Alzheimer's Disease

Fluorescence from dual molecular rotors allows sensitive detection of widespread brain pathology in a mouse model of Alzheimer's disease.

Amyloid fibrils formed by the misfolding and aggregation of proteins are a pathological hallmark of many neurodegenerative conditions including Alzheimer's disease (AD). Although recent studies have shown that pre-fibrillar species including low molecular-weight oligomers are more toxic in vitro than mature fibrils, and correlate better with cognitive decline in AD patients, techniques to study th...

Classification of Alzheimer's Disease by Modeling Brain Networks as Signed Networks under Deep Learning Frameworks.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that remains a global challenge due to its complex pathology and the lack of definitive diagnostic tools. This paper introduces an innovative approach to predicting and analyzing Alzheimer's disease by constructing signed brain network models and leveraging signed graph neural network technologies. By modeling the brain network a...

Vascular cognitive impairment and dementia: Prevention, treatments, mechanisms and management options for the future.

Vascular Cognitive Impairment and Dementia (VCID), the second most common form of dementia, is becoming increasingly prevalent worldwide. However, cur...

Circulating inflammatory proteins predict dementia risk, are linked to structural brain changes and modifiable risk factors.

INTRODUCTION: Systemic inflammation has been identified as a key factor in neurodegeneration but the value of circulating inflammatory proteins in dem...

Multi-omics Mendelian randomization and machine learning identify candidate therapeutic targets for Alzheimer's and Parkinson's diseases.

Neurodegenerative diseases (NDDs), including Alzheimer's disease (AD) and Parkinson's disease (PD), are major public health challenges lacking effecti...

An attention-augmented multimodal classification of alzheimer's disease and parkinson's disease vs healthy controls using MRI, EEG, and SNP data.

Due to the late manifestation of structural symptoms and symptomatic overlap, neurodegenerative diseases such as Parkinson's Disease (PD) and Alzheime...

Sex Differences in Alzheimer's Disease: A Systematic Review of Two Decades of Neuroimaging Research.

OBJECTIVES: Given the heterogeneous nature of Alzheimer's Disease (AD) and its higher prevalence in females, it is crucial to understand sex-related d...

Core Elements of Goals of Care Initiatives Across Eight Health Systems.

For patients with serious illnesses, goals of care conversations improve quality of life and patient and family satisfaction and may reduce healthcare...

Alzheimer's disease prediction via an explainable CNN using genetic algorithm and SHAP values.

Convolutional neural networks (CNNs) are widely recognized for their high precision in image classification. Nevertheless, the lack of transparency in...

Stratifying amyloid burden in early Alzheimer's disease using cascaded attention-guided vision transformer using [¹⁸F]Florbetapir PET.

OBJECTIVES: This study aims to develop a deep learning model to assist physicians in accurately classifying negative, equivocal, and positive β-amyloi...

Utilisation of Machine Learning Approaches Improves RNA-Seq Transcriptome Analyses in Alzheimer's Disease Brain.

UNLABELLED: Alzheimer’s disease (AD) is a neurodegenerative disorder that progressively deteriorates a person’s memory, as well as their ability to th...

Mild Cognitive Impairment Detection System Based on Unstructured Spontaneous Speech: Longitudinal Dual-Modal Framework.

BACKGROUND: In recent years, the incidence of cognitive diseases has also risen with the significant increase in population aging. Among these disease...

Co-occurrence patterns and related risk factors of dementia and Parkinson's disease among older adults across 204 countries and territories: a spatial correspondence and systematic analysis.

BACKGROUND: Dementia and Parkinson's disease (PD) are among the most prevalent neurological disorders globally. Most previous research has focused on ...

Investigating the molecular mechanisms of glutamine metabolism and mitochondria-related biomarkers in Alzheimer's disease through transcriptomics and experimental validation.

BACKGROUND: Alzheimer's disease (AD) is a prevalent neurodegenerative disorder. This study aims to identify biomarkers associated with glutamine metab...

Digital markers and phenotypes of rest-activity rhythms in people with advanced dementia using real-time location data.

BACKGROUND: Disrupted sleep and circadian rhythms in dementia affect rest-activity patterns and impact quality of life, safety, and caregiver burden. ...

The Role of AI in Improving Digital Wellness Among Older Adults: Comparative Bibliometric Analysis.

BACKGROUND: Advances in artificial intelligence (AI) have revolutionized digital wellness by providing innovative solutions for health, social connect...

Association between cognitive status and structural brain changes in Alzheimer's disease: Clinical implication of lightweight deep learning-aided diagnosis.

BACKGROUND: The complex brain changes involved in Alzheimer's disease (AD) development constitute a high-dimensional nonlinear feature space where dee...

Identifying endoplasmic reticulum stress-related key genes of vascular dementia using bioinformatics and biological experiments.

Vascular dementia (VaD) is the second most common type of dementia, yet its pathogenesis is not fully understood, and effective diagnostic and therape...

A causal bidirectional selective state space model for imaging genetics in neurodegenerative diseases.

Brain imaging genetics aims to uncover the pathological mechanisms and improve the diagnosis of brain diseases, particularly neurodegenerative disorde...

An Ensemble Learning Artificial Intelligence Model for Alzheimer's Disease Detection Using OCT.

PURPOSE: There has been significant progress in detecting Alzheimer's disease (AD) using retinal imaging. We developed an ensemble learning-based deep...