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 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.
In order to solve the problem of early classification of Alzheimer’s disease (AD), the conventional linear feature extraction algorithm is difficult to extract the most discriminative information from the high-dimensional features to effectively classify unlabeled samples. Therefore, in order to reduce the redundant features and improve the recognition accuracy, this paper used the supervised locally linear embedding (SLLE) algorithm to transform multivariate data of regional brain volume and cortical thickness to a locally linear space with fewer dimensions. The 412 individuals were collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) including stable mild cognitive impairment (sMCI, n = 93), amnestic mild cognitive impairment (aMCI, n = 96), AD (n = 86) and cognitive normal controls (CN, n = 137). The SLLE algorithm used in this paper is to calculate the nearest neighbors of each sample point by adding the distance correction term, and the locally linear reconstruction weight matrix was obtained from its nearest neighbors, then the low dimensional mapping of the high dimensional data can be calculated. In order to verify the validity of SLLE in the task of classification, the feature extraction algorithms such as principal component analysis (PCA), Neighborhood MinMax Projection (NMMP), locally linear mapping (LLE) and SLLE were respectively combined with support vector machines (SVM) classifier to obtain the accuracy of classification of CN and sMCI, CN and aMCI, CN and AD, sMCI and aMCI, sMCI and AD, and aMCI and AD, respectively. Experimental results showed that our method had improvements (accuracy/sensitivity/specificity: 65.16%/63.33%/67.62%) on the classification of sMCI and aMCI by comparing with the combination algorithm of LLE and SVM (accuracy/sensitivity/specificity: 64.08%/66.14%/62.77%) and SVM (accuracy/sensitivity/specificity: 57.25%/56.28%/58.08%). In detail the accuracy of the combination algorithm of SLLE and SVM is 1.08% higher than the combination algorithm of LLE and SVM, and 7.91% higher than SVM. Thus, the combination of SLLE and SVM is more effective in the early diagnosis of Alzheimer’s disease.
Post-stroke cognitive impairment (PSCI) is the most common dysfunction after stroke, which seriously affects patients’ quality of life and survival time. To strengthen the management and prevention of PSCI, the European Stroke Organization and the European Academy of Neurology jointly developed the guidelines for PSCI in 2021. This paper introduces the background, compilation method and structure, management suggestions and expert consensus of PSCI, the next research direction, etc. Compared with the current prevention and treatment measures of PSCI in China, it aims to provide methodological reference for Chinese scholars to develope PSCI guidelines and reference evidence for clinical prevention and treatment of PSCI.
Alzheimer's disease (AD) is a typical neurodegenerative disease, which is clinically manifested as amnesia, loss of language ability and self-care ability, and so on. So far, the cause of the disease has still been unclear and the course of the disease is irreversible, and there has been no cure for the disease yet. Hence, early prognosis of AD is important for the development of new drugs and measures to slow the progression of the disease. Mild cognitive impairment (MCI) is a state between AD and healthy controls (HC). Studies have shown that patients with MCI are more likely to develop AD than those without MCI. Therefore, accurate screening of MCI patients has become one of the research hotspots of early prognosis of AD. With the rapid development of neuroimaging techniques and deep learning, more and more researchers employ deep learning methods to analyze brain neuroimaging images, such as magnetic resonance imaging (MRI), for early prognosis of AD. Hence, in this paper, a three-dimensional multi-slice classifiers ensemble based on convolutional neural network (CNN) and ensemble learning for early prognosis of AD has been proposed. Compared with the CNN classification model based on a single slice, the proposed classifiers ensemble based on multiple two-dimensional slices from three dimensions could use more effective information contained in MRI to improve classification accuracy and stability in a parallel computing mode.
Objective To evaluate the risk factors for cognitive impairment and their interactions in acute ischemic stroke (IS) patients. Methods IS patients admitted to the Department of Neurology, the People’s Hospital of Mianyang between January 2019 and January 2022 were selected. Patients were divided into a cognitive impairment group and a cognitive normal group. The demographic characteristics and clinical data of the subjects were collected, and the traditional risk factors for cognitive impairment were determined by univariate and multivariate logistic regression analysis. The multifactor dimensionality reduction test was used to detect the possible interactions between risk factors. Results A total of 255 patients were included. Among them, 88 cases (34.5%) in the cognitive impairment group and 167 cases (65.5%) in the cognitive normal group. The results of factor logistic regression analysis showed that after adjusting for covariates, big and medium infarction volume, severe IS, moderate to severe carotid artery stenosis as well as high hypersensitive C-reactive protein (hs-CRP) were associated with post-IS cognitive impairment (P<0.05). The cognitive impairment increased by 22.632 times [odds ratio=22.632, 95% confidence interval (5.980, 85.652), P<0.001] in patients with big and medium infarction volume, severe IS and high hs-CRP. Conclusions The cognitive impairment is common in acute IS. Patients with big and medium infarction volume, non-mild stroke, carotid artery stenosis, high hs-CRP, and non-right sided infarction are prone to cognitive impairment, and there are complex interactions among these risk factors.
Objective To investigate the changes in mitochondrial morphology, structure and function in rats with severe intermittent hypoxia, as well as the effects of intermittent hypoxia and its severity on cognitive function. Methods A total of 18 rats were selected to construct a model of severe intermittent hypoxia, which were divided into a normal control group, an intermittent air control group, and a 5% intermittent hypoxia group for 8 weeks, with 6 rats in each group. The structural and functional changes of mitochondria in the hippocampal CA1 region were observed. A total of 30 rats were randomly divided into 5 groups: a normal control group, an intermittent air control group, a 5% intermittent hypoxia 4-week group, a 5% intermittent hypoxia 6-week group, and a 5% intermittent hypoxia 8-week group, with 6 rats in each group. The cognitive function of the rats in each group was evaluated by Morris water maze experiment. Results In the mitochondria of the hippocampal CA1 region of severely intermittent hypoxic rats, bilayer membranes or multilayer membranes were visible, the mitochondria were swollen, cristae were broken and vacuolated, and their respiratory function was significantly weakened, the membrane permeability was increased, and the membrane potential was reduced. In the Morris water maze, there was no significant difference in swimming speed between the rats. With the prolongation of intermittent hypoxia action time, the latency of finding the hidden platform in each group of rats increased significantly, and the residence time of the target quadrant decreased significantly. Conclusions Mitochondrial structure in the hippocampal CA1 region of the rat brain is destroyed during severe intermittent hypoxia, and dysfunction and cognitive impairment occur. With the prolongation of intermittent hypoxic injury, the degree of cognitive impairment worsens.
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.
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.
Vascular cognitive impairment (VCI), a syndrome induced by cerebrovascular disease and its risk factors, has become a major public health challenge worldwide. Especially in the context of an increasingly aging population, its impact is becoming more significant. In recent years, research has gradually revealed the crucial role of chronic cerebral hypoperfusion (CCH) in the occurrence and development of VCI. CCH leads to long-term ischemia and hypoxia in brain tissue, which seriously threatens mitochondrial function and triggers a series of problems such as mitochondrial oxidative stress, calcium homeostasis disturbance, dynamic abnormalities, autophagy dysregulation, and impaired biogenesis. These issues are extensively involved in the pathological process of VCI. This article provides an overview of the correlation between mitochondrial dysfunction and VCI under CCH conditions, aiming to explore new directions for the treatment of VCI.