Disorders Of Consciousness Patients Research Articles

NIMG-68. A COMPREHENSIVE REVIEW OF THE USE OF FMRI IN EVALUATING FUNCTIONAL CONNECTIVITY AND ACTIVATION IN DISORDERS OF CONSCIOUSNESS

Abstract PURPOSE Disorders of consciousness (DoC) like vegetative and minimally conscious states pose significant diagnostic and prognostic challenges, compounded by the inherent limitations in communicating with affected patients. We performed a scoping review of the literature to examine the application of functional magnetic resonance imaging (fMRI) to the assessment of functional connectivity (FC) and activation in DoC. METHODS We searched the MEDLINE/PubMed, Web of Science, and Embase databases between the years 2004 and 2024 for English-language studies involving fMRI and DoC in adult populations, adhering to PRISMA guidelines. Included studies were evaluated for study design, DoC subtype, fMRI subtype, cohort size, and FC and/or activation findings. RESULTS Our initial search identified 17,475 results, of which 82 studies were ultimately included with a total of 3917 patients. Only one of the included studies was a randomized controlled trial, 24 were prospective cohort studies, three were retrospective cohort studies, and 54 were observational in design. The majority of studies utilized resting-state fMRI (n=54, 65.9%), and the most commonly investigated disorders of consciousness were minimally conscious states (31.9%), vegetative states (14.4%), and unresponsive wakefulness syndromes (12.9%). Among studies with higher levels of evidence like prospective and retrospective studies, FC and activation findings were heterogeneous, with several studies reporting abnormal connectivity in the default mode and auditory networks. Activation responses among patients also varied, with some patients of the same DoC displaying expected activation patterns while others did not. Normal patterns of connectivity and activation were associated with improved prognosis in eight prospective studies and one retrospective study. CONCLUSIONS These findings underscore the complexity of using fMRI to evaluate consciousness in DoC patients. Though fMRI has the potential to identify residual cognitive function, the variability in responses also indicates the need for personalized approaches and further research to standardize fMRI protocols for this purpose.

EEG-Derived Markers to Improve Prognostic Evaluation of Disorders of Consciousness.

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.

Exploring bispectral index as an alternative to polysomnography for long-term sleep monitoring in disorders of consciousness.

Sleep plays a crucial role in neuroplasticity and recovery from brain injury, particularly in disorders of consciousness (DoC), including unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). Traditional sleep monitoring methods like polysomnography (PSG) are complex and often impractical for long-term use in clinical settings. This study aimed to explore the utility of the Bispectral Index (BIS) as a more practical alternative for monitoring sleep patterns in DoC patients. We conducted simultaneous PSG and BIS monitoring on 38 DoC patients (19 UWS and 19 MCS). The study focused on analyzing sleep timing distribution, the effectiveness of BIS in differentiating sleep stages, and its correlation with consciousness levels. Our findings revealed that DoC patients exhibited irregular and fragmented sleep patterns, necessitating extended monitoring periods. The BIS effectively differentiated various sleep stages, with significant differences in BIS values observed across these stages. However, BIS values did not show significant differences between UWS and MCS patients, indicating that BIS primarily indicates wakefulness rather than cognitive awareness. DoC patients have disturbed sleep-wake cycles that require prolonged monitoring. BIS can well distinguish sleep stages in DoC patients, and the distribution of values is similar to that of normal subjects. However, BIS could not distinguish the level of consciousness of DoC patients. The study demonstrates the potential of BIS as a practical tool for long-term sleep monitoring in DoC patients, offering a less intrusive alternative to traditional methods.

Whole brain modelling for simulating pharmacological interventions on patients with disorders of consciousness

Disorders of consciousness (DoC) represent a challenging and complex group of neurological conditions characterised by profound disturbances in consciousness. The current range of treatments for DoC is limited. This has sparked growing interest in developing new treatments, including the use of psychedelic drugs. Nevertheless, clinical investigations and the mechanisms behind them are methodologically and ethically constrained. To tackle these limitations, we combined biologically plausible whole-brain models with deep learning techniques to characterise the low-dimensional space of DoC patients. We investigated the effects of model pharmacological interventions by including the whole-brain dynamical consequences of the enhanced neuromodulatory level of different neurotransmitters, and providing geometrical interpretation in the low-dimensional space. Our findings show that serotonergic and opioid receptors effectively shifted the DoC models towards a dynamical behaviour associated with a healthier state, and that these improvements correlated with the mean density of the activated receptors throughout the brain. These findings mark an important step towards the development of treatments not only for DoC but also for a broader spectrum of brain diseases. Our method offers a promising avenue for exploring the therapeutic potential of pharmacological interventions within the ethical and methodological confines of clinical research.

