Early Detection Of Alzheimer's Disease Research Articles

Integration of Single-Cell and Spatial Transcriptomic Data Reveals Spatial Architecture and Potential Biomarkers in Alzheimer's Disease.

Alzheimer's disease (AD) is a complex neurodegenerative disorder characterized by the gradual loss of neurons and the accumulation of amyloid plaques and neurofibrillary tangles. Despite advancements in the understanding of AD's pathophysiology, the cellular organization and interactions in the prefrontal cortex (PFC) remain elusive. Eight single-cell RNA sequencing (scRNA-seq) datasets from both normal controls and individuals with AD were harmonized. Stringent preprocessing protocols were implemented to uphold dataset integrity. Unsupervised clustering and annotation revealed 22 distinct cell clusters corresponding to 19 unique cell types. The spatial architecture of the PFC region was constructed using the CARD tool. Further analyses encompassed trajectory examination of Oligodendrocyte subtypes, evaluation of regulon activity scores, and spot clustering within white matter regions (WM). Differential expression analysis and functional enrichment assays unveiled molecular signatures linked to AD progression and were validated using microarray data sourced from neurodegenerative disorder patients. Our investigation employs scRNA-seq and spatial transcriptomics to uncover the cellular atlas and spatial architecture of the human PFC in AD. Moreover, our results indicate that Oligodendrocytes are more prevalent in AD patients, showcasing diverse subtypes and spatial organization within WM regions. Each subtype appears to be associated with distinct biological processes and transcriptional regulators, shedding light on their involvement in AD pathology. Notably, the Oligodendrocyte_C6 subtype is linked to neurological damage in AD patients, characterized by heightened expression of genes involved in cell-cell connections, cell membrane stability, and myelination. Additionally, 12 target genes regulated by NFIA were identified, which are upregulated in AD patients and associated with disease progression. Elevated PLXDC2 expression in peripheral blood was also identified, suggesting its potential as a non-invasive biomarker for early AD detection. Our study provides novel insights into the role of Oligodendrocytes in AD and highlights the potential of PLXDC2 as a blood biomarker for non-invasive diagnosis and monitoring of AD patients.

Assessing polyomic risk to predict Alzheimer's disease using a machine learning model.

Alzheimer's disease (AD) is the most common form of dementia in the elderly. Given that AD neuropathology begins decades before symptoms, there is a dire need for effective screening tools for early detection of AD to facilitate early intervention. Here, we used tree-based and deep learning methods to train polyomic prediction models for AD affection status and age at onset, employing genomic, proteomic, metabolomic, and drug use data from UK Biobank. We used SHAP to determine the feature's importance. Our best-performing polyomic model achieved an area under the receiver operating characteristics curve (AUROC) of 0.87. We identified GFAP and CXCL17 proteins to be the strongest predictors of AD, besides apolipoprotein E (APOE) alleles. Increasing the number of cases by including "AD-by-proxy" cases did not improve AD prediction. Among the four modalities, genomics, and proteomics were the most informative modality based on AUROC (area under the receiver operating characteristic curve). Our data suggest that two blood-based biomarkers (glial fibrillary acidic protein [GFAP] and CXCL17) may be effective for early presymptomatic prediction of AD. We developed a polyomic model to predict AD and age-at-onset using omics and medication use data from EHR. We identified GFAP and CXCL17 proteins to be the strongest predictors of AD, besides APOE alleles. "AD-by-proxy" cases, if used in training, do not improve AD prediction. Proteomics was the most informative modality overall for affection status and AAO prediction.

Prediction and clustering of Alzheimer’s disease by race and sex: a multi-head deep-learning approach to analyze irregular and heterogeneous data

Early detection of Alzheimer’s disease (AD) is crucial to maximize clinical outcomes. Most disease progression analyses include people with diagnoses of cognitive impairment, limiting understanding of AD risk among those with normal cognition. The objective was to establish AD progression models through a deep learning approach to analyze heterogeneous, multi-modal datasets, including clustering analyses of population subsets. A multi-head deep-learning architecture was built to process and learn from biomedical and imaging data from the National Alzheimer’s Coordinating Center. Shapley additive explanation algorithms for feature importance ranking and pairwise correlation analysis were used to identify predictors of disease progression. Four primary disease progression clusters (slow, moderate and rapid converters or non-converters) were subdivided into groups by race and sex, yielding 16 sub-clusters of participants with distinct progression patterns. A multi-head and early-fusion convolutional neural network achieved the most competitive performance and demonstrated superiority over a single-head deep learning architecture and conventional tree-based machine-learning methods, with 97% test accuracy, 96% F1 score and 0.19 root mean square error. From 447 features, 2 sets of 100 predictors of disease progression were extracted. Feature importance ranking, correlation analysis and descriptive statistics further enriched cluster analysis and validation of the heterogeneity of risk factors.

