Classification of Alzheimer's Disease Stages: An Approach Using PCA-Based Algorithm

Voxel Extraction and Multiclass Classification of Identified Brain Regions across Various Stages of Alzheimer's Disease Using Machine Learning Approaches

This study sought to investigate how different brain regions are affected by Alzheimer's disease (AD) at various phases of the disease, using independent component analysis (ICA). The study examines six regions in the mild cognitive impairment (MCI) stage, four in the early stage of Alzheimer's disease (AD), six in the moderate stage, and six in the severe stage. The precuneus, cuneus, middle frontal gyri, calcarine cortex, superior medial frontal gyri, and superior frontal gyri were the areas impacted at all phases. A general linear model (GLM) is used to extract the voxels of the previously mentioned regions. The resting fMRI data for 18 AD patients who had advanced from MCI to stage 3 of the disease were obtained from the ADNI public source database. The subjects include eight women and ten men. The voxel dataset is used to train and test ten machine learning algorithms to categorize the MCI, mild, moderate, and severe stages of Alzheimer's disease. The accuracy, recall, precision, and F1 score were used as conventional scoring measures to evaluate the classification outcomes. AdaBoost fared better than the other algorithms and obtained a phenomenal accuracy of 98.61%, precision of 99.00%, and recall and F1 scores of 98.00% each.

Determination of affected brain regions at various stages of Alzheimer's disease

The objective of study was to explore those brain areas that were affected at each stage during the progression of Alzheimer's disease (AD). Six affected brain areas were explored at mild cognitive impairment, four at first stage and six at each of second and third stage of Alzheimer's disease. The common brain regions among these stages were cuneus, precuneus, calcarine cortex, middle frontal gyrus, superior frontal gyrus, and frontal superior medial gyrus. The fMRI data at the resting state of 18 AD patients who were converted from MCI to stage 3 of Alzheimer's were taken from ADNI public source database. Among these patients, there were ten males and eight females. Independent component analysis was used to explore affected brain regions and an algorithm based on deep learning convolutional neural network was proposed for binary classification among the stages of Alzheimer's disease. The proposed CNN model delivered 94.6 % accuracy for separating stage 1 of Alzheimer's disease from mild cognitive impairment. 96.7 % accuracy was acquired to distinguish stage 2 of Alzheimer's disease from mild cognitive impairment, and stage 3 of Alzheimer's disease was separated from mild cognitive impairment with an accuracy of 97.8 %.

Quantifying the impact of Pyramid Squeeze Attention mechanism and filtering approaches on Alzheimer's disease classification

Brain medical imaging and deep learning are important foundations for diagnosing and predicting Alzheimer's disease. In this study, we explored the impact of different image filtering approaches and Pyramid Squeeze Attention (PSA) mechanism on the image classification of Alzheimer's disease. First, during the image preprocessing, we register MRI images and remove skulls, then apply median filtering, Gaussian blur filtering, and anisotropic diffusion filtering to obtain different experimental images. After that, we add the Squeeze and Excitation (SE) mechanism and Pyramid Squeeze Attention (PSA) mechanism to the Fully Convolutional Network (FCN) model respectively, to obtain each MRI image's corresponding feature information of disease probability map. Besides, we also construct Multi-Layer Perceptron (MLP) model's framework, combining feature information of disease probability map with age, gender, and Mini-Mental State Examination (MMSE) of each sample, to get the final classification performance of model. Among them, the accuracy of the MLP-C model combining anisotropic diffusion filtering with the Pyramid Squeeze Attention mechanism can reach 98.85%. The corresponding quantitative experimental results show that different image filtering approaches and attention mechanisms provide effective assistance for the diagnosis and classification of Alzheimer's disease.

Image Classification of Alzheimer’s Disease Based on External-Attention Mechanism and Fully Convolutional Network

Automatic and accurate classification of Alzheimer’s disease is a challenging and promising task. Fully Convolutional Network (FCN) can classify images at the pixel level. Adding an attention mechanism to the Fully Convolutional Network can effectively improve the classification performance of the model. However, the self-attention mechanism ignores the potential correlation between different samples. Aiming at this problem, we propose a new method for image classification of Alzheimer’s disease based on the external-attention mechanism. The external-attention module is added after the fourth convolutional block of the fully convolutional network model. At the same time, the double normalization method of Softmax and L1 norm is introduced to obtain a better classification performance and richer feature information of the disease probability map. The activation function Softmax can increase the degree of fitting of the neural network to the training set, which transforms linearity into nonlinearity, thereby increasing the flexibility of the neural network. The L1 norm can avoid the attention map being affected by especially large (especially small) eigenvalues. The experiments in this paper use 550 three-dimensional MRI images and use five-fold cross-validation. The experimental results show that the proposed image classification method for Alzheimer’s disease, combining the external-attention mechanism with double normalization, can effectively improve the classification performance of the model. With this method, the accuracy of the MLP-A model is 92.36%, the accuracy of the MLP-B model is 98.55%, and the accuracy of the fusion model MLP-C is 98.73%. The classification performance of the model is higher than similar models without adding any attention mechanism, and it is better than other comparison methods.

Dysphagia Risk in Patients Prescribed Rivastigmine: A Systematic Analysis of FDA Adverse Event Reporting System

BACKGROUND

Dysphagia has been reported as an adverse event for patients receiving rivastigmine for Alzheimer's disease (AD) treatment.

