Neurology

Performance of machine learning algorithms in diffusion tensor imaging of movement disorders: an exploratory meta-analysis.

BACKGROUND: Machine learning (ML) applied to diffusion tensor imaging (DTI) has emerged as a promising tool for detecting microstructural brain alterations in movement disorders. However, existing studies vary widely in design, sample size, imaging pipelines, and analytic rigor, resulting in high methodological heterogeneity that limits quantitative comparability. OBJECTIVES: This exploratory meta...

TPCNet: A Temporal Periodicity Convolutional Network for motor imagery EEG decoding in stroke patients.

BACKGROUND: Stroke caused by vascular rupture or blockage has high incidence and leads to significant disability. Motor imagery (MI) electroencephalogram (EEG) is a promising approach to understanding and addressing stroke-related motor impairments. However, the practical application of EEG-based rehabilitation is hindered by an insufficient understanding of the task-specific features and complex ...

Machine learning model predicts prognosis in SARS-CoV-2-infected autoimmune encephalitis patients.

OBJECTIVE: To assess the neurological prognosis of patients with autoimmune encephalitis (AE) after severe acute respiratory syndrome coronavirus 2 (S...

Real-world use of PACS-integrated automated spine numbering in MRI.

PURPOSE: Traditional methods of vertebral identification have predominantly relied on relative approaches, depending on discernible landmarks. Artific...

Probabilistic mapping and automated segmentation of human brainstem white matter bundles.

Brainstem white matter (WM) bundles are essential conduits for neural signals that modulate homeostasis and consciousness. Their architecture forms th...

An interpretable machine learning framework with data-informed imaging biomarkers for diagnosis and prediction of Alzheimer's disease.

Machine learning methods based on imaging and other clinical data have shown great potential for improving the early and accurate diagnosis of Alzheim...

VSSI2p-Net: Physics-guided deep unfolding with L2p-norm and variation sparsity for EEG source imaging.

Electroencephalogram (EEG) source imaging (ESI) is highly underdetermined, which poses a long-standing challenge in neuroimaging. Traditional methods ...

Optimizing deep CNN architecture via hybrid Harris Hawks arithmetic algorithm for EEG meditation classification.

Meditation is a widely recognized practice that enhances mental well-being and cognitive function. Despite advances in EEG meditation neuroscience, ch...

LEFF-ViT: A locally enhanced vision transformer framework for accurate Alzheimer's Disease classification from brain MRI.

Early and accurate diagnosis of Alzheimer's Disease (AD) is critical for effective disease management and progression delay. Researches have been done...

Spatial amyloid-informed multimodal brain age as an early marker of Alzheimer's-related vulnerability and risk stratification.

BACKGROUND: Brain age gap (BAG)-the difference between predicted and chronological age-captures neurobiological aging, but MRI-only models insufficien...

Self-induced large pitch artificial muscles with giant stroke for soft robotic applications.

Achieving large initial coil pitches and contractile strokes in twisted and coiled polymer artificial muscles often requires complex and multi-step fa...

Theoretical and applied research on spatio-temporal graph attention networks for single-trial P300 detection.

Objective.Accurate detection of single-trial P300 ERPs (event-related potentials) is crucial for developing high-performance non-invasive BCIs (brain-...

Diagnostic-Mediated Interactive Superlattices Engineering for Threshold-Saltatory Visual Assessment of Tuberculous Meningitis.

Nonlinear dynamic monitoring is crucial for assessing l-tryptophan (l-Trp) dysregulation progression in tuberculous meningitis (TBM); yet remains chal...

Developing and validating a machine learning model for predicting post-thrombolysis seizures in acute ischemic stroke.

Post-stroke seizures (PSS) manifests variably due to ischemic brain injury, yet its risk factors remain unclear. This study developed a machine learni...

Enhancing Anesthetic Depth Assessment via Unsupervised Machine Learning in Processed Electroencephalography Analysis: Novel Methodological Study.

BACKGROUND: General anesthesia comprises 3 essential components-hypnosis, analgesia, and immobility. Among these, maintaining an appropriate hypnotic ...

Artificial intelligence-powered analysis of operating room turnover: impact of instrument burden.

OBJECTIVE: Improved operating room (OR) efficiency provides greater patient throughput, reduced costs, and maximal patient care. The aim of this study...

Mass spectrometry-based human spatial omics: fundamentals, innovations, and applications.

Mass spectrometry-based spatial omics is a powerful approach for visualizing the spatial organization of proteins, metabolites, lipids, and other biom...

LSTM-GPT-4 Integration for Interpretable Biomedical Signal Classification.

BACKGROUND: Approximately 3.8 billion people lack access to essential health services, and diagnostic interpretation remains a major bottleneck in rem...

Semaphorin 7A Regulates Axon Outgrowth in Subcutaneous White Adipose Tissue.

PURPOSE: Adipose tissue innervation is critical for regulating lipolysis, adipogenesis, and thermogenesis, yet the mechanisms that establish and maint...

Offline AI-assisted fundus imaging for preliminary screening of childhood glaucoma.

PURPOSE: To evaluate the feasibility and performance of AI-assisted portable fundus photography in children with glaucoma. METHODS: This case series d...