Assessment of Visual Pursuit in Patients With Disorders of Consciousness Based on a Brain-Computer Interface.

ObjectiveClinical assessment of consciousness relies on behavioural assessments, which have several limitations. Hence, disorder of consciousness (DOC) patients are often misdiagnosed. In this work, we aimed to compare the repetitive assessment of consciousness performed with a clinical behavioural and a Brain-Computer Interface (BCI) approach.Materials and methodsFor 7 weeks, sixteen DOC patients participated in weekly evaluations using both the Coma Recovery Scale-Revised (CRS-R) and a vibrotactile P300 BCI paradigm. To use the BCI, patients had to perform an active mental task that required detecting specific stimuli while ignoring other stimuli. We analysed the reliability and the efficacy in the detection of command following resulting from the two methodologies.ResultsOver repetitive administrations, the BCI paradigm detected command following before the CRS-R in seven patients. Four clinically unresponsive patients consistently showed command following during the BCI assessments.ConclusionBrain-Computer Interface active paradigms might contribute to the evaluation of the level of consciousness, increasing the diagnostic precision of the clinical bedside approach.SignificanceThe integration of different diagnostic methods leads to a better knowledge and care for the DOC.

BackgroundOccasionally, patients show dramatic recovery from disorders of consciousness (DOC) under intrathecal baclofen (ITB), an established treatment option for severe supraspinal spasticity. Anecdotal explanations for ITB‐related recovery of cognition include modulation of afferent impulses at the spinal level, thereby reducing spasticity‐related proprioceptive information overload within cortico–thalamo–cortical connections.ObjectiveIn this retrospective patient chart analysis, we assessed whether a reduction in spasticity would be associated with an increase in Coma Recovery Scale revised (CRS‐R) scores in a larger sample of patients than previously published.MethodsFrom a hospital‐based ITB treatment register, we extracted data from 26 patients with DOC and severe supraspinal spasticity who improved by >2 points on the Coma Recovery Scale revised (CRS‐R) within 6 months after ITB treatment initiation. We assessed Modified Ashworth scale (MAS) scores and CRS‐R scores on admission (PRE) and 3 and 6 months after initiation of ITB treatment (3M, 6M). We performed correlation analysis of the scores and their respective changes (PRE to 3M, 3M to 6M). We also correlated the time from acute event until ITB initiation to CRS‐R scores at 3M and 6M.ResultsITB led to significant improvement in spasticity based on MAS scores, which did not correlate to the improvements seen in CRS‐R total and subscale scores. Daily ITB dose did neither correlate to MAS scores nor to CRS‐total scores in the whole patient group, but after 3 months, ITB dose correlated to some CRS‐R subscale scores in some patient subgroups. Time until ITB treatment did not correlate to CRS‐R scores later on.ConclusionsOur data confirm that ITB may exert beneficial effects in selected DOC patients with respect to improved cognitive functions, which, however, do not correlate to its antispastic effect. The lack of correlation between time to ITB and CRS‐R outcome, but significant CRS‐R improvements following pump implantation, renders spontaneous remissions unlikely and leaves room for alternative pharmacological mechanisms.

Brain-computer interface (BCI) has been emerging as an assessment tool for patients with disorder of consciousness (DOC). With the advantages of high time resolution, low cost and portable design, EEG based BCI systems are especially suitable for bedside measurement. Recent studies showed the successful application of an EEG based BCI on DOC assessment and communication. However, the effect of repeated BCI measurement on this patient group is not clear. In this study, a tactile BCI paradigm was repeated 12 runs for 10 consecutive days on 5 DOC patients. Although the BCI performance varied among runs and days, every patient reached at least once the accuracy above 60%. Moreover, the Coma Recovery Scale Revised improved on two out of the five patients. This study addressed the significance of repeating a tactile BCI on DOC patients, and indicates a promising recovery effect of a tactile BCI on DOC patients.

