【摘要】 目的 通过比较遗忘型轻度认知障碍（amnestic mild cognitive impairment，aMCI）和血管性认知障碍非痴呆型（vascular cognitive impairment-no dementia，VCI-ND）患者及正常老年人群在简易智能精神状态检查量表（mini mental state examination，MMSE）、听觉词语学习测验（auditory verbal learning test，AVLT）、画钟试验（clock drawing test，CDT）及临床痴呆评定量表（clinical dementia rating scales，CDR）中的表现，进一步分析aMCI和VCI-ND在认知损害方面的不同特点。 方法 选取首都医科大学宣武医院神经内科门诊收治aMCI患者23例及VCI-ND患者27例（CDR＝0.5分），同时选取40名正常老年人（CDR=0分）作为对照组。每位受试者均进行MMSE、AVLT、CDT及CDR等神经心理学量表测查，分析以上3组被试各项神经心理学测查得分之间的差异。 结果 各组受试者的年龄、性别及受教育程度差异无统计学意义（Pgt;0.05），具有可比性。aMCI和VCI-ND组在MMSE、CDT、即刻记忆、延迟记忆及延迟再认检测中的平均值均低于对照组，且差异均具有统计学意义（Plt;0.05）。aMCI和VCI-ND两组除延迟再认检测外，其余各项测查的平均分均无统计学意义（Pgt;0.05）。在延迟再认检测中，aMCI组（6.65±4.00）较VCI-ND组（8.67±2.76）再认词语数量少，两组延迟再认的得分均低于对照组（12.83±1.77），差异有统计学意义（Plt;0.05）。 结论 aMCI和VCI-ND在记忆力、执行能力和信息处理能力方面较正常老年人均有所损害。由于aMCI和VCI-ND不同的病理改变，导致患者存在不同类型的记忆储存和提取机制。【Abstract】 Objective To investigate the different patterns of cognitive impairment in patients with amnestic mild cognitive impairment (amci), vascular cognitive impairment-no dementia (VCI-ND) and normal elder people. Methods A total of 23 patients with aMCI and 27 patients with VCI-ND (CDR＝0.5) and another 40 healthy elder people (CDR=0) were selected. Each individual underwent the neuropsychological tests, including mini mental state examination (MMSE), auditory verbal learning test (AVLT), clock drawing test (CDT), clinical dementia rating scales (CDR) and hamilton rating scale for depression (HAMD). The differences between the three groups were analyzed. Results The differences in age, sexes, and the education background among the three groups were not significant (Pgt;0.05) which meant comparability. The mean scores of MMSE, CDT, instant memory and delayed awareness in aMCI and VIC-ND group were much lower than that in the control group (Plt;0.05). The differences in all the test items except for delayed awareness between aMCI group and VCI-ND groups were not significant (Pgt;0.05). However, in the recall recognition test, these three groups had significant differences: the score in patients with aMCI (6.65±4.00) was much lower than that in patients with VCI-ND (8.67±2.76; Plt;0.05), and the scores of the two groups were both lower than that in the normal aging group (12.83±1.77; Plt;0.05). Conclusion Compared with normal elder people, the cognition of aMCI and VCI-ND patients is impaired severely. The memory tests suggeste that compared with aMCI patients, VCI-ND patients may have different neuropathological changes leading to different mechanism of memory encoding and retrieval.
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.
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.
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.
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.
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.
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.
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.