Background: Upper limb motor dysfunction caused by stroke greatly affects the daily life of patients, significantly reduces their quality of life, and places serious burdens on society. As an emerging rehabilitation training method, brain–computer interface (BCI)–based training can provide closed-loop rehabilitation and is currently being applied to the restoration of upper limb function following stroke. However, because of the differences in the type of experimental clinical research, the quality of the literature varies greatly, and debate around the efficacy of BCI for the rehabilitation of upper limb dysfunction after stroke has continued.Objective: We aimed to provide medical evidence-based support for BCI in the treatment of upper limb dysfunction after stroke by conducting a meta-analysis of relevant clinical studies.Methods: The search terms used to retrieve related articles included “brain-computer interface,” “stroke,” and “upper extremity.” A total of 13 randomized controlled trials involving 258 participants were retrieved from five databases (PubMed, Cochrane Library, Science Direct, MEDLINE, and Web of Science), and RevMan 5.3 was used for data analysis.Results: The total effect size for BCI training on upper limb motor function of post-stroke patients was 0.56 (95% CI: 0.29–0.83). Subgroup analysis indicated that the standard mean differences of BCI training on upper limb motor function of subacute stroke patients and chronic stroke patients were 1.10 (95% CI: 0.20–2.01) and 0.51 (95% CI: 0.09–0.92), respectively (p = 0.24).Conclusion: Brain–computer interface training was shown to be effective in promoting upper limb motor function recovery in post-stroke patients, and the effect size was moderate.

Objective: The primary purpose of our study is to systemically evaluate the effect of repetitive transcranial magnetic stimulation (rTMS) on recovery of dysphagia after stroke.Search Methods: We searched randomized controlled trials (RCTs) and non-RCTs published by PubMed, the Cochrane Library, ScienceDirect, MEDLINE, and Web of Science from inception until April 24, 2021. Language is limited to English. After screening and extracting the data, and evaluating the quality of the selected literature, we carried out the meta-analysis with software RevMan 5.3 and summarized available evidence from non-RCTs.Results: Among 205 potentially relevant articles, 189 participants (from 10 RCTs) were recruited in the meta-analysis, and six non-RCTs were qualitatively described. The random-effects model analysis revealed a pooled effect size of SMD = 0.65 (95% CI = 0.04–1.26, p = 0.04), which indicated that rTMS therapy has a better effect than conventional therapy. However, the subgroup analysis showed that there was no significant difference between low-frequency and high-frequency groups. Even more surprisingly, there were no statistically significant differences between the two groups and the conventional training group in the subgroup analysis, but the combined effect was positive.Conclusion: Our study suggests that rTMS might be effective in treating patients with dysphagia after stroke.

Background: This meta-analysis explores alterations in the gut microbiota of patients with Multiple Sclerosis (MS) using 16S ribosomal RNA (rRNA) gene sequencing. Methods: Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, our comprehensive review spanned major databases, including PubMed, Web of Science, Embase, Cochrane, and Ovid, targeting observational studies that implemented 16S rRNA gene sequencing on fecal specimens. The quality of these studies was meticulously evaluated using the Newcastle-Ottawa scale. Results: Our search yielded 26 relevant studies conducted between 2015-2022, encompassing 2885 participants. No significant differences were observed in alpha diversity indices (Shannon, Chao1, Operational Taxonomic Units (OTU), and Simpson) between MS patients and controls in general. Nonetheless, subgroup analyses according to disease activity using the Shannon index highlighted a significant decrease in microbial diversity during MS’s active phase. Similarly, an evaluation focusing on MS phenotype revealed diminished diversity in individuals with relapsing-remitting MS (RRMS). Microbial composition analysis revealed no consistent increase in pro-inflammatory Bacteroidetes or decrease in anti-inflammatory Firmicutes within the MS cohort. Conclusion: The gut microbiome’s role in MS presents a complex panorama, where alterations in microbial composition might hold greater significance to disease mechanisms than diversity changes. The impact of clinical factors such as disease activity and phenotype are moderately significant, underscoring the need for further research to elucidate these relationships. Prospective research should employ longitudinal methodologies to elucidate the chronological interplay among gut microbiota, disease evolution, and therapeutic strategies.

Although rapid eye movement (REM) sleep is conventionally treated as a unified state, it comprises two distinct microstates: phasic and tonic REM. Recent research emphasizes the importance of understanding the interplay between these microstates, hypothesizing their role in transient shifts between sensory detachment and external awareness. Previous studies primarily employed linear metrics to probe cognitive states, such as oscillatory power, while in this study, we adopt Lempel-Ziv Complexity (LZC), to examine the nonlinear features of electroencephalographic (EEG) data from the REM microstates and to gain complementary insights into neural dynamics during REM sleep. Our findings demonstrate a noteworthy reduction in LZC during phasic REM compared to tonic REM states, signifying diminished EEG complexity in the former. Additionally, we noted a negative correlation between decreased LZC and delta band power, along with a positive correlation with alpha band power. This study highlights the potential of nonlinear EEG metrics, particularly LZC, in elucidating the distinct features of REM microstates. Overall, this research contributes to advancing our understanding of the complex dynamics within REM sleep and opens new avenues for exploring its implications in both clinical and nonclinical contexts.

Objective: This pilot study aimed to evaluate the feasibility and tolerability of motor imagery (MI)-based brain-computer interface (BCI) training with robotic hand assistance for upper limb rehabilitation, and to explore preliminary neural markers in ischemic stroke patients. Methods: Three post-stroke participants performed MI tasks combined with exoskeleton-assisted movements to facilitate rehabilitation training. Electroencephalography (EEG) signals were recorded to assess the neural correlates of MI. Functional outcomes were evaluated using standard assessment tools. Results: Our results demonstrated significant improvements in motor function across all participants. Additionally, EEG analysis revealed event-related desynchronization (ERD) in the high-alpha band power at motor cortex locations, with individual differences in both the frequency and power of neural activity. However, no significant trends in neural activity were observed across the training sessions. Conclusion: These findings suggest that MI-based BCI training, combined with robotic assistance, offer a promising approach for enhancing upper limb function in ischemic stroke patients.