The proper assessment of cognitive functioning is the subject of numerous scientific studies. Current behavioral methods essentially preclude this assessment in patients with disorders of consciousness (DOC). However, the development of eye-tracking technology (ETT) and the establishment of contact via this channel have created an opportunity to diagnose the cognitive function (CF) of DOC patients. The purpose of this study was to assess the level of CF of DOC patients for whom vision is the only channel of communication to introduce the CF assessment tool for DOC patients for whom vision remains the only channel for communication. The clinical multicenter study involved 31 DOC patients, whose attention, language functions, visual-spatial functions, personal orientation, memory, and abstract thinking were assessed three times (T1-T3) using the Cognitive Functions Assessment (CFA) scale, installed on the C-EYE X system. The collected data were compared with the CF assessment results obtained with the Coma Recovery Scale-Revised (CRS-R), and then statistically analyzed. There were no statistically significant differences between different time points. Patients scoring higher on the CRS-R receive higher values on the CFA. Statistically significant and moderate correlations were found between the total CFA and CRS-R scores. The results of the study indicate that the diagnosis of CF made with the use of the CFA allows for an accurate assessment of a wide spectrum of CF as well as makes their assessment independent of the experience of the examiner and the cooperation of the patient. The use of ETT, without reducing the quality of the examination, may reduce the workload of qualified personnel. The study was registered on the ClinicalTrials.gov clinical trials platform ID NCT05536921.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-27996-6.

Making an appropriate diagnosis and effective treatment for hydrocephalus in patients with severe disorders of consciousness (DOC) remains controversial and difficult. Given that the typical symptoms are usually concealed by the limited behavioral responsiveness of patients with severe DOC, hydrocephalus diagnosis is likely to be missed in the clinic. Even if not, the presence of hydrocephalus may reduce the likelihood of DOC recovery, posing a conundrum for clinicians. From December 2013 to January 2023, the clinical data and therapeutic schedule of hydrocephalus in patients with severe DOC at Huashan Hospital's Neurosurgical Emergency Center were studied retrospectively. Sixty-eight patients [mean age (± SD) 52.53 ± 17.03 years, 35 males and 33 females] with severe DOC had been included. The hydrocephalus was discovered after computerized tomography (CT) or magnetic resonance imaging (MRI) revealed enlarged ventricles in the patients. During the hospitalization, the patient underwent a surgical treatment that included a ventriculoperitoneal (V-P) shunt and/or cranioplasty (CP) implantation. Following the surgery, an individualized V-P pressure was established based on the patient's ventricle size and neurological function variation. To account for the improvement in consciousness in patients with severe DOC, the Glasgow Coma Scale (GCS) and the Coma Recovery Scale-Revised (CRS-R) assessments were performed before and after hydrocephalus treatment. All severe DOC patients have varying degrees of ventricular enlargement, deformation, and poor brain compliance. Approximately 60.3% (41/68) of them had low- or negative-pressure hydrocephalus (LPH or NegPH). 45.5% (31/68) of the patients had a one-stage V-P shunt and CP operation performed concurrently, while the remaining 37 patients had a single V-P shunt operation performed independently. Beside of two DOC patients developed surgical complications, 92.4% (61/66) of the survivors showed an improvement in consciousness after hydrocephalus treatment. In patients with severe DOC, LPH or NegPH was common. Secondary hydrocephalus in DOC patients had been largely ignored, hampering their neurological rehabilitation. Even months or years after the onset of severe DOC, active treatment of hydrocephalus can significantly improve patients' consciousness and neurological function. Meanwhile, this study summarized several evidence-based treatment experiences of hydrocephalus in patients with DOC.

Electroencephalography quadratic phase self-coupling correlates with consciousness states and restoration in patients with disorders of consciousness

Over the last 30 years, there has been a growing trend in clinical trials towards assessing novel interventions not only against the benchmark of statistical significance, but also with respect to whether they lead to clinically meaningful changes for patients. In the context of Disorders of Consciousness (DOC), despite a growing landscape of experimental interventions, there is no agreed standard as to what counts as a minimal clinically important difference (MCID). In part, this issue springs from the fact that, by definition, DOC patients are either unresponsive (i.e., in a Vegetative State; VS) or non-communicative (i.e., in a Minimally Conscious State; MCS), which renders it impossible to assess any subjective perception of benefit, one of the two core aspects of MCIDs. Here, we develop a novel approach that leverages published, international diagnostic guidelines to establish a probability-based minimal clinically important difference (pMCID), and we apply it to the most validated and frequently used scale in DOC: the Coma Recovery Scale–Revised (CRS-R). This novel method is objective (i.e., based on published criteria for patient diagnosis) and easy to recalculate as the field refines its agreed-upon criteria for diagnosis. We believe this new approach can help clinicians determine whether observed changes in patients’ behavior are clinically important, even when patients cannot communicate their experiences, and can align the landscape of clinical trials in DOC with the practices in other medical fields.