Exploring EEG-Based Biomarkers for Improved Early Alzheimer's Disease Detection: A Feature-Based Approach Utilizing Machine Learning

This paper presents a comprehensive investigation into Electroencephalogram (EEG) signal processing and analysis techniques aimed at enhancing early diagnosis methods for Alzheimer's Disease (AD). Leveraging a dataset that has EEG data of individuals diagnosed with Mild Cognitive Impairment (MCI), AD, Healthy Controls, and the study explores Preprocessing Methods and Feature Extraction Techniques, with machine learning model notably Support Vector Machines (SVM).In the preprocessing phase, a combination of high pass, lowpass, Savitzky–Golay, and median filters are applied, informed by a comprehensive review of filter comparison literature. Feature extraction encompasses three primary categories: ‘Statistical, ‘Frequency Domain’ and ‘Time Domain’. The scope of this work is to explore features in all these three domains and build SVM based model for efficient classification. In our investigation, we achieved a categorization accuracy of 92% through the utilization of statistical features. Employing time domain features resulted in an accuracy of 87%, while frequency domain features also yielded an 87% accuracy rate in our study. The primary objective of this study is that it aims to enhance early AD diagnosis through advanced EEG signal processing and machine learning techniques, focusing on preprocessing methods, feature extraction, and classification accuracy.

Risk of Alzheimer’s disease and genetically predicted levels of 1400 plasma metabolites: a Mendelian randomization study

Alzheimer’s disease (AD) is a metabolic disorder. Discovering the metabolic products involved in the development of AD may help not only in the early detection and prevention of AD but also in understanding its pathogenesis and treatment. This study investigated the causal association between the latest large-scale plasma metabolites (1091 metabolites and 309 metabolite ratios) and AD. Through the application of Mendelian randomization analysis methods such as inverse-variance weighted (IVW), MR-Egger, and weighted median models, 66 metabolites and metabolite ratios were identified as potentially having a causal association with AD, with 13 showing significant causal associations. During the replication validation phase, six metabolites and metabolite ratios were confirmed for their roles in AD: N-lactoyl tyrosine, argininate, and the adenosine 5’-monophosphate to flavin adenine dinucleotide ratio were found to exhibit protective effects against AD. In contrast, ergothioneine, piperine, and 1,7-dimethyluric acid were identified as contributing to an increased risk of AD. Among them, argininate showed a significant effect against AD. Replication and sensitivity analyses confirmed the robustness of these findings. Metabolic pathway analysis linked “Vitamin B6 metabolism” to AD risk. No genetic correlations were found, but colocalization analysis indicated potential AD risk elevation through top SNPs in APOE and PSEN2 genes. This provides novel insights into AD’s etiology from a metabolomic viewpoint, suggesting both protective and risk metabolites.

A glycan biomarker predicts cognitive decline in amyloid- and tau-negative patients.

