Dementia

Paper Title: LoV3D: Grounding Cognitive Prognosis Reasoning in Longitudinal 3D Brain MRI via Regional Volume Assessments

Longitudinal brain MRI is essential for characterizing the progression of neurological diseases such as Alzheimer's disease assessment. However, current deep-learning tools fragment this process: classifiers reduce a scan to a label, volumetric pipelines produce uninterpreted measurements, and vision-language models (VLMs) may generate fluent but potentially hallucinated conclusions. We present Lo...

From General-Purpose to Disease-Specific Features: Aligning LLM Embeddings on a Disease-Specific Biomedical Knowledge Graph for Drug Repurposing

Identifying new therapeutic uses for existing drugs is a major challenge in biomedicine, especially for complex neurodegenerative conditions such as Alzheimer disease and related dementias (ADRD), where treatment options remain limited and relevant data are often sparse, heterogeneous, and difficult to integrate. Although general-purpose Large Language Model (LLM) embeddings encode rich semantic i...

Characterizing EEG Spectro-Temporal Variability Signatures in Alzheimer's and Parkinson's Disease

We present an EEG-based approach to characterize disease-related spectro-temporal signatures in Alzheimer's disease (AD) and Parkinson's disease (PD)....

An Integrated Molecular Atlas of Alzheimer's Disease

Alzheimer's disease (AD) is a complex neurodegenerative disorder with multifactorial etiology and widespread molecular manifestations. Investigating m...

Evaluating a Locally Deployed 20-Billion Parameter Large Language Model for Automated Abstract Screening in Systematic Reviews

Abstract Background: Systematic reviews (SRs) are essential for evidence-based medicine but require extensive time and resources for abstract screenin...

Streamlining Eligibility Assessment for Alzheimers Disease-Modifying Therapies: Prediction of MMSE Scores Using the Digital Clock and Recall

Introduction: The eligibility of anti-amyloid disease-modifying therapies (DMTs) and their integration into clinical practice in some institutions req...

Longitudinal Impact of NLP-Augmented Clinical Decision Support on Cognitive Decline Detection in German Geriatric Primary Care: A Dynamic Panel Data Analysis Using System GMM Estimation

Background: Cognitive decline and dementia represent major public health challenges in aging populations. Natural language processing (NLP)-augmented ...

Multi-Omics Integration of Transcriptomics and Metabolomics with Machine Learning Uncovers Novel Risk Factors for Alzheimer's disease

Background: Alzheimer's disease (AD) is a neurodegenerative disorder characterized by cognitive decline, memory impairment, and functional deteriorati...

MIRAGE: Knowledge Graph-Guided Cross-Cohort MRI Synthesis for Alzheimer's Disease Prediction

Reliable Alzheimer's disease (AD) diagnosis increasingly relies on multimodal assessments combining structural Magnetic Resonance Imaging (MRI) and El...

The Combinatorial Capacity and Robustness of Hierarchical Concept Coding in the Human Medial Temporal Lobe

The human brain encodes a virtually infinite repertoire of semantic concepts using a finite number of neurons, a feat that defies the capacity limits ...

Align-cDAE: Alzheimer's Disease Progression Modeling with Attention-Aligned Conditional Diffusion Auto-Encoder

Generative AI framework-based modeling and prediction of longitudinal human brain images offer an efficient mechanism to track neurodegenerative progr...

MINT: Multimodal Imaging-to-Speech Knowledge Transfer for Early Alzheimer's Screening

Alzheimer's disease is a progressive neurodegenerative disorder in which mild cognitive impairment (MCI) marks a critical transition between aging and...

Disease Progression and Subtype Modeling for Combined Discrete and Continuous Input Data

Disease progression modeling provides a robust framework to identify long-term disease trajectories from short-term biomarker data. It is a valuable t...

EMAD: Evidence-Centric Grounded Multimodal Diagnosis for Alzheimer's Disease

Deep learning models for medical image analysis often act as black boxes, seldom aligning with clinical guidelines or explicitly linking decisions to ...

Balanced deep learning on multi-omics networks identifies molecular subgroups of pathological brain aging

Abstract Background Neurodegenerative diseases, including Alzheimer's disease (AD), exhibit substantial clinical and molecular heterogeneity, complica...

Probability-Invariant Random Walk Learning on Gyral Folding-Based Cortical Similarity Networks for Alzheimer's and Lewy Body Dementia Diagnosis

Alzheimer's disease (AD) and Lewy body dementia (LBD) present overlapping clinical features yet require distinct diagnostic strategies. While neuroima...

HyPCA-Net: Advancing Multimodal Fusion in Medical Image Analysis

Multimodal fusion frameworks, which integrate diverse medical imaging modalities (e.g., MRI, CT), have shown great potential in applications such as s...

MRC-GAT: A Meta-Relational Copula-Based Graph Attention Network for Interpretable Multimodal Alzheimer's Disease Diagnosis

Alzheimer's disease (AD) is a progressive neurodegenerative condition necessitating early and precise diagnosis to provide prompt clinical management....

Discovery of TDP-43 aggregation inhibitors via a hybrid machine learning framework

TAR DNA-binding protein 43 (TDP-43) aggregation is a hallmark of several neurodegenerative diseases, including amyotrophic lateral sclerosis and front...

Calmodulin controls spatial and temporal specificity of calcium-induced calcium release

Calcium dynamics controls learning and memory. Changes in calcium-induced calcium release (CICR), which is caused by opening ryanodine receptors (RyR)...