Neurology

Do children with cerebral palsy dream of electric legs? The effects of robot-assisted gait training.

Training intensity of robot-assisted gait training in children with cerebral palsy.

AIM: We compared three different intensities of robot-assisted gait training (RAGT) for achieving fa...

Monolithic 2D Perovskites Enabled Artificial Photonic Synapses for Neuromorphic Vision Sensors.

Neuromorphic visual sensors (NVS) based on photonic synapses hold a significant promise to emulate t...

Clinical Outcome Analysis of Robot-Assisted Pedicle Screw Insertion in the Treatment of Ankylosing Spondylitis Complicated with Spinal Fractures.

BACKGROUND: Vague spinal anatomical landmarks in patients with ankylosing spondylitis (AS) make intr...

Early inner plexiform layer thinning and retinal nerve fiber layer thickening in excitotoxic retinal injury using deep learning-assisted optical coherence tomography.

Excitotoxicity from the impairment of glutamate uptake constitutes an important mechanism in neurode...

Molecular Investigation and Preliminary Validation of Candidate Genes Associated with Neurological Damage in Heat Stroke.

Heat stroke (HS) is a severe medical condition characterized by a systemic inflammatory response tha...

Effect of Robotic Rehabilitation on Hand Functions and Quality of Life in Children With Cerebral Palsy: A Prospective Randomized Controlled Study.

OBJECTIVE: This study aimed to examine the impact of robotic hand rehabilitation on hand function an...

Point-Of-Care low-field MRI in acute Stroke (POCS): protocol for a multicentric prospective open-label study evaluating diagnostic accuracy.

INTRODUCTION: Fast and accurate diagnosis of acute stroke is crucial to timely initiate reperfusion ...

The coherence between PSMC6 and α-ring in the 26S proteasome is associated with Alzheimer's disease.

Alzheimer's disease (AD) is a heterogeneous age-dependent neurodegenerative disorder. Its hallmarks ...

Systematic comparison of 3D Deep learning and classical machine learning explanations for Alzheimer's Disease detection.

Black-box deep learning (DL) models trained for the early detection of Alzheimer's Disease (AD) ofte...

Reminiscent music therapy combined with robot-assisted rehabilitation for elderly stroke patients: a pilot study.

BACKGROUND: Although some studies suggest that robot-assisted technology can significantly improve u...

Introducing Region Based Pooling for handling a varied number of EEG channels for deep learning models.

INTRODUCTION: A challenge when applying an artificial intelligence (AI) deep learning (DL) approach ...

Deep learning-based white matter lesion volume on CT is associated with outcome after acute ischemic stroke.

BACKGROUND: Intravenous thrombolysis (IVT) before endovascular treatment (EVT) for acute ischemic st...

Mapping Extracellular Space Features of Viral Encephalitis to Evaluate the Proficiency of Anti-Viral Drugs.

The extracellular space (ECS) is an important barrier against viral attack on brain cells, and dynam...

Special Issue-Biosensors and Neuroscience: Is Biosensors Engineering Ready to Embrace Design Principles from Neuroscience?

In partnership with the Air Force Office of Scientific Research (AFOSR), the National Science Founda...

Exploring Convolutional Neural Network Architectures for EEG Feature Extraction.

The main purpose of this paper is to provide information on how to create a convolutional neural net...

Navigation Learning Assessment Using EEG-Based Multi-Time Scale Spatiotemporal Compound Model.

This study presents a novel method to assess the learning effectiveness using Electroencephalography...

Predictive power of gait and gait-related cognitive measures in amnestic mild cognitive impairment: a machine learning analysis.

INTRODUCTION: Gait disorders and gait-related cognitive tests were recently linked to future Alzheim...

Development and validation of outcome prediction model for reperfusion therapy in acute ischemic stroke using nomogram and machine learning.

OBJECTIVE: To develop logistic regression nomogram and machine learning (ML)-based models to predict...

NADOL: Neuromorphic Architecture for Spike-Driven Online Learning by Dendrites.

Biologically plausible learning with neuronal dendrites is a promising perspective to improve the sp...

Using deep learning to quantify neuronal activation from single-cell and spatial transcriptomic data.

Neuronal activity-dependent transcription directs molecular processes that regulate synaptic plastic...