One of the main problems in Brain Computer Interface (BCI) systems is the non-stationary behavior of the electroencephalography (EEG) signals causing problems in real time applications. Another common problem in BCI systems is the situation where the labeled data are scarce. In this study, we take a semi-supervised learning perspective and propose solving both types of problems by updating the BCI system with labels obtained from the outputs of the classifier. To test the approach, data from motor imagery BCI system are used. Attributes extracted from EEG signals are classified with Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). With respect to the static classifiers, accuracy was improved approximately 4% using the proposed adaptation approach in the case of a training dataset. Even though the difference between the performance of static and adaptive classifiers decreases as the size of training data increases, the accuracy of our proposed adaptive classifier remains higher. The proposed approach has also improved the performance of a BCI system around 4% in the case of non-stationary signals as well.

With the continuous advancement of neuroimaging technologies, clinical research has discovered the phenomenon of cognitive-motor dissociation in patients with disorders of consciousness (DoC). This groundbreaking finding has provided new impetus for the development and application of brain-computer interface (BCI) in clinic. Currently, BCI has been widely applied in DoC patients as an important tool for assessing and assisting behaviorally unresponsive individuals. This paper reviews the current applications of BCI in DoC patients, focusing four main aspects including consciousness detection, auxiliary diagnosis, prognosis assessment, and rehabilitation treatment. It also provides an in-depth analysis of representative key techniques and experimental outcomes in each aspect, which include BCI paradigm designs, brain signal decoding method, and feedback mechanisms. Furthermore, the paper offers recommendations for BCI design tailored to DoC patients and discusses future directions for research and clinical practice in this field.

The Coma Recovery Scale-Revised (CRS-R) is the most recommended clinical tool to examine the neurobehavioral condition of individuals with disorders of consciousness (DOCs). Different studies have investigated the prognostic value of the information provided by the conventional administration of the scale, while other measures derived from the scale have been proposed to improve the prognosis of DOCs. However, the heterogeneity of the data used in the different studies prevents a reliable comparison of the identified predictors and measures. This study investigates which information derived from the CRS-R provides the most reliable prediction of both the clinical diagnosis and recovery of consciousness at the discharge of a long-term neurorehabilitation program. Retrospective observational multisite study. The enrollment was performed in three neurorehabilitation facilities of the same hospital network. A total of 171 individuals with DOCs admitted to an inpatient neurorehabilitation program for a minimum of 3 months were enrolled. Machine learning classifiers were trained to predict the clinical diagnosis and recovery of consciousness at discharge using clinical confounders and different metrics extracted from the CRS-R scale. Results showed that the neurobehavioral state at discharge was predicted with acceptable and comparable predictive value with all the indices and measures derived from the CRS-R, but for the clinical diagnosis and the Consciousness Domain Index, and the recovery of consciousness was predicted with higher accuracy and similarly by all the investigated measures, with the exception of initial clinical diagnosis. Interestingly, the total score in the CRS-R and, especially, the total score in its subscales provided the best overall results, in contrast to the clinical diagnosis, which could indicate that a comprehensive measure of the clinical diagnosis rather than the condition of the individuals could provide a more reliable prediction of the neurobehavioral progress of individuals with prolonged DOC. The results of this work have important implications in clinical practice, offering a more accurate prognosis of patients and thus giving the possibility to personalize and optimize the rehabilitation plan of patients with DoC using low-cost and easily collectable information.

INTRODUCTION: Patients with disorders of consciousness (DoC) represent a complex patient cohort requiring a multidisciplinary approach. Despite the availability of modern national clinical guidelines (2023), real-world clinical practice in Russia continues to be challenged by the use of outdated terminology, insufficient implementation of validated diagnostic scales, and a lack of standardized management approaches, including pain assessment and management. This study was prompted by the need to assess the current situation and identify key issues to improve the quality of care for patients with DoC in Russia. OBJECTIVE: To assess the current practices, awareness, and key challenges faced by Russian healthcare professionals in the diagnosis, treatment, and management of patients with chronic DoC. MATERIALS AND METHODS: In 2024, an anonymous online survey of 204 medical and paramedical professionals (anesthesiologists, neurologists, rehabilitation specialists, psychologists, nursing staff) from various regions of Russia was conducted. The 47-item questionnaire covered demographic data, terminology used, diagnostic scales, treatment and rehabilitation methods, as well as approaches to pain assessment and management. RESULTS: Despite modern clinical guidelines, outdated terminology persists in practice: apallic syndrome (13.3 %), vegetative state. Only 62.9 % of respondents use the validated Coma Recovery Scale-Revised (CRS-R) for DoC diagnosis, while 77.1 % still use non-relevant scales (Glasgow Coma Scale, Full Outline of UnResponsiveness — FOUR). 30 % of respondents, including physicians responsible for diagnosis, use exclusively these non-relevant tools. Significant differences in approaches were identified among specialists of different profiles (intensivists, neurologists, rehabilitation specialists). The majority of respondents (83 %) acknowledge that DoC patients experience pain; however, the assessment methods (autonomic signs, grimacing) are not standardized and can be non-specific. CONCLUSIONS: The study results indicate insufficient awareness and fragmented implementation of modern standards for the diagnosis and management of patients with DoC in Russia. The findings highlight the necessity of developing and implementing educational programs for healthcare professionals aimed at standardizing terminology, promoting the use of validated diagnostic scales (CRS-R), and harmonizing approaches to pain management.

