Alzheimer's Disease Dataset Research Articles

Deep learning based classification for alzheimer's disease detection using MRI images

Alzheimer's disease is a common type of dementia that can cause serious problems in cognitive functions and activities of daily living. Although there is no definitive cure for Alzheimer's disease today, early diagnosis is important to slow down the adverse conditions that may arise and to improve the quality of life. As a result of the development of artificial intelligence technologies and their consistent application in different fields, machine learning techniques have the potential to play an important role in the detection of Alzheimer's disease. In particular, deep learning-based methods, which have the ability to automatically extract patterns from complex patterns, are promising in this field. Recent studies show that the use of deep learning models for Alzheimer's detection on images is becoming widespread. In addition to contributing to the early diagnosis of the disease, these models also show potential in detecting different stages of the disease by analyzing the symptoms in magnetic resonance images. These developments enable the development of more effective treatment methods for patients. However, more studies are needed to evaluate the efficacy and safety of these technologies in clinical applications. In this study, classification studies were performed using MobileNetV2, InceptionV3, Xception, Vgg16 and Vgg19 models for the diagnosis of the disease on a publicly shared Alzheimer's dataset consisting of 6400 different samples and 4 different classes. An accuracy of 99.92% was calculated for the MobileNetV2 model. The performances of the models used in this study were compared with similar studies in the literature and their performances were reported in terms of different metrics. Among the five different models used, the highest accuracy value of 99.92% was obtained with MobileNetV2. It was concluded that the architectures used in the experimental studies produced generally better results than similar studies in the literature.

P02.15.A INVESTIGATING THE ROLE OF CELLULAR SENESCENCE IN BRIDGING GLIOBLASTOMA AND ALZHEIMER’S DISEASE

Abstract BACKGROUND With changing demographics, age-related diseases like neurodegenerative disorders and cancer are becoming increasingly prevalent. We previously demonstrated that over 50% of glioblastoma (GB) patients show Alzheimer’s Disease (AD)-like changes in the tumor-adjacent cortex. However, whether these diseases are biologically linked remains unclear. Here, we investigate cellular senescence, a hallmark of aging, as a shared mechanism. MATERIAL AND METHODS We utilized single cell and single nucleus RNA sequencing datasets from 110 GB patients (GBmap core atlas) and 89 individuals with varying stages of AD (SEA-AD MTG atlas). Following data integration, we assessed senescent cells using three established gene lists, performed differential gene expression and conducted gene set enrichment analysis. Senescence scores were compared across different cell types and patients. Co-expression modules with senescence-associated genes were identified using weighted gene co-expression network analysis (WGCNA). Quantitative neuropathological analysis of published measurements (Iba1, AT8, 6E10) complemented the molecular findings. RESULTS Senescence was found in both GB and AD patient cells including cell types of the microenvironment. Notably, the variability in scores per patient was significantly higher in GB as compared to AD cell types, indicating greater inter-patient heterogeneity. Microglia were specifically enriched for senescence gene sets among glial cells. Subset analysis of microglia through data harmonization identified a proinflammatory GB microglia subtype that shared high similarity with AD microglia and had highest senescence scores. Notably, the increase in senescent microglia was associated with progression to advanced disease stages in both diseases. WGCNA identified 3 consensus modules of senescence-associated genes that were coexpressed with disease-specific genes, like PTEN implicated phosphorylation in the AD module and SPP1 for extracellular matrix remodeling in the GB module. All senescence associated modules showed increased expression in more advanced disease stages. Quantitative analysis of immunohistochemistry measurements in the AD dataset confirmed that samples with a high senescence score showed a significantly higher density of tau pathology and an altered microglia morphology. CONCLUSION Our study identifies cellular senescence as one connection between GB and AD pathology by utilizing atlas-level transcriptomic datasets. Particularly microglia upregulate senescence signatures with disease progression. The co-expression of senescence-associated genes within disease-specific modules underscores the complexity of senescence in this context.

A Reproducibility Focused Meta-Analysis Method for Single-Cell Transcriptomic Case-Control Studies Uncovers Robust Differentially Expressed Genes.

