Neurology

Differentiable Stroke Planning with Dual Parameterization for Efficient and High-Fidelity Painting Creation

In stroke-based rendering, search methods often get trapped in local minima due to discrete stroke placement, while differentiable optimizers lack structural awareness and produce unstructured layouts. To bridge this gap, we propose a dual representation that couples discrete polylines with continuous Bézier control points via a bidirectional mapping mechanism. This enables collaborative optimizat...

High-dimensional Many-to-many-to-many Mediation Analysis

We study high-dimensional mediation analysis in which exposures, mediators, and outcomes are all multivariate, and both exposures and mediators may be high-dimensional. We formalize this as a many (exposures)-to-many (mediators)-to-many (outcomes) (MMM) mediation analysis problem. Methodologically, MMM mediation analysis simultaneously performs variable selection for high-dimensional exposures and...

An Explainable Vision-Language Model Framework with Adaptive PID-Tversky Loss for Lumbar Spinal Stenosis Diagnosis

Lumbar Spinal Stenosis (LSS) diagnosis remains a critical clinical challenge, with diagnosis heavily dependent on labor-intensive manual interpretatio...

Optimizing EEG Graph Structure for Seizure Detection: An Information Bottleneck and Self-Supervised Learning Approach

Seizure detection from EEG signals is highly challenging due to complex spatiotemporal dynamics and extreme inter-patient variability. To model them, ...

CogPic: A Multimodal Dataset for Early Cognitive Impairment Assessment via Picture Description Tasks

The automated evaluation of cognitive status utilizing multimedia technologies presents a promising frontier in early dementia diagnosis. However, the...

BSO-AD: An Ontology for Representing and Harmonizing Behavioral Social Knowledge in ADRD

Objective: Behavioral and social factors (BSFs) substantially influence the risk, onset, and progression of Alzheimer disease and related dementias (A...

Predicting long-term adverse outcomes after neonatal intensive care

Neonates requiring intensive care are at increased risk for long-term neuropsychiatric disorders. However, clinical adoption of risk prediction models...

A unified model for staging amyloid and tau pathology in Alzheimer's disease

Biological staging models are a key tool for assessing the severity of Alzheimer's disease (AD), supporting personalized medicine and playing a critic...

NeuroFM: Toward Precision Neuroimaging with Foundation Models for Individualized Brain Health Estimation

Precision neuroimaging aims to deliver individualized assessments of brain health, yet a single structural MRI does not yield a multidimensional, quan...

MAMGL: A memory-augmented meta-graph learning framework for adolescent major depression disorder diagnosis

Adolescent major depressive disorder (AMDD) is a prevalent and heterogeneous psychiatric condition that emerges during a critical period of brain deve...

Naturalistic Stimulus Reconstruction from fMRI: A Primer in the Natural Scenes Dataset

Reconstructing natural images from brain activity represents one of the most compelling demonstrations of the synergy between modern neuroimaging and ...

BrainYears: A functional EEG-based brain age clock enables intervention-ready measurements of brain aging

Biological brain aging is a major determinant of cognitive decline and neurodegenerative disease, yet scalable and intervention-ready brain aging biom...

The Scaffold Effect: How Prompt Framing Drives Apparent Multimodal Gains in Clinical VLM Evaluation

Trustworthy clinical AI requires that performance gains reflect genuine evidence integration rather than surface-level artifacts. We evaluate 12 open-...

Look, Compare and Draw: Differential Query Transformer for Automatic Oil Painting

This work introduces a new approach to automatic oil painting that emphasizes the creation of dynamic and expressive brushstrokes. A pivotal challenge...

Invasive and Non-Invasive Neural Decoding of Motor Performance in Parkinson's Disease for Personalized Deep Brain Stimulation

Decoding motor performance from brain signals offers promising avenues for adaptive deep brain stimulation (aDBS) for Parkinson's disease (PD). In a t...

Longitudinal Boundary Sharpness Coefficient Slopes Predict Time to Alzheimer's Disease Conversion in Mild Cognitive Impairment: A Survival Analysis Using the ADNI Cohort

Predicting whether someone with mild cognitive impairment (MCI) will progress to Alzheimer's disease (AD) is crucial in the early stages of neurodegen...

D-GATNet: Interpretable Temporal Graph Attention Learning for ADHD Identification Using Dynamic Functional Connectivity

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder whose neuroimaging-based diagnosis remains challenging due ...

Prediction of Major Clinical Endpoints in Atrial Fibrillation at Primary Care Level using Longitudinal Learning Stances

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia worldwide and is strongly associated with increased risks of stroke, heart failure, ...

Narcolepsy Revolution - Protocol and Methodology A diagnostic accuracy study protocol using the Dreem 3 headband for ambulatory diagnosis of narcolepsy in children and young adults

Background Narcolepsy is a rare, lifelong neurological disorder that often begins in childhood or adolescence. Diagnosis is frequently delayed because...

EEG Foundation Model Improves Online Directional Motor Imagery Brain-computer Interface Control

Brain-Computer interfaces (BCIs) offer a link between neural signals and external computation, enabling control of devices for the purposes of restori...