Primary objective This study aimed to verify the reliability and validity of the Japanese version of the Coma Recovery Scale-Revised (CRS-R). Methods Subjects included 59 patients with disorders of consciousness (DOC) due to acquired brain injury. To validate test-retest reliability, Evaluator A assessed the CRS-R twice on the same day (A1, A2). To examine inter-rater reliability, Evaluators A (A2) and B (B) assessed the CRS-R without a time interval. To test concurrent validity, Evaluator A (A1) assessed the CRS-R, Japan Coma Scale (JCS), and the Glasgow Coma Scale (GCS) consecutively. To validate diagnostic accuracy, we evaluated the degree of agreement between A1 and A2 and between A2 and B in their diagnosis of DOC by CRS-R. Results The test-retest (ρ = 0.92) and inter- (ρ = 0.98) reliability of CRS-R were excellent” and Concurrent validity of CRS-R with JCS (ρ = −0.82) and GCS (ρ = 0.92) were high. Results of DOC diagnosis were consistent for 48/59 cases (κ = 0.82) for A1 and A2 and for 54/59 cases (κ = 0.92) for A2 and B. Conlcusion The Japanese version of the CRS-R may be as reliable and valid as the original English and other language versions.

Role of transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) in disorders of consciousness (DOC)

Terminal neurological conditions can affect millions of people worldwide and hinder them from doing their daily tasks and movements normally. Brain computer interface (BCI) is the best hope for many individuals with motor deficiencies. It will help many patients interact with the outside world and handle their daily tasks without assistance. Therefore, machine learning-based BCI systems have emerged as non-invasive techniques for reading out signals from the brain and interpreting them into commands to help those people to perform diverse limb motor tasks. This paper proposes an innovative and improved machine learning-based BCI system that analyzes EEG signals obtained from motor imagery to distinguish among various limb motor tasks based on BCI competition III dataset IVa. The proposed framework pipeline for EEG signal processing performs the following major steps. The first step uses a meta-heuristic optimization technique, called the whale optimization algorithm (WOA), to select the optimal features for discriminating between neural activity patterns. The pipeline then uses machine learning models such as LDA, k-NN, DT, RF, and LR to analyze the chosen features to enhance the precision of EEG signal analysis. The proposed BCI system, which merges the WOA as a feature selection method and the optimized k-NN classification model, demonstrated an overall accuracy of 98.6%, outperforming other machine learning models and previous techniques on the BCI competition III dataset IVa. Additionally, the EEG feature contribution in the ML classification model is reported using Explainable AI (XAI) tools, which provide insights into the individual contributions of the features in the predictions made by the model. By incorporating XAI techniques, the results of this study offer greater transparency and understanding of the relationship between the EEG features and the model’s predictions. The proposed method shows potential levels for better use in controlling diverse limb motor tasks to help people with limb impairments and support them while enhancing their quality of life.

Brain Computer Interface (BCI) systems have gained great visibility in the last years as they represent a quite innovative way of communication and a new instrument aimed at exploring brain functions. A lot of research labs are developing their own BCI system, everyone being involved in some particular aspects of them. At the "Tor Vergata" University our purpose is to develop tools for the evaluation and the optimization of the performances of BCI systems and to delineate some criteria for the analysis and implementation of different BCI systems; also we have defined file formats for BCI data in order to allow the sharing of tools among groups and to create models for the generalization and therefore the unification of the resources. All the tools and routines mentioned are part of the Body Language Framework++ 2.0.