Neurology

Robust validation of neuroimaging and clinical models via the SAR method: A case study based on the ADNI dataset.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and substantial brain atrophy. Early and accurate prediction of disease progression and staging is crucial for timely intervention and effective treatment planning. Previous studies, including those based on artificial intelligence techniques, have employed neuroimaging, biomarkers and clinical ...

Predicting amyotrophic lateral sclerosis stage based on multi-parameter ultrasound: development and validation of an interpretable machine learning model.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) lacks sensitive, objective staging tools to guide clinical management and trials. Existing methods have limited granularity and rely on subjective assessment, while biomarker and imaging approaches can be invasive or impractical for serial use. Ultrasound is a safe, portable imaging modality that can detect neuromuscular changes, but it has not yet b...

Dysfunction of the neurovascular unit as a temporal driver in Alzheimer's pathogenesis.

Extensive studies have shown that cerebrovascular dysfunction is a critical factor in the onset and progression of Alzheimer's disease (AD). Neurovasc...

From Bio-Interface Materials to Neural Integration: The Next-Generation Brain-Machine Interfaces Powered by Hydrogels.

Brain-machine interfaces (BMIs), which serve as revolutionary tools for neural recording, modulation, and rehabilitation, are highly dependent on the ...

Urinary Proteomics: Biological Foundations, Analytical Frameworks, and Clinical Translation Across Human Diseases.

Urinary proteomics has swiftly emerged as a formidable tool for the identification of non-invasive biomarkers and the surveillance of diseases. The pr...

Machine learning model based on plasma proteomics for the identification of Parkinson's disease.

Developing reliable biomarkers capable of differentiating Parkinson's disease from other neurological conditions is crucial for both patient care and ...

The ISLES'24 Dataset: A Multimodal Stroke Imaging Dataset with Hyperacute CT, Acute Postinterventional MRI, and 3-month Clinical Outcomes.

Stroke remains a major global health burden (1,2), although outcomes have improved substantially through imaging-guided therapy and endovascular reper...

A deep dive into ferritin nanoparticle advancements: experimental and computational perspectives.

Ferritin, a natural iron-storage protein, has emerged as a versatile platform in nanotechnology and biomedicine due to its biocompatible 12 nm nanocag...

A comparative evaluation of EEG-based deep learning models for schizophrenia detection with cross-dataset validation and explainable AI.

OBJECTIVES: Schizophrenia is a neuropsychiatric disorder that affects emotional, behavioral, and brain functions that can be tracked using electroence...

Enhancing CNN regressors with contour encoding and self-supervision for improved 3D/2D X-ray to CT registration in spinal surgery navigation.

With advances in deep learning, regression-based methods have shown promising results in 3D/2D medical image registration. However, strict intraoperat...

An EEG-EMG-kinematics dataset from wrist pointing tasks for biomarker research in neurorehabilitation.

This work presents a multimodal dataset containing synchronized electroencephalography (EEG), electromyography (EMG), and kinematic recordings acquire...

CuAgent provides a RAG-assisted intelligent framework to investigate cuproptosis.

Cuproptosis is a novel form of regulated cell death driven by intracellular copper accumulation, leading to lipoylated protein aggregation and Fe-S cl...

RMETNet: A cross-subject motor imagery EEG signal classification model based on TSLANet and riemannian geometry features.

Motor imagery electroencephalogram (MI-EEG) analysis is essential for natural interaction and autonomous control in brain-computer interfaces (BCIs). ...

Physiological predictors of respiratory motor plasticity: A machine-learning reappraisal of phrenic motor facilitation.

Acute intermittent hypoxia (AIH)-induced phrenic long-term facilitation (pLTF) is a well-established form of respiratory motor plasticity and a subtyp...

MRI-based machine learning model to distinguish hippocampal sclerosis (HS) ILAE type 1 and no HS gliosis only in medial temporal lobe epilepsy.

PURPOSE: Despite recent advances in preoperative work-up of drug resistant medial temporal lobe epilepsy (MTLE), predicting post-surgical seizure and ...

Machine learning and meningitis prediction in pediatric invasive pneumococcal disease: a retrospective single-center study.

Evaluating data heterogeneity's impact on convolutional neural network performance in medical imaging.

PURPOSE: Machine learning in medical imaging (MIML) is critical to computer-aided diagnostics. However, data heterogeneity-variation in medical data a...

Enhancing Target Recognition Performance in SSVEP-Based Brain-Computer Interfaces via Deep Neural Networks with Pyramid Squeeze Attention.

Steady state visual evoked potential (SSVEP)-based brain-computer interfaces have been widely studied for their fast response speeds and high informat...

Exploring Information Access in Aging Populations and Those With Dementia and Mild Cognitive Impairment in the United Kingdom: Survey and Focus Group Study.

BACKGROUND: With the growing aging population, technology that supports independent living is increasingly important. Web search systems are well esta...

Letter to the Editor on: Predicting Postoperative Neurological Outcomes in Metastatic Spinal Tumor Surgery Using Machine Learning.