Objective. Coronavirus disease 2019 (COVID-19) outbreak is a major challenge all over the world, without acknowledged treatment. Intravenous immunoglobulin (IVIG) has been recommended to treat critical coronavirus disease 2019 (COVID-19) patients in a few reviews, but the clinical study evidence on its efficacy in COVID-19 patients was lacking. Methods. 325 patients with laboratory-confirmed critical COVID-19 were enrolled from 4 government-designated COVID-19 treatment centres in southern China from December 2019 to March 2020. The primary outcomes were 28-and 60-day mortality, and the secondary outcomes were the total length of in-hospital and the total duration of the disease. Subgroup analysis was carried out according to clinical classification of COVID-19, IVIG dosage and timing. Results. In the enrolled 325 patients, 174 cases used IVIG and 151 cases did not. The 28-day mortality was improved with IVIG after adjusting confounding in overall cohort (P = 0.0014), and the in-hospital and the total duration of disease were longer in the IVIG group (P < 0.001). Subgroup analysis showed that only in patients with critical type, IVIG could significantly reduce the 28-day mortality, decrease the inflammatory response and improve some organ functions (all P < 0.05); the application of IVIG in the early stage (admission ≤ 7 days) with a high dose (> 15 g per day) exhibited significant reduction in 60-day mortality in the critical-type patients. Conclusion. Early administration of IVIG with high dose

Through the hybrid BCI system, command following was detected in four healthy subjects, two of 7 DOC patients, and one LIS patient. We suggest that the hybrid BCI system could be used as a supportive bedside tool to detect awareness in patients with DOC.

Cognitive motor dissociation describes a subset of patients with disorders of consciousness who show neuroimaging evidence of consciousness but no detectable command-following behaviours. Although essential for family counselling, decision-making, and the design of rehabilitation programmes, the prognosis for patients with cognitive motor dissociation remains under-investigated. The current study included 78 patients with disorders of consciousness who showed no detectable command-following behaviours. These patients included 45 patients with unresponsive wakefulness syndrome and 33 patients in a minimally conscious state, as diagnosed using the Coma Recovery Scale-Revised. Each patient underwent an EEG-based brain-computer interface experiment, in which he or she was instructed to perform an item-selection task (i.e. select a photograph or a number from two candidates). Patients who achieved statistically significant brain-computer interface accuracies were identified as cognitive motor dissociation. Two evaluations using the Coma Recovery Scale-Revised, one before the experiment and the other 3 months later, were carried out to measure the patients’ behavioural improvements. Among the 78 patients with disorders of consciousness, our results showed that within the unresponsive wakefulness syndrome patient group, 15 of 18 patients with cognitive motor dissociation (83.33%) regained consciousness, while only five of the other 27 unresponsive wakefulness syndrome patients without significant brain-computer interface accuracies (18.52%) regained consciousness. Furthermore, within the minimally conscious state patient group, 14 of 16 patients with cognitive motor dissociation (87.5%) showed improvements in their Coma Recovery Scale-Revised scores, whereas only four of the other 17 minimally conscious state patients without significant brain-computer interface accuracies (23.53%) had improved Coma Recovery Scale-Revised scores. Our results suggest that patients with cognitive motor dissociation have a better outcome than other patients. Our findings extend current knowledge of the prognosis for patients with cognitive motor dissociation and have important implications for brain-computer interface-based clinical diagnosis and prognosis for patients with disorders of consciousness.

Disorders of consciousness are a heterogeneous mixture of different diseases or injuries. Although some indicators and models have been proposed for prognostication, any single method when used alone carries a high risk of false prediction. This study aimed to develop a multidomain prognostic model that combines resting state functional MRI with three clinical characteristics to predict one year-outcomes at the single-subject level. The model discriminated between patients who would later recover consciousness and those who would not with an accuracy of around 88% on three datasets from two medical centers. It was also able to identify the prognostic importance of different predictors, including brain functions and clinical characteristics. To our knowledge, this is the first reported implementation of a multidomain prognostic model that is based on resting state functional MRI and clinical characteristics in chronic disorders of consciousness, which we suggest is accurate, robust, and interpretable.

Recognizing human emotions based on electroencephalogram (EEG) signals has received a great deal of attentions. Most of the existing studies focused on offline analysis, and real-time emotion recognition using a brain computer interface (BCI) approach remains to be further investigated. In this paper, we proposed an EEG-based BCI system for emotion recognition. Specifically, two classes of video clips that represented positive and negative emotions were presented to the subjects one by one, while the EEG data were collected and processed simultaneously, and instant feedback was provided after each clip. Ten healthy subjects participated in the experiment and achieved a high average online accuracy of 91.5% ± 6.34%. The experimental results demonstrated that the subjects emotions had been sufficiently evoked and efficiently recognized by our system. Clinically, patients with disorder of consciousness (DOC), such as coma, vegetative state, minimally conscious state and emergence minimally conscious state, suffer from motor impairment and generally cannot provide adequate emotion expressions. Consequently, doctors have difficulty in detecting the emotional states of these patients. Therefore, we applied our emotion recognition BCI system to patients with DOC. Eight DOC patients participated in our experiment, and three of them achieved significant online accuracy. The experimental results show that the proposed BCI system could be a promising tool to detect the emotional states of patients with DOC.

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For patients with disorders of consciousness (DOC), such as vegetative state (VS) and minimally conscious state (MCS), detecting and assessing the residual cognitive functions of the brain remain challenging. Emotion-related cognitive functions are difficult to detect in patients with DOC using motor response-based clinical assessment scales such as the Coma Recovery Scale-Revised (CRS-R) because DOC patients have motor impairments and are unable to provide sufficient motor responses for emotion-related communication. In this study, we proposed an EEG-based brain-computer interface (BCI) system for emotion recognition in patients with DOC. Eight patients with DOC (5 VS and 3 MCS) and eight healthy controls participated in the BCI-based experiment. During the experiment, two movie clips flashed (appearing and disappearing) eight times with a random interstimulus interval between flashes to evoke P300 potentials. The subjects were instructed to focus on the crying or laughing movie clip and to count the flashes of the corresponding movie clip cued by instruction. The BCI system performed online P300 detection to determine which movie clip the patients responsed to and presented the result as feedback. Three of the eight patients and all eight healthy controls achieved online accuracies based on P300 detection that were significantly greater than chance level. P300 potentials were observed in the EEG signals from the three patients. These results indicated the three patients had abilities of emotion recognition and command following. Through spectral analysis, common spatial pattern (CSP) and differential entropy (DE) features in the delta, theta, alpha, beta, and gamma frequency bands were employed to classify the EEG signals during the crying and laughing movie clips. Two patients and all eight healthy controls achieved offline accuracies significantly greater than chance levels in the spectral analysis. Furthermore, stable topographic distribution patterns of CSP and DE features were observed in both the healthy subjects and these two patients. Our results suggest that cognitive experiments may be conducted using BCI systems in patients with DOC despite the inability of such patients to provide sufficient behavioral responses.

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