Here we systematically studied the reproducibility of DEGs in previously published Alzheimer's Disease (AD), Parkinson's Disease (PD), and COVID-19 scRNA-seq studies. We found that while transcriptional scores created from differentially expressed genes (DEGs) in individual PD and COVID-19 datasets had moderate predictive power for the case control status of other datasets (mean AUC=0.77 and 0.75, respectively), genes from individual AD datasets had poor predictive power (mean AUC=0.68). We developed a non-parametric meta-analysis method, SumRank, based on reproducibility of relative differential expression ranks across datasets. The meta-analysis genes had improved predictive power (AUCs of 0.88, 0.91, and 0.78, respectively). By multiple other metrics, specificity and sensitivity of these genes were substantially higher than those discovered by dataset merging and inverse variance weighted p-value aggregation methods. The DEGs revealed known and novel biological pathways, and we validate the BCAT1 gene as down-regulated in oligodendrocytes in an AD mouse model. Our analyses show that for heterogeneous diseases, DEGs of individual studies often have low reproducibility, but combining information across multiple datasets promotes the rigorous discovery of reproducible DEGs.

Self-guided Knowledge-Injected Graph Neural Network for Alzheimer's Diseases.

Graph neural networks (GNNs) are proficient machine learning models in handling irregularly structured data. Nevertheless, their generic formulation falls short when applied to the analysis of brain connectomes in Alzheimer's Disease (AD), necessitating the incorporation of domain-specific knowledge to achieve optimal model performance. The integration of AD-related expertise into GNNs presents a significant challenge. Current methodologies reliant on manual design often demand substantial expertise from external domain specialists to guide the development of novel models, thereby consuming considerable time and resources. To mitigate the need for manual curation, this paper introduces a novel self-guided knowledge-infused multimodal GNN to autonomously integrate domain knowledge into the model development process. We propose to conceptualize existing domain knowledge as natural language, and devise a specialized multimodal GNN framework tailored to leverage this uncurated knowledge to direct the learning of the GNN submodule, thereby enhancing its efficacy and improving prediction interpretability. To assess the effectiveness of our framework, we compile a comprehensive literature dataset comprising recent peer-reviewed publications on AD. By integrating this literature dataset with several real-world AD datasets, our experimental results illustrate the effectiveness of the proposed method in extracting curated knowledge and offering explanations on graphs for domain-specific applications. Furthermore, our approach successfully utilizes the extracted information to enhance the performance of the GNN.

Identification of cross-talk pathways and PANoptosis-related genes in periodontitis and Alzheimer's disease by bioinformatics analysis and machine learning.

Periodontitis (PD), a chronic inflammatory disease, is a serious threat to oral health and is one of the risk factors for Alzheimer's disease (AD). A growing body of evidence suggests that the two diseases are closely related. However, current studies have not provided a comprehensive understanding of the common genes and common mechanisms between PD and AD. This study aimed to screen the crosstalk genes of PD and AD and the potential relationship between cross-talk and PANoptosis-related genes. The relationship between core genes and immune cells will be analyzed to provide new targets for clinical treatment. The PD and AD datasets were downloaded from the GEO database and differential expression analysis was performed to obtain DEGs. Overlapping DEGs had cross-talk genes linking PD and OP, and PANoptosis-related genes were obtained from a literature review. Pearson coefficients were used to compute cross-talk and PANoptosis-related gene correlations in the PD and AD datasets. Cross-talk genes were obtained from the intersection of PD and AD-related genes, protein-protein interaction(PPI) networks were constructed and cross-talk genes were identified using the STRING database. The intersection of cross-talk and PANoptosis-related genes was defined as cross-talk-PANoptosis genes. Core genes were screened using ROC analysis and XGBoost. PPI subnetwork, gene-biological process, and gene-pathway networks were constructed based on the core genes. In addition, immune infiltration on the PD and AD datasets was analyzed using the CIBERSORT algorithm. 366 cross-talk genes were overlapping between PD DEGs and AD DEGs. The intersection of cross-talk genes with 109 PANoptosis-related genes was defined as cross-talk-PANoptosis genes. ROC and XGBoost showed that MLKL, DCN, IL1B, and IL18 were more accurate than the other cross-talk-PANoptosis genes in predicting the disease, as well as better in overall characterization. GO and KEGG analyses showed that the four core genes were involved in immunity and inflammation in the organism. Immune infiltration analysis showed that B cells naive, Plasma cells, and T cells gamma delta were significantly differentially expressed in patients with PD and AD compared with the normal group. Finally, 10 drugs associated with core genes were retrieved from the DGIDB database. This study reveals the joint mechanism between PD and AD associated with PANoptosis. Analyzing the four core genes and immune cells may provide new therapeutic directions for the pathogenesis of PD combined with AD.

Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data.

Deciphering the intricate relationships between transcription factors (TFs), enhancers, and genes through the inference of enhancer-driven gene regulatory networks (eGRNs) is crucial in understanding gene regulatory programs in a complex biological system. This study introduces STREAM, a novel method that leverages a Steiner forest problem model, a hybrid biclustering pipeline, and submodular optimization to infer eGRNs from jointly profiled single-cell transcriptome and chromatin accessibility data. Compared to existing methods, STREAM demonstrates enhanced performance in terms of TF recovery, TF-enhancer linkage prediction, and enhancer-gene relation discovery. Application of STREAM to an Alzheimer's disease dataset and a diffuse small lymphocytic lymphoma dataset reveals its ability to identify TF-enhancer-gene relations associated with pseudotime, as well as key TF-enhancer-gene relations and TF cooperation underlying tumor cells.

GenoTools: An Open-Source Python Package for Efficient Genotype Data Quality Control and Analysis.

GenoTools, a Python package, streamlines population genetics research by integrating ancestry estimation, quality control (QC), and genome-wide association studies (GWAS) capabilities into efficient pipelines. By tracking samples, variants, and quality-specific measures throughout fully customizable pipelines, users can easily manage genetics data for large and small studies. GenoTools' "Ancestry" module renders highly accurate predictions, allowing for high-quality ancestry-specific studies, and enables custom ancestry model training and serialization, specified to the user's genotyping or sequencing platform. As the genotype processing engine that powers several large initiatives, including the NIH's Center for Alzheimer's and Related Dementias (CARD) and the Global Parkinson's Genetics Program (GP2). GenoTools was used to process and analyze the UK Biobank and major Alzheimer's Disease (AD) and Parkinson's Disease (PD) datasets with over 400,000 genotypes from arrays and 5000 sequences and has led to novel discoveries in diverse populations. It has provided replicable ancestry predictions, implemented rigorous QC, and conducted genetic ancestry-specific GWAS to identify systematic errors or biases through a single command. GenoTools is a customizable tool that enables users to efficiently analyze and scale genotype data with reproducible and scalable ancestry, QC, and GWAS pipelines.

Identification and immunological characterization of genes associated with ferroptosis in Alzheimer's disease and experimental demonstration.

The incidence of Alzheimer's disease (AD) is rising globally, yet its treatment and prediction of this condition remain challenging due to the complex pathophysiological mechanisms associated with it. Consequently, the objective of the present study was to analyze and characterize the molecular mechanisms underlying ferroptosis‑related genes (FEGs) in the pathogenesis of AD, as well as to construct a prognostic model. The findings will provide new insights for the future diagnosis and treatment of AD. First, the AD dataset GSE33000 from the Gene Expression Omnibus database and the FEGs from FerrDB were obtained. Next, unsupervised cluster analysis was used to obtain the FEGs that were most relevant to AD. Subsequently, enrichment analyses were performed on the FEGs to explore biological functions. Subsequently, the role of these genes in the immune microenvironment was elucidated through CIBERSORT. Then, the optimal machine learning was selected by comparing the performance of different machine learning models. To validate the prediction efficiency, the models were validated using nomograms, calibration curves, decision curve analysis and external datasets. Furthermore, the expression of FEGs between different groups was verified using reverse transcription quantitative PCR and western blot analysis. In AD, alterations in the expression of FEGs affect the aggregation and infiltration of certain immune cells. This indicated that the occurrence of AD is strongly associated with immune infiltration. Finally, the most appropriate machine learning models were selected, and AD diagnostic models and nomograms were built. The present study provided novel insights that enhance understanding with regard to the molecular mechanism of action of FEGs in AD. Moreover, the present study provided biomarkers that may facilitate the diagnosis of AD.

