Alzheimer's disease (AD) is the most common type of dementia and a neurodegenerative disease with progressive cognitive dysfunction as the main feature. How to identify the early changes of cognitive dysfunction and give appropriate treatments is of great significance to delay the onset of dementia. Some other researches have shown that AD is associated with abnormal changes of brain networks. To study human brain functional connectivity characteristics in AD, 16 channels electroencephalogram (EEG) were recorded under resting and eyes-closed condition in 15 AD patients and 15 subjects in the control group. The synchronization likelihood of the full-band and alpha-band (8-13 Hz) data were evaluated, which resulted in the synchronization likelihood coefficient matrices. Considering a threshold T, the matrices were converted into binary graphs. Then the graphs of two groups were measured by topological parameters including the clustering coefficient and global efficiency. The results showed that the global efficiency of the network in full-band EEG was significantly smaller in AD group for the values of T=0.06 and T=0.07, but there was no statistically significant difference in the clustering coefficients between the two groups for the values of T (0.05-0.07). However, the clustering coefficient and global efficiency were significantly lower in AD patients at alpha-band for the same threshold range than those of subjects in the control group. It suggests that there may be decreases of the brain connectivity strength in AD patients at alpha-band of the resting-state EEG. This study provides a support for quantifying functional brain state of AD from the brain network perspective.
Wavelet entropy is a quantitative index to describe the complexity of signals. Continuous wavelet transform method was employed to analyze the spontaneous electroencephalogram (EEG) signals of mild, moderate and severe Alzheimer's disease (AD) patients and normal elderly control people in this study. Wavelet power spectrums of EEG signals were calculated based on wavelet coefficients. Wavelet entropies of mild, moderate and severe AD patients were compared with those of normal controls. The correlation analysis between wavelet entropy and MMSE score was carried out. There existed significant difference on wavelet entropy among mild, moderate, severe AD patients and normal controls (P<0.01). Group comparisons showed that wavelet entropy for mild, moderate, severe AD patients was significantly lower than that for normal controls, which was related to the narrow distribution of their wavelet power spectrums. The statistical difference was significant (P<0.05). Further studies showed that the wavelet entropy of EEG and the MMSE score were significantly correlated (r=0.601-0.799, P<0.01). Wavelet entropy is a quantitative indicator describing the complexity of EEG signals. Wavelet entropy is likely to be an electrophysiological index for AD diagnosis and severity assessment.
ObjectiveTo investigate the quality of life of family caregivers of patients with Alzheimer's disease (AD) and to explore the related factors. MethodsTwenty family caregivers of patients with Alzheimer's disease were surveyed with short form 36 health survey questionnaire between October 2013 and August 2014. ResultsThe subjects who were over 60 years old had lower scores in the dimensions of physical functioning, role limitations due to physical problem and role limitations due to emotional problem than those below 60 years old. Female subjects scored better than male subjects in the dimension of vitality. The sons and daughters had higher scores than the wives and husbands in the dimensions of physical functioning, role limitations due to physical problem and role limitations due to emotional problem. The subjects whose patients had medical insurance scored better than those whose patients with no insurance. The differences above were all statistically significant. The scores of caregivers with senior middle school edudation or above were higher than the caregivers with lower education level in the dimensions of mental health, vitality and general health perceptions. ConclusionThe quality of life of the family members of AD patients is obviously affected by many factors. It is very important to implement planned, targeted, reasonable and effective interventions to enhance the quality of life of these people.
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.
This study aims to explore the diagnosis in patients with Alzheimer's disease (AD) based on magnetic resonance (MR) images, and to compare the differences of bilateral hippocampus in classification and recognition. MR images were obtained from 25 AD patients and 25 normal controls (NC) respectively. Three-dimensional texture features were extracted from bilateral hippocampus of each subject. The texture features that existed significant differences between AD and NC were used as the features in a classification procedure. Back propagation (BP) neural network model was built to classify AD patients from healthy controls. The classification accuracy of three methods, which were principal components analysis, linear discriminant analysis and non-linear discriminant analysis, was obtained and compared. The correlations between bilateral hippocampal texture parameters and Mini-Mental State Examination (MMSE) scores were calculated. The classification accuracy of nonlinear discriminant analysis with a neural network model was the highest, and the classification accuracy of right hippocampus was higher than that of the left. The bilateral hippocampal texture features were correlated to MMSE scores, and the relative of right hippocampus was higher than that of the left. The neural network model with three-dimensional texture features could recognize AD patients and NC, and right hippocampus might be more helpful to AD diagnosis.
