Neurology

GMN4AD: Graph Matching Network for Alzheimer's Disease Diagnosis with Test-Time Domain Adaptation using Multi-centered Structure Magnetic Resonance Imaging

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that affects millions of older adults, with prevalence expected to rise significantly in the coming years. Early diagnosis, particularly during the mild cognitive impairment (MCI) stage, is critical for timely intervention. Structural Magnetic Resonance Imaging (sMRI) has emerged as a key modality for detecting AD-related brain c...

A multi-agent system for spine MRI report generation from multi-sequence imaging

Spinal pathology is a leading cause of pain and disability worldwide. Spine magnetic resonance imaging (MRI) is central to clinical evaluation, yet its interpretation remains complex and time-consuming, requiring integration of information across multiple imaging sequences and anatomical regions. Despite recent advances in automated MRI analysis, effectively combining multi-sequence data while pre...

MipSScs: Artificial neural network-based data integration of 2D/3D single-cell spatial RNA sequence data from virus-infected human cerebral organoids

There is interest in the use of recent single-cell spatial transcriptomic technologies to gain biological insights into disease mechanisms. Previously...

Estimating Individualized Treatment Effects in Acute Ischemic Stroke with Causal Transformation Models (TRAM-DAG): A Multi-Centre Observational Study with External RCT Validation

Personalized medicine in acute ischemic stroke requires moving beyond average treatment effects (ATE) to individualized treatment effect (ITE) estimat...

Reducing the Complexity of Deep Learning Models for EEG Analysis on Wearable Devices

Wearable healthcare devices are the fastest-growing Internet of Things (IoT) sector. Many automated healthcare services rely on two crucial biological...

A Heterogeneous Graph Neural Network Framework for Multi-Horizon Stroke Mortality Prediction

Background: Machine learning models for stroke mortality prediction typically treat each time horizon independently and use flat tabular features that...

Active Learning-Guided Peptide Design for Modulating Condensate Properties upon Recruitment

The physical properties of biomolecular condensates, which form through phase separation, are central to their organisation and function and are incre...

Neural Innervation Invigorates Yolk Sac Biological Functions beyond Nutrient Reservoir during Zebrafish Embryo Development

The zebrafish yolk sac (YS) is traditionally viewed as a nutrient reservoir. By reconstructing the complete progression of embryonic neural developmen...

End-to-End Machine Learning for Depressive State Classification via EEG and fNIRS

The escalating demand for mental healthcare, driven by rising societal stress, highlights the limitations of traditional psychiatric diagnostics. Conv...

Multimodal Ordinal Modeling of Alzheimer's Disease Severity Using Structural MRI and Clinical Data

Neurodegenerative diseases such as Alzheimer's disease (AD) require accurate and scalable tools for assessing disease severity, yet current clinical s...

Candidate Molecular Subtypes of Cognitive Resilience in Alzheimers Disease: A Multi-Cohort Machine Learning and Neuroimaging Study

Background: Cognitive resilience (CR) in Alzheimers disease (AD) refers to preserved cognitive function despite substantial AD pathology. Diverse biol...

The impact of B1+ inhomogeneity on image quality metrics and morphometric statistical inferences at 7 T MRI

Introduction: Structural neuroimaging relies on T1-weighted (T1w) magnetic resonance imaging (MRI) for brain morphometry, yet at 7 Tesla (7 T) transmi...

Real-world safety profile of Enfortumab Vedotin: A comprehensive pharmacovigilance analysis based on the FDA Adverse Event Reporting System (FAERS)

Background: This study aimed to evaluate real-world adverse event (AE) signals of EV to provide evidence-based guidance for its safe clinical applicat...

Sleep EEG Signal Criticality as a Non-Invasive Predictor of Cognitive Decline in Dementia

Early detection of neurodegeneration remains a critical clinical challenge. This study investigates whether sleep EEG signal criticality, quantified v...

SpineReport: Automated 3D Quantification and Reporting of Lumbar Spine Degeneration on MRI

Lumbar spine conditions are a leading cause of disability worldwide, yet reliable quantification of degeneration from MRI remains challenging. In clin...

Interpretable Temporal Facial-Region Motion Analysis for In-the-Wild Parkinson's Disease Video Classification

Reduced facial expressivity is a common motor manifestation of Parkinson's disease (PD), often described as hypomimia or facial bradykinesia. This pap...

White Matter Hyperintensity Burden Modifies the Association Between Atrial Fibrillation and Cerebral Microbleeds

Background: In atrial fibrillation (AF), cerebral microbleed (CMB) burden guides anticoagulation decisions, yet AF is itself inconsistently associated...

Multiplexed temporal SWCNT biosensor combined with convolutional autoencoding identifies ALS-specific serum protein corona signatures

Amyotrophic lateral sclerosis (ALS) lacks a validated blood-based diagnostic, and the field is increasingly moving from single-molecule markers toward...

Comparing Machine Learning Approaches for Predicting CFD-Derived Stroke Risk Indicators in Atrial Fibrillation Patients

Non-valvular atrial fibrillation (AF) is associated with a five-fold increased risk of stroke, mainly due to impaired contractility of the left atrium...

Motor Resonance of Musical Emotion: A Machine Learning Approach to EEG Decoding During Expressive Music Performance

Understanding the neural dynamics underlying expressive musical performance remains a major challenge at the intersection of neuroscience, music cogni...