Computer-aided diagnosis of Alzheimer’s disease and neurocognitive disorders with multimodal Bi-Vision Transformer (BiViT)

Cognitive disorders affect various cognitive functions that can have a substantial impact on individual’s daily life. Alzheimer’s disease (AD) is one of such well-known cognitive disorders. Early detection and treatment of cognitive diseases using artificial intelligence can help contain them. However, the complex spatial relationships and long-range dependencies found in medical imaging data present challenges in achieving the objective. Moreover, for a few years, the application of transformers in imaging has emerged as a promising area of research. A reason can be transformer’s impressive capabilities of tackling spatial relationships and long-range dependency challenges in two ways, i.e., (1) using their self-attention mechanism to generate comprehensive features, and (2) capture complex patterns by incorporating global context and long-range dependencies. In this work, a Bi-Vision Transformer (BiViT) architecture is proposed for classifying different stages of AD, and multiple types of cognitive disorders from 2-dimensional MRI imaging data. More specifically, the transformer is composed of two novel modules, namely Mutual Latent Fusion (MLF) and Parallel Coupled Encoding Strategy (PCES), for effective feature learning. Two different datasets have been used to evaluate the performance of proposed BiViT-based architecture. The first dataset contain several classes such as mild or moderate demented stages of the AD. The other dataset is composed of samples from patients with AD and different cognitive disorders such as mild, early, or moderate impairments. For comprehensive comparison, a multiple transfer learning algorithm and a deep autoencoder have been each trained on both datasets. The results show that the proposed BiViT-based model achieves an accuracy of 96.38% on the AD dataset. However, when applied to cognitive disease data, the accuracy slightly decreases below 96% which can be resulted due to smaller amount of data and imbalance in data distribution. Nevertheless, given the results, it can be hypothesized that the proposed algorithm can perform better if the imbalanced distribution and limited availability problems in data can be addressed.Graphical abstract

Enhancing Alzheimer's disease diagnosis and staging: a multistage CNN framework using MRI.

This study addresses the pervasive and debilitating impact of Alzheimer's disease (AD) on individuals and society, emphasizing the crucial need for timely diagnosis. We present a multistage convolutional neural network (CNN)-based framework for AD detection and sub-classification using brain magnetic resonance imaging (MRI). After preprocessing, a 26-layer CNN model was designed to differentiate between healthy individuals and patients with dementia. After detecting dementia, the 26-layer CNN model was reutilized using the concept of transfer learning to further subclassify dementia into mild, moderate, and severe dementia. Leveraging the frozen weights of the developed CNN on correlated medical images facilitated the transfer learning process for sub-classifying dementia classes. An online AD dataset is used to verify the performance of the proposed multistage CNN-based framework. The proposed approach yielded a noteworthy accuracy of 98.24% in identifying dementia classes, whereas it achieved 99.70% accuracy in dementia subclassification. Another dataset was used to further validate the proposed framework, resulting in 100% performance. Comparative evaluations against pre-trained models and the current literature were also conducted, highlighting the usefulness and superiority of the proposed framework and presenting it as a robust and effective AD detection and subclassification method.

A Siamese network optimization using genetic algorithm for brain diseases

AbstractThe complex nature of human brain tissues is important in ensuring accurate diagnosis to save human lives. Research on early detection of brain diseases has gained significant prominence within medical intelligence using highly complex model architectures with only a single label that cannot be verified. This paper introduces an innovative approach to Siamese Network Genetic Algorithm (SN‐GA) leveraging Siamese contrastive learning for classifying brain images across diverse diseases. Our core architecture is a Bi‐Convolutional Neural Network (Bi‐CNN) optimized by a genetic algorithm to enhance brain image classification. Specifically, five widely recognized transfer learning‐based architectures, namely AlexNet, Efficient‐B0, VGG‐16, ResNet‐50, and Inception‐v3, have been incorporated to evaluate the effectiveness of the proposed SN‐GA system. The performance of these models has been rigorously analyzed and compared using two distinct datasets: Brain tumors and Alzheimer's datasets. The experimental results robustly affirm the efficacy of the proposed Siamese model, yielding exceptional levels of accuracy, precision, and recall, all peaking at 97%. These findings underscore the potential and resilience of the optimized Siamese network in the context of brain disease classification, emphasizing its significance in advancing the field of medical imaging and diagnosis, with implications for early intervention and patient care.

