RESUMEN

Background: Acupuncture is a promising non-pharmaceutical complementary therapy in treating prolonged Disorders of consciousness (pDOC), but solid evidence to support its effectiveness and safety is still lacking. Thus, the purpose of this study is to investigate the efficacy and safety of acupuncture-assisted therapy for pDOC patients. Methods: A single-center, prospective, randomized, conventional-controlled, assessor-and-statistician-blinded trial has been designed and is being conducted at West China Hospital of Sichuan University. A total of 110 participants will be randomly assigned to the experimental group and the control group in a 1:1 allocation ratio and evaluated using Coma Recovery Scale-Revised (CRS-R) at 8 a.m., 12 p.m., and 4 p.m. on 2 consecutive days before enrollment to determine the consciousness level. The experimental group will receive acupuncture combined with conventional treatment, while the control group will receive only conventional treatment during the trial observation period. The treatment duration of both groups will be 20 days. Among them, the frequency of acupuncture-assisted therapy is once a day, with 10 consecutive sessions followed by a day's rest for a total of 24 days. Data will be collected separately during baseline and after the final treatment. For data analysis, both Full Analysis Set (FAS) and Per Protocol Set (PPS) principles will be performed together by applying SPSS 27.0 software. The primary outcome measures are the changes of CRS-R before and after treatment, while the secondary outcome measures are the changes of Full Outline of Unresponsiveness Scale (FOUR), the changes of Nociception Coma Scale-Revised (NCS-R), the changes of Disability Rating Scale (DRS), the changes of Mismatch Negativity (MMN) and P300 before and after treatment, respectively. Discussion: This trial aims to rationally assess the consciousness level from multiple 2 perspectives through subjective evaluation and objective detection by selecting several standardized clinical scales combined with Event-Related Potential (ERP) detection technology. In this way, we will be able to reduce the subjectivity of consciousness assessment and objectively evaluate the clinical efficacy of acupuncture-assisted therapy for pDOC. The study, if proven to be effective and safe enough, will provide a favorable evidence to guide medical decision-making choices and future researches. Clinical trial registration: https://www.chictr.org.cn/, identifier ChiCTR2300076180.

RESUMEN

OBJECTIVES: The aim of this study was to analyze the Chinese version of the Nociception Coma Scale-Revised in patients with prolonged disorders of consciousness within the framework of Rasch modeling, including investigating the invariance of total scores across different etiologies of disorders of consciousness. DESIGN: Prospective psychometric study. PARTICIPANTS: Patients with prolonged disorders of consciousness from the Rehabilitation and Neurology units in hospital. INTERVENTIONS: None. MAIN OUTCOME MEASURE: The Nociception Coma Scale-Revised was undertaken by trained raters and the Coma Recovery Scale-Revised was used to assess patients' consciousness. The psychometric properties within the Rasch model including item-person targeting, reliability and separation, item fit, unidimensionality, and differential item functioning were assessed. RESULTS: 84 patients with prolonged disorders of consciousness (mean age 53 years; mean injury 5 months; 42 with Minimally Conscious State and 42 with Unresponsive Wakefulness Syndrome) of 252 observations were enrolled in the study. Through the procedure of repeated assessment and differential item function, a lower item bias Rasch set was purified. The Rasch model assumptions were examined and met, with item reliability and validity meeting the recommended threshold. CONCLUSIONS: The Chinese version of the Nociception Coma Scale-Revised demonstrated unidimensionality, good reliability and separation, and good item fit, but dissatisfied person fit and item-person targeting. The verbal subscale showed a notable discrepancy between person responses and the difficulty of the items, suggesting limited clinical significance.

RESUMEN

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

Asunto(s)

RESUMEN

BACKGROUND: Awareness is a state of consciousness that enables a subject to interact with the environment. Transient alteration of awareness (AA) is a disabling sign of many types of epileptic seizures. The brain mechanisms of awareness and its alteration are not well known. OBJECTIVE/HYPOTHESIS: Transient and isolated AA induced by electrical brain stimulation during a stereoelectroencephalography (SEEG) recording represents an ideal model for studying the associated modifications of functional connectivity and locating the hubs of awareness networks. METHODS: We investigated the SEEG signals-based brain functional connectivity (FC) changes vs background occurring during AA triggered by three thalamic and two insular stimulations in three patients explored by SEEG in the frame of presurgical evaluation for focal drug-resistant epilepsy. The results were compared to the stimulations of the same sites that did not induce clinical changes (negative stimulations). RESULTS: We observed decreased node strength in the pulvinar, insula, and parietal associative cortices during the thalamic and insular stimulations that induced AA. The link strengths characterizing functional coupling between the thalamus and the insular, prefrontal, temporal, or parietal associative cortices were also decreased. In contrast, there was an increased synchronization between the precuneus and the temporal lateral cortex. These FC changes were absent during the negative stimulations. CONCLUSION: Our study highlights the role of the pulvinar, insular, and parietal hubs in maintaining the awareness networks and paves the way for invasive or non-invasive neuromodulation protocols to reduce AA manifestations during epileptic seizures.

