ObjectiveTo systematically review the diagnostic value of FDG-PET, Aβ-PET and tau-PET for Alzheimer ’s disease (AD).MethodsPubMed, EMbase, The Cochrane Library, CNKI, WanFang Data, VIP and CBM databases were electronically searched to collect diagnostic tests of FDG-PET, Aβ-PET and tau-PET for AD from January 2000 to February 2020. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies; then, meta-analysis was performed by Meta-Disc 1.4 and Stata 14.0 software.ResultsA total of 31 studies involving 3 718 subjects were included. The results of meta-analysis showed that, using normal population as control, the sensitivity/specificity of FDG-PET and Aβ-PET in diagnosing AD were 0.853/0.734 and 0.824/0.771, respectively. Only 2 studies were included for tau-PET and meta-analysis was not performed.ConclusionsFDG-PET and Aβ-PET can provide good diagnostic accuracy for AD, and their diagnostic efficacy is similar. Due to limited quality and quantity of the included studies, more high quality studies are required to verify the above conclusions.
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.
Objective To investigate the effect of continuous positive airway pressure (CPAP) on sleep disorder and neuropsychological characteristics in patients with early Alzheimer’s disease (AD) combined with obstructive sleep apnea hypopnea syndrome (OSAHS). Methods A total of forty-two early AD patients with OSAHS were randomly divided into a CPAP combined treatment group (20 cases) and a simple medicine treatment group (22 cases). The changes of neurocognitive function were assessed by Montreal Cognitive Assessment (MoCA), Mini-mental State Examination (MMSE) and Hopkins Verbal Learning Test-revised (HVLT). Patient Health Questionnaire-9 (PHQ9) was used to evaluate the depression mood changes. The sleep characteristics and respiratory parameters were evaluated by polysomnography. The changes of the patients’ sleep status were assessed by Epworth Sleepiness Scale (ESS) and Pittsburgh Sleep Quality Index (PSQI). The changes of sleep status, cognitive function and mood in the CPAP combined treatment group were compared before and three months after CPAP treatment, and with the simple medicine treatment group. Results After three months of CPAP treatment, the ESS, PSQI and PHQ9 scores in the CPAP combined treatment group were significantly decreased compared with those before treatment, whereas MoCA, MMSE and HVLT (total scores and recall ) in the CPAP combined treatment group were increased compared with those before treatment (P<0.05). After CPAP treatment, the respiratory parameters apnea hypopnea index in the CPAP combined treatment group was significantly lower than that before treatment (P<0.05), and the minimum blood oxygen saturation was significantly higher than that before treatment (P<0.05). However, the sleep characteristics and parameters did not show statistically significant changes compared with those before treatment (P>0.05). The ESS, PSQI and PHQ9 scores were significantly reduced in the CPAP combined treatment group compared with the simple medicine treatment group (P<0.05), while there was no statistically significant changes of cognitive scores between the two groups (P>0.05). Conclusions The degree of low ventilation and hypoxia is alleviated, and the daytime sleepiness and depression is improved in early AD patients with OSAHS after three-month continuous CPAP treatment. Cognitive function is significantly improved, whereas there is no significant change in sleep structure disorder.
40 Hz light flicker stimulation is deemed to hold considerable promise in the treatment of Alzheimer’s disease (AD). However, whether its long-term effect can improve working memory and its related mechanisms remains to be further explored. In this study, 21 adult Wistar rats were randomly divided into the AD light-stimulation group, the AD group and the control group. AD models were established in the first two of these groups, with the light-stimulation group receiving long-term 40 Hz light flicker stimulation. Working memory performance across groups was subsequently evaluated using the T-maze task. To investigate the potential neural mechanisms underlying the effects of 40 Hz light stimulation on working memory, we examined changes in neuronal excitability within the hippocampus (HPC) and medial prefrontal cortex (mPFC), as well as alterations in inter-regional synchronization of neural activity. The findings demonstrated that prolonged 40 Hz light stimulation significantly improved working memory performance in AD model rats. Furthermore, the intervention enhanced the synchronization of neural activity between the hippocampus (HPC) and medial prefrontal cortex (mPFC), as well as the efficiency of information transfer, primarily mediated by theta and low-frequency gamma oscillations. This study provides theoretical support for exploring the mechanisms of 40 Hz light flicker stimulation and its further clinical application in the prevention and treatment of Alzheimer’s disease.
