ObjectiveTo systematically review the efficacy of repetitive transcranial magnetic stimulation (rTMS) on patients with mild cognitive impairment (MCI). MethodsWe searched databases including PubMed, The Cochrane Library (Issue 10, 2015), EMbase, PsycINF, EBSCO, CBM, CNKI, WanFang Data and VIP from inception to October 2015 to collect randomized controlled trials (RCTs) about rTMS for patients with MCI. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Then, meta-analysis was performed by using RevMan 5.3 software. ResultsA total of 5 RCTs involving 180 MCI patients were included. The results of meta-analysis showed that, compared with the control group, rTMS treatment could significantly improve the overall cognitive abilities of MCI patients (SMD=2.53, 95% CI 0.91 to 4.16, P=0.002), as well as the single-domain cognitive performances, including tests for episodic memory (MD=0.98, 95% CI 0.24 to 1.72, P=0.01) and verbal fluency (MD=2.08, 95% CI 0.46 to 3.69, P=0.01). rTMS was a well-tolerated therapy, with slightly more adverse events observed than the control group (RD=0.09, 95% CI 0.00 to 0.18, P=0.04), but cases were mainly transient headache, dizziness and scalp pain. ConclusionrTMS may benefit the cognitive abilities of MCI patients. Nevertheless, due to the limited quantity and quality of included studies, large-scale, multicenter, and high quality RCTs are required to verify the conclusion.
Objective To evaluate diagnostic accuracy of several relevant cut-off points of Montreal cognitive assessment (MoCA) for mild cognitive impairment (MCI) in Chinese middle-aged adults. Methods Databases including PubMed, EMbase, Web of Science, The Cochrane Library (Issue 5, 2016), OVID, CBM, CNKI, VIP, WanFang Data were searched for diagnostic tests about MoCA for MCI from April 9th 2005 to December 31st 2015. Two reviewers independently screened literatures according to the inclusion and exclusion criteria, extracted data and assessed the methodological quality by QUADAS-2 tool. Then, meta-analysis was performed by Stata 14.0 software. Results A total of 27 studies involving 5 755 participants were included with mean ages from 60 to 80 years old. Among them, 1 997 were diagnosed as MCI patients by Petersen criteria. Based on maximal area under the ROC curve as well as optimal pooled sensitivity and specificity, the optimal cutoff value of MoCA was 25/26, the pooled sensitivity was 0.96 with 95%CI 0.93 to 0.97, specificity was 0.83 with 95%CI 0.75 to 0.89, and DOR was 107 with 95%CI 61 to 188. The subgroup analysis with different research designs, different sources of study participants and different MoCA versions all indicated 25/26 as an optimal cut-off value. Conclusion The optimal cutoff value of MoCA in Chinese middle-aged adults for screening MCI by Petersen criteria was 25/26.
Objectives To systematically review the efficacy of multimodal nonpharmacological interventions in mild cognitive impairment (MCI). Methods An electronically search was conducted in PubMed, EMbase, The Cochrane Library, PsycINFO, Web of Science, CINAHL, VIP, CBM, WanFang Data and CNKI databases from inception to November 2017 to collect randomized controlled trials (RCTs) on multimodal nonpharmacological interventions for MCI. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Then, meta-analysis was performed by RevMan 5.3 software. Results A total of 12 RCTs involving 1 359 patients were included. The results of meta-analysis showed that there were no statistical differences between two groups in MMSE scores (SMD=0.33, 95%CI–0.13 to 0.78, P=0.16). However, the MoCA scores (SMD=0.52, 95%CI 0.38 to 0.67, P<0.000 01) and ADAS-Cog scores (SMD=1.13, 95%CI 0.75 to 1.51, P<0.000 01) in the multimodal nonpharmacological interventions group were better than those in the control group. Additionally, multimodal nonpharmacological interventions produced significant effects on ADL (SMD=–0.64, 95%CI –0.83 to–0.45, P<0.000 01), QOL-AD (MD=3.65, 95%CI 1.03 to 6.27, P=0.006) and depression (SMD=–0.83, 95%CI –1.41 to–0.26, P=0.005). There were no statistical differences between two groups on conversion rate to Alzheimer's disease (RR=0.27, 95%CI 0.06 to 1.26, P=0.10). Conclusions The current evidence shows that multimodal nonpharmacological interventions are feasible for patients with MCI as they have positive effects on overall cognitive abilities, daily living skills, and quality of life and depression. Nevertheless, due to the limited quantity and quality of included studies, more high quality studies are required to verify the conclusion.