Early detection of Alzheimer's disease is vital for timely treatment. Existing biomarkers for Alzheimer's disease reflect amyloid- and tau-related pathology, but it is unknown whether the disease can be detected before cerebral amyloidosis is observed. N-glycosylation has been suggested as an upstream regulator of both amyloid and tau pathology, and levels of the N-glycan structure bisecting N-acetylglucosamine (GlcNAc) correlate with tau in blood and CSF already at pre-clinical stages of the disease. Therefore, we aimed to evaluate whether bisecting GlcNAc could predict future cognitive decline in patients from a memory clinic cohort, stratified by amyloid/tau status. We included 251 patients (mean age: 65.6 ± 10.6 years, 60.6% female) in the GEDOC cohort, from the Memory Clinic at Karolinska University Hospital, Stockholm, Sweden. Patients were classified as amyloid/tau positive or negative based on CSF biomarkers. Cognitive decline, measured by longitudinal Mini-Mental State Examination scores, was followed for an average of 10.7 ± 4.1 years and modelled using non-linear mixed effects models. Additionally, bisecting GlcNAc levels were measured in hippocampus and cortex with lectin-based immunohistochemistry in 10 Alzheimer's disease and control brains. We found that CSF bisecting GlcNAc levels were elevated in tau-positive individuals compared with tau-negative individuals, but not in amyloid-positive individuals compared with amyloid-negative individuals. In the whole sample, high levels of CSF bisecting GlcNAc predicted earlier cognitive decline. Strikingly, amyloid/tau stratification showed that high CSF bisecting GlcNAc levels predicted earlier cognitive decline in amyloid-negative patients (β = 2.53 ± 0.85 years, P = 0.003) and tau-negative patients (β = 2.43 ± 1.01 years, P = 0.017), but not in amyloid- or tau-positive patients. Finally, histochemical analysis of bisecting GlcNAc showed increased levels in neurons in hippocampus and cortex of Alzheimer's disease compared with control brain (fold change = 1.44-1.49, P < 0.001). In conclusion, high CSF levels of bisecting GlcNAc reflected neuronal pathology and predicted cognitive decline in amyloid- and tau-negative individuals, suggesting that abnormal glycosylation precedes cerebral amyloidosis and tau hyper-phosphorylation in Alzheimer's disease. Bisecting GlcNAc is a promising novel early biomarker for Alzheimer's disease.

Speech based detection of Alzheimer’s disease: a survey of AI techniques, datasets and challenges

Alzheimer’s disease (AD) is a growing global concern, exacerbated by an aging population and the high costs associated with traditional detection methods. Recent research has identified speech data as valuable clinical information for AD detection, given its association with the progressive degeneration of brain cells and subsequent impacts on memory, cognition, and language abilities. The ongoing demographic shift toward an aging global population underscores the critical need for affordable and easily available methods for early AD detection and intervention. To address this major challenge, substantial research has recently focused on investigating speech data, aiming to develop efficient and affordable diagnostic tools that align with the demands of our aging society. This paper presents an in-depth review of studies from 2018–2023 utilizing speech for AD detection. Following the PRISMA protocol and a two-stage selection process, we identified 85 publications for analysis. In contrast to previous literature reviews, this paper places a strong emphasis on conducting a rigorous comparative analysis of various Artificial Intelligence (AI) based techniques, categorizing them meticulously based on underlying algorithms. We perform an exhaustive evaluation of research papers leveraging common benchmark datasets, specifically ADReSS and ADReSSo, to assess their performance. In contrast to previous literature reviews, this work makes a significant contribution by overcoming the limitations posed by the absence of standardized tasks and commonly accepted benchmark datasets for comparing different studies. The analysis reveals the dominance of deep learning models, particularly those leveraging pre-trained models like BERT, in AD detection. The integration of acoustic and linguistic features often achieves accuracies above 85%. Despite these advancements, challenges persist in data scarcity, standardization, privacy, and model interpretability. Future directions include improving multilingual recognition, exploring emerging multimodal approaches, and enhancing ASR systems for AD patients. By identifying these key challenges and suggesting future research directions, our review serves as a valuable resource for advancing AD detection techniques and their practical implementation.

Blood-Based Biomarkers in Alzheimer's Disease: Advancing Non-Invasive Diagnostics and Prognostics.

Alzheimer's disease (AD), the most prevalent form of dementia, is expected to rise dramatically in incidence due to the global population aging. Traditional diagnostic approaches, such as cerebrospinal fluid analysis and positron emission tomography, are expensive and invasive, limiting their routine clinical use. Recent advances in blood-based biomarkers, including amyloid-beta, phosphorylated tau, and neurofilament light, offer promising non-invasive alternatives for early AD detection and disease monitoring. This review synthesizes current research on these blood-based biomarkers, highlighting their potential to track AD pathology and enhance diagnostic accuracy. Furthermore, this review uniquely integrates recent findings on protein-protein interaction networks and microRNA pathways, exploring novel combinations of proteomic, genomic, and epigenomic biomarkers that provide new insights into AD's molecular mechanisms. Additionally, we discuss the integration of these biomarkers with advanced neuroimaging techniques, emphasizing their potential to revolutionize AD diagnostics. Although large-scale validation is still needed, these biomarkers represent a critical advancement toward more accessible, cost-effective, and early diagnostic tools for AD.

