Latest AI and machine learning research in alzheimer's disease for healthcare professionals.
Integrating resting-state functional magnetic resonance imaging (rs-fMRI) and phenotypic data is a promising way to build a comprehensive population graph for the prediction of brain disorders using graph neural networks (GNNs). However, existing GNN-based methods face two limitations: the complexity of relationships between subjects poses challenges in constructing a well-defined population graph...
BACKGROUND: Artificial intelligence (AI) is increasingly applied to health care, yet concerns about fairness persist, particularly in relation to sociodemographic disparities. Previous studies suggest that socioeconomic status (SES) and sex may influence AI model performance, potentially affecting groups that are historically underserved or understudied. OBJECTIVE: This study aimed to (1) assess a...
AIM: APOE genotype may affect statin therapy response. We conducted a meta-analysis to update and quantify this association across various outcomes. M...
Pathological and neuroimaging changes in the cerebellum of Alzheimer's disease (AD) patients have been well documented. However, the changes in cerebe...
Single-cell and single-nucleus RNA sequencing are used to reveal heterogeneity in cells, showing a growing potential for precision and personalized me...
Alzheimer's disease (AD) classification using machine learning has increasingly relied on multimodal inputs such as Magnetic Resonance Imaging (MRI), ...
Neurodegenerative diseases, like Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), amyotrophic lateral sclerosis (ALS), a...
Deep learning models leveraging human activity data, such as gait, have shown promise for dementia prediction. However, their limited interpretability...
The detection of Alzheimer's Disease (AD) using structural Magnetic Resonance Imaging (MRI) and Machine Learning (ML) often focuses on late-stage atro...
Alzheimer's disease (AD) has a strong genetic predisposition. Genome-wide association studies have identified multiple risk loci, yet many non-coding ...
BACKGROUND: Plasma biomarkers have emerged as robust indicators of Alzheimer's disease (AD) pathology, offering accessible tools for staging and strat...
BACKGROUND: Epigenetic modifications play a vital role in the pathogenesis of human diseases, particularly neurodegenerative disorders such as Alzheim...
Alzheimer's disease (AD) is the most common type of dementia, accounting for at least two-thirds of dementia cases in people aged 65 and older. Numero...
Oxidative stress is a central pathogenic process in the earliest stages of Alzheimer's disease (AD), promoting non-enzymatic protein modifications tha...
BACKGROUND: Prolonged Grief Disorder (PGD) in later life may involve volumetric patterns indicative of accelerated brain aging. This study examined wh...
Alzheimer's disease and related dementias (ADRD) remain underdiagnosed early due to reliance on costly, invasive, and time-intensive assessments, prom...
BACKGROUND AND OBJECTIVES: CONFIDENCE is a culturally tailored intervention to reduce caregiver financial strain, which disproportionately impacts Lat...
BACKGROUND: Psychological distress, particularly symptoms of depression and anxiety (D&A), is highly prevalent among family caregivers of individuals ...
Differentiating between Alzheimer's disease (AD), frontotemporal dementia (FTD), and cognitively normal (CN) subjects remains a significant challenge ...
The genetic mechanisms of ~90% of Alzheimer's disease (AD)-associated variants residing in noncoding DNA remain poorly understood. To address this, we...