Seizures

Bridging the outcome documentation gap in epilepsy surgery: Validating large language model agents for automated Engel and International League Against Epilepsy scoring from clinical notes.

OBJECTIVE: Timely and accurate classification of postepilepsy surgery outcomes using Engel and International League Against Epilepsy (ILAE) scales is essential for clinical follow-up, yet electronic health record documentation often lacks the structured detail needed for reliable scoring. This study aimed to validate large language model (LLM) agents for autonomous extraction of standardized posts...

Fully automated three-dimensional deep learning-based magnetic resonance imaging segmentation of brain cavities in epilepsy surgery.

OBJECTIVE: There are several clinical and research applications for determining the amount of brain tissue resected after epilepsy surgery; however, manual segmentation of postoperative magnetic resonance imaging (MRI) is imprecise and time-consuming. In this study, we developed and benchmarked ResectVol DL, a freely available deep learning-based tool that performs this task automatically. METHODS...

Neural abnormalities in cognitive subprocesses of emotional conflict control in bipolar II disorder: Evidence from ERPs and brain functional networks.

Patients with bipolar disorder (BD) exhibit deficits in emotional conflict control. These abnormalities may be related to alterations in distinct cogn...

An AI-Driven H5P Training for Epilepsy Emergency Management Preparedness in Dental Education.

BACKGROUND: Deficiencies in knowledge and skills related to the management of medical emergencies in dental settings can adversely affect the clinical...

Localizing the Epileptogenic Zone Using SEEG-Based Excitation-Inhibition Dynamics and Spectral Features in Drug-Resistant Epilepsy: A Multicenter Retrospective Study.

Drug-resistant epilepsy (DRE) affects millions of people worldwide and remains a major therapeutic challenge, largely due to the difficulty in precise...

Reconfiguring brain networks via lightweight dynamic connectivity framework: An EEG-based stress validation.

In recent years, Electroencephalographic (EEG) analysis has gained prominence in stress research when combined with AI and Machine Learning (ML) model...

Slow-wave-modulated high-frequency oscillations reveal early network abnormalities in pre-pathological 5XFAD mice.

Alzheimer's disease (AD) is characterized by progressive disruption of large-scale neural networks, leading to abnormal brain oscillatory activity, ye...

Glymphatic-meningeal lymphatic dysfunction in epilepsy: Novel mechanistic insights and therapeutic avenues.

Despite advances in pharmacotherapy, approximately one-third of individuals with epilepsy develop drug-resistant epilepsy (DRE), accounting for a disp...

Multimodal assessment of minimally conscious state and cognitive motor dissociation in neurocritical care: A critical review.

Minimally conscious state (MCS) is characterized by inconsistent but clearly discernible clinical and behavioral evidence of consciousness. Cognitive ...

The entropic brain today.

Introduced in 2014 and revised in 2018, the entropic brain hypothesis has accrued a wealth of supportive evidence. The hypothesis states that-along a ...

Supervised autoencoder for gradient and BCG artifact removal in EEG during simultaneous EEG-fMRI.

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide complementary views of brain function, but MRI-indu...

A Dataset of Microelectrode Recordings from Deep Brain Stimulation Procedures.

Precise intraoperative localisation of subcortical brain structures remains a critical challenge in deep brain stimulation, yet openly available micro...

Transformer-based emotion recognition in interactive art: A multimodal neural approach.

Understanding how interactive digital art affects emotional states is essential to advance research into the interface between affective neuroscience ...

Development and validation of an explainable prediction model for post-stroke epilepsy in patients with ischaemic stroke following mechanical thrombectomy: a multicentre retrospective cohort study.

OBJECTIVE: In this retrospective multicentre cohort study, we aimed to develop and validate an interpretable machine learning (ML) model for early pos...

Distinct mental fatigue mechanisms under prolonged work and night shifts Implications for safety management in a real-world oil drilling site.

OBJECTIVE: This study aimed to investigate how shift work and prolonged working hours affect mental fatigue under real-world working conditions, focus...

Integrating weighted gene co-expression network analysis and machine learning reveal that NDUFA4L2 alleviates KA-induced HT22 cell neurotoxicity, apoptosis, oxidative stress, and mitochondrial dysfunction.

Temporal lobe epilepsy (TLE) is one of the most common types of epilepsy, with frequent seizures often leading to cognitive, emotional, and psychiatri...

Deep learning based depth of anaesthesia monitoring using EEG: a 4-layer CNN model with PSD and BSR correlation features.

Monitoring the depth of anaesthesia (DoA) through electroencephalogram (EEG) analysis plays a major role in maintaining patient safety and guiding opt...

A multimodal epilepsy dataset of paired 3-Tesla and 7-Tesla MRI and intracranial EEG.

There is an increasing need to integrate multimodal datasets in epilepsy research, particularly to correlate electrophysiology with imaging in patient...

Quantitative sleep EEG identifies CSF core biomarker-related subgroups in Alzheimer's disease.

Early detection and biological characterization of Alzheimer's disease (AD) remain challenging, as current diagnostic approaches rely on invasive cere...

Use of artificial intelligence in magnetic resonance imaging across the epileptic patient's journey: A meta-analysis of four clinical applications.

OBJECTIVE: The application of artificial intelligence/machine learning (AI/ML) to magnetic resonance imaging (MRI) promises to enhance and support cli...