Neurology

Predicting Antiseizure Medication Outcomes in Early Diagnosed Epilepsy: A Multimodal Framework Using EEG, MRI, and Clinical Data

Accurate prediction of antiseizure medication (ASM) outcomes is crucial for optimising epilepsy treatment. We propose a multi-modal deep learning framework that integrates electroencephalography (EEG), magnetic resonance imaging (MRI), clinical factors, and molecular drug features to enhance ASM outcome prediction. Our approach includes EEG Q-Net, a pre-trained quantisation model capturing finegra...

Automated Detection of Faciobrachial Dystonic Seizures Related Events in LGI1 Autoimmune Encephalitis Patients with Wearables

To evaluate the potential of wrist-worn wearable devices to detect and quantify Faciobrachial Dystonic Seizures (FBDS) and related events associated with leucine-rich glioma Inactivated-1 (LGI1)-IgG autoimmune encephalitis (LGI1 AIE). Seven patients and four control subjects were monitored with Empatica E4 wristbands in both hospital and ambulatory environments. The analysis focused on the pre- an...

Optimized Feature Selection and Advanced Machine Learning for Stroke Risk Prediction in Revascularized Coronary Artery Disease Patients

Coronary artery disease (CAD) is a leading cause of mortality, with stroke being a major complication following coronary revascularization procedures ...

Interpretable multivariate survival models: Improving predictions for conversion from mild cognitive impairment to Alzheimer’s disease (AD) via data fusion and machine learning

Accurately predicting which individuals with mild cognitive impairment (MCI) will progress to Alzheimer’s disease (AD) can improve patient care. This ...

FFT Power Relationships Applied to EEG Signal Analysis: A Meeting between Visual Analysis of EEG and Its Quantification

This study presents a novel computational approach for analyzing electroencephalogram (EEG) signals, focusing on the distribution and variability of e...

Pose AI prediction of neurological status in the Neuroscience Intensive Care Unit

The neurological exam is pivotal in assessing patients with neurological conditions but has severe limitations: it can vary between examiners, it may ...

Transformer-based long-term predictor of subthalamic beta activity in Parkinson’s disease

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a mainstay treatment for patients with Parkinson’s disease (PD). The adaptive DBS app...

Precise perivascular space segmentation on magnetic resonance imaging from Human Connectome Project-Aging

Perivascular spaces (PVS) are cerebrospinal fluid-filled tunnels around brain blood vessels, crucial for the functions of the glymphatic system. Chang...

Artificial Intelligence algorithm for real-time detection and counting of Trypanosoma cruzi parasites using smartphone microscopy

Chagas disease affects 6–7 million people worldwide and causes approximately 12,000 deaths annually. Diagnostic methods vary by disease stage, with se...

Integration of Deep Learning Annotations with Functional Genomics Improves Identification of Causal Alzheimer’s Disease Variants

Genetic variants associated with Alzheimer’s disease (AD) through genome-wide association studies (GWAS) are challenging to interpret because most lie...

Performance of DeepSeek, Qwen 2.5 MAX, and ChatGPT Assisting in Diagnosis of Corneal Eye Diseases, Glaucoma, and Neuro-Ophthalmology Diseases Based on Clinical Case Reports

This study evaluates the diagnostic performance of several AI models, including Deepseek, in diagnosing corneal diseases, glaucoma, and neuro□ophthalm...

Integrating Multilevel, Multidomain and Multimodal Neuroimaging Factors to Predict Early Alcohol Exposure Trajectories Using Explainable AI

Alcohol consumption tends to increase from childhood to adolescence, and risk factors at the individual, family, and environmental level (multilevel, ...

Machine learning-based calculation of neurovascular compression surface area correlates with post-microvascular decompression pain outcomes for trigeminal neuralgia

Machine learning-generated segmentations of the trigeminal nerve and nearby blood vessels have the potential to quantify the magnitude of neurovascula...

Profile of deaths mentioning ischemic and hemorrhagic stroke in Brazil: a population-based machine learning analysis

Brazil has the highest stroke rates in Latin America. The aim of this study was to investigate the profile of deaths mentioning stroke in Brazil betwe...

A Pilot Study Comparing Speech Characteristics in People with Parkinson’s Disease and Controls Dancing Weekly Over 5-years

Parkinson’s Disease (PD) is a neurodegenerative disorder that affects motor and non-motor functions. Speech impairments, such as reduced variability i...

Machine Learning Classification of Mild Cognitive Impairment using Advanced Multi-Shell Diffusion MRI and CSF Biomarkers

Machine learning applied to neuroimaging can help with medical diagnosis and early detection by identifying biomarkers of subtle changes in brain stru...

Leveraging Deep Learning to Enhance MRI for Brain Disorders

The limited availability and high cost of 7 Tesla (7T) structural MRI hinder its widespread application despite its superior imaging quality. This stu...

Neuroimaging-AI endophenotypes reveal underlying mechanisms and genetic factors contributing to progression and development of four brain disorders

Recent work leveraging artificial intelligence has offered promise to dissect disease heterogeneity by identifying complex intermediate brain phenotyp...

Computational Strategies for Depression Detection and Treatment: The Role of Behavioral Activation and Neurobiological Insights – A Systematic Review

Depression is a multifaceted disorder with neurobiological, behavioral, and environmental components. This review aims to explore how artificial intel...

OpenSpindleNet: An Open-Source Deep Learning Network for Reliable Sleep Spindle Detection

Sleep spindles, an oscillatory brain activity occurring during light non-rapid eye movement (NREM) sleep, are important for memory consolidation and c...