This article combines researches and experiments of mild cognitive impairment (MCI) from 2005 to 2018. It makes a conclusion among psychological evaluation, imaging studies, nerve electrophysiology, neural circuit and mental models, and concludes the changes of patients with MCI, which helps to make a definite diagnosis of MCI in clinical practice. Due to the research above we can find the suitable way to improve the sensitivity and specificity of discovery of MCI, improve the predictive power of its development, and intervene potential Alzheimer’s disease effectively.
ObjectivesTo systematically review the epidemiological characteristics of mild cognitive impairment (MCI) in Chinese elderly population.MethodsPubMed, EMbase, The Cochrane Library, CNKI, VIP, WanFang Data and CBM databases were electronically searched to collect studies on the epidemiological characteristics of mild cognitive impairment in the elderly in China from inception to May 2019. Two reviewers independently screened literature, extracted data and assessed risk of bias of included studies. Then, meta-analysis was performed by using Stata 12.0 software.ResultsA total of 25 studies involving 56 720 patients were included. The results of meta-analysis showed that the prevalence of MCI in Chinese elderly population was 14% (95%CI 12% to 17%), in which 12.1% (95%CI 9.7% to 14.5%) was male and 14.8% (95%CI 12.5% to 17.2%) was female. The prevalence of MCI was 8% (95%CI 6.0% to 10.1%) in the elderly aged 60 to 69, 13.1% (95%CI 10.6% to 15.6%) in the elderly aged 70 to 79 and 23.4% (95%CI 18.3% to 28.6%) in the elderly aged above 80. The prevalence of MCI was 23% (95%CI 18.3% to 27.6%) in the elderly who were illiterate, 15.2% (95%CI 11.2% to 19.2%) among the elderly with a primary education and 9.8% (95%CI 7.1% to 12.6%) among the elderly with an education above junior high school. The prevalence of MCI was 9.9% (95%CI 5.5% to 14.2%) in urban areas, and 16.7% (95%CI 11.2% to 22.2%) in rural areas. The prevalence of MCI was 12.1% (95%CI 7.7% to 16.5%) in married individuals and 17.1% (95%CI 13.9% to 20.2%) in single individuals. The prevalence of MCI was 15.4% (95%CI 11.4% to 19.4%) in northern China, 14.1% (95%CI 11.1% to 17.2%) in eastern China, 5.4% (95%CI 3.9% to 6.9%) in northeast China, 13% (95%CI 6.2% to 19.8%) in Central-south China, 11.7% (95%CI 10.2% to 13.2%) in the southwest China and 17.4% (95%CI 2.5% to 32.3%) in northwest China. By using the diagnostic criteria proposed by Petersen, the prevalence of MCI was 15.2% (95%CI 11.8% to 18.7%), and was 12.4% (95%CI 9.4% to 15.4%) using the criteria of the DSM-Ⅳ.ConclusionsThe prevalence of MCI is high in China, and varies with gender, age, education, location, marital status, region and diagnostic criteria.
Objective To systematically review the influencing factors of mild cognitive impairment in type 2 diabetic patients. MethodsPubMed, Web of Science, EMbase, The Cochrane Library, CNKI, WanFang Data, VIP, and CBM databases were electronically searched to collect studies on the influencing factors of mild cognitive impairment in patients with type 2 diabetes from inception to December 31, 2021. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias of included studies; then, meta-analysis was performed by using RevMan 5.4 software and Stata 12.0 software. ResultsA total of 32 studies involving 7 519 subjects were included. The results of the meta-analysis showed that the main influencing factors of mild cognitive impairment in type 2 diabetic patients were age, duration of type 2 diabetes, educational level, cerebral infarction, hypertension, smoking, insulin resistance index, glycosylated hemoglobin, and homocysteine. ConclusionCurrent evidence shows that some factors such as age, duration, and educational level are the main influencing factors of mild cognitive impairment in type 2 diabetic patients. Due to the limited quality and quantity of the included studies, more high quality studies are needed to verify the above conclusions.