Study of a precise treatment protocol for patients with consciousness disorders based on the brain network analysis of functional magnetic resonance imaging.

How to conduct objective and accurate individualized assessments of patients with disorders of consciousness (DOC) and carry out precision rehabilitation treatment technology is a major rehabilitation problem that needs to be solved urgently. In this study, a multi-layer brain network was constructed based on functional magnetic resonance imaging (fMRI) to analyze the structural and functional brain networks of patients with DOC at different levels and to find regulatory targets (imaging markers) with recovery potential for DOC. Then repeated transcranial magnetic stimulation (rTMS) was performed in DOC patients to clinically validate. The brain network connectivity of DOC patients with different consciousness states is different, and the most obvious brain regions appeared in the olfactory cortex and precuneus. rTMS stimulation could effectively improve the consciousness level of DOC patients and stimulate the occipital lobe (specific regions found in this study) and the dorsolateral prefrontal cortex (DLPFC), and both parts had a good consciousness recovery effect. In clinical work, personalized stimulation regimen treatment combined with the brain network characteristics of DOC patients can improve the treatment effect.

Untargeted Metabolomic Analyses of Body Fluids to Differentiate TBI DOC and NTBI DOC.

To investigate the metabolomic differences between Traumatic brain injury (TBI) disorder of consciousness (DOC) patients and non-traumatic brain injury (NTBI) DOC patients by using cerebrospinal fluid (CSF), serum and urine samples beneficial to understand the pathological mechanism differences between the two etiologies, provide potential clues for the subsequent treatment and prognosis, and investigate the metabolome differences and similarities between TBI and NTBI among three different body fluids. In total, 24 TBI DOC subjects and 29 NTBI DOC subjects were enrolled. CSF, serum and urine samples from TBI DOC and NTBI DOC patients were collected and analyzed by performing UPLC-MS. The statistical methods and pathway analyses were applied to discover potential biomarkers and altered metabolic functions. When comparing TBI DOC and NTBI DOC, 36, 31 and 52 differential metabolites were obtained in CSF, serum and urine, respectively. The functional analysis of differential metabolites obtained in CSF, serum and urine were all related to amino acid metabolism. Except for amino acid metabolism, metabolic biomarkers in CSF, serum and urine mainly focus on central function, cognitive function, necrosis and apoptosis and neurological function, respectively. In CSF, the highest AUC was 0.864 (Isoproturon) and 0.816 (Proline betaine). Then, the AUC of NFurfurylformamide in serum was 0.941, while the AUC of Dihydronepetalactone and Doxepin N-oxide glucuronide were 1.0 in urine. CSF, serum and urine metabolomic analyses could differentiate TBI DOC from NTBI DOC and functional analyses showed a metabolic change difference between TBI DOC and NTBI DOC.

Clinical characteristics and outcomes of patients with antibody-related autoimmune encephalitis presenting with disorders of consciousness: A prospective cohort study.

To explore the clinical characteristics, short- and long-term functional outcomes, and risk factors for antibody-related autoimmune encephalitis (AE) in patients with disorders of consciousness (DoC). Clinical data were collected from AE patients admitted to Xuanwu Hospital of Capital Medical University from January 2012 to December 2021, and patients were followed up for up to 24 months after immunotherapy. A total of 312 patients with AE were included: 197 (63.1%) with anti-NMDAR encephalitis, 71 (22.8%) with anti-LGI1 encephalitis, 20 (6.4%) with anti-GABAbR encephalitis, 10 (3.2%) with anti-CASPR2 encephalitis, 10 (3.2%) with anti-GAD65 encephalitis, and 4 (1.3%) with anti-AMPAR2 encephalitis. Among these patients, 32.4% (101/312) presented with DoC, and the median (interquartile range, IQR) time to DoC was 16 (7.5, 32) days. DoC patients had higher rates of various clinical features of AE (p < .05). DoC was associated with elevated lumbar puncture cerebrospinal fluid (CSF) pressure, CSF leukocyte count, and specific antibody titer (p < .05). A high percentage of patients in the DoC group had a poor prognosis at discharge and at 6 months after immunotherapy (p < .001), but no significant difference in prognosis was noted between the DoC group and the non-DoC group at 12 and 24 months after immunotherapy. Dyskinesia (OR = 3.266, 95% CI: 1.550-6.925, p = .002), autonomic dysfunction (OR = 5.871, 95% CI: 2.574-14.096, and p < .001), increased CSF pressure (OR = 1.007, 95% CI: 1.001-1.014, p = .046), and modified Rankin scale (mRS) score ≥3 at the initiation of immunotherapy (OR = 7.457, 95% CI: 3.225-18.839, p < .001) were independent risk factors for DoC in AE patients. DoC is a relatively common clinical symptom in patients with AE, especially critically ill patients. Despite requiring longer hospitalization, DoC mostly improves with treatment of the primary disease and has a good long-term prognosis after aggressive life support and combination immunotherapy.

