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.
ObjectivesTo systematically review the efficacy and safety of butylphthalide soft capsule with routine treatment for Alzheimer’s disease (AD).MethodsDatabases including CNKI, WanFang Data, VIP, CBM, PubMed, EMbase, and The Cochrane Library were electronically searched from September 2002 to July 2018 to collect randomized controlled trials of butylphthalide soft capsule with routine treatment for Alzheimer’s disease. The trial was screened based on inclusion and exclusion criteria, and the methodological quality of the included trial was assessed. Meta-analysis was then performed by Revman 5.3 software.ResultsA total of 8 studies involving 576 patients were included. The butylphthalide soft capsule group included 283 patients and the control group included 293 patients. The result of meta-analysis showed that butylphthalide soft capsule with routine treatment (Donepezil hydrochloride or Memantine or EGb761) significantly improved the score of mini-mental state examination (MMSE) (MD=3.19, 95% CI 2.69 to 3.69, P<0.001) and clinical efficacy (RR=1.36, 95%CI 1.21 to 1.53, P<0.001). There was no significant difference in number of adverse events between the butylphthalide group and the control group (RR=1.13, 95%CI 0.77 to 1.67, P=0.52).ConclusionsBased on the routine treatment, combining with butylphthalide soft capsule can further facilitate cognitive function of AD and improve clinical efficacy. At the same time, no increase in adverse reactions has been found. However, due to the low quality of the included studies, more high quality randomized controlled trials are required to verify the results.
Objective To evaluate the efficacy and safety of memantine in the treatment of Alzheimer’s disease (AD). Methods The randomized controlled trials (RCTs) about memantine vs. donepezil for patients with AD from January 1989 to July 2011 were searched in CBM, CNKI, WanFang Data, MEDLINE, OVID, EMbase and The Cochrane Library. Two reviewers independently screened the literatures, extracted the data, and evaluated the methodological quality. Then meta-analyses were conducted by using RevMan 5.0 software. Results The total 12 RCTs were included. Among the 2 716 patients involved, 1 459 were in the memantine group, while the other 1 302 were in the donepezil group. The results of meta-analyses showed that the efficacy of the memantine group was superior to that of the donepezil group in MMSE (MD=0.53, 95%CI 0.21 to 0.85, P=0.001), CIBIC-Plus (MD= –0.19, 95%CI –0.31 to –0.07, P=0.002), NPI (MD= –2.9, 95%CI –4.57 to –1.22, P=0.000 7) and SIB (MD=3.12, 95%CI 0.57 to 5.67, P=0.02), with significant differences; but the efficacy of the two groups was similar in ADCS-ADL19 (MD=0.29, 95%CI –0.03 to 0.60, P=0.07). There was no significant difference between the two groups in incidence of side effects (RR=1.14, 95%CI 0.94 to 1.38, P=0.17), but the tolerability of the memantine group was much better (RR=0.78, 95%CI 0.63 to 0.97, P=0.03). Conclusion Based on the current studies, memantine is superior to donepezil in treating Alzheimer’s disease (AD) at present. Although the side effects are similar to donepezil, memantine has much better intolerability and is considered to be safe and effective. For the quality restrictions and possible publication bias of the included studies, more double blind RCTs with high quality are required to further assess the effects.
Objective To systematically review the effect of vitamin D (VitD) supplementation on cognitive function in people with cognitive impairment and non-cognitive disorders. MethodsThe PubMed, Web of Science, Cochrane Library, EMbase, CBM, CNKI, WanFang Data and VIP databases were searched to collect randomized controlled trials (RCTs) about the effect of VitD supplementation on cognitive function of patients with cognitive impairment or non-cognitive disorders from inception to March, 2022. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias of the included studies. Meta-analysis was then performed using RevMan 5.4 software. Results A total of 19 articles including 8 684 cases were included. The results of meta-analysis showed that mini-mental state examination (MMSE) score (MD=1.70, 95%CI 1.20 to 2.21, P<0.01), Montreal cognitive assessment (MoCA) score (MD=1.51, 95%CI 1.00 to 2.02, P<0.01), Wechsler Adult Intelligence Scale-Revised (WAIS-RC) score (MD=9.12, 95%CI 7.77 to 10.47, P<0.01) and working memory (SMD=1.87, 95%CI 1.07 to 2.67, P<0.01) in the VitD group of patients with cognitive impairment were all better than those in the control group. However, the overall cognitive function and working memory of the non-cognitive impairment population were not significantly different compared with the control group. In terms of language fluency and language memory, there was no significant difference between the VitD group and the control group. In terms of the executive functions, at the intervention time of> 6 months, the VitD and control groups were statistically significant (SMD=0.15, 95%CI 0.01 to 0.28, P=0.03). Conclusion Current evidence suggests that VitD supplementation can effectively improve the overall cognitive function and working memory of patients with cognitive impairment, and has a positive effect on executive function at an intervention time of >6 months. Due to the limited quality and quantity of the included studies, more high-quality studies are needed to verify the above conclusion.
Biological markers play a pivotal role in the early and accurate diagnosis of Alzheimer’s disease, enabling precise identification and monitoring of therapeutic interventions. The detection of central β-amyloid and Tau proteins has become an indispensable tool in clinical trials. Recent years have witnessed substantial progress in the development of readily accessible and cost-effective blood biomarkers. This comprehensive article provides a comprehensive overview of the clinical applications of blood biomarkers, encompassing β-amyloid, phosphorylated Tau protein, neurofilament light chain protein, and glial fibrillary acidic protein, all of which have demonstrated clinical relevance in Alzheimer’s disease diagnosis. Notably, phosphorylated Tau protein exhibits superior diagnostic efficacy. The incorporation of blood biomarkers facilitates early screening, accurate diagnosis, and efficacious treatment of Alzheimer’s disease.