Hybridization of Machine Learning Models for Alzheimers Disease Classification

Alzheimer's disease (AD), is a gradual cognitive decline and memory impairment. It is a major health concern worldwide. Despite intensive research efforts, accurate and early diagnosis remains difficult to achieve, largely due to the complexity of AD pathology and the absence of definitive biomarkers. Existing diagnostic approaches often rely on costly and invasive procedures, leading to delays in diagnosis and treatment initiation, and limiting the effectiveness of therapeutic interventions. To overcome these issues, this work suggests a novel approach for AD classification using EEG signals. EEG signals offer a non-invasive and cost-effective means of assessing brain activity, making them an attractive candidate for biomarker discovery and disease classification. The proposed work integrates preprocessing, feature extraction, and classification methodologies to accurately differentiate between AD, normal/healthy states, and Frontotemporal Dementia (FTD). The proposed solution begins with Sequential Savitzky-Golay filtering (SEQ-SG) to enhance the quality of EEG signals by reducing noise and enhancing relevant features. Subsequently, an Improved Principal Component Analysis (IPCA) approach is employed for feature extraction, incorporating feature scaling using StandardScaler to ensure uniform contribution from all features. Finally, classification is achieved using a hybrid approach named HMLCAD (Hybridization of Machine Learning for Classification of Alzheimer's Disease), which combines Random Forest and Gradient Boosting through a voting classifier ensemble. This methodology offers a promising framework for accurate and early detection of AD, enabling timely intervention and improved patient outcomes.

DEVELOPMENT AND DATA ANALYSIS OF A ROBO-PEN FOR ALZHEIMER’S DISEASE DIAGNOSIS: PRELIMINARY RESULTS

Alzheimer’s Disease (AD) poses a significant challenge in contemporary medicine, necessitating early and accurate diagnostic methods to manage its progression effectively. This study explores the development and application of the Robo-pen, an innovative diagnostic tool designed to detect early signs of cognitive decline through detailed handwriting analysis. The Robo-pen, equipped with an MPU-9250 sensor, captures three-dimensional coordinates, velocity, and acceleration of handwriting movements, crucial for assessing spatial control, movement consistency, speed variations, and the ability to modulate movement speed and force–parameters often disrupted in cognitive impairments like AD. Participants included 20 patients diagnosed with AD and 18 healthy controls, matched in age and educational levels. Data collection involved tasks such as sentence rewriting, figure redrawing, and digit rewriting, processed using CoolTerm software at a sampling rate of 18 Hz. Descriptive statistics revealed that the AD group exhibited lower mean values for gyroscope and acceleration data, indicating slower and less variable movements compared to the control group. T-tests confirmed significant differences (p &lt; 0.001) across all measured parameters between the AD and control groups. The results support the potential of the Robo-pen as a non-invasive, cost-effective diagnostic tool for early detection of AD. By capturing subtle neuromotor changes, the Robo-pen facilitates earlier diagnosis and timely intervention, potentially altering the disease trajectory and improving patient outcomes. This study marks a significant advancement in the early detection of AD, highlighting the Robo-pen’s promise as a transformative tool in neurodegenerative disease diagnosis and management.

Screening for early Alzheimer's disease: enhancing diagnosis with linguistic features and biomarkers.

Research has shown that speech analysis demonstrates sensitivity in detecting early Alzheimer's disease (AD), but the relation between linguistic features and cognitive tests or biomarkers remains unclear. This study aimed to investigate how linguistic features help identify cognitive impairments in patients in the early stages of AD. This study analyzed connected speech from 80 participants and categorized the participants into early-AD and normal control (NC) groups. The participants underwent amyloid-β positron emission tomography scans, brain magnetic resonance imaging, and comprehensive neuropsychological testing. Participants' speech data from a picture description task were examined. A total of 15 linguistic features were analyzed to classify groups and predict cognitive performance. We found notable linguistic differences between the early-AD and NC groups in lexical diversity, syntactic complexity, and language disfluency. Using machine learning classifiers (SVM, KNN, and RF), we achieved up to 88% accuracy in distinguishing early-AD patients from normal controls, with mean length of utterance (MLU) and long pauses ratio (LPR) serving as core linguistic indicators. Moreover, the integration of linguistic indicators with biomarkers significantly improved predictive accuracy for AD. Regression analysis also highlighted crucial linguistic features, such as MLU, LPR, Type-to-Token ratio (TTR), and passive construction ratio (PCR), which were sensitive to changes in cognitive function. Findings support the efficacy of linguistic analysis as a screening tool for the early detection of AD and the assessment of subtle cognitive decline. Integrating linguistic features with biomarkers significantly improved diagnostic accuracy.

