Neurology

Eye movement features are associated with cognitive and mobility decline.

BackgroundEye movement is a vital indicator of neurodegenerative diseases, brain health, and behavior. However, existing knowledge is limited to patient populations or cross-sectional samples. Little is known about eye movement in association with longitudinal cognitive and mobility decline in aging.ObjectiveInvestigate relationships between eye movement features with cognitive impairment, includi...

Large language models in artificial intelligence to answer patient questions in spine surgery: an evaluation of current evidence.

INTRODUCTION: Large language models (LLMs) are increasingly being explored in healthcare, particularly for enhancing patient education. In spine surgery, LLMs have the potential to enhance communication and support patients through perioperative care. However, concerns remain regarding the accuracy, readability, and overall reliability of these tools in delivering patient-facing information. This ...

Multimodal attention-enhanced network of segmenting acute ischemic stroke from perfusion images.

Acute ischemic stroke (AIS) is a major cause of long-term disability and mortality worldwide. Accurate segmentation of stroke lesions, particularly th...

Focal-DenseNet: A Risk Assessment Framework for Alzheimer's Disease in Heterogeneous MRI Data.

Alzheimer's disease (AD) is a progressive neurodegenerative disease of the nervous system, which has become an important public health issue attractin...

Development and Validation of Machine Learning Models for Predicting Early Cognitive Decline Using Home Sensor-Derived Behavioral Data: Sensors in-Home for Elder Wellbeing (SINEW) Cohort Study.

BACKGROUND: As the global population continues to age, the prevalence of geriatric conditions, including dementia and frailty, is also increasing. Ear...

A feasibility study of deep learning-based segmentation of the inferior alveolar nerve on magnetic resonance neurography.

Anterior midcingulate cortex activity during pain catastrophizing predicts and sequentially tracks reduction in pain interference after cognitive behavioral therapy in fibromyalgia.

Fibromyalgia (FM) is a chronic pain disorder characterized by widespread pain, fatigue, and functional disability. Cognitive behavioral therapy (CBT) ...

Artificial intelligence for educating family caregivers of people with dementia: A mixed methods systematic review.

Dementia is a growing global health challenge, with the majority of individuals living with dementia receiving care from family members. However, many...

A scalable EEG-based spatial neglect detection system in augmented reality for stroke patients.

BACKGROUND: Spatial neglect is a common visuospatial attention disorder following a stroke. To overcome weaknesses associated with classic pen-and-pap...

Self-Supervised Learning With Adaptive Graph Modeling for EEG-Based Epileptic Seizure Classification.

OBJECTIVE: Epileptic seizure classification using EEG signals remains a significant challenge due to complex spatial-temporal dependencies, limited la...

OPTIMUS: Predicting Multivariate Outcomes in Alzheimer's Disease Using Multi-Modal Data Amidst Missing Values.

OBJECTIVE: Alzheimer's disease, a progressive neurodegenerative disorder, involves neural, genetic, and proteomic factors and impacts multiple cogniti...

Energy-Efficient EEG-Based Autism Spectrum Disorder Detection Using a Hyperbolic Attention Neural Network.

Long-term physiological monitoring using wearable wireless systems represents a paradigm change in next-generation e-health applications. Specifically...

Channel Transformer-Based Generative Adversarial Network With Multi-Instance Attention and Nutcracker Optimization for Automatic Seizure Detection Using EEG.

The current literature on automatic seizure detection based on EEG has obtained significant accuracy, but most of them still have difficulties in proc...

Optimized Seizure Detection in EEG Using Dual-Branch Feature Fusion and Machine Learning Technique.

Epilepsy is a neurological disorder of the brain that generates seizures due to abnormal electrical activity. The diagnosis and management of the dise...

Scalp Electroencephalography Markers of Hidden Interictal Epileptiform Discharges in the Mesial Temporal Lobe.

Interictal epileptiform discharges (IEDs) are crucial for epilepsy diagnosis but are often undetectable on scalp EEG (scEEG). This study aims to devel...

EEG Emotion Recognition With Uncertainty-Aware Contrastive Learning and Frequency-Aware Self-Attention.

Electroencephalography (EEG) emotion recognition plays a key role in improving human-machine interactions. Advanced algorithms have been proposed for ...

A Review of Vagus Nerve Stimulation for Disease: Comprehensive Theory and Evidence for Mechanisms of Action.

Vagus nerve stimulation (VNS) is an established neuromodulatory therapy approved for epilepsy, depression, obesity, stroke rehabilitation, rheumatoid ...

Impact of Robotic Assisted Treadmill Gait Training on Walking Performance in Children With Spastic Diplegic Cerebral Palsy: A Randomized Controlled Study.

BACKGROUND AND PURPOSE: Children diagnosed with diplegic cerebral palsy frequently experience gait difficulties that can significantly affect their in...

Improved Hybrid Local Binary Structural Pattern Shallow Graph Deep Convolutional Attention Neural Networks With Synergistic Fibroblast Optimization for Automated Epileptic Seizure Detection and Diagnosis in EEG Signals.

Epileptic seizure (ES) detection from electroencephalography (EEG) signals is difficult because of noise and the intricate, patient-specific nature of...

Countermovement Jump Force-Time Mechanics Differentiate ACL Injury Status in Elite Alpine Ski Racers.

Biomechanical assessments of stretch-shortening cycle (SSC) movements such as the countermovement jump (CMJ) are used to evaluate neuromuscular functi...