Seizures

Integrating multisequence radiomics and clinical features to predict seizure recurrence after gross total resection of pediatric low-grade epilepsy-associated brain tumors.

OBJECTIVE: This study aimed to develop a predictive model integrating clinical features and multisequence MRI radiomics to forecast postoperative seizure outcomes in pediatric patients with low-grade epilepsy-associated tumors (LEATs) who underwent gross total resection (GTR). METHODS: In this study, we propose a novel radiomics-based approach to predict seizure recurrence. The model was further o...

Synaptic dysfunction-related gene expression spatially correlates with widespread cortical network atrophy in temporal lobe epilepsy.

Temporal lobe epilepsy (TLE) exhibits marked lateralized gray matter alterations, yet whole-brain network vulnerability patterns, particularly those independent of seizure laterality, remain incompletely understood. Furthermore, the spatial correspondence between macroscopic network disruptions and underlying molecular architectures lacks systematic characterization, limiting mechanistic insights ...

Evaluating the biological meaning of neural network decisions in EEG-based MCI detection.

OBJECTIVE: High accuracy in medical classification tasks does not ensure that neural networks reason in ways consistent with clinical or neurobiologic...

High-frequency EEG synchronization modes as a stable biometric signature in humans.

Electroencephalography (EEG) offers a promising modality for biometric identification, though balancing performance, interpretability, and robustness ...

Point-of-Care Artificial Intelligence Measure of Seizure Burden Associates With Clinical Outcome at Discharge.

OBJECTIVES: Point-of-care (POC) electroencephalography (EEG) enabled with artificial intelligence (AI) algorithms hold the potential to address gaps i...

Machine learning models for predicting postpartum convulsions using clinical indicators from PMA Ethiopia data.

Postpartum convulsions, defined as seizure episodes occurring after childbirth during the postpartum period, remain a major cause of maternal morbidit...

Characterizing Central-Autonomic Dynamics During an Episodic Memory Task Using Multi-Modal Neural and Cardiomechanical Signals.

OBJECTIVE: Memory function underlies mental and behavioral health. While the role of the central nervous system (CNS) during episodic memory encoding ...

Cross-Hemispheric Spatial-Temporal Attention Network for Decoding Silent Speech From EEG.

OBJECTIVE: Speech, as the core of advanced human cognition, is fundamental to social interaction and daily life. Electroencephalogram (EEG)-based spee...

A Hierarchical Multimodal Framework for Sedation Monitoring in ICU Patients.

In the intensive care unit (ICU), monitoring sedation levels is crucial. Clinicians often rely on intermittent behavioral scales like the Richmond Agi...

An Active Dry-Contact Continuous EEG Monitoring System for Seizure Detection Applications in Clinical Neurophysiology.

OBJECTIVE: Young children and infants, especially newborns, are highly susceptible to seizures, which, if undetected and untreated, can lead to severe...

BioGAP-Ultra: A Modular Edge-AI Platform for Wearable Multimodal Biosignal Acquisition and Processing.

The growing demand for continuous physiological monitoring and human-machine interaction in real-world settings calls for wearable platforms that are ...

Can Artificial Intelligence Chatbots Plan Therapeutic Ketogenic Diets for Children With Epilepsy?

RATIONALE: Ketogenic Diet Therapy (KDT) is an effective but complex treatment for paediatric drug-resistant epilepsy. Access to trained dietitians lim...

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 rea...

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...

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...

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...

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...

EEG-based dataset explicitly targets the transitions between sitting and standing for exploring neural activation patterns in Motor Imagery and execution.

This study presents the first publicly accessible electroencephalography (EEG) dataset explicitly targeting sit-to-stand and stand-to-sit transitions ...

Multi-scale dynamic cooperative attention network for Auditory Attention Detection.

Humans exhibit a remarkable capacity to concentrate on particular auditory inputs amid multiple simultaneous speakers, as seen in cocktail party setti...

EEG biomarkers can predict early-stage Alzheimer's disease and correlate with intracerebral pathology: a multimodal machine learning study.

BACKGROUND: Early recognition of Alzheimer's disease (AD) is crucial for timely intervention and delaying disease progression. Electroencephalogram (E...