AIMC Topic: Alzheimer Disease
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Multimodal attention fusion deep self-reconstruction presentation model for Alzheimer's disease diagnosis and biomarker identification.
Artificial cells, nanomedicine, and biotechnology
Dec 1, 2025
The unknown pathogenic mechanisms of Alzheimer's disease (AD) make treatment challenging. Neuroimaging genetics offers a method for identifying disease biomarkers for early diagnosis, but traditional approaches struggle with complex non-linear, multi...
Transformer attention-based neural network for cognitive score estimation from sMRI data.
Computers in biology and medicine
Sep 1, 2025
Accurately predicting cognitive scores based on structural MRI holds significant clinical value for understanding the pathological stages of dementia and forecasting Alzheimer's disease (AD). Some existing deep learning methods often depend on anatom...
BrainAGE latent representation clustering is associated with longitudinal disease progression in early-onset Alzheimer's disease.
Journal of neuroradiology = Journal de neuroradiologie
Sep 1, 2025
INTRODUCTION: Early-onset Alzheimer's disease (EOAD) population is a clinically, genetically and pathologically heterogeneous condition. Identifying biomarkers related to disease progression is crucial for advancing clinical trials and improving ther...
Emerging blood biomarkers in Alzheimer's disease: a proteomic perspective.
Clinica chimica acta; international journal of clinical chemistry
Aug 15, 2025
Early detection of Alzheimer's disease (AD) remains a formidable clinical challenge, but emerging blood-based assays show promise for identifying at-risk individuals long before cognitive symptoms arise. This is the first comprehensive synthesis comp...
Clinical Trial Eligibility Criteria Decomposition and Parsing with Large Language Models.
Studies in health technology and informatics
Aug 7, 2025
Clinical trial eligibility criteria, often presented as complex free text, pose significant challenges for automated processing. This study introduces a Decomposition and Parsing (DP) workflow to address these challenges by systematically breaking do...
Brain Age Prediction: Deep Models Need a Hand to Generalize.
Human brain mapping
Aug 1, 2025
Predicting brain age from T1-weighted MRI is a promising marker for understanding brain aging and its associated conditions. While deep learning models have shown success in reducing the mean absolute error (MAE) of predicted brain age, concerns abou...
Multi-Target Drug Design in Alzheimer's Disease Treatment: Emerging Technologies, Advantages, Challenges, and Limitations.
Pharmacology research & perspectives
Aug 1, 2025
Alzheimer's disease (AD) is a complex and multifactorial neurodegenerative disorder, recognized as the most prevalent form of dementia. It is characterized by multiple pathological processes, including amyloid-beta accumulation, neurofibrillary tangl...
Selection, visualization, and explanation of deep features from resting-state fMRI for Alzheimer's disease diagnosis.
Psychiatry research. Neuroimaging
Aug 1, 2025
Despite the remarkable achievements of deep learning networks in analyzing neuroimaging data for various tasks linked to brain functions and disorders, the opaque nature of these models and their interpretability challenges pose significant barriers ...
Predicting metabolite-disease associations based on dynamic adaptive feature learning architecture.
Computer methods and programs in biomedicine
Aug 1, 2025
BACKGROUND AND OBJECTIVE: In recent years, the association between metabolites and complex human diseases has increasingly been recognized as a major research focus. Traditional wet-lab experiments are considered time-consuming and labor-intensive, w...
Brain age prediction from MRI scans in neurodegenerative diseases.
Current opinion in neurology
Aug 1, 2025
PURPOSE OF REVIEW: This review explores the use of brain age estimation from MRI scans as a biomarker of brain health. With disorders like Alzheimer's and Parkinson's increasing globally, there is an urgent need for early detection tools that can ide...
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