Neurology

Seizure detection using hierarchical temporal trend integration and self-supervised learning.

Early, reliable detection of epileptic seizures from electroencephalography (EEG) remains challenging due to label scarcity, inter patient variability, and the non stationary nature of clinical recordings. This work introduces Federated Temporal Learning (FTL), an annotation efficient framework that couples a supervised seizure classifier with a self supervised pretext task, Temporal Sequence Cons...

CKLF1 Serves as a Pivotal Mediator of Blood-Brain Barrier Disruption and Neuroinflammation via PI3K/AKT/mTOR Signaling in Ischemic Stroke.

Ischemic stroke (IS) is accompanied by blood-brain barrier (BBB) disruption and neuroinflammatory activation, but the upstream regulatory mechanisms remain incompletely defined. Here, integrated single-cell RNA sequencing, regulatory network analysis, and machine-learning screening identified Chemokine-like factor 1 (CKLF1) as a candidate endothelial regulator associated with IS. Functional valida...

Predicting big five personality traits from resting-state EEG connectivity: a connectome predictive modeling study.

Personality traits are stable individual differences linked to important life outcomes including mental health, occupational functioning, and interper...

Detecting scoliosis at scale using automated cobb angle analysis in the UK biobank.

PURPOSE: Adult degenerative scoliosis arises after skeletal maturity in an initially normal spine, primarily driven by age-related degeneration. The C...

IShuffleNet-PCNN hybrid classifier within an MResU-Net-based framework for spinal cord injury detection and classification using CT images.

BACKGROUND: Spinal cord injury (SCI) is a serious medical condition. Spinal Cord Injury limits the movement of the body, blocks the nervous system and...

Global research trends and hotspots in robot-assisted spinal surgery for spinal deformity: a bibliometric analysis.

This bibliometric study maps the global landscape and evolutionary trajectories of robot-assisted spinal surgery in spinal deformity by analyzing 121 ...

Patient-Specific Non-Invasive Epileptogenic Zone Localization via High-Resolution Time-Frequency Representations and CNN Deep Learning.

Stereoelectroencephalography (SEEG)-guided radiofrequency thermocoagulation is the mainstream treatment for drug-resistant epilepsy (DRE), yet non-inv...

Motion Artifact-Aware Self-Supervised Representation Learning for 3D Brain MRI Motion Artifact Reduction.

Patient motion remains a source of image degradation in brain MRI, leading to signal loss, blurring, and geometric distortion that compromise quantita...

Distinct tDCS montages act via dissociable lateralization mechanisms to enhance motor function in chronic stroke.

The imbalance in interhemispheric functional connectivity following stroke fundamentally impedes motor recovery. Although transcranial direct current ...

Federated learning for MRI-based BrainAGE: A multicenter study on post-stroke functional outcome prediction.

OBJECTIVE: Brain-predicted age difference (BrainAGE) is a neuroimaging biomarker reflecting brain health, with potential implications for post-stroke ...

Teaching Signal Synchronization in Deep Neural Networks with Prospective Neurons.

Working memory requires the brain to maintain information from the recent past to guide ongoing behavior. Neurons can contribute to this capacity by s...

A public generalizable AI tool for automated segmentation of coronal brain tissue slabs for 3D neuropathology.

Advances in image registration and machine learning have recently enabled volumetric analysis of postmortem brain tissue from conventional photographs...

Comprehensive Analysis of Starvation Response-Related Genes in the Diagnosis of Ischemic Stroke: Based on Machine Learning and Single-Cell RNA Sequencing Data.

BACKGROUND: The pathogenesis of ischemic stroke (IS) involves a starvation response (SR); however, the mechanistic link between the two remains unclea...

Automated autism spectrum disorder detection using EEG signals and time-frequency visibility graphs.

Early and objective screening of Autism Spectrum Disorder (ASD) remains challenging because conventional diagnosis primarily relies on behavioural ass...

A Combined Deep Learning Approach to Screen Patients for Neuromuscular Pathology.

Neuromuscular diseases (NMD), comprising over 600 different conditions, severely impact nerve and/or muscle function and lead to significant morbidity...

Validation of artificial intelligence-assisted CBCT analysis for predicting inferior alveolar nerve proximity to impacted mandibular third molars: a diagnostic accuracy study.

OBJECTIVES: This study aimed to evaluate the diagnostic accuracy of an artificial intelligence (AI)-assisted cone-beam computed tomography (CBCT) anal...

CT-based prediction of hematoma expansion and adverse outcomes after intracerebral hemorrhage: evidence appraisal, artificial intelligence translation, and GeroScience perspectives.

Spontaneous intracerebral hemorrhage (ICH) is a highly lethal and disabling form of stroke, in which hematoma expansion (HE) is a major and potentiall...

Anatomical Localization of Intracranial Electrodes Using Synthetic MRI Generated from Computed Tomography.

BACKGROUND AND OBJECTIVES: MRI and computed tomography (CT) are commonly combined to localize intracranial electrodes in deep brain stimulation (DBS)....

Comparison of responses from google and large language models to the top frequently asked questions on lumbar spinal stenosis: an evaluation of accuracy and completeness.

PURPOSE: Lumbar spinal stenosis (LSS) is a common degenerative spinal condition and a leading cause of pain and disability in adults. With increasing ...

Artificial intelligence in ultrasound imaging for carpal tunnel syndrome: what clinicians need to know.

This review examines artificial intelligence (AI) applications in ultrasound imaging for carpal tunnel syndrome diagnosis. Deep learning models have a...