Identifying Proteomic Prognostic Markers for Alzheimer's Disease with Survival Machine Learning: the Framingham Heart Study.

Protein abundance levels, sensitive to both physiological changes and external interventions, are useful for assessing the Alzheimer's disease (AD) risk and treatment efficacy. However, identifying proteomic prognostic markers for AD is challenging by their high dimensionality and inherent correlations. Our study analyzed 1128 plasma proteins, measured by the SOMAscan platform, from 858 participants 55 years and older (mean age 63 years, 52.9% women) of the Framingham Heart Study (FHS) Offspring cohort. We conducted regression analysis and machine learning models, including LASSO-based Cox proportional hazard regression model (LASSO) and generalized boosted regression model (GBM), to identify protein prognostic markers. These markers were used to construct a weighted proteomic composite score, the AD prediction performance of which was assessed using time-dependent area under the curve (AUC). The association between the composite score and memory domain was examined in 339 (of the 858) participants with available memory scores, and in an independent group of 430 participants younger than 55 years (mean age 46, 56.7% women). Over a mean follow-up of 20 years, 132 (15.4%) participants developed AD. After adjusting for baseline age, sex, education, and APOE ε4+ status, regression models identified 309 proteins (P ≤ 0.2). After applying machine learning methods, nine of these proteins were selected to develop a composite score. This score improved AD prediction beyond the factors of age, sex, education, and APOE ε4+ status across 15 to 25 years of follow-up, achieving its peak AUC of 0.84 in the LASSO model at the 22-year follow-up. It also showed a consistent negative association with memory scores in 339 participants (beta = -0.061, P = 0.046), 430 independent participants (beta = -0.060, P = 0.018), and the pooled 769 samples (beta = -0.058, P = 0.003). These findings highlight the utility of proteomic markers in improving AD prediction and emphasize the complex pathology of AD. The composite score may aid early AD detection and efficacy monitoring, warranting further validation in diverse populations.

Early detection of Alzheimer's disease using the MEMORIES mnemonic

AbstractThe rising incidence and death rates linked to Alzheimer's disease (AD) highlight an urgent issue. Genetic screening is celebrated as a significant advancement for its early detection capabilities, pinpointing those at risk before the emergence of symptoms. Yet, the limited availability of these technologies highlights a critical gap in widespread application. This review pivots to the potential of presymptomatic clinical assessments as a readily available, economical, and simple strategy for early detection. Traditionally, AD diagnosis relies on the late‐stage identification of cognitive deterioration, functional impairments, and neuropsychiatric symptoms, coinciding with advanced brain degeneration. Conversely, emerging research identifies early indicators preceding significant degeneration, manifesting years before clinical symptoms. We introduce a mnemonic, MEMORIES, to categorize these prodromal: Metabolism changes, Eye/visual impairments, March (refer to gait disturbances), Olfactory dysfunction, Rhythm (blood pressure and heart rate), Insensitivity of the tongue, Ears (hearing loss), and Stool alterations. Recognizing these prodromal through clinical examinations provides a valuable strategy for initiating preventative actions against brain degeneration. This approach advocates for broadening the screening lens beyond genetic screening to encompass clinical evaluations, enhancing early detection and intervention opportunities for AD.

Advanced interpretable diagnosis of Alzheimer's disease using SECNN-RF framework with explainable AI.

Early detection of Alzheimer's disease (AD) is vital for effective treatment, as interventions are most successful in the disease's early stages. Combining Magnetic Resonance Imaging (MRI) with artificial intelligence (AI) offers significant potential for enhancing AD diagnosis. However, traditional AI models often lack transparency in their decision-making processes. Explainable Artificial Intelligence (XAI) is an evolving field that aims to make AI decisions understandable to humans, providing transparency and insight into AI systems. This research introduces the Squeeze-and-Excitation Convolutional Neural Network with Random Forest (SECNN-RF) framework for early AD detection using MRI scans. The SECNN-RF integrates Squeeze-and-Excitation (SE) blocks into a Convolutional Neural Network (CNN) to focus on crucial features and uses Dropout layers to prevent overfitting. It then employs a Random Forest classifier to accurately categorize the extracted features. The SECNN-RF demonstrates high accuracy (99.89%) and offers an explainable analysis, enhancing the model's interpretability. Further exploration of the SECNN framework involved substituting the Random Forest classifier with other machine learning algorithms like Decision Tree, XGBoost, Support Vector Machine, and Gradient Boosting. While all these classifiers improved model performance, Random Forest achieved the highest accuracy, followed closely by XGBoost, Gradient Boosting, Support Vector Machine, and Decision Tree which achieved lower accuracy.

