Neurology

NewRep: a classification model for online pattern recognition based on HD-EMG signals.

High density (HD) electromyography (EMG) signals have been widely used for pattern recognition (PR) in myoelectric control, particularly for gesture recognition. Although neural networks generally outperform traditional machine learning models in EMG PR, the limited accuracy in recognition remains a critical challenge. To further improve the online classification of neural networks in EMG PR, this...

Integrated bioinformatics and experimental analysis identify apoptosis- and pyroptosis-related hub genes as candidate diagnostic biomarkers in Alzheimer's disease.

Apoptosis and pyroptosis-mediated neuronal death represent major pathogenic mechanisms underlying Alzheimer's disease (AD). Given the potential crosstalk between these two forms of cell death, investigation of a single death pathway may be insufficient to identify robust diagnostic biomarkers for AD. Therefore, this study aimed to explore hub genes involved in both apoptosis and pyroptosis as pote...

EEG-X: An Integrated Framework for Automated Quantitative EEG Analysis in Epilepsy Diagnosis.

Accurate interpretation of electroencephalography (EEG) remains a major challenge in epilepsy diagnosis, particularly given that patient heterogeneity...

Feature integration of [18F]FDG PET brain imaging using deep learning for sensitive cognitive decline detection.

BACKGROUND: Distinguishing individuals with cognitive decline (CD), including early Alzheimer's disease, from cognitively normal (CN) individuals is e...

Neural network-enhanced investigation of ferroptosis and druggability in early-onset alzheimer's disease.

Alzheimer's disease (AD) is a complex neurodegenerative disorder which is multifactorial in nature. Some of its characteristics are slow cognitive dec...

A Convolutional Neural Network Combining Multi-scale Convolution and Functional Connectivity for Predicting rTMS Efficacy in MUD.

PURPOSE: Repetitive transcranial magnetic stimulation (rTMS) is a promising neuromodulation approach for treating methamphetamine use disorder (MUD), ...

Hybrid self-supervised EEG emotion representation: masked reconstruction joint with mutual information bounds.

EEG signals are widely used in affective computing and brain informatics for emotion recognition due to their non-invasiveness. Deep learning and self...

Technical advancements in robot-assisted radical prostatectomy: systematic review and bibliometric analysis.

Robot-assisted radical prostatectomy has transformed the surgical management of localized prostate cancer, yet its research landscape remains incomple...

Brain Region-Centered MultiModal Hypergraph Fusion for MCI Conversion Prediction.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder, with mild cognitive impairment (MCI) as its prodromal stage. Accurate MCI conver...

Reticular pseudodrusen load is associated with an increased risk of stroke.

BACKGROUND/AIMS: Reticular pseudodrusen (RPD) are increasingly recognised as a distinct phenotype in the age-related macular degeneration (AMD) diseas...

Deep learning for multimodal physiological signal based assessment of sleep disordered breathing.

Sleep disordered breathing (SDB) is commonly assessed using polysomnography (PSG), which records multiple physiological signals during sleep. Accurate...

An ROI-Guided Imaging Informatics Framework for Image-Language Differentiation of Tuberculous and Pyogenic Spondylitis on MRI.

This study aims to develop and validate an MRI-based image-language collaborative framework for differentiating tuberculous spondylitis (TB) from pyog...

DIFNet: A Dual-Branch Interactive Fusion Network for Femoral Nerve Segmentation in Ultrasound Images.

Accurate femoral nerve (FN) segmentation in ultrasound images is crucial for nerve blocks but remains challenging due to its tortuous structure, large...

Integrating remote testing and machine learning to identify markers of cerebellar ataxia at home.

BACKGROUND: In-person assessments face accessibility, scalability, and geographic diversity challenges, especially for rare diseases. Additionally, Ce...

[Overview and conclusions of the STROKE data platform of the National Laboratory for Translational Neuroscience and the associated clinical studies].

Acute ischemic and hemorrhagic stroke are among the leading causes of mortality and long-term disability worldwide. In addition to the results of rand...

Age-Related Brain Atrophy Mediates a Composite Outcome of One-Year Ischemic Stroke Recurrence and All-Cause Mortality Through YKL-40-Related Inflammatory Pathways: A Structural Equation Model.

Chronological age is a strong predictor of poor outcomes after ischemic stroke but may not fully capture underlying biological vulnerability. This stu...

PatientSpace: A multimodal graph-based latent representation framework for modeling neurodegenerative disease heterogeneity.

Neurodegenerative diseases such as Alzheimer's disease (AD) and frontotemporal dementia (FTD) exhibit substantial biological and clinical heterogeneit...

SMFF-Net: Spatiotemporal-frequency Multi-domain Feature Fusion Network for EEG-based brain state detection.

The timely and reliable detection of driver fatigue is crucial for reducing driving risks and improving traffic safety. EEG-based deep learning approa...

Analytical construction of lump, rogue, and multi-wave structures in a nonlinear neuron membrane model via bilinear neural network approach.

In this article, we study a nonlinear neuron membrane model describing the propagation of action potentials along nerve fibers, incorporating nonlinea...

Multimodal molecular mapping of the vasculature in human cortex reveals lipid markers of cerebral amyloid angiopathy.

Cerebral amyloid angiopathy (CAA) commonly co-occurs with Alzheimer's disease (AD), yet the molecular changes that accompany vascular [Formula: see te...