RESUMEN

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

RESUMEN

Background: The findings regarding the prognosis of prolonged disorders of consciousness (PDOC) vary widely among different studies. This study aims to investigate the mortality, consciousness recovery and disabilities of patients with PDOC after brain injury. Methods: A total of 204 patients with PDOC were included in a longitudinal cohort study, including 129 males and 75 females. There were 112 cases of traumatic brain injury (TBI), 62 cases of cerebral hemorrhage (CH), 13 cases of cerebral infarction (CI) and 17 cases of ischemic hypoxic encephalopathy (IHE). The status of consciousness at 1, 2, 3, 6, 12, 18, 24, 36, 48 months of the disease course was assessed or followed up using the Revised Coma Recovery Scale (CRS-R). If the patients were conscious, the disability Rating Scale (DRS) was also performed. The prognosis of different PDOC including coma, vegetative state (VS) and minimal conscious state (MCS) was analyzed. The survival patients were screened for variables and included in multivariate binary Logistic regression to screen the factors affecting the recovery of consciousness. Results: The mortality rates at 12, 24, 36, and 48 months were 10.7, 23.4, 38.9, and 68.4%, respectively. The median time of death was 18 months (8.75, 29). The probability of MCS regaining consciousness was higher than VS (p < 0.05), with the degree of disability left lower than VS (p < 0.05). There was no significant difference between MCS- and MCS+ groups in terms of the probability of regaining consciousness, the extent of residual disability, and mortality rates (p > 0.05). The mortality rate of coma was higher than that of other PDOC (p < 0.05). The mortality rate of MCS was lower than that of VS, but the difference was not statistically significant (p > 0.05). The probability of consciousness recovery after TBI was the highest and the mortality rate was the lowest. The possibility of consciousness recovery in IHE was the least, and the mortality rate of CI was the highest. The cause of brain injury and initial CRS-R score were the factors affecting the consciousness recovery of patients (p < 0.05). Conclusion: The prognosis of MCS is more favorable than VS, with comparable outcomes between MCS- and MCS+, while comatose patients was the poorest. TBI has the best prognosis and IHE has the worst prognosis.

Asunto(s)

RESUMEN

BACKGROUND: There is no diagnostic assessment procedure with moderate or strong evidence of use, and evidence for current means of treating prolonged disorders of consciousness (pDOC) is sparse. This may be related to the fact that the mechanisms of pDOC have not been studied deeply enough and are not clear enough. Therefore, the aim of this study was to explore the mechanism of pDOC using functional near-infrared spectroscopy (fNIRS) to provide a basis for the treatment of pDOC, as well as to explore preclinical markers for determining the arousal of pDOC patients. METHODS: Five minutes resting-state data were collected from 10 pDOC patients and 13healthy adults using fNIRS. Based on the concentrations of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) in the time series, the resting-state cortical brain functional connectivity strengths of the two groups were calculated, and the functional connectivity strengths of homologous and heterologous brain networks were compared at the sensorimotor network (SEN), dorsal attention network (DAN), ventral attention network (VAN), default mode network (DMN), frontoparietal network (FPN), and visual network (VIS) levels. Univariate binary logistic regression analyses were performed on brain networks with statistically significant differences to identify brain networks associated with arousal in pDOC patients. The receiver operating characteristic (ROC) curves were further analyzed to determine the cut-off value of the relevant brain networks to provide clinical biomarkers for the prediction of arousal in pDOC patients. RESULTS: The results showed that the functional connectivity strengths of oxyhemoglobin (HbO)-based SEN∼SEN, VIS∼VIS, DAN∼DAN, DMN∼DMN, SEN∼VIS, SEN∼FPN, SEN∼DAN, SEN∼DMN, VIS∼FPN, VIS∼DAN, VIS∼DMN, HbR-based SEN∼SEN, and SEN∼DAN were significantly reduced in the pDOC group and were factors that could reflect the participants' state of consciousness. The cut-off value of resting-state functional connectivity strength calculated by ROC curve analysis can be used as a potential preclinical marker for predicting the arousal state of subjects. CONCLUSION: Resting-state functional connectivity strength of cortical networks is significantly reduced in pDOC patients. The cut-off values of resting-state functional connectivity strength are potential preclinical markers for predicting arousal in pDOC patients.