ObjectiveTo systematically review the relationship between Cadmium (Cd) level and Alzheimer's disease (AD). MethodWe searched PubMed, EMbase, CNKI, WanFang Data and CBM databases from inception to December 2014 to collect case-control studies about the relationship between Cd level and AD. Two reviewers screened literature, extracted data and evaluated the risk of bias of included studies, and then meta-analysis was performed by using RevMan 5.3 software. ResultsA total of 11 studies were included, among them 8 studies were included into final meta-analysis. Three studies including 154 patients and 141 controls reported the relationship of serum Cd concentrations and AD, and the result of meta-analysis showed that the higher serum Cd level was found in the AD group than the control group (SMD=0.36, 95%CI 0.12 to 0.59, P=0.003). Six studies including 358 patients and 423 controls reported the relationship of blood Cd concentrations and AD, and the result of meta-analysis showed that there was no significant difference of blood Cd levels between both groups (SMD=0.35, 95%CI -0.14 to 0.84, P=0.16). ConclusionSerum Cd concentrations may be associated with AD, but blood Cd concentrations not. Due to the limitation of quality and quantity of the included studies, more high quality studies are needed to verify the above conclusion.
With the exacerbation of aging population in China, the number of patients with Alzheimer's disease (AD) is increasing rapidly. AD is a chronic but irreversible neurodegenerative disease, which cannot be cured radically at present. In recent years, in order to intervene in the course of AD in advance, many researchers have explored how to detect AD as early as possible, which may be helpful for effective treatment of AD. Imaging genomics is a kind of diagnosis method developed in recent years, which combines the medical imaging and high-throughput genetic omics together. It studies changes in cognitive function in patients with AD by extracting effective information from high-throughput medical imaging data and genomic data, providing effective guidance for early detection and treatment of AD patients. In this paper, the association analysis of magnetic resonance image (MRI) with genetic variation are summarized, as well as the research progress on AD with this method. According to complexity, the objects in the association analysis are classified as candidate brain phenotype, candidate genetic variation, genome-wide genetic variation and whole brain voxel. Then we briefly describe the specific methods corresponding to phenotypic of the brain and genetic variation respectively. Finally, some unsolved problems such as phenotype selection and limited polymorphism of candidate genes are put forward.
The pathogenesis of Alzheimer's disease (AD), a common neurodegenerative disease, is still unknown. It is difficult to determine the atrophy areas, especially for patients with mild cognitive impairment (MCI) at different stages of AD, which results in a low diagnostic rate. Therefore, an early diagnosis model of AD based on 3-dimensional convolutional neural network (3DCNN) and genetic algorithm (GA) was proposed. Firstly, the 3DCNN was used to train a base classifier for each region of interest (ROI). And then, the optimal combination of the base classifiers was determined with the GA. Finally, the ensemble consisting of the chosen base classifiers was employed to make a diagnosis for a patient and the brain regions with significant classification capability were decided. The experimental results showed that the classification accuracy was 88.6% for AD vs. normal control (NC), 88.1% for MCI patients who will convert to AD (MCIc) vs. NC, and 71.3% for MCI patients who will not convert to AD (MCInc) vs. MCIc. In addition, with the statistical analysis of the behavioral domains corresponding to ROIs (i.e. brain regions), besides left hippocampus, medial and lateral amygdala, and left para-hippocampal gyrus, anterior superior temporal sulcus of middle temporal gyrus and dorsal area 23 of cingulate gyrus were also found with GA. It is concluded that the functions of the selected brain regions mainly are relevant to emotions, memory, cognition and the like, which is basically consistent with the symptoms of indifference, memory losses, mobility decreases and cognitive declines in AD patients. All of these show that the proposed method is effective.
Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder. Due to the subtlety of symptoms in the early stages of AD, rapid and accurate clinical diagnosis is challenging, leading to a high rate of misdiagnosis. Current research on early diagnosis of AD has not sufficiently focused on tracking the progression of the disease over an extended period in subjects. To address this issue, this paper proposes an ensemble model for assisting early diagnosis of AD that combines structural magnetic resonance imaging (sMRI) data from two time points with clinical information. The model employs a three-dimensional convolutional neural network (3DCNN) and twin neural network modules to extract features from the sMRI data of subjects at two time points, while a multi-layer perceptron (MLP) is used to model the clinical information of the subjects. The objective is to extract AD-related features from the multi-modal data of the subjects as much as possible, thereby enhancing the diagnostic performance of the ensemble model. Experimental results show that based on this model, the classification accuracy rate is 89% for differentiating AD patients from normal controls (NC), 88% for differentiating mild cognitive impairment converting to AD (MCIc) from NC, and 69% for distinguishing non-converting mild cognitive impairment (MCInc) from MCIc, confirming the effectiveness and efficiency of the proposed method for early diagnosis of AD, as well as its potential to play a supportive role in the clinical diagnosis of early Alzheimer's disease.
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.