Alzheimer's Disease

A Cautionary Tale of Self-Supervised Learning for Imaging Biomarkers: Alzheimer's Disease Case Study

Discovery of sensitive and biologically grounded biomarkers is essential for early detection and mon...

Integrating Acoustic, Prosodic, and Phonological Features for Automatic Alzheimer's Detection

Early and accurate diagnosis of Alzheimer's Disease (AD) is critical for effective intervention. Whi...

Anatomically Guided Latent Diffusion for Brain MRI Progression Modeling

Accurately modeling longitudinal brain MRI progression is crucial for understanding neurodegenerativ...

A Computer Vision Hybrid Approach: CNN and Transformer Models for Accurate Alzheimer's Detection from Brain MRI Scans

Early and accurate classification of Alzheimers disease (AD) from brain MRI scans is essential for t...

ComPRePS: Unlocking Scalable AI Analysis for Computational Renal Pathology

Digital pathology using whole slide imaging (WSI) and artificial intelligence (AI) has the potential...

ARMARecon: An ARMA Convolutional Filter based Graph Neural Network for Neurodegenerative Dementias Classification

Early detection of neurodegenerative diseases such as Alzheimer's Disease (AD) and Frontotemporal De...

Graph Neural Network Reveals the Local Cortical Morphology of Brain Aging in Normal Cognition and Alzheimers Disease

Estimating brain age (BA) from T1-weighted magnetic resonance images (MRIs) provides a useful approa...

DW-DGAT: Dynamically Weighted Dual Graph Attention Network for Neurodegenerative Disease Diagnosis

Parkinson's disease (PD) and Alzheimer's disease (AD) are the two most prevalent and incurable neuro...

Predicting Continuous Cognitive Decline: The Generalizability of a Multimodal Machine Learning Approach Including Structural MRI and Non-Brain Data

Aging is often accompanied by cognitive decline, but the extent, timing, and severity of this proces...

ATN Classification and Machine-Learned Plasma Biomarker Phenotypes Reveal Distinct Alzheimer's Pathology in a Population-Based Cohort

BackgroundThe ATN (Amyloid/Tau/Neurodegeneration) framework provides a theory-driven approach to Alz...

Multimodal attention fusion deep self-reconstruction presentation model for Alzheimer's disease diagnosis and biomarker identification.

The unknown pathogenic mechanisms of Alzheimer's disease (AD) make treatment challenging. Neuroimagi...

Distinct neurodynamics of functional brain networks in Alzheimer's disease and frontotemporal dementia as revealed by EEG

Objective While Alzheimer's disease (AD) and frontotemporal dementia (FTD) show some common memory...

Driving as a Diagnostic Tool: Scenario-based Cognitive Assessment in Older Drivers From Driving Video

We introduce scenario-based cognitive status identification in older drivers from Naturalistic dri...

An Explainable Transformer Model for Alzheimer's Disease Detection Using Retinal Imaging

Alzheimer's disease (AD) is a neurodegenerative disorder that affects millions worldwide. In the a...

Transformer Model for Alzheimer's Disease Progression Prediction Using Longitudinal Visit Sequences

Alzheimer's disease (AD) is a neurodegenerative disorder with no known cure that affects tens of m...

Robust brain age estimation from structural MRI with contrastive learning

Estimating brain age from structural MRI has emerged as a powerful tool for characterizing normati...

Calibrated Self-supervised Vision Transformers Improve Intracranial Arterial Calcification Segmentation from Clinical CT Head Scans

Vision Transformers (ViTs) have gained significant popularity in the natural image domain but have...

Beyond Black-Box AI: Interpretable Hybrid Systems for Dementia Care

The recent boom of large language models (LLMs) has re-ignited the hope that artificial intelligen...

Differential dementia detection from multimodal brain images in a real-world dataset.

INTRODUCTION: Artificial intelligence (AI) models have been applied to differential dementia detecti...

Correction to "Machine learning prediction of tau-PET in Alzheimer's disease using plasma, MRI, and clinical data".

Non-traditional socio-environmental and geospatial determinants of Alzheimer's disease-related dementia mortality.

IMPORTANCE: Recent data point to the impact of non-traditional environmental and social factors on A...