Neurology

Prediction of post-stroke brain swelling using biomechanical modelling and deep neural networks.

Malignant stroke is a life-threatening condition, with mortality rates reaching up to 80% among patients managed conservatively. Brain swelling volume and midline shift are pivotal clinical markers for predicting stroke outcomes. However, brain oedema typically peaks two to five days post-stroke onset, which significantly delays the implementation of timely interventions. Early prediction of these...

A longitudinal and explainable 2.5D deep learning framework for Alzheimer's disease progression using ADNI MRI.

Early identification of Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI), is important for timely clinical assessment and disease management. Structural T1-weighted magnetic resonance imaging (MRI) captures macroscopic neurodegenerative changes associated with disease progression; however, developing deep learning models that are both methodologically rigorous and ...

Trends in the studies of pharmacoresistant epilepsy- a review based on literature analysis (2015-2025).

Drug-resistant epilepsy (DRE) is a complex neurological disease that accounts for 30%-40% of all epilepsy cases. Its pathogenesis and treatment have a...

Predicting one-year mortality risk in ICU patients with ischemic stroke using multi-algorithm machine learning and a nomogram.

Introduction: Ischemic stroke is a leading cause of mortality, and patients requiring intensive care unit (ICU) admission carry a guarded prognosis. W...

Adult Day Centers as Partners in Baccalaureate Nursing Dementia Education.

This pilot study explored how adult day centers can serve as transformative clinical learning environments for nursing students to learn dementia care...

Transcriptional signatures and topological reorganization of morphometric similarity networks in temporal lobe epilepsy with unilateral hippocampal sclerosis.

OBJECTIVE: To delineate morphometric similarity network (MSN) topological abnormalities and their underlying spatial transcriptomics in the normal-app...

Individualized treatment strategies and long-term prognosis of congenital hydrocephalus: an integrated analysis based on multicenter retrospective data and machine learning.

INTRODUCTION: Hydrocephalus is a common pediatric neurological disorder characterized by abnormal head enlargement, intellectual disability, visual im...

Advancing diagnostic biomarkers in Alzheimer's disease: interdisciplinary innovations and technological frontiers.

Developing diagnostic biomarkers for Alzheimer's disease (AD) is at the cutting edge of interdisciplinary research and technical advancement. This com...

Predictive Value of Machine Learning for Poststroke Mortality Risk: Systematic Review and Meta-Analysis.

BACKGROUND: People with stroke face a high mortality risk, and an accurate prediction model is essential to the guidance of clinical decision-making i...

Physiologically inspired modeling of cortical dynamics through spiking neural networks.

the characterization of neural activity underlying neurophysiological function presents a major challenge in computational neuroscience. Several metho...

Analyzing multiple-sclerosis progression: stage-specific biomarker insights via explainable machine learning.

BACKGROUND: Multiple Sclerosis (MS) is a chronic autoimmune disease where early diagnosis from Clinically Isolated Syndrome (CIS) remains challenging....

Polymer Drug Conjugate: A Revolution in Drug Delivery.

Polymer-drug conjugates (PDCs) represent a remarkable advancement in modern medicine, leveraging the physicochemical properties of polymers to enhance...

Physics-informed graph neural networks for robust cross-patient epileptic seizure prediction via chimera state detection.

BACKGROUND: Epilepsy affects approximately 50 million individuals worldwide, with 30% experiencing drug-resistant seizures despite optimal pharmacolog...

EEG-AI: An agentic system for AI-assisted semi-automated EEG preprocessing and artifact removal.

BACKGROUND: EEG is widely used to identify neural markers, personalize treatments, and evaluate interventions. However, low signal-to-noise ratio and ...

Classification of depressed and non-depressed MCI and non-depressed cognitively normal individuals using resting-state metrics: A multi-group study with machine learning and graph reinforcement learning.

Depressive symptoms frequently co-occur in individuals with Mild Cognitive Impairment (MCI) and are thought to accelerate neurodegenerative progressio...

Clinical applications of EEG connectivity in acute brain injuries: A systematic review.

OBJECTIVE: While connectivity methods have been widely studied as predictors of recovery in chronic disorders of consciousness (DoC), evidence for EEG...

U-Mamba-Spectra: A novel generative and explainable framework for camel milk adulteration detection using near-infrared spectral learning.

Camel milk is vulnerable to adulteration due to its high value and limited supply. This study proposes an interpretable framework, U-Mamba-Spectra, fo...

Exploration of an admittance control method for multi-segment spinal motion loading.

BACKGROUND: In vitro testing is a fundamental approach for advancing spinal biomechanics research. However, existing loading methods still exhibit not...

Machine learning in epilepsy.

Epilepsy is a complex neurological disorder characterized by pathological processes that unfold across multiple biological scales, from cellular excit...

AI-Driven Multimodal Analysis of Neuroimaging and Speech Data for Diagnosis of Alzheimer's, Parkinson's, and Epilepsy.

This study investigates the application of machine learning (ML) techniques combined with neuroimaging and speech signal processing for the early dete...