Resting-State fMRI and Machine Learning as Diagnostic Tools for Alzheimer's Disease

: Alzheimer's disease (AD) presents a significant challenge in healthcare, necessitating accurate and timely diagnosis for effective management. Resting-state functional magnetic resonance imaging (Rs-fMRI) has emerged as a valuable tool for understanding neural correlates and the early detection of AD. This article reviews recent advancements in utilizing Rs-fMRI in combination with machine learning (ML) techniques for early AD diagnosis. First, we discuss the underlying principles of Rs-fMRI, highlighting its ability to detect alterations in brain functional connectivity (FC) patterns associated with AD. We then explore the potential of ML algorithms, particularly support vector machines (SVMs), in analyzing Rs-fMRI data and discriminating between AD patients and healthy controls. We indicate the challenges and opportunities in integrating Rs-fMRI and ML, such as in data preprocessing, feature selection, and model interpretation. We also address the importance of large-scale, multi-site studies to validate the robustness and generalizability of the proposed approaches. Overall, the integration of Rs-fMRI and ML holds great promise as a non-invasive, objective, and sensitive diagnostic tool for AD, potentially enabling early detection and personalized treatment strategies. However, further studies are warranted to optimize methodologies, enhance interpretability, and facilitate clinical translation.

Early Alzheimer's Disease Detection: A Review of Machine Learning Techniques for Forecasting Transition from Mild Cognitive Impairment.

Alzheimer's disease is a weakening neurodegenerative condition with profound cognitive implications, making early and accurate detection crucial for effective treatment. In recent years, machine learning, particularly deep learning, has shown significant promise in detecting mild cognitive impairment to Alzheimer's disease conversion. This review synthesizes research on machine learning approaches for predicting conversion from mild cognitive impairment to Alzheimer's disease dementia using magnetic resonance imaging, positron emission tomography, and other biomarkers. Various techniques used in literature such as machine learning, deep learning, and transfer learning were examined in this study. Additionally, data modalities and feature extraction methods analyzed by different researchers are discussed. This review provides a comprehensive overview of the current state of research in Alzheimer's disease detection and highlights future research directions.

Retina as a potential biomarker for the early stage of Alzheimer's disease spectrum.

To characterize the retinal microvasculature and structure in subjective cognitive decline (SCD) and identify the potential biomarker for the early stage of the Alzheimer's disease (AD) spectrum. In this study, 35 patients with SCD, 36 with cognitive impairment, and 29 with normal cognition (NC) were enrolled. Optical coherence tomography angiography was employed to assess retinal vascular density, fovea avascular zone area, and retinal thickness. The parameters reflecting retinal perfusion and structure were compared among the three groups. In addition, the association between retinal parameters, cerebral blood flow (CBF), and peripheral blood biomarkers in the SCD stage was analyzed. The superficial vascular complex (SVC) vascular density in the macula and retinal nerve fiber layer thickness in the peripapillary were significantly reduced in individuals with SCD compared to NC. Furthermore, there was a positive correlation between macular ganglion cell complex thickness and CBF in SCD. The retinal microvasculature and structure exhibit alterations in individuals with SCD. Macular ganglion cell complex thickness demonstrates correlations with cerebral perfusion. The retina holds potential as a novel biomarker for early detection of AD.

Human Leukocyte Antigen and microRNAs as Key Orchestrators of Mild Cognitive Impairment and Alzheimer's Disease: A Systematic Review.

