Neurology

Situation-Based Neuromorphic Memory in Spiking Neuron-Astrocyte Network.

Mammalian brains operate in very special surroundings: to survive they have to react quickly and eff...

Prediction of clinical efficacy of acupuncture intervention on upper limb dysfunction after ischemic stroke based on machine learning: a study driven by DSA diagnostic reports data.

OBJECTIVE: To develop a machine learning-based model for predicting the clinical efficacy of acupunc...

Retrospective cohort study based on the MIMIC-IV database: analysis of factors influencing all-cause mortality at 30 days, 90 days, 1 year, and 3 years in patients with different types of stroke.

OBJECTIVE: This study aims to evaluate key factors influencing the short-term and long-term prognosi...

Editorial: Protecting privacy in neuroimaging analysis: balancing data sharing and privacy preservation.

Enhancing home-based physical activity for neurodivergent children: adapting the program with AI and universal design.

PURPOSE: While it is common practice for schools across the United States to include neurodivergent ...

Inclusion of hypocretin-1 improved performance of poor sleep quality prediction for elderly patients with acute ischemic stroke: a prospective cohort study.

BACKGROUND: Hypocretin-1 is a vital neurotransmitter in regulating the sleep-wake cycle and provides...

Machine learning-based 28-day mortality prediction model for elderly neurocritically Ill patients.

BACKGROUND: The growing population of elderly neurocritically ill patients highlights the need for e...

An EEG-based emotion recognition method by fusing multi-frequency-spatial features under multi-frequency bands.

BACKGROUND: Recognition of emotion changes is of great significance to a person's physical and menta...

Artificial intelligence-enhanced navigation for nerve recognition and surgical education in laparoscopic colorectal surgery.

BACKGROUND: Devices that help educate young doctors and enable safe, minimally invasive surgery are ...

A hybrid CNN-Bi-LSTM model with feature fusion for accurate epilepsy seizure detection.

BACKGROUND: The diagnosis and treatment of epilepsy continue to face numerous challenges, highlighti...

EEG Signals Classification Related to Visual Objects Using Long Short-Term Memory Network and Nonlinear Interval Type-2 Fuzzy Regression.

By gaining insights into how brain activity is encoded and decoded, we enhance our understanding of ...

Alzheimer's disease recognition using graph neural network by leveraging image-text similarity from vision language model.

Alzheimer's disease (AD), a progressive neurodegenerative condition, notably impacts cognitive funct...

From Genes to Metabolites: HSP90B1's Role in Alzheimer's Disease and Potential for Therapeutic Intervention.

Alzheimer's disease (AD) is a prototypical neurodegenerative disorder, predominantly affecting indiv...

A hybrid network using transformer with modified locally linear embedding and sliding window convolution for EEG decoding.

. Brain-computer interface(BCI) is leveraged by artificial intelligence in EEG signal decoding, whic...

Advances in physiological and clinical relevance of hiPSC-derived brain models for precision medicine pipelines.

Precision, or personalized, medicine aims to stratify patients based on variable pathogenic signatur...

Xixin Decoction's novel mechanism for alleviating Alzheimer's disease cognitive dysfunction by modulating amyloid-β transport across the blood-brain barrier to reduce neuroinflammation.

PURPOSE: Xixin Decoction (XXD) is a classical formula that has been used to effectively treat dement...

An EEG-based framework for automated discrimination of conversion to Alzheimer's disease in patients with amnestic mild cognitive impairment: an 18-month longitudinal study.

BACKGROUND: As a clinical precursor to Alzheimer's disease (AD), amnestic mild cognitive impairment ...

DDEvENet: Evidence-based ensemble learning for uncertainty-aware brain parcellation using diffusion MRI.

In this study, we developed an Evidential Ensemble Neural Network based on Deep learning and Diffusi...

Cognitive load detection through EEG lead wise feature optimization and ensemble classification.

Cognitive load stimulates neural activity, essential for understanding the brain's response to stres...

DHCT-GAN: Improving EEG Signal Quality with a Dual-Branch Hybrid CNN-Transformer Network.

Electroencephalogram (EEG) signals are important bioelectrical signals widely used in brain activity...

Effectiveness of robot-assisted task-oriented training intervention for upper limb and daily living skills in stroke patients: A meta-analysis.

PURPOSE: Stroke is one of the leading causes of acquired disability in adults in high-income countri...