Neurology

Synaptic dysfunction-related gene expression spatially correlates with widespread cortical network atrophy in temporal lobe epilepsy.

Temporal lobe epilepsy (TLE) exhibits marked lateralized gray matter alterations, yet whole-brain network vulnerability patterns, particularly those independent of seizure laterality, remain incompletely understood. Furthermore, the spatial correspondence between macroscopic network disruptions and underlying molecular architectures lacks systematic characterization, limiting mechanistic insights ...

Robust end-to-end stratification of amyotrophic lateral sclerosis patients via recurrent variational autoencoder and consensus clustering.

OBJECTIVE: This study aims to develop a data-driven methodology for stratifying Amyotrophic Lateral Sclerosis (ALS) patients based on longitudinal disease progression patterns, using a novel deep learning framework that combines a Recurrent Variational Autoencoder (RVA) with consensus clustering to identify clinically meaningful subgroups. METHODS: The RVA integrates Peephole Long Short-Term Memor...

Speech-based machine learning for detecting neuropsychiatric symptoms in Alzheimer's disease.

BackgroundNeuropsychiatric symptoms (NPS) are common in Alzheimer's disease (AD) and mild cognitive impairment (MCI), yet their detection relies on su...

Artificial intelligence-driven data expansion for the validation of spinopelvic parameter correlations in asymptomatic subjects.

BACKGROUND: The study of spinopelvic alignment in asymptomatic individuals is essential for understanding physiological sagittal balance and establish...

Integrative Transcriptomic, Network, and Machine Learning Analyses Identify Genistein and Resveratrol-Associated Therapeutic Targets in Alzheimer's Disease.

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder characterized by complex molecular alterations across multiple brain regions. ...

Bioactive compounds for neuroinflammation and neuropathic pain management: molecular and cellular mechanisms.

Bioactive compounds are a promising multi-target strategy for managing neuroinflammation and chronic pain. Bioactive compounds such as Paeonol, Kaempf...

AI-Driven Design and Clinical Translation of Nucleotide-Peptide and Peptide-Drug Conjugates.

While nucleotide-peptide conjugates and peptide-drug conjugates demonstrate a considerable evolution as next-generation therapeutics, the successful t...

Evaluating the biological meaning of neural network decisions in EEG-based MCI detection.

OBJECTIVE: High accuracy in medical classification tasks does not ensure that neural networks reason in ways consistent with clinical or neurobiologic...

Accuracy of Automated Deep Learning versus Expert Clinicians for Diagnosis of Acute Lacunar Stroke on CT Perfusion.

BACKGROUND: Accurate diagnosis of lacunar stroke in the acute setting is challenging and often depends on MRI or clinical suspicion. CT perfusion (CTP...

A Hybrid Rule-Based and Large Language Model Artificial Intelligence Systems for Electrodiagnostic Reporting: Two-Center Retrospective Evaluation.

INTRODUCTION/AIMS: Electrodiagnostic (EDX) studies comprise nerve conduction studies (NCS) and needle electromyography (EMG). However, EDX reporting i...

AI-derived versus surgeon-performed instrumentation in adolescent idiopathic scoliosis: a biomechanical simulation analysis.

PURPOSE: Posterior spinal fusion (PSF) with pedicle screws is the standard treatment for adolescent idiopathic scoliosis (AIS) with curves > 45°, yet ...

Predicting diffusion-FLAIR mismatch from B1000 and ADC without FLAIR: A deep learning-based approach.

Diffusion-FLAIR mismatch (DFM), defined as the discrepancy between diffusion-weighted imaging (DWI) and FLAIR sequences, is a key imaging biomarker us...

Long-term exposure to wildfire smoke particulate matter and incident stroke: a US nationwide study.

BACKGROUND AND AIMS: Wildfire smoke is a growing public health concern, particularly due to its high fine particulate matter (PM2.5) concentrations. W...

Proteomic signatures of early retinal neurodegeneration in type 2 diabetes mellitus.

BACKGROUND: Retinal neurodegeneration is an early and independent feature of diabetic retinal disease and has been proposed as a window into the syste...

KGT: Knowledge-guided graph transformer for neurodegenerative disease diagnosis and brain age prediction with MRI.

Deep learning methods have significantly advanced the analysis of brain imaging data for various downstream tasks such as disease diagnosis and age pr...

High-frequency EEG synchronization modes as a stable biometric signature in humans.

Electroencephalography (EEG) offers a promising modality for biometric identification, though balancing performance, interpretability, and robustness ...

Deep learning-based neuroanatomical profiling reveals population-specific brain changes in multiple sclerosis: a large-scale Middle Eastern study.

BACKGROUND: Multiple sclerosis (MS) affects 2.8 million individuals worldwide, with Middle Eastern populations remaining underrepresented in neuroimag...

Performance comparison of a neuro-symbolic large language model system versus conventional AI models and human experts in cholangitis management.

BACKGROUND: Large language models (LLMs) have shown promising results in medical decision support; Background: Large language models (LLMs) have demon...

Deep learning-driven decoding of ubiquitination: from regulatory mechanisms to targeted protein degradation.

BACKGROUND: Ubiquitination is a highly dynamic post-translational modification that plays central roles in protein homeostasis, signal transduction, i...

Comorbidities for Predicting Progression Independent of Relapse Activity in Multiple Sclerosis Treated With B-Cell Depletion.

BACKGROUND: Progression independent of relapse activity (PIRA) has been shown to account for a majority of the disability accumulation in relapsing-re...