Neurology

Classifying Alzheimer's Disease Using a Finite Basis Physics Neural Network.

The disease amyloid plaques, neurofibrillary tangles, synaptic dysfunction, and neuronal death gradually accumulate throughout Alzheimer's disease (AD), resulting in cognitive decline and functional disability. The challenges of dataset quality, interpretability, ethical integration, population variety, and picture standardization must be addressed using deep learning for the functional magnetic r...

Fusion of brain imaging genetic data for alzheimer's disease diagnosis and causal factors identification using multi-stream attention mechanisms and graph convolutional networks.

Correctly diagnosing Alzheimer's disease (AD) and identifying pathogenic brain regions and genes play a vital role in understanding the AD and developing effective prevention and treatment strategies. Recent works combine imaging and genetic data, and leverage the strengths of both modalities to achieve better classification results. In this work, we propose MCA-GCN, a Multi-stream Cross-Attention...

Assessing the prognostic impact of body composition phenotypes on surgical outcomes and survival in patients with spinal metastasis: a deep learning approach to preoperative CT analysis.

OBJECTIVE: The prognostic significance of body composition phenotypes for survival in patients undergoing surgical intervention for spinal metastases ...

DCSENets: Interpretable deep learning for patient-independent seizure classification using enhanced EEG-based spectrogram visualization.

Neurologists often face challenges in identifying epileptic activities within multichannel EEG recordings, requiring extensive hours of analysis. Comp...

Development of a Virtual Robot Rehabilitation Training System for Children with Cerebral Palsy: An Observational Study.

This paper presents the development of a robotic system for the rehabilitation and quality of life improvement of children with cerebral palsy (CP). T...

Multivariate Modelling and Prediction of High-Frequency Sensor-Based Cerebral Physiologic Signals: Narrative Review of Machine Learning Methodologies.

Monitoring cerebral oxygenation and metabolism, using a combination of invasive and non-invasive sensors, is vital due to frequent disruptions in hemo...

Machine Learning Recognizes Stages of Parkinson's Disease Using Magnetic Resonance Imaging.

Neurodegenerative diseases (NDs), such as Alzheimer's disease (AD) and Parkinson's disease (PD), are debilitating conditions that affect millions worl...

Effect of Ultrasound-Guided Extra-Prevertebral Fascial Suprascapular Nerve and Infraclavicular Brachial Plexus Block on Postoperative Analgesia and Phrenic Nerve Function in Shoulder Arthroscopy: A Pilot Study.

PURPOSE: The suprascapular nerve is situated between the prevertebral fascia and the superficial layer of deep cervical fascia and on the surface of t...

Neuropathology of focal epilepsy: the promise of artificial intelligence and digital Neuropathology 3.0.

Focal lesions of the human neocortex often cause drug-resistant epilepsy, yet ​surgical resection of the epileptogenic region has been proven as a suc...

Augmented Effect of Combined Robotic Assisted Gait Training and Proprioceptive Neuromuscular Facilitation-irradiation Technique on Muscle Activation and Ankle Kinematics in Hemiparetic Gait: A Preliminary Study.

BackgroundProprioceptive neuromuscular facilitation (PNF) alone has limited effectiveness in restoring gait, while robotic-assisted gait training (RAG...

Exploring transparency: A comparative analysis of explainable artificial intelligence techniques in retinography images to support the diagnosis of glaucoma.

Machine learning models are widely applied across diverse fields, including nearly all segments of human activity. In healthcare, artificial intellige...

A U-Net based partial convolutional time-domain separation model to identify motor units from surface electromyographic signals in real time.

This study proposed a U-Net based partial convolutional time-domain model for a real-time high-density surface electromyography (HD-sEMG) decompositio...

Explainable machine learning on clinical features to predict and differentiate Alzheimer's progression by sex: Toward a clinician-tailored web interface.

Alzheimer's disease (AD), the most common neurodegenerative disorder world-wide, presents sex-specific differences in its manifestation and progressio...

Accuracy deficits during robotic time-constrained reaching are related to altered prefrontal cortex activity in children with cerebral palsy.

BACKGROUND: The prefrontal cortex (PFC) is an important node for action planning in the frontoparietal reaching network but its role in reaching in ch...

Machine Learning and Statistical Analyses of Sensor Data Reveal Variability Between Repeated Trials in Parkinson's Disease Mobility Assessments.

Mobility tasks like the Timed Up and Go test (TUG), cognitive TUG (cogTUG), and walking with turns provide insights into the impact of Parkinson's dis...

Enhancing Deep-Learning Classification for Remote Motor Imagery Rehabilitation Using Multi-Subject Transfer Learning in IoT Environment.

One of the most promising applications for electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is motor rehabilitation through motor ima...

Cholinesterase Inhibitory Activity of Paeoniflorin: Molecular Dynamics Simulation, and In Vitro Mechanistic Investigation.

Alzheimer's disease (AD), a neurological disorder, is one of the major reasons for memory loss in the world. AD is characterized by a sequela of cogni...

Catalpol promotes hippocampal neurogenesis and synaptogenesis in rats after multiple cerebral infarctions by mitochondrial regulation: involvement of the Shh signaling pathway.

INTRODUCTION: Ischemic stroke greatly threatens human life and health. Neuro-restoration is considered to be the critical points in reestablishing neu...

Association between Alzheimer's disease pathologic products and age and a pathologic product-based diagnostic model for Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) has a major negative impact on people's quality of life, life, and health. More research is needed to determine t...

An integrated machine learning approach delineates an entropic expansion mechanism for the binding of a small molecule to α-synuclein.

The mis-folding and aggregation of intrinsically disordered proteins (IDPs) such as α-synuclein (αS) underlie the pathogenesis of various neurodegener...