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.
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.
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.
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.
It is generally considered that various regulatory activities between genes are contained in the gene expression datasets. Therefore, the underlying gene regulatory relationship and the biologically useful information can be found by modeling the gene regulatory network from the gene expression data. In our study, two unsupervised matrix factorization methods, independent component analysis (ICA) and nonnegative matrix factorization (NMF), were proposed to identify significant genes and model the regulatory network using the microarray gene expression data of Alzheimer's disease (AD). By bio-molecular analyzing of the pathways, the differences between ICA and NMF have been explored and the fact, which the inflammatory reaction is one of the main pathological mechanisms of AD, is also emphasized. It was demonstrated that our study gave a novel and valuable method for the research of early detection and pathological mechanism, biomarkers' findings of AD.
ObjectiveTo systematically review the correlation between apolipoprotein E (ApoE) polymorphism and sporadic Alzheimer's disease (SAD) in Chinese population. MethodsThe case-control studies about the relationship between ApoE polymorphism and SAD in Chinese population were electronically retrieved in PubMed, EMbase, CBM, The Cochrane Library (Issue 8, 2013), CNKI, VIP, and WanFang Data from the date of their establishment to August 2013. Literature screening according to the inclusion and exclusion criteria, data extraction and methodological quality assessment of the included stuides were completed by two reviewers independently. Meta-analysis was then conducted using Stata 12.0 software. ResultsA total of 50 case-control studies invovling 3 396 cases and 4 917 controls were finally included. The results of meta-analysis showed that, in Chinese, the risk of SAD was 2.89 times higher in population with allele ε4 than in population with allele ε3 (OR=2.89, 95%CI 2.61 to 3.19, P < 0.001); 7.24 times higher in those with ε4/ε4 genotype than in those with ε3/ε3 genotype (OR=7.24, 95%CI 5.11 to 10.24, P < 0.001); 2.90 times higher in ε3/ε4 genotype than in ε3/ε3 genotype (OR=2.90, 95%CI 2.56 to 3.29, P < 0.001); 2.11 times higher in ε2/ε4 genotype than in ε3/ε3 genotype (OR=2.11, 95%CI 1.64 to 2.72, P < 0.001); and no statistic significance was found in the risk of SAD compared ε2/ε3, ε2/ε2 genotypes and ε2 allele with ε3/ε3 genotype and ε3 allele. ConclusionFor Chinese population, ApoE allele ε4 is significantly associated with the onset of SAD, and genotype ε4/ε4 is a high risk factor of SAD. While allele ε2 is not associated with the onset of SAD. Since a great deal of current studies failed to conduct stratified analysis, it is suggested to further conduct relevant relevant studies according to clinical classification of SAD and patients' characteristics.
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.
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.
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.
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.