Asunto(s)

RESUMEN

Objective: This study pursued a meta-analysis to evaluate the predictive accuracy of machine learning (ML) models in determining disorders of consciousness (DOC) among patients with traumatic brain injury (TBI). Methods: A comprehensive literature search was conducted to identify ML applications in the establishment of a predictive model of DOC after TBI as of August 6, 2023. Two independent reviewers assessed publication eligibility based on predefined criteria. The predictive accuracy was measured using areas under the receiver operating characteristic curves (AUCs). Subsequently, a random-effects model was employed to estimate the overall effect size, and statistical heterogeneity was determined based on I2 statistic. Additionally, funnel plot asymmetry was employed to examine publication bias. Finally, subgroup analyses were performed based on age, ML type, and relevant clinical outcomes. Results: Final analyses incorporated a total of 46 studies. Both the overall and subgroup analyses exhibited considerable statistical heterogeneity. Machine learning predictions for DOC in TBI yielded an overall pooled AUC of 0.83 (95% CI: 0.82-0.84). Subgroup analysis based on age revealed that the ML model in pediatric patients yielded an overall combined AUC of 0.88 (95% CI: 0.80-0.95); among the model subgroups, logistic regression was the most frequently employed, with an overall pooled AUC of 0.85 (95% CI: 0.83-0.87). In the clinical outcome subgroup analysis, the overall pooled AUC for distinguishing between consciousness recovery and consciousness disorders was 0.84 (95% CI: 0.82-0.85). Conclusion: The findings of this meta-analysis demonstrated outstanding accuracy of ML models in predicting DOC among patients with brain injuries, which presented substantial research value and potential of ML application in this domain.

RESUMEN

Disorders of Consciousness (DoC) result in profound functional impairment, adversely affecting the lives of a predominantly younger patient population. Currently, effective treatment options for those who have reached chronicity (prolonged symptom duration over 4 weeks) are extremely limited, with the majority of such cases facing life-long dependence on carers and a poor quality of life. Here we briefly review the current evidence on caseload, diagnostic and management options in the United Kingdom (UK), United States of America (USA) and the European Union (EU). We identify key differences as well as similarities in these approaches across respective healthcare systems, highlighting unmet needs in this population. We subsequently present past efforts and the most recent advances in the field of surgical modulation of consciousness through implantable neurostimulation systems. We examine the ethical dilemmas that such a treatment approach may pose, proposing mediating solutions and methodological adjustments to address these concerns. Overall, we argue that there is a strong case for the utilisation of deep brain stimulation (DBS) in the DoC patient cohort. This is based on both promising results of recent clinical trials as well as technological developments. We propose a revitalization of surgical neuromodulation for DoC with a multicenter, multidisciplinary approach and strict monitoring guidelines, in order to not only advance treatment options but also ensure the safeguarding of patients' welfare and dignity.

RESUMEN

The aim of this prospective, observational cohort study was to investigate and assess diverse neuroimaging biomarkers to predict patients' neurological recovery after coma. 32 patients (18-76 years, M = 44.8, SD = 17.7) with disorders of consciousness participated in the study. Multimodal neuroimaging data acquired during the patient's hospitalization were used to derive cortical glucose metabolism (18F-fluorodeoxyglucose positron emission tomography/computed tomography), and structural (diffusion-weighted imaging) and functional connectivity (resting-state functional MRI) indices. The recovery outcome was defined as a continuous composite score constructed from a multivariate neurobehavioral recovery assessment administered upon the discharge from the hospital. Fractional anisotropy-based white matter integrity in the anterior forebrain mesocircuit (r = 0.72, p < .001, 95% CI: 0.87, 0.45), and the functional connectivity between the antagonistic default mode and dorsal attention resting-state networks (r = - 0.74, p < 0.001, 95% CI: - 0.46, - 0.88) strongly correlated with the recovery outcome. The association between the posterior glucose metabolism and the recovery outcome was moderate (r = 0.38, p = 0.040, 95% CI: 0.66, 0.02). Structural (adjusted R2 = 0.84, p = 0.003) or functional connectivity biomarker (adjusted R2 = 0.85, p = 0.001), but not their combination, significantly improved the model fit to predict the recovery compared solely to bedside neurobehavioral evaluation (adjusted R2 = 0.75). The present study elucidates an important role of specific MRI-derived structural and functional connectivity biomarkers in diagnosis and prognosis of recovery after coma and has implications for clinical care of patients with severe brain injury.

