Neurology

Beyond FDG: a paradigm shift in precision localization of epileptogenic zones in refractory epilepsy using multimodal molecular imaging (PET/CT) and isotropic 3D MRI fusion.

Surgical resection for drug-resistant focal epilepsy relies on the precise presurgical localization of the epileptogenic zone (EZ). Although [1⁸F]FDG-PET has historically served as a diagnostic cornerstone, its clinical utility is often constrained by anatomy-metabolism decoupling. This non-specific glucose hypometabolism frequently extends beyond the true EZ due to the bystander effect, complicat...

A Framework for Predicting Neurofeedback Treatment Response in ADHD Using EEG Functional Connectivity and Genetic Algorithm-Driven Channel Selection.

In this article, we present a computational framework for predicting treatment response to neurofeedback (NF) among patients with Attention-Deficit/Hyperactivity Disorder (ADHD). The proposed framework uses functional brain connectivity analysis of electroencephalogram (EEG) signals acquired during an early-to-mid NF treatment window to classify participants as eventual responders or non-responder...

Smartphone-Based Brunnstrom Stage Classification of Hemiparetic Gait via Skeleton-Attention-LSTM-Inception Network.

OBJECTIVE: The accurate assessment of hemiparetic gait after stroke was essential for understanding the extent of motor impairment and for guiding reh...

Predicting gross-total resection in skull base chondrosarcoma: a multi-institutional machine learning study.

OBJECTIVE: The aim of this study was to develop and validate an anatomy-driven model that predicts gross-total resection (GTR) of skull base chondrosa...

Unmasking data leakage in EEG-ADHD literature: a rigorous, interpretable SOTA framework (DSAEN).

The field of translational Electroencephalogram-Artificial Intelligence (EEG-AI) faces a significant methodological challenge regarding epoch-wise dat...

First-pass aspiration versus stent retriever thrombectomy: machine learning study of treatment effect heterogeneity.

BACKGROUND: Randomized trials have shown equivocal outcomes between a direct aspiration as first-pass technique (ADAPT) and stent retriever (SR) strok...

Development and Validation of a Machine Learning Classification Algorithm for Differentiating Frontotemporal Dementia from Alzheimer's Disease Using Automated Brain MRI Volumetry.

BACKGROUND AND PURPOSE: Frontotemporal dementia (FTD) is characterized by frontal and anterior temporal lobe atrophy and progressive changes in behavi...

Safety of clonidine and dexmedetomidine in peripheral nerve blocks: limitations do not preclude relevance. Response to Br J Anaesth. 2026; 137: 299-300.

EEG-based automated evaluation of automotive sound quality using ensemble deep learning.

The evaluation of automotive sound quality is of considerable significance for improving driving comfort. However, existing methodologies suffer from ...

Early Identification of Recovery Potential After Acute Brain Injury Using Functional Near-Infrared Spectroscopy.

BACKGROUND: Accurate early prognostication in patients with acute brain injury remains a major challenge in neurocritical care. Conventional bedside a...

The past and future of neuropsychological assessment: Back to the origins to face digital technological changes.

BACKGROUND: The procedures used for the neuropsychological assessment of patients with dementia have been undergoing significant transformations relat...

Research advances in key genes and regulatory mechanisms of posttranslational modifications in Parkinson's disease.

BACKGROUND: The genesis of Parkinson's disease (PD), a common central neurodegenerative disorder, involves dysregulation of protein posttranslational ...

The underlying mechanisms of tDCS-Based cognitive enhancement in unpredictable task switching: A machine learning-based approach for EEG signal analysis.

Transcranial direct current stimulation (tDCS) enhances cognitive abilities yet has highly inconsistent outcomes, highlighting the need to clarify its...

MRI-free amyloid PET quantification using a deep learning model and white matter reference.

BackgroundAccurate quantification of standardized uptake value ratio (SUVR) in amyloid PET is essential for Alzheimer's disease (AD) diagnosis but typ...

Advancing spine connectomics and neural integration through machine learning and neuroengineering: a narrative review.

PURPOSE: Spinal connectomics is increasingly shifting understanding of the spinal cord from a simple reflex relay toward an active system that contrib...

Intelligent recognition and segmentation of anatomical structures in spinal endoscopy: a deep learning approach with 1000 annotated images.

BACKGROUND: Accurate intraoperative identification of anatomical structures is critical for ensuring the safety and efficacy of spinal endoscopic surg...

Large-Scale Plasma Proteomics Profiles for Predicting Atrial Fibrillation Associated Stroke Risk : Type of manuscript: Original Research.

BACKGROUND: Stroke is a major complication of atrial fibrillation (AF), and risk prediction using the congestive heart failure, hypertension, age, dia...

Large language models as a screening tool for the qualification of patients with Parkinson's disease for device-aided therapies.

Motor complications become increasingly prominent as Parkinson's disease (PD) progresses. Although device-aided therapies (DAT) improve symptoms contr...

Zebrafish as a versatile model in biomedical research, from disease modeling to regenerative medicine: a review.

Zebrafish are an effective animal model widely utilized in biomedical research. They are known for their rapid reproduction and substantial genetic si...

Ethical Considerations of Artificial Intelligence in Dementia Research: A Review of Reviews.

BACKGROUND AND OBJECTIVES: To examine the ethical considerations related to artificial intelligence technologies in dementia across diagnostic, manage...