Neurology

CausalTCC: causal temporal contrastive learning for automated Alzheimer's disease biomarker discovery with bio-electrical signals.

OBJECTIVE: Learning robust representations from scarce labeled bio-electrical time-series data remains a critical challenge in clinical diagnosis. While contrastive learning has shown promise, existing approaches often overlook the intrinsic causal dynamics inherent in physiological signals, leading to over-smoothed representations. This study presents CausalTCC, an end-to-end framework for causal...

Cognitive and brain function enhancement in Gen X group after personalized, AI supervised EEG-neurofeedback training.

Interventions supporting medical care and enhancing quality of life in neurodegenerative or age-related cognitive decline are strongly needed. Electroencephalographic (EEG) neurofeedback can enable users to modulate their brain activity through real-time feedback. However, evidence for its clinical effectiveness remains inconclusive, partly due to limited personalization and insufficient task...

Dynamic functional graph-Laplacian priors integrated with optimization for EEG source localization.

OBJECTIVE: Electroencephalography (EEG) source localization is an ill-posed inverse problem in which conventional methods often rely on static anatomi...

Exploring the Link Between Intravoxel Incoherent Motion Measured Brain Diffusivity During Wakefulness and Sleep Macrostructure in the Elderly.

Intravoxel incoherent motion (IVIM) is a diffusion-weighted magnetic resonance imaging (MRI) method that models slow (D, tissue diffusivity) and fast ...

Minimizing chronic groin pain after inguinal hernia surgery: An augmented evidence review.

BACKGROUND: Chronic post-operative inguinal pain (CPIP) is a significant complication following inguinal hernia repair. Evidence on the surgical facto...

An EEG-based hybrid machine learning approach for CT scan triage in mild traumatic brain injury.

Mild traumatic brain injury (mTBI) frequently prompts computed tomography (CT) imaging in emergency departments, despite a high proportion of negative...

Identifying unique gait phenotypes across neuromotor conditions using wearable inertial sensors and topological data analysis.

Wearable sensors quantify gait and mobility in detail, but translating high-dimensional data into clinically actionable insights remains challenging i...

An adaptive attention U-network for recognizing ultrasound images.

ObjectiveThe traditional method of intraspinal anesthesia relies on surface anatomical landmarks for positioning, which is associated with a low accur...

A digital marker for stratifying cardiovascular metabolic comorbidities among the middle-aged and elderly adults.

Cardiovascular Metabolic Comorbidities (CMM) share common physiological mechanisms in inflammation and immunity, oxidative stress, and insulin resista...

A-eye: Automated 3D MRI segmentation and morphometric feature extraction for eye and orbit atlas construction.

In this study we introduce automated 3D segmentation of the healthy human adult eye and orbit from Magnetic Resonance Images, to improve ophthalmic di...

Music-induced cortical plasticity: Protocol for a systematic review.

Music engages sensory, motor, cognitive, and emotional systems, making it a powerful model for studying experience-dependent neuroplasticity. Although...

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PURPOSE: We aimed to develop a machine learning model to predict activities of daily living (ADL) at discharge in stroke patients and identify key pre...

Variations of global brain asymmetry are associated with aging and related diseases.

Lateralization is a hallmark of brain organization, yet the structural basis underlying this phenomenon remains a critical, unresolved question in cog...

Targeting SLK protects against cerebral ischemia-reperfusion injury by regulating USP8-mediated HIF-1α stabilization and RhoA/ROCK activation.

INTRODUCTION: Acute ischemic stroke (AIS) represents a major global contributor to mortality and chronic disability, with few effective therapeutic ta...

Intelligent differentiation between Parkinson's disease and essential tremor using wearable sensors and machine learning: a temporal validation study.

BACKGROUND: Parkinson's disease (PD) and essential tremor (ET) are prevalent movement disorders with overlapping clinical presentations but divergent ...

Explicit Inclusion of Diabetes Mellitus Without Retinopathy Within Diabetic Retinopathy Prediction.

PURPOSE: The purpose of this study was to evaluate whether explicitly modeling diabetes mellitus (DM) without diabetic retinopathy (DR) as its own sta...

Archetypal Visual Field Analysis of Patients With Chronic Leber Hereditary Optic Neuropathy in Relation to Visual Recovery.

PURPOSE: To use the machine learning algorithm archetypal analysis (AA) to characterize visual field (VF) loss patterns in patients with chronic Leber...

Readability and Linguistic Characteristics of Alzheimer's Disease and Related Dementias Prevention, Symptom, and Treatment Information from Generative Artificial Intelligence Chatbots.

PURPOSE: To examine the readability and linguistic characteristics of Alzheimer's disease and related dementias (ADRD) prevention, symptom, and treatm...

Development and Validation of Machine Learning Models to Optimize Imaging and Referrals for Dizziness in the Emergency Department.

BACKGROUND: Dizziness and vertigo are common emergency department (ED) presentations, but only 2%-5% receive a serious diagnosis, such as stroke or tr...

Post-stroke rehabilitation in inflammatory rheumatic diseases: outcome measures, digital tools, and patient education perspectives.

Inflammatory rheumatic diseases (IRDs) represent a significant risk factor for cerebrovascular events, independent of traditional cardiovascular risk ...