The expression of inflamma-miRs and human leukocyte antigen (HLA) haplotypes could indicate mild cognitive impairment (MCI) and Alzheimer's disease (AD). We used international databases to conduct a systematic review of studies on HLA variants and a meta-analysis of research on microRNAs (miRNAs). We aimed to analyze the discriminative value of HLA variants and miRNAs in MCI, AD and controls to evaluate the protective or causative effect of HLA in cognitive decline, establish the role of miRNAs as biomarkers for the early detection of AD, and find a possible link between miRNAs and HLA. Statistical analysis was conducted using Comprehensive Meta-analysis software, version 2.2.050 (Biostat Inc., Englewood, NJ, USA). The effect sizes were estimated by the logarithm base 2 of the fold change. The systematic review revealed that some HLA variants, such as HLA-B*4402, HLA-A*33:01, HLA-A*33:01, HLA-DPB1, HLA-DR15, HLA-DQB1*03:03, HLA-DQB1*06:01, HLA-DQB1*03:01, SNPs on HLA-DRB1/DQB1, and HLA-DQA1, predisposed to cognitive decline before the occurrence of AD, while HLA-A1*01, HLA-DRB1∗13:02, HLA-DRB1*04:04, and HLA-DRB1*04:01 demonstrated a protective role. The meta-analysis identified let-7 and miR-15/16 as biomarkers for the early detection of AD. The association between these two miRNA families and the HLA variants that predispose to AD could be used for the early screening and prevention of MCI.

Prediction of Alzheimer’s disease with MRI image segmentation and multi-layered dynamic feed-forward neural network

Alzheimer's disease (AD) is a kind of dementia characterized by memory loss in early stages. Damage to the nerve in the brain responsible for learning, reasoning, and remembering (a cognitive function) causes these symptoms. Treatment and patient care are most successful when they follow a timely and exact diagnosis. In this research, a Multi-Layered Dynamic- Feed Forward Neural Network (MLD-FFNN) method is used to analyse Magnetic resonance imaging (MRI) images to make accurate AD predictions. The approach has three essential components: image preprocessing, image segmentation, and classification. MRI datasets are used in the study to categorize subjects as non-demented, very mildly demented, mildly demented, or moderately demented. The initial step, data preprocessing, improves the quality of MRI images. Histogram equalization was designated as one of the methods discussed. Images' contrast may be improved with the use of technique called histogram equalization. Segmentation of images follows after preprocessing. Here, MRI images must be segmented into informative sections. The Gaussian filter (GF) method is used for image segmentation. When smoothing or filtering images, a method is used. Finally, a MLD-FFNN method is utilized for categorization. The segmented MRI scans are presumably the input for this neural network, which is trained to predict whether or not a patient has AD. This research shows effectiveness for early detection and treatment of AD because of its potential for precise prediction. Based on experimental results, this investigation achieved higher accuracy than other methods. The automated AD classification has the potential to serve as a useful aid for medical professionals in making diagnoses of AD and determining the best course of therapy.

MULTIMEDIA CONTENT ANALYSIS FOR ALZHEIMER’S DISEASE DIAGNOSIS USING MRI SCANS AND DEEP LEARNING

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss, with early diagnosis being crucial for effective intervention. Magnetic Resonance Imaging (MRI) is a valuable tool for detecting structural brain changes associated with AD. However, accurate and automated analysis of MRI scans remains a challenge due to the complexity and variability in brain structures. Traditional methods for analyzing MRI scans for AD diagnosis often rely on manual interpretation or basic image processing techniques, which can be time-consuming and prone to variability. There is a need for advanced automated methods that can accurately segment brain structures and extract relevant features for reliable diagnosis. This study proposes a novel approach for AD diagnosis using MRI scans, combining Conditional Attention U-Net for segmentation and Ant Colony Optimization (ACO) for feature extraction. The Conditional Attention U-Net enhances segmentation accuracy by incorporating conditional attention mechanisms to focus on relevant features while minimizing background noise. ACO is employed to optimize feature extraction by simulating the foraging behavior of ants, which efficiently selects and refines key features related to AD. The proposed model was evaluated on a dataset of 500 MRI scans, comparing performance with traditional methods using metrics such as Dice Similarity Coefficient (DSC) and classification accuracy. The Conditional Attention U-Net achieved an average DSC of 0.89 for segmentation of key brain regions, outperforming conventional methods by 10%. The ACO-enhanced feature extraction resulted in a classification accuracy of 92% for AD diagnosis, representing a 7% improvement over baseline methods. The combination of these techniques demonstrated a significant enhancement in both segmentation precision and diagnostic accuracy, showcasing the effectiveness of the proposed approach for early AD detection.