Parallel Multilink Group Joint ICA: Fusion of 3D Structural and 4D Functional Data Across Multiple Resting fMRI Networks.

Multimodal neuroimaging research plays a pivotal role in understanding the complexities of the human brain and its disorders. Independent component analysis (ICA) has emerged as a widely used and powerful tool for disentangling mixed independent sources, particularly in the analysis of functional magnetic resonance imaging (fMRI) data. This paper extends the use of ICA as a unifying framework for multimodal fusion, introducing a novel approach termed parallel multilink group joint ICA (pmg-jICA). The method allows for the fusion of gray matter maps from structural MRI (sMRI) data to multiple fMRI intrinsic networks, addressing the limitations of previous models. The effectiveness of pmg-jICA is demonstrated through its application to an Alzheimer's dataset, yielding linked structure-function outputs for 53 brain networks. Our approach leverages the complementary information from various imaging modalities, providing a unique perspective on brain alterations in Alzheimer's disease. The pmg-jICA identifies several components with significant differences between HC and AD groups including thalamus, caudate, putamen with in the subcortical (SC) domain, insula, parahippocampal gyrus within the cognitive control (CC) domain, and the lingual gyrus within the visual (VS) domain, providing localized insights into the links between AD and specific brain regions. In addition, because we link across multiple brain networks, we can also compute functional network connectivity (FNC) from spatial maps and subject loadings, providing a detailed exploration of the relationships between different brain regions and allowing us to visualize spatial patterns and loading parameters in sMRI along with intrinsic networks and FNC from the fMRI data. In essence, developed approach combines concepts from joint ICA and group ICA to provide a rich set of output characterizing data-driven links between covarying gray matter networks, and a (potentially large number of) resting fMRI networks allowing further study in the context of structure/function links. We demonstrate the utility of the approach by highlighting key structure/function disruptions in Alzheimer's individuals.

Downregulation of miR-181c-5p in Alzheimer’s disease weakens the response of microglia to Aβ phagocytosis

Alzheimer’s disease (AD) is an age-associated neurodegenerative disease. Recently, studies have demonstrated the potential involvement of microRNA-181c-5p (miR-181c-5p) in AD. However, the mechanism through which miR-181c-5p is responsible for the onset and progression of this disease remains unclear, and our study aimed to explore this problem. Differential expression analysis of the AD dataset was performed to identify dysregulated genes. Based on hypergeometric analysis, AD differential the upstream regulation genes miR-181c-5p was found. We constructed a model where SH-SY5Y and BV2 cells were exposed to Aβ1-42 to simulate AD. Levels of tumor necrosis factor-alpha, interleukin-6, and IL-1β were determined using enzyme-linked immunosorbent assay or reverse transcription quantitative polymerase chain reaction. Phosphorylation levels of p-P38 and P38 were detected by Western blot. The level of apoptosis in BV2 cells under Aβ1-42 stress was exacerbated by miR-181c-5p mimic. Downregulated miR-181c-5p impaired the phagocytosis and degradation of Aβ by BV2 cells. The release of proinflammatory cytokines in BV2 cells with Aβ1-42 stress was alleviated by miR-181c-5p upregulation. Additionally, miR-181c-5p downregulation alleviated the phosphorylation of P38 in Aβ1-42-induced SH-SY5Y cells. In conclusion, miR-181c-5p improves the phagocytosis of Aβ by microglial cells in AD patients, thereby reducing neuroinflammation.

Enhancing clinical diagnostics: novel denoising methodology for brain MRI with adaptive masking and modified non-local block.

