Dementia

miRNA-mRNA Interaction Network Analysis in Alzheimer's Disease for Biomarker Discovery

Alzheimer's disease (AD) is a complex neurodegenerative disorder characterized by widespread dysregulation of gene expression and regulatory pathways. MicroRNAs (miRNAs) act as key post-transcriptional regulators by modulating messenger RNAs (mRNAs), and their disruption can influence synaptic function, neuroinflammation, and neuronal survival. In this study, we present an integrative transcriptom...

Multimodal Visual Surrogate Compression for Alzheimer's Disease Classification

High-dimensional structural MRI (sMRI) images are widely used for Alzheimer's Disease (AD) diagnosis. Most existing methods for sMRI representation learning rely on 3D architectures (e.g., 3D CNNs), slice-wise feature extraction with late aggregation, or apply training-free feature extractions using 2D foundation models (e.g., DINO). However, these three paradigms suffer from high computational co...

Feature Integration of FDG PET Brain Imaging Using Deep Learning for Sensitive Cognitive Decline Detection

Background Distinguishing individuals with cognitive decline (CD), including early Alzheimers disease, from cognitively normal (CN) individuals is ess...

An explainable framework for the relationship between dementia and glucose metabolism patterns

High-dimensional neuroimaging data presents challenges for assessing neurodegenerative diseases due to complex non-linear relationships. Variational A...

Cortex-Grounded Diffusion Models for Brain Image Generation

Synthetic neuroimaging data can mitigate critical limitations of real-world datasets, including the scarcity of rare phenotypes, domain shifts across ...

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 algo...

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 medici...

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...

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...

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...

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...

Artificial intelligence models using F-wave responses predict amyotrophic lateral sclerosis.

Nerve conduction F-wave studies contain crucial information about subclinical motor dysfunction that can be used to diagnose patients with amyotrophic...

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...