SFT-SGAT: A semi-supervised fine-tuning self-supervised graph attention network for emotion recognition and consciousness detection

Emotional recognition is highly important in the field of brain-computer interfaces (BCIs). However, due to the individual variability in electroencephalogram (EEG) signals and the challenges in obtaining accurate emotional labels, traditional methods have shown poor performance in cross-subject emotion recognition. In this study, we propose a cross-subject EEG emotion recognition method based on a semi-supervised fine-tuning self-supervised graph attention network (SFT-SGAT). First, we model multi-channel EEG signals by constructing a graph structure that dynamically captures the spatiotemporal topological features of EEG signals. Second, we employ a self-supervised graph attention neural network to facilitate model training, mitigating the impact of signal noise on the model. Finally, a semi-supervised approach is used to fine-tune the model, enhancing its generalization ability in cross-subject classification. By combining supervised and unsupervised learning techniques, the SFT-SGAT maximizes the utility of limited labeled data in EEG emotion recognition tasks, thereby enhancing the model’s performance. Experiments based on leave-one-subject-out cross-validation demonstrate that SFT-SGAT achieves state-of-the-art cross-subject emotion recognition performance on the SEED and SEED-IV datasets, with accuracies of 92.04% and 82.76%, respectively. Furthermore, experiments conducted on a self-collected dataset comprising ten healthy subjects and eight patients with disorders of consciousness (DOCs) revealed that the SFT-SGAT attains high classification performance in healthy subjects (maximum accuracy of 95.84%) and was successfully applied to DOC patients, with four patients achieving emotion recognition accuracies exceeding 60%. The experiments demonstrate the effectiveness of the proposed SFT-SGAT model in cross-subject EEG emotion recognition and its potential for assessing levels of consciousness in patients with DOC.

The integrity of thalamo-dorsolateral prefrontal cortex tract: a key factor in residual consciousness in disorders of consciousness patients.

The mesocircuit model describes a complex network that includes the prefrontal cortical-striatopallidal-thalamo-cortical loop systems and is involved in the mechanism underlying consciousness in patients with disorders of consciousness (DoC). Inhibitory signals to the thalamus become hyperactive in DoC patients, leading to a loss of consciousness. Reactivating this mesocircuit system is important for recovering consciousness in these patients. We investigated how the residual integrity of the thalamo-dorsolateral prefrontal cortex tract (TDLPFCT) influences consciousness in patients with DoC. This retrospective case-control study included three groups: prolonged DoC (n = 20), stroke without DoC (n = 20), and healthy controls (n = 20). Diffusion tensor imaging (DTI) was performed at least 4 weeks after the onset. Thalamo-DLPFC tracts were reconstructed using diffusion tensor tractography, and fractional anisotropy (FA) and tract volume (TV) were measured for each hemisphere. Consciousness was assessed using the revised coma recovery scale (CRS-R) within a week of brain imaging. Significant differences in DLPFCT TV were observed across all three groups, in both affected and less-affected lobes, with the DoC group showing the greatest reduction. A significant correlation was found between the TV of the less-affected TDLPFCT and CRS-R score. The integrity of the TDLPFCT, particularly in the less affected hemisphere, is associated with consciousness levels in patients with prolonged DoC. This finding suggests its potential importance in assessing prognosis and further developing therapeutic strategies for patients with DoC.

