Alzheimer's Disease

Expert Evaluation of the Perceived Accuracy, Relevance, and Safety of Large Language Model-Generated Patient Information in Geriatrics: Cross-Condition Study.

BACKGROUND: Large language models (LLMs) are increasingly used to generate patient-oriented medical information. In geriatrics, such information must balance accuracy, relevance, and safety, as older adults may be particularly susceptible to misleading or harmful advice. However, systematic evaluations of expert perceptions across multiple geriatric conditions remain limited. OBJECTIVE: This study...

Learning patient-specific spatial biomarker dynamics via operator learning for Alzheimer's disease progression.

Alzheimer's disease (AD) is a complex, multifactorial neurodegenerative disorder with substantial heterogeneity in progression and treatment response. Despite recent therapeutic advances, predictive models capable of accurately forecasting individualized future biomarker states remain limited. Here, we present a machine learning-based operator learning framework for personalized modeling of AD pro...

Reducing annotation burden in medical imaging with ADGNET: A semi-supervised deep learning strategy.

We propose ADGNET, a semi-supervised framework for Alzheimer's disease (AD) diagnosis that jointly optimizes image reconstruction and classification t...

Disentangling amyloid polymorphs in normal aging and Alzheimer's disease using dual-probe spectral imaging.

Variability in Alzheimer's disease (AD) clinical presentation complicates mechanistic studies and therapeutic outcome prediction. Brain protein aggreg...

Developing machine learning-enhanced WHODAS 2.0 short forms for persons with dementia.

BACKGROUND: Disability assessment in dementia is important for care planning, but the full World Health Organization Disability Assessment Schedule 2....

Cognitive Reserve in rural and urban populations: Insights from two aging cohorts in southern India.

BACKGROUND: There are no Indian studies estimating Cognitive Reserve (CR) across rural and urban aging populations. METHODS: We estimated CR from two ...

Balancing Speed and Sensitivity: Echo-Planar Accelerated MRI for ARIA-H Screening in Anti-Aβ Therapeutics.

The recent advent of anti-amyloid-β monoclonal antibodies has introduced new demands for MRI-based screening of amyloid-related imaging abnormalities,...

Deep Learning Reconstruction Enables Diagnostic-Quality 0.4T Knee and Spine MRI in One-Third of the Time.

There has been a growing interest in low-field MRI due to its lower costs, enabling an increase in accessibility of MRI worldwide. Long scan times are...

Touch Modulates Gamma-Band Network Dynamics in the Infant Brain.

Touch is a foundational sensory modality in early development, playing a pivotal role in shaping social, emotional, and cognitive functions. This stud...

Contextualizing Socioeconomic Status Effects on Adolescent Brain Structure and Cognitive Skills: Evidence From the ABCD Study.

Youths' socioeconomic status (SES) correlates with academic, cognitive, and neural outcomes, partly driven by influences on the developmental environm...

GPND-AI NULISA: A 15-Protein AI classifier for diagnosis and co-pathology profiling across neurodegenerative diseases.

INTRODUCTION: Accurate clinical diagnosis of neurodegenerative diseases remains challenging, particularly when individuals have mixed pathologies. We ...

Bibliometric analysis of Alzheimer's and dementia research in Latin America.

INTRODUCTION: Dementia is increasing rapidly in Latin America and the Caribbean (LAC), but research output remains limited. Tracking publication trend...

Multimodal deep learning neuroimaging approach to enhance CT-based diagnosis of Alzheimer's disease.

Neuroimaging plays a critical role in the diagnosis of Alzheimer's disease (AD), with Magnetic Resonance Imaging (MRI) and Positron Emission Tomograph...

The applications of single-cell and spatial transcriptomics in neuroscience and brain disorders.

Spatial transcriptomics extends traditional transcriptomic methods by quantifying gene expression within intact tissues while preserving each cell's p...

Discovering non-linear dynamics of miRNAs in Alzheimer's disease-related cognitive impairment: a cross-species approach with explainable machine learning.

BACKGROUND: MicroRNA (miRNA) biomarker studies in Alzheimer's disease (AD) typically assume monotonic relationships between expression levels and dise...

Interpretable Deep Survival Analysis of Alzheimer's Disease via Metabolic Genetic Variants.

BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disease. Traditional models for estimating AD onset cannot capture nonlinear i...

Large Language Model and Pediatric Neurosurgery - A Neurosurgeon's Perspective on the Artificial Nervous System.

Large language models (LLMs) are rapidly transforming healthcare, yet their implications for pediatric neurosurgery remain underexplored. This narrati...

Neural Stem Cell-Derived Extracellular Vesicles: The Next Frontier in Neurological and Neurodegenerative Diseases.

The present manuscript provides a comprehensive overview of neural stem cell (NSC)-derived extracellular vesicles (NSC-EVs( as a cell-free approach to...

Interpretable multivariate survival models: Improving predictions for conversion from mild cognitive impairment to Alzheimer's disease via data fusion and machine learning.

Accurately predicting which individuals with mild cognitive impairment (MCI) will progress to Alzheimer's disease (AD) can improve patient care. This ...

Machine learning to diagnose, classify and predict phenoconversion in isolated REM sleep behavior disorder.

Rapid eye movement (REM) sleep behaviour disorder (RBD), particularly its idiopathic/isolated form (iRBD), is a prodromal marker for α-synucleinopathi...