Objective To generate eukaryotic expression vector of pcDNA3.1-β-site amyloid precursor protein cleaving enzyme (BACE) and obtain its transient expression in COS-7 cells. Methods A 1.5 kb cDNA fragment was amplified from the total RNA of the human neuroblastoma cells by the RT-PCR method and was cloned into the plasmid pcDNA3.1. The vector was identified by the double digestion with restriction enzymes BamHI and XhoI and was sequenced by the Sanger-dideoxy-mediated chain termination. The expression of the BACE gene was detected by immunocytochemistry. Results The results showed that the cDNA fragment included 1.5 kb total coding region. The recombinant eukaryotic cell expression vector of pcDNA3.1-BACE was constructed successfully, and the sequence of insert was identical to the published sequence. The COS-7 cells transfected with the pcDNA3.1BACE plasmid expressed a high level of the BACE protein in the cytoplasm. Conclusion The recombinant plasmid pcDNA3.1-BACE can provide a very useful tool for the research on the cause of Alzheimer’s disease and lay an important foundation for preventing Alzheimer’s disease.
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.
Alzheimer’s disease (AD) is a progressive and irreversible neurodegenerative disease. Neuroimaging based on magnetic resonance imaging (MRI) is one of the most intuitive and reliable methods to perform AD screening and diagnosis. Clinical head MRI detection generates multimodal image data, and to solve the problem of multimodal MRI processing and information fusion, this paper proposes a structural and functional MRI feature extraction and fusion method based on generalized convolutional neural networks (gCNN). The method includes a three-dimensional residual U-shaped network based on hybrid attention mechanism (3D HA-ResUNet) for feature representation and classification for structural MRI, and a U-shaped graph convolutional neural network (U-GCN) for node feature representation and classification of brain functional networks for functional MRI. Based on the fusion of the two types of image features, the optimal feature subset is selected based on discrete binary particle swarm optimization, and the prediction results are output by a machine learning classifier. The validation results of multimodal dataset from the AD Neuroimaging Initiative (ADNI) open-source database show that the proposed models have superior performance in their respective data domains. The gCNN framework combines the advantages of these two models and further improves the performance of the methods using single-modal MRI, improving the classification accuracy and sensitivity by 5.56% and 11.11%, respectively. In conclusion, the gCNN-based multimodal MRI classification method proposed in this paper can provide a technical basis for the auxiliary diagnosis of Alzheimer’s disease.
Amyloid β-protein (Aβ) deposition is an important prevention and treatment target for Alzheimer’s disease (AD), and early detection of Aβ deposition in the brain is the key to early diagnosis of AD. Magnetic resonance imaging (MRI) is the perfect imaging technology for the clinical diagnosis of AD, but it cannot display the plaque deposition directly. In this paper, based on two feature selection modes-filter and wrapper, chain-like agent genetic algorithm (CAGA), principal component analysis (PCA), support vector machine (SVM) and random forest (RF), we designed six kinds of feature learning classification algorithms to detect the information (distribution) of Aβ deposition through magnetic resonance image pixels selection. Firstly, we segmented the brain region from brain MR images. Secondly, we extracted the pixels in the segmented brain region as a feature vector (features) according to rows. Thirdly, we conducted feature learning on the extracted features, and obtained the final optimal feature subset by voting mechanism. Finally, using the final optimal selected features, we could find and mark the corresponding pixels on the MR images to show the information about Aβ plaque deposition by elastic mapping. According to the experimental results, the proposed pixel features learning methods in this paper could extract and reflect Aβ plaque deposition, and the best classification accuracy could be as high as 80%, thereby showing the effectiveness of the methods. The proposed methods can precisely detect the information of the Aβ plaque deposition, thereby being helpful for improving classification accuracy of diagnosis of AD.
ObjectiveTo systematically review the data of peripheral inflammatory markers in patients with Alzheimer’s disease (AD) and vascular dementia (VaD) to further indicate pathogenesis and antidiastole.MethodsPubMed, EMbase, The Cochrane Library, CNKI, WanFang Data and VIP databases were electronically searched to collect studies on peripheral inflammatory markers in patients with AD and VaD from inception to July 2020. Two reviewers independently screened literature, extracted data, and assessed risk of bias of included studies, and meta-analysis was performed by using Stata 15.1SE software.ResultsA total of 30 studies involving 2 377 patients were included. The results of meta-analysis showed that the IL-6 level was higher in VaD group than that in AD group (SMD=?0.477, 95%CI ?0.944 to ?0.009, P=0.046). However, there were no statistical difference in peripheral IL-1β (SMD=?0.034, 95%CI ?0.325 to 0.257, P=0.818), TNF-α (SMD=0.409, 95%CI ?0.152 to 0.970, P=0.153) or CRP (SMD=0.277, 95%CI ?0.228 to 0.782, P=0.282) levels.ConclusionsThese findings suggest that IL-6 may be sensitive markers to distinguish AD from VaD. Due to limited quality and quantity of the included studies, more high-quality studies are required to verify the conclusions.