38%) with an observable response to verbal commands.
CONCLUSIONS: Approximately one in four participants without an observable response to commands performed a cognitive task on fMRI or EEG as compared with one in three participants with an observable response to commands. (Funded by the James S. McDonnell Foundation and others.).

Numerous studies have shown that musical stimulation can activate corresponding functional brain areas. Electroencephalogram (EEG) activity during musical stimulation can be used to assess the consciousness states of patients with disorders of consciousness (DOC). In this study, a musical stimulation paradigm and verifiable criteria were used for consciousness assessment. Twenty-nine participants (13 healthy subjects, 6 patients in a minimally conscious state (MCS) and 10 patients in a vegetative state (VS)) were recruited, and EEG signals were collected while participants listened to preferred and relaxing music. Fusion features based on differential entropy (DE), common spatial pattern (CSP), and EEG-based network pattern (ENP) features were extracted from EEG signals, and a convolutional neural network-long short-term memory (CNN-LSTM) model was employed to classify preferred and relaxing music.The results showed that the average classification accuracy for healthy subjects reached 85.58%. For two of the patients in the MCS group, the classification accuracies reached 78.18% and 66.14%, and they were diagnosed with emergence from MCS (EMCS) two months later. The accuracies of three patients in the VS group were 58.18%, 64.32% and 62.05%, with two patients showing slight increases in scale scores. Our study suggests that musical stimulation could be an effective method for consciousness detection, with significant diagnostic implications for patients with DOC.

Prolonged disorder of consciousness (DoC) is a rising challenge. Pediatric data on diagnosis and prognosis of prolonged DoC were too limited and heterogeneous, making it difficult to define the natural course and evaluate the prognosis. The present study explored the emergence from the Minimally Conscious State (eMCS) incidence at different months postinjury drawing the natural course, and detected the predictors of the incidence in children with prolonged DoC. A hospital-based prospective cohort study was conducted. Kaplan-Meier curves, as well as univariate and multivariate COX regression analysis, were performed. The study enrolled 383 pediatric DoC individuals, including 220 males (57.4%), with an average age of 3.9 (1.9-7.3) years. The median duration between onset and rehabilitation is 30.0 (21.0-46.0) days. At enrollment, the ratio of vegetative state/unresponsive wakefulness syndrome (VS/WUS) to MCS is 78.9%-21.1%. Traumatic brain injury and infection are the major etiologies (36.8% and 37.1%, respectively), followed by hypoxia cerebral injury (12.3%). For children with prolonged DoC, the cumulative incidence of eMCS at months 3, 6, 12, and 24 was 0.510, 0.652, 0.731, 0.784 VS 0.290, 0.418, 0.539, 0.603 in the traumatic VS non-traumatic subgroup, respectively. For children in a persistent vegetative state (PVS), the cumulative incidence of emergence at months in 3, 6, 12, 24, 36 and 48 was testified as 0.439, 0.591, 0.683, 0.724, 0.743 and 0.743 in the traumatic subgroup, and 0.204, 0.349, 0.469, 0.534, 0.589 and 0.620 in the non-traumatic subgroup. Participants who exhibit any of the following four demographical and/or clinical characteristics-namely, older than 4 years at onset, accepted rehabilitation within 28 days of onset, remained MCS at enrollment, or with etiology of traumatic brain injuries-had a significantly positive outcome of consciousness recovery (eMCS). Moreover, both prolongation of the central somatosensory conductive time (CCT) (level 2) and absence of N20 (level 3) independently predict a negative outcome. In children with prolonged DoC, we found that 12 months postinjury was critical to eMCS, and a preferred timepoint to define chronic vegetative state (VS). The characteristics including age, etiology, time before rehabilitation, consciousness state, and SEP results were useful predictors of conscious recovery.Trial registration Registered 06/11/2018, the registration number is chiCTR1800019330 (chictr.org.cn). Registered prospectively.

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).

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.

BACKGROUND: Accurate evaluation of level of disorder of consciousness (DOC) is clinically challenging.
OBJECTIVE: This study aimed to establish a distinctive DOC-related pattern (DOCRP) for assessing disease severity and distinguishing unresponsive wakefulness syndrome (UWS) from minimally conscious state (MCS).
METHODS: Fifteen patients with DOC and eighteen health subjects with F-18-fluorodeoxyglucose (F-18-FDG) positron emission tomography (PET) were enrolled in this study. All patients were assessed by Coma Recovery Scale-Revised (CRS-R) and all individuals were randomly divided into two cohorts (Cohort A and B). DOCRP was identified in Cohort A and subsequently validated in Cohort B and A+B. We also assessed the discriminatory power of DOCRP between MCS and UWS.
RESULTS: The DOCRP was characterized bilaterally by relatively decreased metabolism in the medial and lateral frontal lobes, parieto-temporal lobes, cingulate gyrus and caudate, associated with relatively increased metabolism in the cerebellum and brainstem. DOCRP expression exhibited high accuracy in differentiating DOC patients from controls (P<0.0001, AUC=1.000), and furthermore could effectively distinguish MCS from UWS (P=0.037, AUC=0.821, sensitivity: 85.7 %, specificity: 75.0 %). Particularly in the subgroup of DOC patients survived global hypoxic-ischemic brain injury, DOCRP expression exhibited even better discriminatory power between MCS and UWS (P=0.046, AUC=1.000).
CONCLUSIONS: DOCRP might serve as an objective biomarker in distinguishing between UWS and MCS, especially in patients survived global hypoxic-ischemic brain injury.
BACKGROUND: ChiCTR2300073717 (Chinese clinical trial registry site, http://www.chictr.org).

