Alzheimer's Disease

Early Dementia Diagnosis in Older Adults through Machine Learning: A Cross-Sectional fMRI Data Analysis

Background: Early diagnosis of dementia can significantly improve care planning and patient outcomes while delaying progression. Machine learning algorithms can identify patterns in clinical and neuroimaging data that may aid in the early detection of dementia risk factors. Objective: To evaluate the performance of the ensemble machine learning pipeline for classifying dementia status utilizing de...

A retrieval-augmented generation large language model framework for accurate dementia identification from electronic health records

Objective Accurate and scalable disease phenotyping from electronic health records (EHRs) is foundational for predictive modeling and precision medicine. Traditional rule- and keyword-based approaches are limited by inconsistent documentation and inability to capture clinical nuance. We aim to evaluate whether large language models (LLMs) can overcome these limitations to improve dementia phenotyp...

Biophysically realistic network-level transport model of tau progression with exosome-mediated release and uptake processes

The spatiotemporal progression of tau aggregates in neurodegenerative diseases like Alzheimer's follows the brain's structural connectome, yet a profo...

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 monitoring of Alzheimer's disease (AD). Structural MR...

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. While previous studies have explored speech-based bio...

Anatomically Guided Latent Diffusion for Brain MRI Progression Modeling

Accurately modeling longitudinal brain MRI progression is crucial for understanding neurodegenerative diseases and predicting individualized structura...

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 timely clinical intervention and improved patient o...

ComPRePS: Unlocking Scalable AI Analysis for Computational Renal Pathology

Digital pathology using whole slide imaging (WSI) and artificial intelligence (AI) has the potential to transform diagnostic workflows, but adoption r...

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 Dementia (FTD) is essential for reducing the risk of...

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 approach to map the anatomic features of brain senescenc...

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 neurodegenerative diseases (NDs) worldwide, for which e...

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 process is subject to large inter-individual variability...

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 Alzheimers disease (AD) classification using binary b...

Histological aging signatures enable tissue-specific disease prediction from blood

Aging, the leading risk factor for numerous diseases, manifests through diverse structural and architectural changes in human tissues, providing an op...

NEuRT: A Transformer-Based Model for Explainable Neuronal Activity Analysis

The study of neuronal activity is essential for understanding brain function and its alterations in neurodegenerative diseases. Advances in in vivo im...

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. Neuroimaging genetics offers a method for identifying diseas...

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 deficits, these two disorders show partially over...

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 driving videos and large vision models. In recent tim...

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 absence of effective treatment options, early diagn...

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 millions of people worldwide. Early detection of AD...