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.
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.
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.
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.
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.
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.
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.
【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.