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.
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.
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.
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.
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.
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.
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.
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.