Neurology

Common biomarkers of Alzheimer disease and postoperative delirium.

Alzheimer disease (AD) and Postoperative delirium (POD) may share a common mechanism, but their shared genes and potential novel therapeutic targets remains unknown. We selected datasets GSE48350, GSE4226, and GSE16759 containing AD and control samples from the GEO database for AD model development, and use the GSE63061 dataset for AD model validation. The GSE163943 dataset containing PODs and con...

European Science for Health Research Needs and Priorities.

The European research landscape for developing new diagnostic, preventive, and therapeutic interventions is fraught with challenges. Scarcity of qualified talent, limited funding for laboratories and research infrastructure, geopolitical competition, and increasing complexity of data systems all require creativity, leadership, and vision to advance science and translate research results into effec...

Fluid-based biomarkers of amyotrophic lateral sclerosis: recent advances and future prospects.

Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disorder with no definitive cure. The absence of specific diagnostic biomarkers...

Predict neuromuscular performance in human epidural electrical stimulation: phase 1 trial interim results.

BACKGROUND: Epidural electrical stimulation (EES) has emerged as a promising therapy for restoring motor function in patients with paralysis. A primar...

TiON/NiOx Heterojunction Neuron for CMOS-Compatible Hardware Implementation of Activation Function in Neural Network.

This study presents a compact neuron device based on a PN heterojunction neuron to enable hardware-level deep neural networks (HDNNs) with improved in...

Assessment system for short-term lower cranial nerve dysfunction following medulla oblongata glioma surgery: risk stratification and optimal surgical strategy.

OBJECTIVE: Medullary gliomas pose significant surgical risks, particularly the risk of postoperative lower cranial nerve (LCN) dysfunction, which prof...

EEG-based dataset explicitly targets the transitions between sitting and standing for exploring neural activation patterns in Motor Imagery and execution.

This study presents the first publicly accessible electroencephalography (EEG) dataset explicitly targeting sit-to-stand and stand-to-sit transitions ...

Multi-scale dynamic cooperative attention network for Auditory Attention Detection.

Humans exhibit a remarkable capacity to concentrate on particular auditory inputs amid multiple simultaneous speakers, as seen in cocktail party setti...

RNA-centric approaches in amyotrophic lateral sclerosis: prospects for future therapeutics.

INTRODUCTION: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by profound clinical and molecular heterogeneity...

Explainable machine learning for the prediction of Alzheimer's disease-related cognitive impairment: a consensus feature selection approach.

BACKGROUND: Early identification of Alzheimer's disease-related cognitive impairment remains challenging, and existing machine learning (ML) models of...

A survey of deep learning techniques in detecting neurological disorders using MRI.

Magnetic resonance imaging (MRI) is widely regarded as the most reliable non-invasive imaging modality for detecting neurological disorders. However, ...

EEG biomarkers can predict early-stage Alzheimer's disease and correlate with intracerebral pathology: a multimodal machine learning study.

BACKGROUND: Early recognition of Alzheimer's disease (AD) is crucial for timely intervention and delaying disease progression. Electroencephalogram (E...

Different pattern: a new EEG-based method for mental performance detection.

BACKGROUND: This study focuses on detecting mental performance from EEG signals. It provides both classification and explanation results. For this pur...

A prototype-augmented graph representation learning framework for identifying brain disorder-associated genes and facilitating drug repurposing.

Many genetic loci were identified as associated with neuropsychiatric disorders and neurodegenerative disorders by Genome-wide association studies (GW...

Evaluating Equity in AI-Supported Functional Assessment: Agreement Between Clinician Judgment and Digital Metrics in Stroke Rehabilitation.

Evaluating agreement between AI-generated functional assessment metrics and clinician judgment in stroke rehabilitation is important for understanding...

Sex-specific molecular and cellular phenotypes during resolution of neuropathic pain in dorsal root ganglia.

The dorsal root ganglion (DRG) is examined for sex-dependent phenotypes in sensory neurons, satellite glial cells (SGCs), and local macrophages follow...

Advancing Alzheimer Disease Prediction With Large Language Model-Based Linguistic Feature Analysis: Development and Validation Study.

BACKGROUND: Alzheimer disease (AD) is a progressive neurodegenerative disorder with rapidly growing global prevalence. Early detection is critical for...

Capturing multi-disease states on a spectrum with machine learning and routine clinical data.

BACKGROUND: Diseases exist on spectra of risk factors, cellular perturbations, organ dysfunction, and clinical manifestations. It is unknown whether t...

Epilepsy seizure prediction based on ViT-2DCNN spatio-temporal fusion model.

OBJECTIVE: Epilepsy is a chronic neurological disorder characterized by recurrent and sudden seizures. Accurate prediction of epileptic seizures holds...

Multimodal phenotypic classification of generalized anxiety and panic using structural MRI data and psychosocial factors: machine learning results from the German National Cohort (NAKO) study.

Anxiety disorders are common and impairing mental health conditions. Using data from 26,378 adults in the German National Cohort Study (NAKO), we inve...