OBJECTIVE: The patient being minimally conscious state (MCS) may benefit from wake-up interventions aimed at improving quality of life and have a higher probability of recovering higher level of consciousness compared to patients with the unresponsive wakefulness syndrome (UWS). However, differentiation of the MCS and UWS poses challenge in clinical practice. This study aimed to explore glucose metabolic pattern (GMP) obtained from 18F-labeled-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) in distinguishing between UWS and MCS.
METHODS: Fifty-seven patients with disorders of consciousness (21 cases of UWS and 36 cases of MCS) who had undergone repeated standardized Coma Recovery Scale-Revised (CRS-R) evaluations were enrolled in this prospective study. 18F-FDG-PET was carried out in all patients and healthy controls (HCs). Voxel-based scaled subprofile model/principal component analysis (SSM/PCA) was used to generate GMPs. The expression score of whole-brain GMP was obtained, and its diagnostic accuracy was compared with the standardized uptake value ratio (SUVR). The diagnostic efficiency was validated by one-year later clinical outcomes.
RESULTS: UWS-MCS GMP exhibited hypometabolism in the frontal-parietal cortex, along with hypermetabolism in the unilateral lentiform nucleus, putamen, and anterior cingulate gyrus. The UWS-MCS-GMP expression score was significantly higher in UWS compared to MCS patients (0.90 ± 0.85 vs. 0 ± 0.93, p < 0.001). UWS-MCS-GMP expression score achieved an area under the curve (AUC) of 0.77 to distinguish MCS from UWS, surpassing that of SUVR based on the frontoparietal cortex (AUC = 0.623). UWS-MCS-GMP expression score was significantly correlated with the CRS-R score (r = -0.45, p = 0.004) and accurately predicted the one-year outcome in 73.7% of patients.
CONCLUSIONS: UWS and MCS exhibit specific glucose metabolism patterns, the UWS-MCS-GMP expression score significantly distinguishes MCS from UWS, making SSM/PCA a potential diagnostic methods in clinical practice for individual patients.

BACKGROUND: Decannulation for people in a persistent vegetative state (PVS) is challenging and relevant predictors of successful decannulation have yet to be identified.
OBJECTIVE: This study aimed to explore the predictors of tracheostomy decannulation outcomes in individuals in PVS and to develop a nomogram.
METHODS: In 2022, 872 people with tracheostomy in PVS were retrospectively enrolled and their data was randomly divided into a training set and a validation set in a 7:3 ratio. Univariate and multivariate regression analyses were performed on the training set to explore the influencing factors for decannulation and nomogram development. Internal validation was performed using 5-fold cross-validation. External validation was performed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) on both the training and validation sets.
RESULTS: Data from 610 to 262 individuals were used for the training and validation sets, respectively. The multivariate regression analysis found that duration of tracheostomy tube placement≥30 days (Odds Ratio [OR] 0.216, 95 % CI 0.151-0.310), pulmonary infection (OR 0.528, 95 %CI 0.366-0.761), hypoproteinemia (OR 0.669, 95 % CI 0.463-0.967), no passive standing training (OR 0.372, 95 % CI 0.253-0.547), abnormal swallowing reflex (OR 0.276, 95 % CI 0.116-0.656), mechanical ventilation (OR 0.658, 95 % CI 0.461-0.940), intensive care unit (ICU) duration>4 weeks (OR 0.517, 95 % CI 0.332-0.805), duration of endotracheal tube (OR 0.855, 95 % CI 0.803-0.907), older age (OR 0.981, 95 % CI 0.966-0.996) were risk factors for decannulation failure. Conversely, peroral feeding (OR 1.684, 95 % CI 1.178-2.406), passive standing training≥60 min (OR 1.687, 95 % CI 1.072-2.656), private caregiver (OR 1.944, 95 % CI 1.350-2.799) and ICU duration<2 weeks (OR 1.758, 95 % CI 1.173-2.634) were protective factors conducive to successful decannulation. The 5-fold cross-validation revealed a mean area under the curve of 0.744. The ROC curve C-indexes for the training and validation sets were 0.784 and 0.768, respectively, and the model exhibited good stability and accuracy. The DCA revealed a net benefit when the risk threshold was between 0 and 0.4.
CONCLUSIONS: The nomogram can help adjust the treatment and reduce decannulation failure.
BACKGROUND: Clinical registration is not mandatory for retrospective studies.

This study aims to describe resting state networks (RSN) in patients with disorders of consciousness (DOC)s after acute severe traumatic brain injury (TBI).
Adult patients with TBI with a GCS score <8 who remained in a coma, minimally conscious state (MCS), or unresponsive wakefulness syndrome (UWS), between 2017 and 2020 were included. Blood-oxygen-level dependent imaging was performed to compare their RSN with 10 healthy volunteers.
Of a total of 293 patients evaluated, only 13 patients were included according to inclusion criteria: 7 in coma (54%), 2 in MCS (15%), and 4 (31%) had an UWS. RSN analysis showed that the default mode network (DMN) was present and symmetric in 6 patients (46%), absent in 1 (8%), and asymmetric in 6 (46%). The executive control network (ECN) was present in all patients but was asymmetric in 3 (23%). The right ECN was absent in 2 patients (15%) and the left ECN in 1 (7%). The medial visual network was present in 11 (85%) patients. Finally, the cerebellar network was symmetric in 8 patients (62%), asymmetric in 1 (8%), and absent in 4 (30%).
A substantial impairment in activation of RSN is demonstrated in patients with DOC after severe TBI in comparison with healthy subjects. Three patterns of activation were found: normal/complete activation, 2) asymmetric activation or partially absent, and 3) absent activation.