Neurology

Digital Sleep-Wake Cycle Metrics and Dementia Prediction in Older Adults.

IMPORTANCE: Disruptions in the sleep-wake cycle have been reported in the preclinical period of dementia; whether they contribute to dementia prediction remains unclear. OBJECTIVE: To examine associations of accelerometer-derived sleep-wake cycle metrics with incident dementia and their contribution to dementia risk prediction in models containing age and known risk factors. DESIGN, SETTING, AND P...

EffortNet: A Deep Learning Framework for Objective Assessment of Speech Enhancement Technologies Using EEG-Based Alpha Oscillations.

This paper presents EffortNet, a novel deep learning framework for decoding listening effort at the individual level from electroencephalography (EEG) during speech comprehension. Quantifying listening effort remains a significant challenge in speech-hearing research. We collected 64-channel EEG data from 122 participants during speech comprehension under four conditions: clean, noisy, MMSE-enhanc...

Evaluating Medical Students' Perceptions of AI-Assisted Clinical Documentation (CarePilot): Cross-Sectional Study.

BACKGROUND: Artificial intelligence (AI) is increasingly being integrated into health care to streamline documentation and improve clinician efficienc...

Sleep loss overrides sleep-preparatory behavior to promote sleep.

In the period preceding sleep, humans and other animals display a stereotyped repertoire of behaviors-including hygiene-related activities and prepari...

Predicting categorical and continuous Alzheimer's disease outcomes from a single MRI scan.

Deep learning (DL) has shown success in predicting Alzheimer's disease (AD) diagnosis, yet continuous measures such as cognitive assessment remain cri...

N2G calibrator: a cross-subject adversarial learning framework for neural signal-driven gait tracking in Parkinson's disease.

Adaptive deep brain stimulation (aDBS) has enabled machine learning models to track motor states from neural signals with improved accuracy, aiming to...

Explainable machine learning for predicting longitudinal dementia status: Establishing a leakage-free benchmark.

Dementia research often suffers from methodological pitfalls such as label-information and subject-information leakages. Leveraging the longitudinal O...

Formulaic and novel language dissociate in Alzheimer's clinical syndrome: evidence for the Dual-Process Model.

INTRODUCTION: Spontaneous speech is commonly disrupted in persons with Alzheimer's disease (AD) and/or Alzheimer's clinical syndrome (ACS). Importantl...

Performance evaluation of an EEG-guided robotic glove with machine learning models for hand rehabilitation in injured athletes.

BackgroundAssistive rehabilitation technologies play a crucial role in improving motor recovery for individuals with hand injuries particularly athlet...

Estimation of dynamic spinal loads during manual lifting using smartphone-based markerless motion capture.

This study evaluated the feasibility of dynamic spinal load estimation using kinematic data collected with a smartphone-based markerless motion captur...

Interpersonal Predictors of Grief Among Caregivers of Persons With Dementia.

OBJECTIVE: Family caregivers of persons with dementia experience grief as the care recipients' dementia advances. Here, we explore how various interpe...

Intraoperative neurophysiological monitoring (IONM) in neurosurgery: A critical appraisal of established practices, ongoing controversies, and future trajectories.

Intraoperative neurophysiological monitoring (IONM) has evolved from a novel technique into an evidence-based standard treatment method for high-risk ...

Aging beyond diagnosis: the MRI brain age gap across disorders.

The brain age gap (BAG), the difference between magnetic resonance imaging-predicted brain age and chronological age, is a proposed marker of neurobio...

Transcriptomic Analysis and Multiple Machine Learning Approaches Identify ZDHHC20 and Its Highly Correlated Gene AK5 as Biomarkers in Multiple System Atrophy.

Multiple system atrophy (MSA) is a fatal neurodegenerative disorder lacking effective diagnostic tools. While protein palmitoylation is crucial for ne...

Comprehensive benchmarking and explainable machine learning analysis of EEG imagery activity recognition.

Motor imagery (MI)-based brain-computer interfaces (BCIs) enable users to control external devices using EEG signals, offering great potential in assi...

Enhancing Drug Response Prediction in Epilepsy with Emerging Multimodal Models: Focus on Clinical, Pharmacologic, and Genomic Factors.

Epilepsy is a common neurological disorder, with approximately one-third of the affected population developing drug-resistant epilepsy despite the exp...

Early laboratory-based differentiation of severe influenza pneumonia and influenza-associated encephalopathy in children using a multi-model, stability-aware machine-learning workflow: a single-center retrospective cohort study.

BACKGROUND: Influenza-associated encephalopathy and acute necrotizing encephalopathy (ANE) are rare but devastating complications of pediatric influen...

REST deficiency and neurogenic-to-gliogenic shift in down syndrome human cerebral organoids.

Down syndrome (DS) features impaired cortical neurogenesis and excess gliogenesis, yet the temporal regulatory events driving this imbalance remain un...

Adaptive fusion of EEG and NIRS with explainable AI reveals neurophysiological markers of cognitive flexibility.

Cognitive flexibility enables individuals to adapt to changing rules, goals, or uncertainty. This study evaluates the discriminative power of electroe...

Progress in Precision Drug Delivery Systems for Neurodegenerative Diseases with Muscle Atrophy.

Neurodegenerative diseases frequently co-occur with skeletal muscle atrophy, creating a complex comorbid condition that significantly accelerates func...