Neurology

Accuracy of the rapid-response electroencephalography's Automated Seizure Burden Estimator: A follow-up validation study of version 8 (AccuRASE II).

OBJECTIVE: Ceribell Inc.'s point-of-care electroencephalographic (EEG) system and artificial intelligence-based Automated Seizure Burden Estimator (ASBE; ClarityPro) have US Food and Drug Administration clearance for diagnosing electrographic status epilepticus (ESE). The AccuRASE study using ASBE version 6 (V6) showed high negative predictive value (NPV) but limited sensitivity and positive predi...

EEG Connectivity Signatures in Active vs. Passive Mental Fatigue Settings.

In real-world occupational settings, mental fatigue commonly emerges from the combination of sleep deprivation with prolonged cognitive and physical workload. However, this multidimensional fatigue profile is rarely captured in controlled experimental paradigms that examine brain activation and fatigue-related responses. Consequently, the validity and transferability of cognitive fatigue biomarker...

Hypoperfusion Intensity Ratio as a Predictive Marker in Pre-Endovascular Treatment Perfusion MRI for Acute Middle Cerebral Artery Occlusion with Intracranial Atherosclerosis.

Introduction Identifying the cause of MCA occlusion before endovascular treatment (EVT) in acute ischemic stroke is useful. Hypoperfusion intensity ra...

AneuSI: A software tool to isolate a region of interest in cerebral aneurysms from full geometrical models.

BACKGROUND AND OBJECTIVE: Modeling cerebral aneurysms using patient-specific geometries demands significant computational resources, particularly when...

Deep Learning Based on Swin-Transformer and 3D U-Net: Implant Three-Dimensional Position Planning.

INTRODUCTION AND AIMS: This study aimed to devise a deep learning-based model for the automated identification of anatomical mandibular lingual concav...

DGCD-3D: Difference-guided conditional diffusion model for low-field 3D MRI enhancement to assist stroke assessment.

Low-field (LF) magnetic resonance imaging (MRI) plays a crucial role in assisting clinicians with rapid stroke diagnosis. However, its inherent limita...

Investigating the feasibility and acceptability of the TeleRehabilitation of balance clinical and economic Decision Support System (TeleRehaB DSS) in adults at risk of falls: study protocol for a multicentre clinical trial.

INTRODUCTION: Falls are a significant concern for older adults, particularly those with neurological, vestibular, cognitive and post-viral conditions,...

Expression patterns of risk genes associated with three evolutionarily relevant syndromes in rhesus macaque and human brains.

Depressive disorder (DD), Alzheimer's disease (AD), and schizophrenia (SZ) are evolutionarily relevant traits that disrupt neural networks supporting ...

Efficient epileptic seizure prediction using single channel EEG signal and knowledge distillation on deep neural networks.

Epilepsy is a common neurological disease, and in some patients, abnormal changes in brain activity typically begin before the onset of a seizure. Ele...

Aggregating XAI-based explanations to identify spectral-spatial patterns in CNN-based resting-state EEG classification.

Convolutional neural networks (CNNs) achieve high performance in electroencephalographic (EEG) classification tasks; however, their decision-making me...

Discrimination of generalized anxiety and major depressive disorders by machine learning of resting-state functional MRI.

OBJECTIVE: To address the clinical difficulty of differentiating Generalized Anxiety Disorder (GAD) from Major Depressive Disorder (MDD), this study a...

Machine learning for predicting institutionalization and mortality risks among older home care recipients with routinely collected need assessment data: explainable AI for long-term care.

BACKGROUND: Identifying older home care recipients at risk of institutionalization in advance is crucial for providing preventive services. Supporting...

Decoding motor imagery related to major mimetic muscles from electroencephalography.

BACKGROUND: Functional and aesthetic deficits in individuals with facial nerve paralysis (FNP) significantly impair their quality of life. By decoding...

Design of an artificial intelligence model to screen spontaneous speech to detect Alzheimer's Disease.

There is a shortage of physicians trained in the specialized care of Alzheimer's disease (AD). One possible solution is to use machine learning (ML)/a...

AI-driven tripartite classification for optimizing wearable bioelectronics in depression management.

Current disease-sensing devices primarily focus on distinguishing between healthy and diseased states, effective for diagnosis but limited in guiding ...

SinTransNet: an EEG-based deep learning framework for infantile epileptic spasms syndrome detection.

Infantile Epileptic Spasms Syndrome (IESS) represents a severe form of developmental epileptic encephalopathy in infancy, characterized by clusters of...

Correlation between SHR and 28-day all-cause mortality risk in patients with spontaneous intracerebral hemorrhage.

BACKGROUND: The stress hyperglycemia ratio (SHR) has recently been suggested as a dependable indication for predicting adverse outcomes in critically ...

EXPRESS: Advances in artificial intelligence for neuroimaging.

Most neuroimaging applications involve a multi-step pipeline encompassing image acquisition, reconstruction, enhancement, registration, segmentation, ...

Viral Infections and Neurodegenerative Diseases: Reinterpreting the Crosstalk Through a Dual-Role Lens.

Neurodegenerative diseases (NDDs) are multifactorial disorders with increasing evidence implicating viral infections in their pathogenesis. However, c...

Real-World Generalizability of Alzheimer's Volumetric MRI Machine-Learning Models: External Validation with British Data.

PURPOSE: Assessing generalizability and performance of machine learning models in clinical settings is crucial. In this study, we aimed to test our mo...