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.
Alzheimer’s disease (AD) is a common and serious form of elderly dementia, but early detection and treatment of mild cognitive impairment can help slow down the progression of dementia. Recent studies have shown that there is a relationship between overall cognitive function and motor function and gait abnormalities. We recruited 302 cases from the Rehabilitation Hospital Affiliated to National Rehabilitation Aids Research Center and included 193 of them according to the screening criteria, including 137 patients with MCI and 56 healthy controls (HC). The gait parameters of the participants were collected during performing single-task (free walking) and dual-task (counting backwards from 100) using a wearable device. By taking gait parameters such as gait cycle, kinematics parameters, time-space parameters as the focus of the study, using recursive feature elimination (RFE) to select important features, and taking the subject’s MoCA score as the response variable, a machine learning model based on quantitative evaluation of cognitive level of gait features was established. The results showed that temporal and spatial parameters of toe-off and heel strike had important clinical significance as markers to evaluate cognitive level, indicating important clinical application value in preventing or delaying the occurrence of AD in the future.
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.
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.
Objective To analyze the efficacy of music therapy on the rehabilitation of post-stroke cognitive impairment (PSCI) and to provide a reference for rehabilitation intervention methods for PSCI. Methods Patients hospitalized in Beijing Bo’Ai Hospital, China Rehabilitation Research Center and diagnosed with PSCI between December 2020 and July 2022 were prospectively selected. According to the random number table method, patients were divided into a music therapy group and a control group. Both groups were given conventional neurology medication, nursing care, and conventional rehabilitation. The music therapy group received additional music therapy training, and both groups received treatment for one month. The Montreal Cognitive Assessment (MoCA), National Institute of Health Stroke Scale (NIHSS), Fugl-Meyer Assessment Scale (FMA), and modified Barthel Index (MBI) were used before and after treatment to assess patients’ cognitive function, degree of neurological deficits, motor function and activities of daily live. Results A total of 48 patients were included, with 24 patients in both groups. There was no statistically significant difference in gender, age, education level, stroke type, lesion location, comorbidities, history of myocardial infarction or peripheral vascular disease, and smoking status between the two groups of patients (P>0.05). Before and after treatment, most patients in the two groups did not score in terms of language and delayed recall scores, and the difference were not statistically significant (P>0.05). There was no statistically significant difference in MoCA scores, visual space and executive function, naming, attention, calculation, abstract thinking, and orientation scores between the two groups of patients before treatment (P>0.05). After treatment, the MoCA score, visual space and executive function, naming, attention, calculation, abstract thinking, and orientation scores of the music therapy group improved compared to before treatment (P<0.05), while the MoCA score, visual space and executive function, naming, attention, and orientation scores of the control group improved compared to before treatment (P<0.05). After treatment, the improvement in MoCA scores [5.0 (3.0, 6.0) vs. 2.5 (1.0, 4.0)], attention [1.0 (0.0, 1.0) vs. 0.0 (0.0, 1.0)], and abstract thinking scores [0.0 (0.0, 1.0) vs. 0.0 (0.0, 0.0)] in the music therapy group were better than that in the control group (P<0.05). There was no statistically significant difference in NIHSS, FMA, and MBI scores between the two groups of patients before treatment (P>0.05), and both groups improved after treatment compared to before treatment (P<0.05). After treatment, there was no statistically significant difference in the improvement of NIHSS, FMA, and MBI scores between the two groups of patients (P>0.05). Conclusions Compared with conventional rehabilitation therapy, training combined with music therapy is more beneficial for improving cognitive function in PSCI patients, especially in the cognitive domains of attention and abstract thinking. However, significant advantages have not been found in improving the degree of neurological impairment, limb motor function, and daily living activities.
With the wide application of deep learning technology in disease diagnosis, especially the outstanding performance of convolutional neural network (CNN) in computer vision and image processing, more and more studies have proposed to use this algorithm to achieve the classification of Alzheimer’s disease (AD), mild cognitive impairment (MCI) and normal cognition (CN). This article systematically reviews the application progress of several classic convolutional neural network models in brain image analysis and diagnosis at different stages of Alzheimer’s disease, and discusses the existing problems and gives the possible development directions in order to provide some references.
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.
Cerebral small vessel disease refers to a series of clinical, imaging, and pathological syndromes caused by various factors affecting small blood vessels in the brain. Cognitive impairment is one of the most common complications of cerebral small vessel disease. Current researches have found that cognitive impairment is related to various factors such as hypoxia. Hyperbaric oxygen therapy can achieve certain therapeutic effects by improving hypoxia. This article reviews the pathogenesis of cerebral small vessel disease, biomarkers of cerebral small vessel disease, research progress on hyperbaric oxygen therapy for cognitive impairment, and focuses on the research progress of hyperbaric oxygen therapy for mild cognitive impairment and dementia, providing more references for clinical treatment.
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.
Hypoxia and other factors are related to cognitive impairment. Hyperbaric oxygen therapy can improve tissue oxygen supply to improve brain hypoxia. Based on the basic principle of hyperbaric oxygen therapy, hyperbaric oxygen has been widely used in recent years for cognitive impairment caused by stroke, brain injury, neurodegenerative disease, neuroinflammatory disease and metabolic encephalopathy. This article will review the basic mechanism of hyperbaric oxygen, and summarize and discuss the improvement of hyperbaric oxygen therapy on cognitive and brain diseases, in order to provide relevant reference for clinical treatment.