Neurology

Latest AI and machine learning research in neurology for healthcare professionals.

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Risk prediction of stroke-associated pneumonia in acute ischemic stroke with atrial fibrillation using machine learning models.

Stroke-associated pneumonia (SAP) is a serious complication of acute ischemic stroke (AIS), signific...

Simplified two-compartment neuron with calcium dynamics capturing brain-state specific apical-amplification, -isolation and -drive.

Mounting experimental evidence suggests the hypothesis that brain-state-specific neural mechanisms, ...

Coherence resonance, parameter estimation and self-regulation in a thermalsensitive neuron.

In this work, two capacitors connected by a thermistor are used to explore the electrical property o...

Efficient system for classifying cyclic alternating pattern phases in sleep.

Electroencephalogram (EEG) signals are a popular tool to analyze sleep patterns. Cyclic alternating ...

PhyTransformer: A unified framework for learning spatial-temporal representation from physiological signals.

As a modal of physiological information, electroencephalogram (EEG), surface electromyography (sEMG)...

Unveiling the invisible: How cutting-edge neuroimaging transforms adolescent depression diagnosis.

Yu 's study has advanced the understanding of the neural mechanisms underlying major depressive diso...

NeuroDetect: Deep Learning-Based Signal Detection in Phase-Modulated Systems with Low-Resolution Quantization.

This manuscript introduces NeuroDetect, a model-free deep learning-based signal detection framework ...

Layer-Specific Colocalization of Microglia with Amyloid Plaques in the Middle Temporal Gyrus Predicts Cognitive Decline in Alzheimer's Disease.

Alzheimer's disease (AD), the most common form of dementia, is marked by cognitive decline and amylo...

Preoperative Differentiation of Spinal Schwannoma and Meningioma Using Machine Learning-Based Models: A Systematic Review and Meta-Analysis.

BACKGROUND: Regarding the differences in surgical approaches for spinal schwannomas and meningiomas,...

Morphometric and radiomics analysis toward the prediction of epilepsy associated with supratentorial low-grade glioma in children.

OBJECTIVES: Understanding the impact of epilepsy on pediatric brain tumors is crucial to diagnostic ...

The Future of Parasomnias.

Parasomnias are abnormal behaviours or mental experiences during sleep or the sleep-wake transition....

Harnessing the hybrid machine learning methods for stroke risk classification.

Stroke is a leading global cause of death, with 80% of cases considered preventable through early de...

Functional MRI Analysis of Cortical Regions to Distinguish Lewy Body Dementia From Alzheimer's Disease.

OBJECTIVE: Cortical regions such as parietal area H (PH) and the fundus of the superior temporal sul...

Cognitive Dysfunction in the Addictions (CDiA): protocol for a neuron-to-neighbourhood collaborative research program.

Substance use disorders (SUDs), including Alcohol Use Disorder, are pressing global public health pr...

AI-based staging, causal hypothesis and progression of subjects at risk of Alzheimer's disease: a multicenter study.

INTRODUCTION: In 2024, 11 European scientific societies/organizations and one patient advocacy assoc...

Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods.

Driven by the remarkable capabilities of machine learning, brain-computer interfaces (BCIs) are carv...

Neurophysiological Approaches to Lie Detection: A Systematic Review.

Lie detection is crucial in domains such as security, law enforcement, and clinical assessments. Tr...

Alzheimer's diagnosis by an efficient pipelined gene selection model based on statistical and biological data analysis.

Diagnosing Alzheimer's disease based on gene expression data extracted from microarrays is still an ...

Decision support system based on ensemble models in distinguishing epilepsy types.

This study aimed to classify patients' focal (frontal, temporal, parietal, occipital), multifocal, a...

Development and Validation of a Prognostic Model for Independent Walking in Children with Cerebral Palsy Based on Machine Learning.

OBJECTIVE: To develop and validate machine learning-based models for predicting independent walking ...

Brain metabolic imaging-based model identifies cognitive stability in prodromal Alzheimer's disease.

The recent approval of anti-amyloid pharmaceuticals for the treatment of Alzheimer's disease (AD) ha...

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