Asunto(s)

RESUMEN

INTRODUCTION:  Epidural spinal cord stimulation is a minimally invasive procedure with a growing list of indications. It has a good safety profile and analgesic effect, reduces the severity of spasticity, and activates various brain regions. The purpose of this study is to evaluate the clinical outcome of epidural spinal cord stimulation in patients with spastic syndrome and chronic disorders of consciousness resulting from severe traumatic brain injury (sTBI). METHODS: Between 2021 and 2023, an epidural spinal cord stimulation test was performed in 34 patients with central paresis, severe hypertonia, and chronically altered consciousness following sTBI. The severity of spastic syndrome was assessed using a modified Ashworth scale. All patients underwent implantation of a cylindrical eight-contact test epidural electrode at C3-C5 cervical level, followed by neurostimulation and selection of individual modes. Tonic stimulation at a frequency of 60 Hz, "burst" mode, or a combination of the two was used. RESULTS:  Epidural spinal cord stimulation was administered for an average of 4 ± 1.5 days, with tonic stimulation mode applied in 15 (44.1%) patients, "burst" mode in 10 (29.4%), and a combination of two in nine (26.5%) patients. A reduction in spasticity with clinical improvement was observed in 21 patients (61.8%). The Ashworth scale scores for distal and proximal upper extremities decreased from 3 points to 2.5 points and from 3 points to 2 points, respectively. This was significant in the right upper limbs (p = 0.0152 distally and p = 0.0164 proximally). Significant improvements were also seen in the lower extremities. Active movements in paretic limbs increased or appeared in 12 patients (35.3%), while a heightened level of consciousness was observed in six patients (17.6%). Permanent neurostimulator implantation was performed in 12 patients (35.3%), with no reported surgical complications. CONCLUSION:  Epidural spinal cord stimulation shows promise as an invasive rehabilitation method for patients with sTBI sequelae. Its use reduced the severity of spastic syndrome in over half of patients and increased active movements in paretic limbs in over a third. Notably, neuromodulation at the cervical level yielded pronounced effects on the upper extremities, both proximally and distally. Findings regarding consciousness level improvement are particularly intriguing but warrant further validation through randomized trials.

RESUMEN

Objectives: We performed the current research to describe healthcare providers' perspectives toward withdrawal of life-sustaining treatment (WLST) and advanced directive (AD) of patients with unresponsive wakefulness syndrome (UWS) and to identify influencing factors of their perspectives. Methods: Healthcare providers were recruited during a professional conference on disorders of consciousness (DoC). Participants completed self-administered questionnaires which included demographics, personal perspectives regarding WLST and the perception of ADs. Results: A total of 230 Chinese healthcare providers (female: 69.7%) were included. Only a small proportion reported positive attitudes toward withdrawing artificial nutrition and hydration (35.2%), antibiotics (30.9%), and do-not-resuscitation orders (23.5%) in UWS patients. As for predictors' identification, religion was significantly associated with the positive attitude toward DNR order (p = 0.004). Moreover, although 47.4% of the participants had never heard of ADs before of conference, almost all of them would consider ADs (95.7%) thereafter, especially for non-neurologists (p = 0.033). Conclusion: The propensity to WLST for UWS in China is low and perspective on WLST is significantly associated with individual characteristics. The attitudes of healthcare providers toward integrating ADs in the decisional process are positive. Future research regarding ADs and their predictors should be carried out to improve the quality of end-of-life care of UWS in China.

