Neurology

From Observation to Prediction: Machine Learning Analysis of Progression of Visual loss in Nonarteritic Anterior Ischemic Optic Neuropathy.

OBJECTIVE: To determine whether combinations of modifiable clinical/systemic risk factors and structured trial variables predict early disease progression in acute NAION, as a clinical-feature benchmark, using machine learning for multivariable analysis. DESIGN: Secondary analysis of a multicenter, double-masked, sham-controlled, randomized clinical trial. SUBJECTS: We analyzed 589 study eyes with...

Efficacy of a social robot-assisted training for socio-cognitive and cognitive functions in Parkinson's disease: A comparison with an active control group.

This study evaluated the efficacy of 12-week of Socially Assistive Robot (SAR) training program on socio-cognitive and cognitive functions in 11 individuals with early-stage Parkinson's disease (PD) and 9 healthy older adults. Both groups underwent the same intervention with the Pepper robot. Pre- and post-assessments were analyzed using mixed-design repeated-measures ANOVAs. Results showed high a...

Recognizing EEG responses to active TMS vs. sham stimulations in different TMS-EEG datasets: A machine learning approach.

Transcranial Magnetic Stimulation with simultaneous Electroencephalogram (TMS-EEG) allows for the assessment of neurophysiological properties of corti...

Silencer variants are key drivers of gene up-regulation in Alzheimer's disease.

The genetic mechanisms of ~90% of Alzheimer's disease (AD)-associated variants residing in noncoding DNA remain poorly understood. To address this, we...

Robot-assisted therapy following stroke: what effects on quality of life, cognitive and psychosocial outcomes? A systematic review.

OBJECTIVE: Robot-assisted therapy (RAT) has shown promise in post-stroke motor recovery. However, its effects on non-motor outcomes remain unclear. Th...

Postmarketing Safety of Transcranial Magnetic Stimulation: A 10-Year MAUDE Database Analysis of Adverse Events and Technological Advancements.

Background: Transcranial magnetic stimulation (TMS) is an FDA-cleared neuromodulation technique with expanding clinical applications beyond major depr...

Enhancing low-dose PET image recovery via edge perception and Mamba-guided network architecture.

Positron emission tomography (PET) is a sensitive molecular imaging technique used extensively in cancer diagnosis, neurology, and cardiovascular dise...

Disrupted and reorganized connectivity of brain networks in multiple sclerosis: a systematic review and meta-analysis of resting-state functional MRI.

BACKGROUND: Abnormalities in large-scale brain functional networks are implicated in multiple sclerosis (MS) and linked to clinical impairments, but r...

Association between leukocyte telomere length and neurodegenerative diseases: a prospective cohort in the UK Biobank.

BACKGROUND: This study aims to investigate the association between leukocyte telomere length (LTL) and the risk of incident NDDs using a large-scale c...

NeuroCLIP: A Multimodal Contrastive Learning Method for rTMS-treated Methamphetamine Addiction Analysis.

Methamphetamine dependence poses a significant global health challenge, yet its assessment and the evaluation of treatments like repetitive transcrani...

Subject-Adaptive EEG Decoding via Filter-Bank Neural Architecture Search for BCI Applications.

Individual differences pose a significant challenge in brain-computer interface (BCI) research. Designing a universally applicable network architectur...

Bioinspired adaptive pupil reflex based on liquid-metal shape-shifters for machine vision.

Inspired by the evolutionary diversification of biological eyes for environmental adaptation, recently emerged artificial counterparts offer a variety...

Machine Learning-Guided Engineering of Protein Phase Separation Properties in Immune Regulation.

Phase separation (PS) underpins compartmentalization in living cells, facilitating the formation of membraneless organelles and the regulation of cell...

Physician Perspectives on Web-Based Real-World Statistics for Better-Informed Drug Selection in Epilepsy: Mixed Methods Study.

BACKGROUND: Lately, big data studies have shown promise in using patient characteristics to rank the likelihood of retention of antiseizure medication...

Deep Learning-Based Epileptic Seizure Detection from EEG Signals and PPG signals Using LSTM and CNN Models.

Epilepsy is a chronic neurological disorder characterized by recurrent and unpredictable seizures that significantly affect patients' health and quali...

Terrestrial and Airborne Laser Scanning Dataset of Trees in the Shivalik Range, India with Field Measurements and Leaf-Wood Classifications.

Annotated datasets are essential for training and evaluating machine learning models in forest ecology. This dataset provides high-resolution, annotat...

Polysomnography Dataset for Sleep Analysis in Ischemic Stroke Patients.

Sleep architecture and integrity significantly influence neural recovery and cognitive restoration. These are particularly relevant in ischemic stroke...

A physics-informed graph neural network to approximate docking-based binding affinity for DYRK2 in Alzheimer's drug repurposing.

Alzheimer's disease (AD) requires the discovery of new therapeutic targets, but traditional molecular docking methods for virtual screening are often ...

AI-Enabled Surveillance and Modelling for Counterfeit Botulinum Toxin A: Risk Projection, Patient Safety, and Systemic Reform of Pharmacovigilance.

BACKGROUND: Counterfeit Botulinum Toxin A (BoNT-A) poses a growing global threat, particularly in aesthetic medicine where regulatory oversight is min...

A Multi-parametric MRI and Machine Learning Study of Cerebellar Structure in Youth with Neurofibromatosis Type 1.

BACKGROUND: Neurofibromatosis type 1 (NF1) is a genetic condition caused by pathogenic variants of the NF1 gene. While alterations in cerebral structu...