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.
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.
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.
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 obverse the changes of macular choroidal thickness (CT) in patients with mild to moderate Alzheimer’s disease (AD).MethodsThis was a case-control study. Twenty-one patients with mild to moderate AD confirmed by Neurology Department of Jinhua Central Hospital from November 2016 to June 2018 and 21 age-matched control subjects were concluded in the study. There was no significant difference in age (t=0.128), intraocular pressure (t=0.440) and axial length (t=1.202) between the two groups (P＞0.05). There was significant difference in mini-mental state examination score (t=8.608, P＜0.05). CT was measured by OCT with enhanced depth imaging technique in the subfoveal choroid, at 0.5 mm and 1.0 mm from the center of the fovea nasal (NCT0.5, 1.0 mm), temporal (TCT0.5, 1.0 mm), superior (SCT0.5, 1 .0 mm), and inferior (ICT0.5, 1.0 mm). Independent-samples t test was used to compare the results obtained from these two groups.ResultsSFCT (t=2.431), NCT0.5, 1.0 mm (t=3.341, 2.640), TCT0.5, 1.0 mm (t=3.340, 2.899), SCT0.5, 1.0 mm (t=3.576, 3.751) and ICT0.5, 1.0 mm (t=2.897, 2.903) were significantly thinner in AD eyes than those in control eyes.ConclusionCompared with healthy subjects, patients with mild to moderate AD showed a significant reduction in CT.
With the intensified aging problem, the study of age-related diseases is becoming more and more significant. Alzheimer's disease is a kind of dementia, with senile plaques and neurofibrillary tangles as the main pathological features, and has become one of the major diseases that endanger the health of the elderly. This review is concentrated on the research of the early assessment of Alzheimer's disease. The current situation of early diagnosis of the disease is analyzed, and a prospect of the future development of early assessment means of the disease is also made in the paper.
ObjectiveTo conduct a meta-analysis comparing the accuracy of artificial intelligence (AI)-assisted diagnostic systems based on 18F-fluorodeoxyglucose PET/CT (18F-FDG PET/CT) and structural MRI (sMRI) in the diagnosis of Alzheimer's disease (AD). MethodsOriginal studies dedicated to the development or validation of AI-assisted diagnostic systems based on 18F-FDG PET/CT or sMRI for AD diagnosis were retrieved from the Web of Science, PubMed, and Embase databases. Studies meeting the inclusion criteria were collected, and the risk of bias and clinical applicability of the included studies were assessed using the PROBAST checklist. The pooled sensitivity, specificity, and area under the summary receiver operating characteristic (SROC) curve (AUC) were calculated using a bivariate random-effects model. ResultsTwenty-six studies met the inclusion criteria, yielding a total of 38 2×2 contingency tables related to diagnostic performance. Specifically, 24 contingency tables were based on 18F-FDG PET/CT to distinguish AD patients from normal cognitive (NC) controls, and 14 contingency tables were based on sMRI for the same purpose. The meta-analysis results showed that for 18F-FDG PET/CT, the AI-assisted diagnostic systems had a pooled sensitivity, specificity, and SROC-AUC of 89% (95%CI 88% to 91%), 93% (95%CI 91% to 94%), and 0.96 (95%CI 0.93 to 0.97), respectively. For sMRI, the AI-assisted diagnostic systems had a pooled sensitivity, specificity, and SROC-AUC of 88% (95%CI 85% to 90%), 90% (95%CI 87% to 92%), and 0.94 (95%CI 0.92 to 0.96), respectively. ConclusionAI-assisted diagnostic systems based on either 18F-FDG PET/CT or sMRI demonstrated similar performance in the diagnosis of AD, with both showing high accuracy.
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.
Objective To observe the changes of retinal nerve fiber layer (RNFL) thickness in patients with Alzheimer's disease (AD). Methods Twenty eyes of 40 patients with mild and (or) moderate AD confirmed by clinical examination (AD group) were included in the study. There were 11 males and 9 females with an average age of (72.75±8.25) years. Age and gender-matched normal 20 objectives were in the normal control group. Among them, there were 11 males and 9 females with a mean age of (71.05±7.08) years. There was no significant difference in gender composition, age and intraocular pressure between the two groups (P＞0.05). There were significant differences in visual acuity, cup disc ratio and mini-mental state examination score (P＜0.05). All eyes underwent high-resolution optical coherence tomography (OCT) examination. With a diameter of 3.4 mm and a center on the center of the optic disc, circular fast scans on optic disc were performed to obtain an average disc RNFL thickness, signal threshold ＞6. Computer image analysis system was used to measure the RNFL thickness from superior, inferior, temporal and nasal quadrants, and the average RNFL thickness. The changes of RNFL thickness between the two groups and between different eyes of the same group were compared. Results Compared with the normal control group, the average (t=5.591), superior (t=8.169, 8.053) and inferior (t=12.596, 11.377) thickness of RNFL in both eyes in AD group were thinner, the differences were significant (P＜0.05); the temporal (t=1.966, 0.838）and nasal (t=2.071, 0.916) thickness of RNFL in both eyes of AD group were thinner, but the difference was not statistically significant (P＞0.05). There was no significant difference of the mean and different quadrant RNFL thickness between different eyes in AD group and normal control group (AD group: t=0.097, 0.821, 0.059, 0.020, 0.116; normal control group: t=0.791, 1.938, 1.806, 2.058, 1.005; P＞0.05). Conclusion The RNFL thickness around the optic disc in AD patients is thinner; This occurs first in superior and inferior quadrants of the optic disc.
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.