Neurology

AI-Enhanced Nanophotonic Heterochain Sensor Enables Multiplexed Biomarker Detection across Serum, Urine, and Saliva for Stroke Differentiation.

Background: Rapid and accurate identification of stroke subtype is critical for timely intervention, yet current diagnostic assays are limited by long turnaround times, dependency on centralized laboratories, and insufficient sensitivity in the ultra-early stage. Methods: We developed a nanophotonic heterochain biosensing platform integrated with deep learning-assisted image analysis for multiplex...

Subject-independent emotion recognition with EEG bispectral quadratic phase coupling features and explainable machine learning.

Subject-independent emotion recognition from electroencephalography (EEG) is constrained by nonlinear neural dynamics and inter-subject variability. This study characterises nine bispectral quadratic phase coupling (QPC) descriptors extracted from frontal EEG rhythms of the DEAP dataset, selects a compact subset via a genetic algorithm under nested leave-one-subject-out (LOSO) cross-validation, an...

Altered EEG markers of reward learning during abstinence in alcohol dependence: A probabilistic reversal learning study.

OBJECTIVE: This study investigated neurophysiological and behavioural adaptations in reward learning and decision making which may contribute to the d...

The Brain Imaging and Neurophysiology Dataset of large-scale multimodal neural data.

The Brain Imaging and Neurophysiology Dataset (BIND) represents one of the largest multi-institutional, multimodal, clinical neuroimaging repositories...

Improved Prediction of Invasive Bacterial Infections in Febrile Infants Using Machine Learning.

BACKGROUND: Existing guidelines for febrile infants aged 8 to 60 days use clinical appearance, age, and laboratory test results to assess the risk of ...

Prediction of cognitive impairment through speech data analysis: A comparative evaluation of deep learning models.

BACKGROUND: The early detection of cognitive impairments, such as mild cognitive impairment (MCI) and Alzheimer's disease (AD), is essential for timel...

Integrating multi-omics analysis to identify potential biomarkers and regulatory networks of ischemic stroke.

Ischemic stroke (IS) is a leading cause of death and long-term disability worldwide, with a complex and multifactorial pathophysiology that is still i...

A hybrid CNN-GCN framework for interpretable Alzheimer's disease diagnosis from MRI scans.

Medical image analysis for Alzheimer's Disease (AD) diagnosis faces two key challenges: capturing spatial dependencies between anatomically connected ...

Differential cerebral white matter tract alterations in generalized anxiety disorder revealed by ultra-high-field 7 T diffusion-weighted imaging.

Generalized anxiety disorder (GAD) is characterized by chronic worry and emotional dysregulation, yet its underlying white matter (WM) microstructural...

A translational multimodal machine-learning prototype predicting valproate response in epilepsy treatment.

OBJECTIVE: Epilepsy affects ~1% of the global population and often requires lifelong antiseizure medication (ASM) therapy. Valproic acid (VPA) is a co...

Multimodal MRI marker of cognition explains the association between cognition and mental health in the UK Biobank.

Cognitive dysfunction often co-occurs with psychopathology. Advances in neuroimaging and machine learning have led to neural indicators that predict i...

Explainable Deep Learning Framework for Multimodal Brain Tumor Classification via Neuroimaging Attribute Extraction.

Accurate grading of brain tumors from multiparametric MRI is a critical step in treatment planning, yet deep learning models trained for this task rem...

Comparing Stakeholders' Perspectives on Parkinson Disease Management and Digital Technologies: Exploratory International Survey.

BACKGROUND: Parkinson disease (PD) is a progressive neurodegenerative disorder that poses complex challenges for persons with PD, informal caregivers,...

Postangiography Prediction of Renal Replacement Therapy in Acute Myocardial Infarction-Related Cardiogenic Shock: Least Absolute Shrinkage and Selection Operator Nomogram Development and Validation.

BACKGROUND: Acute kidney injury critically impacts outcomes in cardiogenic shock secondary to acute myocardial infarction (CS-AMI). Acute kidney injur...

HRGNN: Hierarchical region-aware graph neural network for interpretable EEG-based emotion recognition.

Neurophysiological studies have shown that cortical information processing involves complex interactions among multiple functional brain regions. Howe...

SHAP analysis of an improved EEG-based mental workload classification framework: utilizing data augmentation and explainable AI.

Mental workload (MWL) classification using electroencephalogram (EEG) signals is crucial for cognitive neuroscience and is also a challenging research...

National trends and comparative outcomes of insulin versus non-insulin therapy in acute ischemic stroke patients treated with mechanical thrombectomy: a retrospective cohort study using the national inpatient sample.

Background diabetes mellitus is prevalent among patients with acute ischemic stroke (AIS). The prognostic significance of long-term insulin treatment ...

Artificial intelligence and neurodegeneration: state of the art and explainability.

BACKGROUND: Alzheimer disease (AD) and Parkinson disease (PD) are an increasing healthcare concern and growing cause of disability in our century. The...

Single-trial EEG-based classification reveals Instrument-Specific Timbre Perception via traditional Machine Learning Classifiers.

Many users of hearing aids report challenges when listening to music. In the future, it may be possible to develop hearing aids that monitor brain act...

Limited Femoral Joint-Line Alteration and Preserved Extension Lateral Laxity Enhanced Early Outcomes in restricted kinematic alignment TKA.

BACKGROUND: Restricted kinematic alignment (rKA) total knee arthroplasty (TKA) seeks to restore native alignment while limiting excessive correction. ...