Permutation conditional mutual information to quantify TMS-evoked cortical connectivity in disorders of consciousness

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.

MutaPT: A Multi-Task Pre-Trained Transformer for Classifying State of Disorders of Consciousness Using EEG Signal.

Deep learning (DL) has been demonstrated to be a valuable tool for classifying state of disorders of consciousness (DOC) using EEG signals. However, the performance of the DL-based DOC state classification is often challenged by the limited size of EEG datasets. To overcome this issue, we introduce multiple open-source EEG datasets to increase data volume and train a novel multi-task pre-training Transformer model named MutaPT. Furthermore, we propose a cross-distribution self-supervised (CDS) pre-training strategy to enhance the model's generalization ability, addressing data distribution shifts across multiple datasets. An EEG dataset of DOC patients is used to validate the effectiveness of our methods for the task of classifying DOC states. Experimental results show the superiority of our MutaPT over several DL models for EEG classification.

Global Field Time-Frequency Representation-Based Discriminative Similarity Analysis of Passive Auditory ERPs for Diagnosis of Disorders of Consciousness.

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.

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.

Correlation between Thalamocortical Tract and Default Mode Network with Consciousness Levels in Hypoxic-Ischemic Brain Injury Patients: A Comparative Study Using the Coma Recovery Scale-Revised.

BACKGROUND The thalamocortical tract (TCT) links nerve fibers between the thalamus and cerebral cortex, relaying motor/sensory information. The default mode network (DMN) comprises bilateral, symmetrical, isolated cortical regions of the lateral and medial parietal and temporal brain cortex. The Coma Recovery Scale-Revised (CRS-R) is a standardized neurobehavioral assessment of disorders of consciousness (DOC). In the present study, 31 patients with hypoxic-ischemic brain injury (HI-BI) were compared for changes in the TCT and DMN with consciousness levels assessed using the CRS-R. MATERIAL AND METHODS In this retrospective study, 31 consecutive patients with HI-BI (17 DOC,14 non-DOC) and 17 age- and sex-matched normal control subjects were recruited. Magnetic resonance imaging was used to diagnose HI-BI, and the CRS-R was used to evaluate consciousness levels at the time of diffusion tensor imaging (DTI). The fractional anisotropy (FA) values and tract volumes (TV) of the TCT and DMN were compared. RESULTS In patients with DOC, the FA values and TV of both the TCT and DMN were significantly lower compared to those of patients without DOC and the control subjects (p<0.05). When comparing the non-DOC and control groups, the TV of the TCT and DMN were significantly lower in the non-DOC group (p<0.05). Moreover, the CRS-R score had strong positive correlations with the TV of the TCT (r=0.501, p<0.05), FA of the DMN (r=0.532, p<0.05), and TV of the DMN (r=0.501, p<0.05) in the DOC group. CONCLUSIONS This study suggests that both the TCT and DMN exhibit strong correlations with consciousness levels in DOC patients with HI-BI.

Uncovering sympathetic nervous system dysfunction in disorders of consciousness via heart rate variability during head-up tilt test.

Few standardized tools are available for evaluation of disorders of consciousness (DOC). The potential of heart rate variability (HRV) during head-up tilt (HUT) test was investigated as a complementary evaluation tool. Twenty-one DOC patients and 21 healthy participants were enrolled in this study comparing clinical characteristics and HRV time- and frequency-domain outcomes and temporal changes during HUT test. During the 1st-5th min of the HUT, DOC group showed a significant increase and decrease in log low frequency (LF) (p = 0.045) and log normalized high frequency (nHF) (p = 0.02), respectively, compared to the supine position and had lower log normalized LF (nLF) (p = 0.004) and log ratio of low-to-high frequency (LF/HF) (p = 0.001) compared to healthy controls. As the HUT continued from the 6th to the 20th min, DOC group exhibited a significant increase in log LF/HF (16th-20th min) (p < 0.05), along with a decrease in log nHF (6th-10th and 16th-20th min) (p < 0.05) and maintained lower log LF, log nLF, and log LF/HF than controls (p < 0.05). 1st-10th min after returning to the supine position, DOC group demonstrated a significant decrease in log nHF (p < 0.01) and increases in log LF/HF (p < 0.01) and had lower log LF (p < 0.01) and log nLF (p < 0.05) compared to controls. In contrast, the control group exhibited a significant decrease in log nHF (p < 0.05) and increase in log LF/HF (p < 0.05) throughout the entire HUT test. Notably, no significant differences were observed when comparing time-domain outcomes reflecting parasympathetic nervous system between the two groups. HRV during HUT test indicated a delayed and attenuated autonomic response, particularly in the sympathetic nervous system, in DOC patients compared with healthy individuals.

