Neurology

An artificial neural network based on radial basis methodology using delay effects in the Parkinson's disease model.

The purpose of this study is to find the numerical solutions of the delay Parkinson's disease model by employing a computing neural network framework. The disease model is divided into five components: healthy brain neurons, infected brain neurons, activated microglia cells, extracellular α-synuclein, and the activated T-cell population. A two-layered neural network scheme using radial basis funct...

Associations of epigenetic age acceleration with motor impairment: Evidence from the Parkinson's Progression Markers Initiative cohort.

Motor decline is a hallmark of Parkinson's disease (PD) and biological aging, yet the specific relationship between systemic biological aging and neuromotor function remains under-characterized. This study leveraged longitudinal phenotypic and whole-blood DNA methylation data from the Parkinson's Progression Markers Initiative (PPMI) to evaluate associations between seven epigenetic aging measures...

Enriched resting-state EEG prediction of cognitive decline in prodromal Alzheimer's disease: a machine-learning approach.

OBJECTIVE: We evaluated the accuracy of standard machine learning (ML) algorithms in predicting 1-year cognitive decline in Alzheimer's disease patien...

Perfusion-derived cerebral CT angiography with artificial intelligence iterative reconstruction as a replacement for routine cerebral CT angiography in acute ischemic stroke.

OBJECTIVES: To investigate the feasibility of replacing routine cerebral CT angiography (CTA) with CT perfusion (CTP)-derived cerebral CTA by using ar...

Hippocampal-parietal directed connectivity mediates brain network reconfiguration between internal and external attention: An intracranial EEG study.

Attention is a cornerstone of cognitive function, and understanding its neural mechanisms is of great significance for both cognitive science and clin...

Ultrastructural Evidence of Altered Dendritic Morphology in the Prefrontal Cortex of Alzheimer's Model Rats.

Alzheimer's disease (AD) is a neurodegenerative disorder with synaptic pathology as a core theme in aging-related research. Conventional imaging and l...

Ultrasensitive Fluorescent Detection of Sarin Simulant Vapor via Micropit Array-Mediated Mass Transfer and Photonic Modulation.

Gas-solid interface fluorescent sensors hold considerable promise for nerve agent vapor detection. However, their sensitivity is limited by insufficie...

Multimodal AI for Alzheimer Disease Diagnosis: Systematic Review of Datasets, Models, and Modalities.

BACKGROUND: Early detection of Alzheimer disease (AD) is essential for timely intervention; yet, diagnostic performance varies widely across modalitie...

Effectiveness of Thoracic Paravertebral Block in Minimally Invasive Vertebroplasty: A Retrospective Cohort Study Focusing on Pain Trajectories and Sagittal Spinal Alignment.

PURPOSE: To evaluate the effectiveness of ultrasound-guided thoracic paravertebral block (TPVB) combined with general anesthesia (GA) versus GA alone ...

Nonlinear dynamical analysis of chaos, fractal complexity, and entropy in EEG signals during music therapy in ICU burn patients.

Music therapy (MT) is known to influence brain dynamics; however, its effects on nonlinear electroencephalogram (EEG) characteristics in clinical sett...

Robot-Assisted Therapy for Upper Limb Rehabilitation After Stroke: Umbrella Review.

BACKGROUND: Stroke is a leading cause of long-term upper limb disability, severely impacting patients' independence and quality of life. Robot-assiste...

Personalized prediction of initial valproic acid dose in children with epilepsy using machine learning techniques.

INTRODUCTION: Valproic acid (VPA) is widely prescribed antiepileptic drug in children because of its broad-spectrum efficacy. However, marked inter-in...

EEG-RCPNet: a cross-subject cognitive impairment detection model based on limited-channel eeg signals.

Development and validation of a prediction model for long-term cognitive frailty risk in stroke patients based on CHARLS data.

BACKGROUND: This study aimed to develop and validate machine learning (ML) models for predicting the risk of cognitive frailty in community-dwelling e...

Establishment of a novel deep learning-based method for gait analysis after peripheral nerve injury in pigs.

BACKGROUND: Peripheral nerve injury with deficits has poor functional prognosis, making motor function assessment during nerve regeneration crucial. R...

Automated estimation of perivascular space and brain morphometry from deep learning-reconstructed three-dimensional T1-weighted MRI: comparison with the conventional technique.

PURPOSE: To evaluate the utility of deep learning-based reconstruction (DLR) three-dimensional T1-weighted imaging (T1-WI) in improving fine structura...

Optimized Complex-Valued Spatio-Temporal Graph Convolutional Networks for attention deficit hyperactivity disorder detection in pediatric EEG signals.

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental condition affecting mood, anxiety, learning, and sleep. Electroencephalogram (...

Prediction of depressive episodes based on clinical features, cognitive characteristics, inflammation-related proteins, and EEG data.

The absence of clinically validated biomarkers and objective diagnostic protocols hinders the accurate and effective diagnosis of depression. Although...

A vision transformer model for the detection of glaucoma from optic disc photographs.

This study uses a deep learning algorithm to analyze optic disc photographs (ODPs) and classify eyes as glaucomatous or healthy based on optic nerve a...

Neuroanatomy reflects individual variability in impulsivity in youth.

Individual differences in neural circuits underlying emotional regulation, motivation, and decision-making are implicated in many psychiatric illnesse...