Neurology

Development and validation of a deep survival model to predict time to seizure from routine electroencephalography.

OBJECTIVE: This study was undertaken to develop and validate a deep survival model (EEGSurvNet) that...

EEGMoE: A Domain-Decoupled Mixture-of-Experts Model for Self-Supervised EEG Representation Learning.

Existing deep learning models for electroencephalogram (EEG) are typically tailored for specific tas...

Classification of Alzheimer's Disease by Modeling Brain Networks as Signed Networks under Deep Learning Frameworks.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that remains a global challenge...

Vascular cognitive impairment and dementia: Prevention, treatments, mechanisms and management options for the future.

Vascular Cognitive Impairment and Dementia (VCID), the second most common form of dementia, is becom...

EEG microstate features under visual feedback gain conditions exhibit high sensitivity in identifying early Parkinson's disease patients.

BACKGROUND: Early Parkinson's disease (PD) presents with subtle symptoms and lacks specific diagnost...

Circulating inflammatory proteins predict dementia risk, are linked to structural brain changes and modifiable risk factors.

INTRODUCTION: Systemic inflammation has been identified as a key factor in neurodegeneration but the...

Unlocking the silent signals: Motor kinematics as a new frontier in early detection of mild cognitive impairment.

The increasing global prevalence of mild cognitive impairment (MCI) necessitates a paradigm shift in...

Multimodal diagnosis of Parkinson's disease with an internet-based collaborative agent architecture of medical language models.

Parkinson's disease (PD) remains one of the most prevalent neurodegenerative disorders, where delays...

Insights into the interplay between stroke and depression through lipid metabolism-related diagnostic genes.

Stroke, a result of acute cerebrovascular disease that causes cerebral dysfunction, often coexists w...

Identifying the state of cerebrovascular autoregulation using the pressure reactivity index versus the mean flow index: Why metric matters.

Cerebral autoregulation maintains stable cerebral blood flow despite fluctuations in cerebral perfus...

Using Expert-Cited Features to Detect Leg Dystonia in Cerebral Palsy.

OBJECTIVES: Leg dystonia in cerebral palsy (CP) is debilitating but remains underdiagnosed. Routine ...

Transfer learning from 2D natural images to 4D fMRI brain images via geometric mapping.

Functional magnetic resonance imaging (fMRI) allows real-time observation of brain activity through ...

Identifying and predicting gait stability metrics in people with stroke in uneven-surface walking using machine learning.

People with stroke (PwS) face increased fall risk on uneven surfaces; however, gait stability under ...

Biomarker discovery in Lennox-Gastaut syndrome: Advances and challenges in electrophysiological, genetic, neuroimaging, and neuroinflammatory approaches.

In Lennox-Gastaut Syndrome (LGS), a severe developmental and epileptic encephalopathy, the absence o...

U2AD: Uncertainty-based unsupervised anomaly detection framework for detecting T2 hyperintensity in MRI spinal cord.

T2 hyperintensities in spinal cord MR images are crucial biomarkers for conditions such as degenerat...

Rebuilding the pelvis: advances in robotic-assisted management of complex pelvic fractures.

Complex pelvic fractures are infamously challenging to fix surgically because of their fine anatomy ...

EEG-to-gait decoding via phase-aware representation learning.

Accurate decoding of lower-limb motion from EEG signals is essential for advancing brain-computer in...

Comparative analysis of machine learning algorithms for predicting stereotactic coordinates of the centromedian nucleus.

OBJECTIVE: Deep brain stimulation (DBS) of the centromedian nucleus (CM) of the thalamus is a promis...

Evaluating AI Research Quality in Myasthenia Gravis: A Longitudinal Study Using the CLARITY Framework (2020-2024).

Multisequence MRI Enables High-Fidelity FDG-PET Synthesis for Epilepsy Using GANs.

BACKGROUND: FDG-PET aids presurgical epilepsy evaluation but is limited by access and radiation expo...

Japan Brain Bank Net (JBBN).

Autopsies have long been performed to determine the cause of death in the medical field. In fact, au...