Due to the aging population intensifies, the number of people suffering from mild cognitive impairment (MCI) or dementia is expected to increase, which may lead to a series of public health and social health problems. In the absence of drugs to prevent the transformation of MCI into dementia, it is urgent to find effective non-pharmacological therapies to delay the progress of cognitive impairment. This article will review the diagnosis of MCI and the research progress of non-pharmacological therapies, focusing on the non-pharmacological therapies related to MCI in recent years, including exercise intervention, cognitive intervention, physical and mental exercise, dietary intervention, electroacupuncture, repeated transcranial magnetic stimulation, and multi-component intervention, in order to provide an effective treatment for preventing or delaying the progression of MCI to dementia.
The recurrent neural network architecture improves the processing ability of time-series data. However, issues such as exploding gradients and poor feature extraction limit its application in the automatic diagnosis of mild cognitive impairment (MCI). This paper proposed a research approach for building an MCI diagnostic model using a Bayesian-optimized bidirectional long short-term memory network (BO-BiLSTM) to address this problem. The diagnostic model was based on a Bayesian algorithm and combined prior distribution and posterior probability results to optimize the BO-BiLSTM network hyperparameters. It also used multiple feature quantities that fully reflected the cognitive state of the MCI brain, such as power spectral density, fuzzy entropy, and multifractal spectrum, as the input of the diagnostic model to achieve automatic MCI diagnosis. The results showed that the feature-fused Bayesian-optimized BiLSTM network model achieved an MCI diagnostic accuracy of 98.64% and effectively completed the diagnostic assessment of MCI. In conclusion, based on this optimization, the long short-term neural network model has achieved automatic diagnostic assessment of MCI, providing a new diagnostic model for intelligent diagnosis of MCI.
Objective To systematically review the efficacy of six cognitive interventions on cognitive function of patients with mild cognitive impairment after stroke. Methods The PubMed, EMbase, Cochrane Library, SinoMed, WanFang Data and CNKI databases were electronically searched to collect randomized controlled trials on the effects of non-drug interventions on the cognitive function of patients with mild cognitive impairment after stroke from inception to March 2023. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias of the included studies. Network meta-analysis was then performed using Openbugs 3.2.3 and Stata 16.0 software. Results A total of 72 studies involving 4 962 patients were included. The results of network meta-analysis showed that the following five cognitive interventions improved the cognitive function of stroke patients with mild cognitive impairment: cognitive control intervention (SMD=−1.28, 95%CI −1.686 to −0.90, P<0.05) had the most significant effect on the improvement of cognitive function, followed by computer cognitive training (SMD=−1.02, 95%CI −1.51 to −0.53, P<0.05), virtual reality cognitive training (SMD=−1.20, 95%CI −1.78 to −0.62, P<0.05), non-invasive neural regulation (SMD=−1.09, 95%CI −1.58 to −0.60, P<0.05), and cognitive stimulation (SMD=−0.94, 95%CI −1.82 to −0.07, P<0.05). Conclusion Five cognitive interventions are effective in improving cognitive function for stroke patients with mild cognitive impairment, among which cognitive control intervention is the most effective. Due to the limited quantity and quality of the included studies, more high-quality studies are needed to verify the above conclusion.
ObjectiveTo evaluate the efficacy of different non-pharmacological interventions on cognitive function in elderly patients with mild cognitive impairment by the network meta-analysis. MethodsThe PubMed, Embase, Cochrane Library, CINAHL, CNKI, WanFang Data, VIP and CBM databases were electronically searched to collect randomized controlled trials (RCTs) related to the objectives from inception to November 2022. Two reviewers independently screened literature, extracted data and assessed the risk of bias of the included studies. The network meta-analysis was then performed by using Stata 16.0 and Open BUGS 3.2.3 software. ResultsA total of 43 RCTs involving 2 986 patients were included, which involved 8 non-drug intervention methods. The best probability ranking results of the network meta-analysis showed that on the simple mental state scale (MMSE) scores: rTMS > acupressure > acupuncture therapy > exercise therapy > cognitive training > multicomponent intervention > VR > conventional care > health education, and on the Montreal cognitive assessment scale (MoCA) scores: VR > exercise therapy > rTMS > acupuncture therapy > acupressure > cognitive training > health education > conventional care. Conclusion Current evidence shows that rTMS, acupressure, VR, exercise therapy and acupuncture may be effective interventions to improve cognitive function in elderly patients with mild cognitive impairment. Due to the limited quality and quantity of the included studies, more high quality studies are needed to verify the above conclusion.