This study seeks to explore the early signs of cognitive impairment in patients with obstructive sleep apnea hypopnea syndrome (OSAHS). According to polysomnography, twenty patients diagnosed with OSAHS and twenty normal controls underwent event-related potential (ERP) examination including mismatch negativity (MMN) and P300. Compared with normal controls, OSAHS patients showed significantly prolonged latency of MMN and P300 at Cz. After controlling age and body mass index (BMI), MMN latency positively correlated with apnea hypopnea index (AHI), oxygen reduction index, stage N1 sleep and arousal index, while MMN latency negatively correlated with stage N3 sleep and mean blood oxygen saturation; and P300 latency positively related to AHI and oxygen reduction index; no relationships were found among MMN latency, MMN amplitude, P300 latency and P300 amplitude. These results suggest that the brain function of automatic processing and controlled processing aere impaired in OSAHS patients, and these dysfunction are correlated with nocturnal repeatedly hypoxemia and sleep structure disturbance.
We in the present research proposed a classification method that applied infomax independent component analysis (ICA) to respectively extract single modality features of structural magnetic resonance imaging (sMRI) and positron emission tomography (PET). And then we combined these two features by using a method of weight combination. We found that the present method was able to improve the accurate diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Compared AD to healthy controls (HC): the study achieved a classification accuracy of 93.75%, with a sensitivity of 100% and a specificity of 87.64%. Compared MCI to HC: classification accuracy was 89.35%, with a sensitivity of 81.85% and a specificity of 99.36%. The experimental results showed that the bi-modality method performed better than the individual modality in comparison to classification accuracy.
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.
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.
Mild cognitive impairment (MCI) is a clinical transition state between age-related cognitive decline and dementia. Researchers can use neuroimaging and neurophysiological techniques to obtain structural and functional information about the human brain. Using this information researchers can construct the brain network based on complex network theory. The literature on graph theory shows that the large-scale brain network of MCI patient exhibits small-world property, which ranges intermediately between Alzheimer's disease and that in the normal control group. But brain connectivity of MCI patients presents topologically structural disorder. The disorder is significantly correlated to the cognitive functions. This article reviews the recent findings on brain connectivity of MCI patients from the perspective of multimodal data. Specifically, the article focuses on the graph theory evidences of the whole brain structural and functional and the joint covariance network disorders. At last, the article shows the limitations and future research directions in this field.
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.
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.
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.
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.