Alzheimer's Disease

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, particularly the hemorrhage subtype (ARIA-H). In this editorial, we discuss the study by Loftus and colleagues, which evaluates the diagnostic performance of echo-planar accelerated gradient-recalled echo (GRE) and susceptibility-weighted imaging (S...

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 currently needed to obtain high-quality images. The aim of our study was to accelerate low-field 0.4T musculoskeletal MRI using deep learning and evaluate maximum acceleration via assessment by two expert radiologists. A cascades of independently re...

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...

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...

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 neurode-generative diseases. Advances in in vivo i...

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 ...

Assessment of Large Language Models for Tracheostomy Care in Pediatrics: ChatGPT vs Claude.

INTRODUCTION: This study aimed to evaluate the accuracy, completeness, clarity, and relevance of responses provided by two freely available large lang...

Predicting Peptide Aggregation with Protein Language Model Embeddings.

Amyloid fibrils, a form of peptide aggregates, are associated with multiple diseases and hinder the development of therapeutics. The experimental char...

Amyloid-beta statuses prediction with free water MR imaging features in Alzheimer's disease using machine learning models.

Development of a community home-based dual-supplier cooperation care model for people with dementia based on an intelligent decision-assistance system: protocol of a 1-year randomised controlled trial in China.

INTRODUCTION: Dementia imposes significant care and financial burdens on families and countries globally. While high-quality home-based care is crucia...

Establishing a Diagnostic Model Based on Plasma CDR1as and Alpha-Synuclein for Mild Cognitive Impairment in Type 2 Diabetes.

Emerging evidence implicates cerebellar degeneration-related protein 1 antisense transcript (CDR1as) and alpha-synuclein (α-Syn) in diabetes and cogni...

Data-driven thresholds for standardized classification of severe Alzheimer's disease neuropathology using digital neuropathology.

Alzheimer's disease neuropathological changes (ADNC)-operationalized with semi-quantitative parameters-represent the consensus-based gold standard for...

Dementia screening with the Rivermead Behavioural Memory Test: A machine learning analysis.

BackgroundWith the advent of anti-amyloid-β monoclonal antibody therapies and the growing societal burden of dementia, early identification of Alzheim...