Neurology

Prediction of Neurological Outcome and Mortality in Cardiac Arrest Patients: An Explainable Machine Learning Study Integrating HRV, EEG, and Clinical Features.

OBJECTIVE: Early and accurate prediction of neurological outcomes and mortality in comatose patients after cardiac arrest remains challenging. Multimodal data integrating heart and brain electrophysiological signals may improve prognostic accuracy, but distinct predictive patterns underlying neurological recovery versus survival are not well characterized. METHODS: We analyzed 331 patients from th...

Towards generalizable seizure monitoring: EpiVLM for cross-environment detection and classification.

The translation of automated seizure detection from controlled clinical units to real-world settings is hindered by heterogeneous recording conditions and limited expert monitoring. We introduce EpiVLM, a multimodal vision-language system that combines clinically structured prompts with video reasoning for cross-environment seizure monitoring. Evaluated on a robust and diverse dataset of 232 video...

Fusing Structural Phenotypes with Functional Data for Early Prediction of Primary Angle-Closure Glaucoma Progression.

PURPOSE: To classify eyes as slow or fast glaucoma progressors in patients with primary angle-closure glaucoma (PACG) using an integrated approach com...

Classification of familial and non-familial ADHD using auto-encoding network and binary hypothesis testing.

Family heritage is one of the most powerful risk factors for attention-deficit/hyperactivity disorder (ADHD). Children with familial ADHD (ADHD-F) and...

Targeting Tau-Mitochondrial Crosstalk in Alzheimer's Disease: Integrative Multi-Omics and Artificial Intelligence-Driven Tools for the Development of Disease-Modifying Therapeutics.

Alzheimer's disease (AD) is a progressive neurodegenerative illness marked by cognitive impairment, synaptic dysfunction and neuronal death. Tau prote...

Simulated wildfire smoke particulate matter elicits STING-dependent astrocyte-mediated inflammatory signaling and neurotoxicity.

Changing global wildfire landscapes necessitate exploration of the effects of exposure to wildfire smoke on health and disease. Exposure to this toxic...

Effects of peripheral nerve damage on promoting maladaptive plasticity in the spinal cord and brain.

Peripheral nerves can acquire damage through trauma, demyelinating diseases, inflammatory or immune-mediated insults, cancer, metabolic disorders, med...

[Clinical practice guidelines for the application of intelligent technology in spinal surgery (2026 edition)].

In response to the challenges of insufficient precision and limited safety associated with traditional techniques in the diagnosis and treatment of co...

Genetic algorithm-optimized machine learning approaches for EEG-based silent speech decoding.

The phases of human communication consist of speech perception, production, and imagination. The objective of this work is to understand and analyse t...

Volumetric Analysis of Deep Cerebellar and Thalamic Nuclei in Spastic Cerebral Palsy.

OBJECTIVE: Cerebral palsy (CP) encompasses various movement disorders, most commonly spasticity, although other motor phenotypes such as dystonia may ...

DSA-NRP: No-Reflow Prediction from Angiographic Perfusion Dynamics in Stroke EVT.

Following successful large-vessel recanalization via endovascular thrombectomy (EVT) for acute ischemic stroke (AIS), some patients experience a compl...

Lightweight deep learning model for nonconvulsive status epilepticus diagnosis using EEG time-frequency analysis.

Nonconvulsive Status Epilepticus (NCSE) is a persistent epileptic seizure state whose detection primarily relies on visual EEG inspection. Automated a...

A clinical-radiomics model based on multiparametric MRI for discriminating solitary primary spinal tumors from solitary spinal metastases.

OBJECTIVE: To develop and validate a clinical-radiomics model based on multiparametric MRI for differentiating solitary primary spinal tumors from sol...

Deep Learning and Radiomics Assessment for Highly Myopic Glaucoma Detection Based on Fundus Photography.

PURPOSE: To investigate spatially distinctive features in fundus photographs of highly myopic glaucoma (HMG) by integrating radiomics and deep learnin...

Neuromodulation techniques for managing overactive bladder.

Overactive bladder (OAB) represents a significant burden and has a considerable impact on the quality of life (QoL) of affected patients. The treatmen...

Developmental Profile of Physiological High-Frequency Oscillations in the Human Brain.

High-frequency oscillations (HFOs), transient burst of ≥ 80 Hz activity, are increasingly recognized as promising EEG biomarkers of the epileptogenic ...

Identification and Validation of Glycosylation-Related Biomarkers in the Hippocampus for Alzheimer's Disease Diagnosis and Drug Repurposing.

As a crucial type of post-translational modification, glycosylation plays a fundamental role in maintaining cellular homeostasis and is closely associ...

EEG microstate dynamics and metal metabolism in Parkinson's disease dementia: an exploratory study with machine learning-assisted sensitivity analysis.

BACKGROUND: Electroencephalogram (EEG) microstates effectively characterise cognitive-related brain networks, and metal homeostasis is crucial for mai...

Isolating Eye-Movement Artifacts from EEG Signals.

The electroencephalogram (EEG) provides a direct measure of brain electrical activity but is typically contaminated by artifacts, most notably those a...

Diagnostic value of machine-learning using conventional magnetic resonance imaging markers for pediatric idiopathic intracranial hypertension: a retrospective study.

BACKGROUND: Pediatric idiopathic intracranial hypertension can be challenging to diagnose; magnetic resonance imaging (MRI) signs are considered suppo...