ObjectiveIn this study, we aimed to investigate the effects of non-invasive brain stimulation (NIBS) on cognitive and motor functions in patients with multiple sclerosis (pwMS).MethodsA literature search was performed in the Cochrane Library, Embase, PubMed, Web of Science, Medline, CNKI, and Wan fang. The time interval used for database construction was up to December 2022, and the language was not limited. The collected trials were subsequently screened, the data were extracted, the quality was evaluated, and the effect sizes were computed using STATA/MP Version 13 for outcome analysis. Standard mean difference (SMD) and 95% confidence interval (CI) were calculated for domain of interest.ResultsIn total, 17 articles that examined 364 patients with multiple sclerosis were included in this analysis. Non-invasive brain stimulation did not improve the overall cognitive function [SMD = 0.18, 95% CI (−0.32, 0.69), P = 0.475] but helped improve motor function in patients [SMD = 0.52, 95% CI (0.19, 0.85), P = 0.002]. Moreover, this study specifically indicated that non-invasive brain stimulation improved alerting [SMD = 0.68, 95% CI (0.09, 1.26), P = 0.02], whereas non-invasive brain stimulation intervention improved motor function in patients aged <45 years [SMD = 0.67, 95% CI (0.23, 1.10), P = 0.003] and in patients with expanded disability status scale scores (EDSS) <3.5 [SMD = 0.82, 95% CI (0.22, 1.42), P = 0.007]. In particular, NIBS contributed to the improvement of spasticity in pwMS [SMD = 0.68, 95% CI (0.13, 1.23), P = 0.015].ConclusionThese results of this present study provide evidence that non-invasive brain stimulation could improve alertness in pwMS. Furthermore, NIBS may help pwMS with motor function and those who are under 45 years of age or with EDSS < 3.5 improve their motor function. For the therapeutic use of NIBS, we recommend applying transcranial magnetic stimulation as an intervention and located on the motor cortex M1 according to the subgroup analysis of motor function. These findings warrant verification.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022301012.

BackgroundWith the development of quantitative electroencephalography (QEEG), an increasing number of studies have been published on the clinical use of QEEG in the past two decades, particularly in the diagnosis, treatment, and prognosis of neuropsychiatric disorders. However, to date, the current status and developing trends of this research field have not been systematically analyzed from a macroscopic perspective. The present study aimed to identify the hot spots, knowledge base, and frontiers of QEEG research in neuropsychiatric disorders from 2000 to 2021 through bibliometric analysis.MethodsQEEG-related publications in the neuropsychiatric field from 2000 to 2021 were retrieved from the Web of Science Core Collection (WOSCC). CiteSpace and VOSviewer software programs, and the online literature analysis platform (bibliometric.com) were employed to perform bibliographic and visualized analysis.ResultsA total of 1,904 publications between 2000 and 2021 were retrieved. The number of QEEG-related publications in neuropsychiatric disorders increased steadily from 2000 to 2021, and research in psychiatric disorders requires more attention in comparison to research in neurological disorders. During the last two decades, QEEG has been mainly applied in neurodegenerative diseases, cerebrovascular diseases, and mental disorders to reveal the pathological mechanisms, assist clinical diagnosis, and promote the selection of effective treatments. The recent hot topics focused on QEEG utilization in neurodegenerative disorders like Alzheimer's and Parkinson's disease, traumatic brain injury and related cerebrovascular diseases, epilepsy and seizure, attention-deficit hyperactivity disorder, and other mental disorders like major depressive disorder and schizophrenia. In addition, studies to cross-validate QEEG biomarkers, develop new biomarkers (e.g., functional connectivity and complexity), and extract compound biomarkers by machine learning were the emerging trends.ConclusionThe present study integrated bibliometric information on the current status, the knowledge base, and future directions of QEEG studies in neuropsychiatric disorders from a macroscopic perspective. It may provide valuable insights for researchers focusing on the utilization of QEEG in this field.