The electroencephalogram (EEG) signal is a general reflection of the neurophysiological activity of the brain, which has the advantages of being safe, efficient, real-time and dynamic. With the development and advancement of machine learning research, automatic diagnosis of Alzheimer’s diseases based on deep learning is becoming a research hotspot. Started from feedforward neural networks, this paper compared and analysed the structural properties of neural network models such as recurrent neural networks, convolutional neural networks and deep belief networks and their performance in the diagnosis of Alzheimer’s disease. It also discussed the possible challenges and research trends of this research in the future, expecting to provide a valuable reference for the clinical application of neural networks in the EEG diagnosis of Alzheimer’s disease.
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 a chronic central neurodegenerative disease. The pathological features of AD are the extracellular deposition of senile plaques formed by amyloid-β oligomers (AβOs) and the intracellular accumulation of neurofibrillary tangles formed by hyperphosphorylated tau protein. In this paper, an in vitro pathological model of AD based on neuronal network chip and its real-time dynamic analysis were presented. The hippocampal neuronal network was cultured on the microelectrode array (MEA) chip and induced by AβOs as an AD model in vitro to simultaneously record two firing patterns from the interneurons and pyramidal neurons. The spatial firing patterns mapping and cross-correlation between channels were performed to validate the degeneration of neuronal network connectivity. This biosensor enabled the detection of the AβOs toxicity responses, and the identification of connectivity and interactions between neuronal networks, which can be a novel technique in the research of AD pathological model in vitro.
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.
Alzheimer's disease is a common neuro-degenerative disease. The clinical diagnosis mainly depends on the patient's complaint, the score of mini-mental state examination and Montreal cognitive assessment scale, and the comprehensive judgment of MRI and other imaging examinations. Retina is homologous to brain tissue, and their vascular systems have similar physiological characteristics to small blood vessels in the brain. Numerous studies found that the thickness of retinal nerve fiber layer, visual function, retinal blood vessels and retinal oxygen saturation were changed in AD patients to different degrees. To explore the formation mechanism and significance of ocular fundus changes in AD patients will be helpful to select specific, sensitive and simple methods for early observation and evaluation of AD.
ObjectiveTo systematically review the diagnostic value of miRNAs for Alzheimer’s disease (AD).MethodsPubMed, Web of Science, EMbase, The Cochrane Library, CNKI, WanFang Data, VIP, and CBM databases were electronically searched to collect diagnostic tests of miRNAs for AD from inception to October 31, 2020. Two researchers independently screened literature, extracted data, and assessed the risk of bias of the included studies. RevMan 5.3 and Stata 14.0 software were used for meta-analysis. ResultsA total of 22 studies involving 4 006 subjects were included. The meta-analysis results showed that the pooled sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio, and the areas under the working characteristic curve of miRNA in AD diagnosis were 0.83 (95%CI 0.79 to 0.87), 0.80 (95%CI 0.76 to 0.83), 4.07 (95%CI 3.37 to 4.92), 0.21 (95%CI 0.17 to 0.27), 19.20 (95%CI 12.96 to 28.48) and 0.88 (95%CI 0.85 to 0.90), respectively. ConclusionThe current evidence shows that miRNAs have a high diagnostic value for AD. However, because of the limited quality and quantity of the included studies, more high-quality studies are required to verify the above conclusion.
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.
Objective To analyze the characteristic and temporal trend in mortality and disease burden of Alzheimer’s disease (AD) and other forms of dementia in Guangzhou from 2008 to 2019, and estimate the disease burden attributable to smoking to provide evidence for promoting local health policy of prevention and intervention of dementia. Methods Based on the data of Guangzhou surveillance point of the National Mortality Surveillance System (NMSS), the crude mortality, standardized mortality, years of life lost (YLL) of AD and other dementia were calculated. The indirect method was used to estimate years lived with disability (YLD) and disability-adjusted life years (DALY).The distribution and changing trends of the index rates were compared from 2008 to 2019 using Joinpoint Regression Program. Based on the data of Guangzhou Chronic Disease and Risk Factors Monitoring System in 2013, the indexes of disease burden of AD and other forms of dementia attributable to smoking in 2018 was calculated. Results The standardized mortality rate, YLL rate, YLD rate and DALY rate of AD and other forms of dementia in Guangzhou increased from 0.45/100 000, 0.05‰, 0.02‰ and 0.07 ‰ in 2008 to 1.28/100 000, 0.15‰, 0.07‰ and 0.22‰ in 2019, respectively. The average annual changing trend was statistically significant (AAPC=11.30%, 13.09%, 13.09%, 13.09%, P<0.001）. In most years, the mortality and disease burden of women were higher than those of men, but men had higher growing trend than women in standardized mortality rate, YLL rate, YLD rate and DALY rate from 2008 to 2019, with a slower growing speed after the year 2012.The disease burden of dementia attributable to smoking in men was significantly higher than that in women. Conclusion The mortality and disease burden of AD and other forms of dementia in Guangzhou have dramatically increased over the past twelve years. Intervention against modifiable factors such as smoking, and prevention and screening for dementia in key populations should be strengthened. Support policies for dementia care management should be adopted to reduce the disease burden caused by premature death and disability.