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.

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.

ST-SCGNN: A Spatio-Temporal Self-Constructing Graph Neural Network for Cross-Subject EEG-Based Emotion Recognition and Consciousness Detection.

In this paper, a novel spatio-temporal self-constructing graph neural network (ST-SCGNN) is proposed for cross-subject emotion recognition and consciousness detection. For spatio-temporal feature generation, activation and connection pattern features are first extracted and then combined to leverage their complementary emotion-related information. Next, a self-constructing graph neural network with a spatio-temporal model is presented. Specifically, the graph structure of the neural network is dynamically updated by the self-constructing module of the input signal. Experiments based on the SEED and SEED-IV datasets showed that the model achieved average accuracies of 85.90% and 76.37%, respectively. Both values exceed the state-of-the-art metrics with the same protocol. In clinical besides, patients with disorders of consciousness (DOC) suffer severe brain injuries, and sufficient training data for EEG-based emotion recognition cannot be collected. Our proposed ST-SCGNN method for cross-subject emotion recognition was first attempted in training in ten healthy subjects and testing in eight patients with DOC. We found that two patients obtained accuracies significantly higher than chance level and showed similar neural patterns with healthy subjects. Covert consciousness and emotion-related abilities were thus demonstrated in these two patients. Our proposed ST-SCGNN for cross-subject emotion recognition could be a promising tool for consciousness detection in DOC patients.

Music, occupational, physical, and speech therapy interventions for patients in disorders of consciousness: An umbrella review.

Current clinical guidelines recommend that a multidisciplinary team inclusive of allied healthcare practitioners deliver assessment and intervention for disorders of consciousness. Allied health professionals include music, occupational, physical, and speech therapists. These allied health clinicians are challenged to select interventions due to a lack of evidence-based recommendations regarding rehabilitation interventions that support recovery of consciousness. This umbrella review synthesizes available systematic reviews (SRs) that describe occupational, speech and language, physical and/or musical therapeutic interventions for people with disorders of consciousness. Identify and summarize evidence from systematic reviews (SRs) that examine allied healthcare interventions for patients with disorders of consciousness. Additionally, this umbrella review aims to evaluate the impact of allied health interventions on recovery of consciousness, methodological quality and risk of bias for the included systematic reviews. An umbrella review was completed. The review was reported according to the Preferred Reporting Items for Overview of Reviews (PRIOR) guidance. Five academic databases (PubMed, CINAHL, PsycInfo, Web of Science, and the Cochrane Library) were searched for SRs and/or meta-analyses of allied health (i.e., music, occupational, physical, and speech therapy) interventions for disorders of consciousness. For included studies, data were extracted and quality of the SRs appraised using the A Measurement Tool to Assess Systematic Reviews (AMSTAR) 2 checklist. Data extracted from each SR identified the authors and years of primary studies, interventions, comparators, and outcomes related to recovery of consciousness (i.e., neurobehavioral/cognitive), functional status, physiological response pain, and adverse events. Rehabilitation interventions were categorized and described. Fifteen SRs were included and three of these reviews conducted meta-analyses. Identified rehabilitation interventions included: 1) sensory stimulation, 2) median nerve stimulation, 3) communication/environmental control through assistive technology, 4) mobilization, and 5) music-based therapy. SRs were published between 2002 and 2022 and included 2286 participants. Using the AMSTAR 2, the quality of reviews was critically low (k = 6), low (k = 3), moderate (k = 4), and high (k = 2). SRs within this umbrella review demonstrated significant heterogeneity in research methods and use of outcome measures to evaluate the recovery of consciousness within the primary studies. These factors influenced the ability to conduct meta-analyses. Sensory stimulation, median nerve stimulation, music therapy and mobilization are all interventions that demonstrate some level of benefit, but current SRs fail to prove benefit through high-level quality evidence. There is an indisputable need for continued rehabilitation research to expand options for treatment modalities and to ensure that the interventions being applied to DoC rehabilitation are evidence-based to improve consciousness and recovery.