RESUMEN

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

Asunto(s)

RESUMEN

OBJECTIVE: To estimate rates and time to reach emergence of consciousness from vegetative state/unresponsive wakefulness syndrome (VS/UWS), and explore factors associated with improved recovery in children and adolescents with disorders of consciousness (DoC) following severe traumatic and non-traumatic brain injury. METHODS: Analytical, retrospective, cohort study. Clinical records of consecutively referred patients admitted in VS/UWS to a neurological rehabilitation institute in Argentina, between 2005 and 2021 were reviewed. Seventy children and adolescents were included in the analysis. A specialized 12-week rehabilitation program was administered, and emergence was defined by scores ≥44 points on the Western Neuro Sensory Stimulation Profile (WNSSP), sustained for at least 3 weeks on consecutive weekly evaluations. RESULTS: Emergence from VS/UWS to consciousness occurred within 5.4 (SD 2.6) weeks in almost one-third of patients. Multivariate Cox regression analysis showed emergence was significantly lower in patients with hypoxic ischemic encephalopathy compared to patients with other non-traumatic etiologies [HRadj 0.23 (95% CI 0.06-0.89); p = 0.03)]. CONCLUSIONS: Our findings reinforce growing evidence on the impact of etiology on DoC recovery in pediatric populations, ultimately influencing treatment and family-related decisions in child neurorehabilitation.

RESUMEN

For patients with disorders of consciousness (DoC), accurate assessment of residual consciousness levels and cognitive abilities is critical for developing appropriate rehabilitation interventions. In this study, we investigated the potential of electrooculography (EOG) in assessing language processing abilities and consciousness levels. Patients' EOG data and related electrophysiological data were analysed before and after explicit language learning. The results showed distinct differences in vocabulary learning patterns among patients with varying levels of consciousness. While minimally conscious patients showed significant neural tracking of artificial words and notable learning effects similar to those observed in healthy controls, whereas patients with unresponsive wakefulness syndrome did not show such effects. Correlation analysis further indicated that EOG detected vocabulary learning effects with comparable validity to electroencephalography, reinforcing the credibility of EOG indicator as a diagnostic tool. Critically, EOG also revealed significant correlations between individual patients' linguistic learning performance and their Oromotor/verbal function as assessed through behavioural scales. In conclusion, this study explored the differences in language processing abilities among patients with varying consciousness levels. By demonstrating the utility of EOG in evaluating consciousness and detecting vocabulary learning effects, as well as its potential to guide personalised rehabilitation, our findings indicate that EOG indicators show promise as a rapid, accurate and effective additional tool for diagnosing and managing patients with DoC.

Asunto(s)

RESUMEN

Brain connectivity, allowing information to be shared between distinct cortical areas and thus to be processed in an integrated way, has long been considered critical for consciousness. However, the relationship between functional intercortical interactions and the structural connections thought to underlie them is poorly understood. In the present work, we explore both functional (with an EEG-based metric: the median weighted symbolic mutual information in the theta band) and structural (with a brain MRI-based metric: fractional anisotropy) connectivities in a cohort of 78 patients with disorders of consciousness. Both metrics could distinguish patients in a vegetative state from patients in minimally conscious state. Crucially, we discovered a significant positive correlation between functional and structural connectivities. Furthermore, we showed that this structure-function relationship is more specifically observed when considering structural connectivity within the intra- and inter-hemispheric long-distance cortico-cortical bundles involved in the Global Neuronal Workspace (GNW) theory of consciousness, thus supporting predictions of this model. Altogether, these results support the interest of multimodal assessments of brain connectivity in refining the diagnostic evaluation of patients with disorders of consciousness.

RESUMEN

BACKGROUND: Disorders of consciousness (DOC) incorporate stages of awareness and arousal. Through coma arousal therapy sensory deprivation experienced by patients with DOC can be mitigated. Nevertheless, consensus concerning its effectiveness on these patients is still fractional. PURPOSE: This review aims to investigate the effectiveness of coma arousal therapies on patients with DOC. METHODS: A meta-analysis was performed by searching electronic databases using search terms, the studies investigating the effect of coma arousal therapy in patients with DOC using the Coma Recovery Scale-Revised and Glasgow Coma Scale as outcome measures were included. The risk of bias was assessed, using Cochrane and Joanna Briggs Institute critical appraisal tools. Further, analysis was conducted for the included studies. RESULTS: Out of 260 studies, 45 trials were reviewed and assessed for bias, with 31 studies included for analysis. The analysis demonstrates a significant difference in pre- and post - sensory stimulation, vagus nerve stimulation, transcranial magnetic stimulation, and transcranial direct current stimulation. Sensory stimulation showed the greatest mean difference of -4.96; 95% CI = -5.76 to - 4.15. The patients who underwent intervention after 3 months of illness showed significant improvement. CONCLUSION: The result shows that sensory stimulation, transcranial magnetic stimulation, and transcranial direct stimulation can improve behavioral outcomes of patients with DOC, wherein sensory stimulation is found to be more effective.

RESUMEN

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.

Asunto(s)