Alzheimer’s disease (AD) is the most common degenerative disease of the nervous system. Studies have found that the 40 Hz pulsed magnetic field has the effect of improving cognitive ability in AD, but the mechanism of action is not clear. In this study, APP/PS1 double transgenic AD model mice were used as the research object, the water maze was used to group dementia, and 40 Hz/10 mT pulsed magnetic field stimulation was applied to AD model mice with different degrees of dementia. The behavioral indicators, mitochondrial samples of hippocampal CA1 region and electrocardiogram signals were collected from each group, and the effects of 40 Hz pulsed magnetic field on mouse behavior, mitochondrial kinetic indexes and heart rate variability (HRV) parameters were analyzed. The results showed that compared with the AD group, the loss of mitochondrial crest structure was alleviated and the mitochondrial dynamics related indexes were significantly improved in the AD + stimulated group (P < 0.001), sympathetic nerve excitation and parasympathetic nerve inhibition were improved, and the spatial cognitive memory ability of mice was significantly improved (P < 0.05). The preliminary results of this study show that 40 Hz pulsed magnetic field stimulation can improve the mitochondrial structure and mitochondrial kinetic homeostasis imbalance of AD mice, and significantly improve the autonomic neuromodulation ability and spatial cognition ability of AD mice, which lays a foundation for further exploring the mechanism of ultra-low frequency magnetic field in delaying the course of AD disease and realizing personalized neurofeedback therapy for AD.
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.
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.
Alzheimer’ s disease is the most common kind of dementia without effective treatment. Via early diagnosis, early intervention after diagnosis is the most effective way to handle this disease. However, the early diagnosis method remains to be studied. Neuroimaging data can provide a convenient measurement for the brain function and structure. Brain structure network is a good reflection of the fiber structural connectivity patterns between different brain cortical regions, which is the basis of brain’s normal psychology function. In the paper, a brain structure network based on pattern recognition analysis was provided to realize an automatic diagnosis research of Alzheimer’s disease and gray matter based on structure information. With the feature selection in pattern recognition, this method can provide the abnormal regions of brain structural network. The research in this paper analyzed the patterns of abnormal structural network in Alzheimer’s disease from the aspects of connectivity and node, which was expected to provide updated information for the research about the pathological mechanism of Alzheimer’s disease.
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.
In order to solve the problem of early classification of Alzheimer’s disease (AD), the conventional linear feature extraction algorithm is difficult to extract the most discriminative information from the high-dimensional features to effectively classify unlabeled samples. Therefore, in order to reduce the redundant features and improve the recognition accuracy, this paper used the supervised locally linear embedding (SLLE) algorithm to transform multivariate data of regional brain volume and cortical thickness to a locally linear space with fewer dimensions. The 412 individuals were collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) including stable mild cognitive impairment (sMCI, n = 93), amnestic mild cognitive impairment (aMCI, n = 96), AD (n = 86) and cognitive normal controls (CN, n = 137). The SLLE algorithm used in this paper is to calculate the nearest neighbors of each sample point by adding the distance correction term, and the locally linear reconstruction weight matrix was obtained from its nearest neighbors, then the low dimensional mapping of the high dimensional data can be calculated. In order to verify the validity of SLLE in the task of classification, the feature extraction algorithms such as principal component analysis (PCA), Neighborhood MinMax Projection (NMMP), locally linear mapping (LLE) and SLLE were respectively combined with support vector machines (SVM) classifier to obtain the accuracy of classification of CN and sMCI, CN and aMCI, CN and AD, sMCI and aMCI, sMCI and AD, and aMCI and AD, respectively. Experimental results showed that our method had improvements (accuracy/sensitivity/specificity: 65.16%/63.33%/67.62%) on the classification of sMCI and aMCI by comparing with the combination algorithm of LLE and SVM (accuracy/sensitivity/specificity: 64.08%/66.14%/62.77%) and SVM (accuracy/sensitivity/specificity: 57.25%/56.28%/58.08%). In detail the accuracy of the combination algorithm of SLLE and SVM is 1.08% higher than the combination algorithm of LLE and SVM, and 7.91% higher than SVM. Thus, the combination of SLLE and SVM is more effective in the early diagnosis of Alzheimer’s disease.