Neurology

The Science of Preventing Delirium: Trends, Knowledge Gaps, and Future Directions Revealed Through Bibliometric Insights.

PURPOSE: To examine global research trends in delirium prevention through a bibliometric analysis and provide a structured overview of its development and emerging directions. DESIGN: A bibliometric analysis. METHODS: Data were retrieved from the Web of Science Core Collection, focusing on publications between January 1, 2015, and December 31, 2024. The analysis utilized VOSviewer, CiteSpace, and ...

Changes in collateral status during transfer for thrombectomy are predictive of functional outcome in large vessel occlusion stroke.

Collateral circulation that determines infarct progression in large vessel occlusion (LVO) is implicitly regarded as stationary. We investigated collateral circulation changes during interfacility transfer for endovascular thrombectomy (EVT) and its association with functional outcome in anterior circulation LVO stroke. Seventy consecutive patients with middle cerebral artery occlusion transferred...

Dynamic Spatio-Temporal Fusion Network Via Hierarchical Self-Attention for Seizure Prediction.

The use of deep learning for EEG-based seizure prediction has grown rapidly in recent years. However, existing studies fail to effectively encode spat...

Multiscale Coupling From Mastication to Retronasal Aroma Perception: The PG-DTCFN Model and Multiphysics Simulation.

Retronasal olfaction is central to food flavor perception, yet its multiscale mechanisms from the oral processing to the central nervous system lack s...

First-episode psychosis and mood disorder onset in individuals with epilepsy.

BACKGROUND: Psychiatric disorders represent a major burden for patients with epilepsy (PwE). This study examined how demographic, epilepsy-related, an...

Anchor-free temporal localization of apnea events from EEG/EOG with state-space models.

Reduced-channel polysomnography (PSG) and electroencephalography/electrooculography (EEG/EOG) signals can support obstructive sleep apnea (OSA) screen...

Machine learning for predicting full-count FDG PET brain images from low-count acquisitions in suspected dementia: a clinical and quantitative evaluation.

Objective.Artificial intelligence methods for denoising low-count FDG PET brain images are usually evaluated using image quality metrics alone, with l...

Multi-parameter scoliosis evaluation from dual-view x-rays via a local sine-based projection model.

OBJECTIVE: To develop a robust and accurate multi-parameter assessment framework for adolescent idiopathic scoliosis (AIS) based on spinal X-ray image...

Brain organoids in Parkinson's disease drug development: Human-specific models for translational discovery.

Parkinson's disease (PD) poses a major unmet therapeutic challenge, with most drug candidates failing in clinical translation despite promising animal...

Measuring cardiac stroke volume through in-ear audio sensing.

Stroke volume, the volume of blood ejected by the left ventricle during a contraction, is a key metric of cardiovascular health. Currently, stroke vol...

Deep learning-based automatic measurement of spinal alignment and implant detection in scoliosis radiographs.

Deep learning-based automated analysis offers the potential to streamline workflows, improve reproducibility, and reduce clinician workload. We develo...

Artificial Intelligence in Ischemic Stroke Lesion Segmentation: A Narrative Review of Deep Learning Methods, Clinical Utility, and Future Directions.

Ischemic stroke management is time-sensitive, and lesion segmentation supports treatment selection, prognostication, and reproducible quantification. ...

Context-agnostic machine learning for Parkinson's disease motor symptom detection using wearable sensors.

BACKGROUND: Parkinson's disease is a rapidly growing neurodegenerative disorder with various motor and non-motor symptoms, affecting millions of peopl...

Mapping Artificial Intelligence Applications in Dementia Caregiving: A Scoping Review of Technologies, Functions, and Evidence Gaps.

PURPOSE: As symptoms progress in people with Alzheimer's disease and related dementias (ADRD), caregivers assume increasing responsibility. The object...

The write-cost bottleneck: energy constraints on continual learning in brains and machines.

In recent decades, neural energy-budget research has extensively quantified signaling costs-the "read" side of computation-including action potentials...

Deep Model Families for EEG-Based Multi-Class Dementia Classification.

Deep learning (DL) has shown considerable promise for EEG-based dementia assessment; however, rigorous cross-family comparisons under leakage-free and...

α-Synuclein Seeds Amplification Assays for Parkinson's Disease and Related Synucleinopathies.

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by the pathological misfolding and aggregation of α-synuclein (α-sy...

NREP is involved in xenon's protection against ischemic stroke injury through modulating microglial M1 polarization and neuroinflammation.

BACKGROUND: Ischemic stroke (IS) is associated with high mortality and disability rates, and secondary neuroinflammation is a key driver of exacerbate...

Machine learning-based prediction of hospital-associated complications after tibial fracture surgery in older patients: a nationwide Japanese database study.

BACKGROUND: Older adults requiring emergency surgery for acute tibial fractures are vulnerable to hospital-associated complications (HACs), but admiss...

EEG-DBNet: a dual-branch framework for temporal-spectral representation learning of motor imagery electroencephalography.

PURPOSE: Motor imagery electroencephalography (MI-EEG) decoding remains challenging due to low signal-to-noise ratio and complex temporal-spectral cha...