Medical image denoising has been a subject of extensive research, with various techniques employed to enhance image quality and facilitate more accurate diagnostics. The evolution of denoising methods has highlighted impressive results but struggled to strike equilibrium between noise reduction and edge preservation which limits its applicability in various domains. This paper manifests the novel methodology that integrates an adaptive masking strategy, transformer-based U-Net Prior generator, edge enhancement module, and modified non-local block (MNLB) for denoising brain MRI clinical images. The adaptive masking strategy maintains the vital information through dynamic mask generation while the prior generator by capturing hierarchical features regenerates the high-quality prior MRI images. Finally, these images are fed to the edge enhancement module to boost structural information by maintaining crucial edge details, and the MNLB produces the denoised output by deriving non-local contextual information. The comprehensive experimental assessment is performed by employing two datasets namely the brain tumor MRI dataset and Alzheimer's dataset for diverse metrics and compared with conventional denoising approaches. The proposed denoising methodology achieves a PSNR of 40.965 and SSIM of 0.938 on the Alzheimer's dataset and also achieves a PSNR of 40.002 and SSIM of 0.926 on the brain tumor MRI dataset at a noise level of 50% revealing its supremacy in noise minimization. Furthermore, the impact of different masking ratios on denoising performance is analyzed which reveals that the proposed method showed PSNR of 40.965, SSIM of 0.938, MAE of 5.847, and MSE of 3.672 at the masking ratio of 60%. Moreover, the findings pave the way for the advancement of clinical image processing, facilitating precise detection of tumors in clinical MRI images.

Hybrid federated learning with brain-region attention network for multi-center Alzheimer's disease detection

Identifying reproducible and interpretable biomarkers for Alzheimer's disease (AD) detection remains a challenge. AD detection using multi-center datasets can expand the sample size to improve robustness but might lead to a data privacy problem. Moreover, due to the high cost of labeling data, a lot of unlabeled data in each center is not fully utilized. To address this, a hybrid FL (HFL) framework is proposed that not only uses unlabeled data to train deep learning networks, but also achieves data privacy protection. We propose a novel Brain-region Attention Network (BANet), which highlights important regions via attention to represent the region of interest (ROIs).Specifically, we use a brain template to extract ROI signals from the preprocessed structure magnetic resonance imaging (sMRI) data. In addition, we add a self-supervised loss to the current loss to guide the attention map generation to learn the representations from unlabeled data. Finally, we evaluate our method on a multi-center database which is constructed using five AD datasets. The experimental results show that the proposed method performs better than state-of-the-art methods, achieving mean accuracy rates of 85.69 %, 63.34 %, and 69.89 % on the AD vs. NC, MCI vs. NC, and AD vs. MCI respectively. The source code is available for reproducibility at: https://github.com/yuliangCarmelo/HFL.

Deciphering the effect of phytosterols on Alzheimer’s disease and Parkinson’s disease: the mediating role of lipid profiles

BackgroundStudies have suggested that blood circulating phytosterols, plant-derived sterols analogous to cholesterol, were associated with blood lipid levels and the risk of Alzheimer’s disease (AD) and Parkinson’s disease (PD). This Mendelian randomization (MR) study is performed to determine the causal effect of circulating phytosterols on AD and PD and evaluate the mediation effect of blood lipids.MethodsLeveraging genome-wide association studies summary-level data for phytosterols, blood lipids, AD, and PD, univariable and multivariable MR (MVMR) analyses were conducted. Four types of phytosterols (brassicasterol, campesterol, sitosterol, and stigmasterol), three blood lipids parameters (high-density lipoprotein cholesterol [HDL-C], non-HDL-C, and triglyceride), two datasets for AD and PD were used. Inverse-variance weighted method was applied as the primary analysis, and false discovery rate method was used for adjustment of multiple comparisons.ResultsUsing the largest AD dataset, genetically proxied higher levels of stigmasterol (OR = 0.593, 95%CI = 0.431–0.817, P = 0.004) and sitosterol (OR = 0.864, 95%CI = 0.791–0.943, P = 0.004) significantly correlated with a lower risk of AD. No significant associations were observed between all four types of phytosterols levels and PD. MVMR estimates showed that the above causal associations were missing after integrating the blood lipids as exposures. Sensitivity analyses confirmed the robustness of these associations, with no evidence of pleiotropy and heterogeneity.ConclusionThe study supports a potential beneficial role of blood stigmasterol and sitosterol in reducing the risk of AD, but not PD, which is dependent on modulating blood lipids. These insights highlight circulating stigmasterol and sitosterol as possible biomarkers and therapeutic targets for AD.

