Latest AI and machine learning research in neurology for healthcare professionals.
OBJECTIVE: Non-invasive Brain-Computer Interfaces (BCIs) based on Code-Modulated Visual Evoked Potentials (c-VEPs) using electroencephalography (EEG) signals require robust classification algorithms. It is unclear whether the best approach is to use a similarity measure or to follow a discriminant method. METHODS: We propose a multiple-classifier binary convolutional Siamese (MCBCS) network for si...
Neuroimaging provides essential tools for characterizing brain activity and inter-regional connectivity through modalities that capture complementary aspects of brain organization. At the same time, extracting meaningful neural signatures remains challenging because each modality introduces its own sources of variability, including measurement noise, spatial and temporal distortions, heterogeneous...
OBJECTIVE: Magnetic susceptibility source separation provides profound insights into tissue pathophysiology by disentangling the underlying paramagnet...
Traditionally, stroke has been characterized as an acute focal brain injury; however, its clinical consequences extend beyond neurological deficits to...
Cerebral hemorrhage is a critical public health issue marked by high incidence, disability, and mortality. In China, short-video platforms have become...
BACKGROUND: Autism Spectrum Disorder (ASD) is characterized by challenges in social interaction, communication, and restricted or repetitive behaviors...
Early identification of individuals at risk of developing psychosis enables timely intervention and better clinical outcomes. Current approach relies ...
BACKGROUND: The aim of the current study was to investigate the predictive values of computed tomographic angiography-derived radiomics features (RFs)...
Effects of stroke therapies area highly time dependent but onset-to-treatment times for recanalizing treatment are mostly beyond optimal time windows....
Study DesignScoping review.ObjectivesTo map spine literature on large language models, characterize reported use cases, and identify evidence gaps lim...
BackgroundEmerging evidence suggests that extracranial tissues and immune-glymphatic interactions may contribute to neurodegenerative processes in Alz...
Accurate lower-limb joint moment estimation is essential for clinical gait analysis in children with cerebral palsy (CP), yet conventional methods req...
Objective, scalable assessment of executive function in Attention-Deficit/Hyperactivity Disorder (ADHD) is limited by subjective rating scales and sin...
Membrane molecular recognition features (MemMoRFs) are lipid-binding intrinsically disordered regions (IDRs) that undergo disorder-to-order transition...
BACKGROUND: Voice-based deep learning models for Parkinson disease (PD) and dementia screening report areas under the curve (AUCs) of 0.85-0.97, but r...
BACKGROUND: Literature reviews rely on rigorous title and abstract screening by researchers, which is time-consuming. AI-assisted literature screening...
OBJECTIVE: To identify significant predictors for individual American Spinal Injury Association Impairment Scale (AIS) grades, and develop a clinical ...
G protein-coupled receptors (GPCRs) are major therapeutic targets for central nervous system disorders, with more than 500 approved drugs targeting th...
BACKGROUND/OBJECTIVES: We investigated whether the interthalamic adhesion (IA), a midline structure connecting the thalami, is altered in multiple scl...
Human Activity Recognition (HAR) using wearable sensors is relevant to rehabilitation, assistive robotics, and mobile health applications. This study ...