Neurology

A novel and accurate EEG emotion classification model based on multiple attention local binary patterns.

BACKGROUND: Electroencephalography (EEG) signals play a crucial role in understanding brain activity because they provide useful information about real emotions and intentions. Many machine learning models have been used for automatic EEG-based emotion classification. However, previous studies remain limited by restricted feature representations and insufficient subject-independent validation. MET...

Mechanistic Insights into PET-MPs-Aggravated Ischemic Stroke via Integrated Computational-Experimental Approaches.

Microplastics, especially the environmentally pervasive polyethylene terephthalate microplastics (PET-MPs), are important environmental pollutants, and their potential risks to the health of the nervous system are increasingly a concern. Evidence indicates the presence of PET-MPs in human tissues, including the brain; however, their specific role in cerebrovascular diseases such as ischemic stroke...

Beyond clinical predictors: Determining the impact of psychosocial factors in chronic pain after spinal cord injury.

UNLABELLED: ▒: Although chronic pain is common after traumatic SCI, prognostic models have traditionally prioritized clinical injury characteristics, ...

Deep learning algorithm for automatic detection of acute ischemic stroke on noncontrast brain CT.

This study aimed to evaluate the diagnostic performance of a deep learning-based algorithm for detecting acute ischemic stroke (AIS), including small ...

AI-Driven discovery of brain-penetrant mTOR-independent autophagy enhancers for Alzheimer's disease.

Current Alzheimer's disease therapies offer limited efficacy and are often accompanied by significant side effects, underscoring the urgent need for n...

Machine learning model to estimate momentary status of activities of daily living in people with early-stage Parkinson's disease: an experimental study in home setting.

PURPOSE: The purpose of this study was to develop a model that estimates the momentary status of activities of daily living (ADLs) in people with Park...

An AI-Driven Pipeline for Localization, Segmentation, and Classification of Carpal Tunnel Syndrome Using Ultrasound Images of the Median Nerve.

BACKGROUND: Carpal tunnel syndrome (CTS), the most common peripheral neuropathy, is currently diagnosed by clinical suspicion supported by tools such ...

Development of Machine Learning-Driven Dual-Task Gait Test Model for Cognitive Impairment Screening.

OBJECTIVE: To develop an artificial intelligence (AI)-aided dual-task gait test model for scalable, high-throughput cognitive impairment screening. DE...

Frequency-Specific Alterations of Spontaneous Brain Activity in Stroke Patients with Upper Limb Motor Dysfunction: A Multi-Metric rs-fMRI and Machine Learning Study.

BACKGROUND: Post-stroke upper limb motor dysfunction is associated with complex alterations in brain function, but the frequency-specific characterist...

EEG monitoring in the operating room: current uses and perspectives - a narrative review.

CONTEXT AND IMPORTANCE: With over 300 million surgeries performed under general anaesthesia annually, optimising perioperative brain health has become...

A century of coffee and tea research in cognitive health and Alzheimer's disease: Structural, thematic, and translational insights (1911-2025).

BackgroundAlthough studies have explored tea and coffee in relation to Alzheimer's disease, no century-scale analysis has jointly examined both within...

Multimodal machine learning for early risk stratification of post-stroke cognitive impairment.

BackgroundPost-stroke cognitive impairment (PSCI) is a major vascular contributor to dementia, significantly impacting long-term recovery and quality ...

Integrating AI in Medicinal Chemistry for Accelerated Drug Discovery: A Comprehensive SAR (CSAR) Optimization Strategy and Discovery of Potent ALDH3A1 Inhibitors.

Developing potent, selective small-molecule inhibitors remains a major challenge in drug discovery. ALDH3A1, a detoxifying aldehyde dehydrogenase isof...

Analysis of electroencephalogram microstates and spectral power in children and adolescents with attention deficit hyperactivity disorder.

Attention-Deficit/Hyperactivity Disorder (ADHD) is a widely recognized neurodevelopmental disorder characterized by inattention, hyperactivity, and im...

Machine learning-based MRI radiomics identifies patients with degenerative cervical myelopathy and predicts baseline function.

PURPOSE: The diagnosis of Degenerative cervical myelopathy (DCM) relies on clinical evaluation and conventional MRI, yet early symptoms are subtle and...

Beyond Broca and Wernicke: Epilepsy surgery in the language areas.

Epilepsy surgery in language areas is challenged by the intricacies of presurgical workup and surgical planning. In recent decades, the view of langua...

Optimizing cervical breath sound acquisition protocol in dysphagia patients: an orthogonal experimental study.

BACKGROUND: Dysphagia is a highly prevalent condition among patients with stroke. Cervical auscultation serves as a crucial screening method for dysph...

Robotic Technologies for Hand Recovery After Stroke: A Reflection on Research Paths in Honor of Dr. Steven Wolf.

Over the past 35 years, my work has focused on developing and studying robotic technologies to promote hand and arm recovery after stroke. In this Poi...

Integrated machine learning and computational approaches for identifying NLRP3 inhibitors from Jeevaneeya Rasayana in epilepsy.

NOD-Like Receptor Protein-3 (NLRP3) inflammasome emerged as a crucial therapeutic target in epilepsy, playing a significant role in regulating inflamm...

Proteomic and transcriptomic analyses identify the role of RBM25 in the malignant progression of glioma.

Glioma is a primary tumor derived from central nervous system glial cells. RNA binding motif protein 25 (RBM25) has been implicated in glioma progress...