Development of a Machine Learning Model for Diagnosis of Alzheimer's Disease Stages Using Brain Image Patterns

The goal of this project was to increase the diagnostic accuracy of AI modelings in the detection of Alzheimer's disease. Alzheimer's disease is a complex condition that requires early diagnosis for effective treatment. However, current diagnostic methods can have limitations. In this study, we developed a more advanced research model to detect AD. By utilizing both linear regression models and neural network, our model achieved excellent results with a test accuracy of 81.34% for the neural network model and 93.05% for the linear model Unlike earlier models that focused on binary classification of Alzheimer's disease, our model goes beyond that by classifying different stages and severities of Alzheimer's. We utilized a large dataset found on Kaggle called the "Alzheimer's Dataset" [1] consisting of 5,121 images of four classes (Mild Demented, Non Demented, Very Mild Demented, and Moderate Demented). This research redemonstrates the potential of machine learning to improve diagnostic accuracy for Alzheimer's disease.

Utilising VGG-16 of Convolutional Neural Network for Medical Image Classification

Medical image classification, which involves accurately classifying anomalies or abnormalities within images, is an important area of attention in healthcare domain. It requires a fast and exact classification to ensure appropriate and timely treatment to the patients. This paper introduces a model based on Convolutional Neural Network (CNN) that utilises the VGG16 architecture for medical image classification, specifically in brain tumour and Alzheimer dataset. The VGG16 architecture, is known for its remarkable ability to extract important features, that is crucial in medical image classification. To enhance the precision of diagnosis, a detailed experimental setup is conducted, which includes the careful selection and organisation of a collection of medical images that cover different illnesses and anomalies to the dataset. The architecture of the model is then adjusted to achieve optimal performance in for image classification. The results show the model's efficiency in identifying anomalies in medical images especially for brain tumour dataset. The sensitivity, specificity, and F1-score evaluation metrics are presented, emphasising the model's ability to accurately differentiate between various medical image diseases.

Integrated Bulk and Single-Cell RNA-Sequencing Reveals the Effects of Circadian Rhythm Disruption on the Metabolic Reprogramming of CD4+ T Cells in Alzheimer's Disease.

Although growing evidence suggests close correlations between Alzheimer's disease (AD) and circadian rhythm disruption (CRD), few studies have focused on the influence of circadian rhythm on levels of immune cells in AD. We aimed to delineate the mechanism underlying the effects of circadian related genes on T cell immune function in AD. A total of 112 brain samples were used to construct the CRD-related model by performing weighted gene co-expression network analysis and machine learning algorithms (LASSO, SVM-RFE, and RF). The ssGSEA method was used to calculate the CRDscore in order to quantify CRD status. Using single-cell transcriptome data of CSF cells, we investigated the CD4+ T cell metabolism and cell-cell communication in high- and low-risk CRD groups. Connectivity map (CMap) was applied to explore small molecule drugs targeting CRD, and the expression of the signature gene GPR4 was furthervalidated in AD. The CRDscore algorithm, which is based on 23 circadian-related genes, can effectively classify the CRD status in AD datasets. The single-cell analysis revealed that the CD4+ T cells with high CRDscore were characterized by hypometabolism. Cell communication analysis revealed that CD4+ T cells might be involved in promoting CD8+ T cell adhesion under CRD, which may facilitate T cell infiltration into the brain parenchyma. Overall, this study indicates the potential connotation of circadian rhythm in AD, providing insights into understanding T cell metabolic reprogramming under CRD.

STW-MD: a novel spatio-temporal weighting and multi-step decision tree method for considering spatial heterogeneity in brain gene expression data.

Gene expression during brain development or abnormal development is a biological process that is highly dynamic in spatio and temporal. Previous studies have mainly focused on individual brain regions or a certain developmental stage. Our motivation is to address this gap by incorporating spatio-temporal information to gain a more complete understanding of brain development or abnormal brain development, such as Alzheimer's disease (AD), and to identify potential determinants of response. In this study, we propose a novel two-step framework based on spatial-temporal information weighting and multi-step decision trees. This framework can effectively exploit the spatial similarity and temporal dependence between different stages and different brain regions, and facilitate differential gene analysis in brain regions with high heterogeneity. We focus on two datasets: the AD dataset, which includes gene expression data from early, middle and late stages, and the brain development dataset, spanning fetal development to adulthood. Our findings highlight the advantages of the proposed framework in discovering gene classes and elucidating their impact on brain development and AD progression across diverse brain regions and stages. These findings align with existing studies and provide insights into the processes of normal and abnormal brain development.