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.

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.

BACKGROUND: The Patient Right to Autonomy Act (PRAA), implemented in Taiwan in 2019, enables the creation of advance decisions (AD) through advance care planning (ACP). This legal framework allows for the withholding and withdrawal of life-sustaining treatment (LST) or artificial nutrition and hydration (ANH) in situations like irreversible coma, vegetative state, severe dementia, or unbearable pain. This study aims to investigate preferences for LST or ANH across various clinical conditions, variations in participant preferences, and factors influencing these preferences among urban residents.
METHODS: Employing a survey of legally structured AD documents and convenience sampling for data collection, individuals were enlisted from Taipei City Hospital, serving as the primary trial and demonstration facility for ACP in Taiwan since the commencement of the PRAA in its inaugural year. The study examined ADs and ACP consultation records, documenting gender, age, welfare entitlement, disease conditions, family caregiving experience, location of ACP consultation, participation of second-degree relatives, and the intention to participate in ACP.
RESULTS: Data from 2337 participants were extracted from electronic records. There was high consistency in the willingness to refuse LST and ANH, with significant differences noted between terminal diseases and extremely severe dementia. Additionally, ANH was widely accepted as a time-limited treatment, and there was a prevalent trend of authorizing a health care agent (HCA) to make decisions on behalf of participants. Gender differences were observed, with females more inclined to decline LST and ANH, while males tended towards accepting full or time-limited treatment. Age also played a role, with younger participants more open to treatment and authorizing HCA, and older participants more prone to refusal.
CONCLUSIONS: Diverse preferences in LST and ANH were shaped by the public\'s current understanding of different clinical states, gender, age, and cultural factors. Our study reveals nuanced end-of-life preferences, evolving ADs, and socio-demographic influences. Further research could explore evolving preferences over time and healthcare professionals\' perspectives on LST and ANH decisions for neurological patients..

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).
CONCLUSIONS: 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.
CONCLUSIONS: Graph-theoretical features are shown to discriminate between these two neurophysiologically similar conditions, and may thus support the clinical diagnosis.

Recovery of consciousness after coma remains one of the most challenging areas for accurate diagnosis and effective therapeutic engagement in the clinical neurosciences. Recovery depends on preservation of neuronal integrity and evolving changes in network function that re-establish environmental responsiveness. It typically occurs in defined steps: it begins with eye opening and unresponsiveness in a vegetative state, then limited recovery of responsiveness characterizes the minimally conscious state, and this is followed by recovery of reliable communication. This review considers several points for novel interventions, for example, in persons with cognitive motor dissociation in whom a hidden cognitive reserve is revealed. Circuit mechanisms underlying restoration of behavioral responsiveness and communication are discussed. An emerging theme is the possibility to rescue latent capacities in partially damaged human networks across time. These opportunities should be exploited for therapeutic engagement to achieve individualized solutions for restoration of communication and environmental interaction across varying levels of recovery.

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.

Non-invasive brain stimulation is considered as a promising technology for treating patients with disorders of consciousness (DOC). Various approaches and protocols have been proposed; however, few of them have shown potential effects on patients with vegetative state (VS). This study aimed to explore the neuro-modulation effects of intermittent theta burst stimulation (iTBS) on the brains of patients with VS and to provide a pilot investigation into its possible role in treating such patients.
We conducted a sham-controlled crossover study, a real and a sham session of iTBS were delivered over the left dorsolateral prefrontal cortex of such patients. A measurement of an electroencephalography (EEG) and a behavioral assessment of the Coma Recovery Scale-Revised (CRS-R) were applied to evaluate the modulation effects of iTBS before and after stimulation.
No meaningful changes of CRS-R were found. The iTBS altered the spectrum, complexity and functional connectivity of the patients. The real stimulation induced a trend of decreasing of delta power at T1 and T2 in the frontal region, significant increasing of permutation entropy at the T2 in the left frontal region. In addition, brain functional connectivity, particularly inter-hemispheric connectivity, was strengthened between the electrodes of the frontal region. The sham stimulation, however, did not induce any significant changes of the brain activity.
One session of iTBS significantly altered the oscillation power, complexity and functional connectivity of brain activity of VS patients. It may be a valuable tool on modulating the brain activities of patients with VS.

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.
OBJECTIVE: 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.
CONCLUSIONS: 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.