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.
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.
This study uses mind-control game training to intervene in patients with mild cognitive impairment to improve their cognitive function. In this study, electroencephalogram (EEG) data of 40 participants were collected before and after two training sessions. The continuous complexity of EEG signals was analyzed to assess the status of cognitive function and explore the effect of mind-control game training on the improvement of cognitive function. The results showed that after two training sessions, the continuous complexity of EEG signal of the subject increased (0.012 44 ± 0.000 29, P < 0.05) and amplitude of curve fluctuation decreased gradually, indicating that with increase of training times, the continuous complexity increased significantly, the cognitive function of brain improved significantly and state was stable. The results of this paper may show that mind-control game training can improve the status of the brain cognitive function, which may provide support and help for the future intervention of cognitive dysfunction.
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.
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.
Normal brain aging and a serious of neurodegenerative diseases may lead to decline in memory, attention and executive ability and poorer quality of life. The mechanism of the decline is not clear now and is still a hot issue in the fields of neuroscience and medicine. A large number of researches showed that resting state functional brain networks based functional magnetic resonance imaging (fMRI) are sensitive and susceptive to the change of cognitive function. In this paper, the researches of brain functional connectivity based on resting fMRI in recent years were compared, and the results of subjects with different levels of cognitive decline including normal brain aging, mild cognitive impairment (MCI) and Alzheimer’s disease (AD) were reviewed. And the changes of brain functional networks under three different levels of cognitive decline are introduced in this paper, which will provide the basis for the detection of normal brain aging and clinical diseases.
The cognitive impairment of type 2 diabetes patients caused by long-term metabolic disorders has been the current focus of attention. In order to find the related electroencephalogram (EEG) characteristics to the mild cognitive impairment (MCI) of diabetes patients, this study analyses the EEG synchronization with the method of multi-channel synchronization analysis--S estimator based on phase synchronization. The results showed that the S estimator values in each frequency band of diabetes patients with MCI were almost lower than that of control group. Especially, the S estimator values decreased significantly in the delta and alpha band, which indicated the EEG synchronization decrease. The MoCA scores and S value had a significant positive correlation in alpha band.
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.
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.
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.