OBJECTIVE

The purpose of this study was to determine the association between dysphagia and the usage of rivastigmine by using the pharmacovigilance data from the FDA Adverse Event Reporting System (FAERS).

METHODS

The risk of dysphagia in patients who took rivastigmine was compared with those of patients who took other medications. In addition, this study sought to determine if the dysphagia risk was influenced by sex, age, dosage, and medication routes of administration.

RESULTS

When compared to patients prescribed donepezil, galantamine, or memantine, individuals prescribed rivastigmine were almost twice as likely to report dysphagia as an adverse event. The dysphagia risk in individuals prescribed rivastigmine is comparable to individuals prescribed penicillamine but significantly higher than clozapine, drugs of which have been previously shown to be associated with elevated dysphagia likelihood. Individuals older than 80 were 122% more likely to report having dysphagia after being prescribed rivastigmine than patients that were 50-70 years of age. Oral administration of rivastigmine was associated with approximately 2 times greater likelihood of reporting dysphagia relative to users of the transdermal patch. In addition, dysphagia showed higher association with pneumonia than other commonly reported adverse events.

CONCLUSION

Patients prescribed rivastigmine were at greater risk of reporting dysphagia as an adverse event than patients prescribed many other medicines. This increase in dysphagia occurrence may be attributed to the dual inhibition of both acetylcholinesterase and butyrylcholinesterase.

The Association Between Use of Rivastigmine and Pneumonia: Systematic Analysis of FDA Adverse Event Reporting System

BACKGROUND

Pneumonia is an inflammatory condition induced by infection of the lungs and is frequently a cause of morbidity and mortality among patients with Alzheimer's disease (AD). Some studies have shown a correlation between acetylcholinesterase inhibitor use and elevated pneumonia risk.

OBJECTIVE

The purpose of this study was to perform a comparative analysis of the number of reported pneumonia cases in individuals prescribed rivastigmine relative to the association between pneumonia risk for other therapeutics including over-the-counter drugs and other AD therapeutics, as reported to the FDA Adverse Event Reporting System (FAERS) database.

METHODS

A disproportionality analysis was conducted to investigate the association between using rivastigmine and risk of pneumonia. Age, gender, dosage, route of administration, temporality, and geographic distribution of reported cases were also assessed.

RESULTS

Patients prescribed rivastigmine were more likely to report pneumonia as an adverse event than many drugs except galantamine. Males were found to be 46%more likely than females to report pneumonia as an adverse event while likelihood of pneumonia diagnosis increases 3-5-fold in patients older than 65 years of age.

CONCLUSION

The observed elevated frequency of aspiration pneumonia in patients prescribed rivastigmine may be due to an induced cholinergic crisis that is selective for the medulla oblongata, resulting in gastrointestinal distress, impaired swallowing, heightened salivation, and labored breathing. The observed elevated frequency of infectious pneumonia in patients prescribed rivastigmine may also be linked to overstimulation of neurons in the medulla oblongata and downstream suppression of localized inflammatory responses.

A cross-sectional study in healthy elderly subjects aimed at development of an algorithm to increase identification of Alzheimer pathology for the purpose of clinical trial participation

Background

In the current study, we aimed to develop an algorithm based on biomarkers obtained through non- or minimally invasive procedures to identify healthy elderly subjects who have an increased risk of abnormal cerebrospinal fluid (CSF) amyloid beta42 (Aβ) levels consistent with the presence of Alzheimer’s disease (AD) pathology. The use of the algorithm may help to identify subjects with preclinical AD who are eligible for potential participation in trials with disease modifying compounds being developed for AD. Due to this pre-selection, fewer lumbar punctures will be needed, decreasing overall burden for study subjects and costs.

Methods

Healthy elderly subjects (n = 200; age 65–70 (N = 100) and age > 70 (N = 100)) with an MMSE > 24 were recruited. An automated central nervous system test battery was used for cognitive profiling. CSF Aβ1-42 concentrations, plasma Aβ1-40, Aβ1-42, neurofilament light, and total Tau concentrations were measured. Aβ1-42/1-40 ratio was calculated for plasma. The neuroinflammation biomarker YKL-40 and APOE ε4 status were determined in plasma. Different mathematical models were evaluated on their sensitivity, specificity, and positive predictive value. A logistic regression algorithm described the data best. Data were analyzed using a 5-fold cross validation logistic regression classifier.

Results

Two hundred healthy elderly subjects were enrolled in this study. Data of 154 subjects were used for the per protocol analysis. The average age of the 154 subjects was 72.1 (65–86) years. Twenty-four (27.3%) were Aβ positive for AD (age 65–83). The results of the logistic regression classifier showed that predictive features for Aβ positivity/negativity in CSF consist of sex, 7 CNS tests, and 1 plasma-based assay. The model achieved a sensitivity of 70.82% (± 4.35) and a specificity of 89.25% (± 4.35) with respect to identifying abnormal CSF in healthy elderly subjects. The receiver operating characteristic curve showed an AUC of 65% (± 0.10).

Conclusion

This algorithm would allow for a 70% reduction of lumbar punctures needed to identify subjects with abnormal CSF Aβ levels consistent with AD. The use of this algorithm can be expected to lower overall subject burden and costs of identifying subjects with preclinical AD and therefore of total study costs.

Trial registration

ISRCTN.org identifier: ISRCTN79036545 (retrospectively registered).

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-021-00874-9.