Neurology

Multimodal MRI insights into hippocampal pathological changes across the Alzheimer's disease continuum: advances and challenges.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder, with early and progressive hippocampal pathology serving as a hallmark across the AD continuum, including subjective cognitive decline, mild cognitive impairment, and AD dementia. Single-modal MRI fails to fully capture multilevel hippocampal neuropathology, hindering accurate early diagnosis and prognostic prediction of AD. Thi...

A Novel Adversarial Approach for EEG Dataset Refinement: Enhancing Generalization Through Proximity-to-Boundary Scoring.

As deep learning (DL) performs remarkably in pattern recognition from complex data, it is used to interpret user intentions from electroencephalography (EEG) signals. However, the DL models trained on EEG datasets have low generalization ability owing to numerous noisy samples in datasets. Therefore, prior research has focused on distinguishing and eliminating noisy samples from datasets. One intu...

Impact of oncological biomarkers and clinical factors on survival outcomes in renal cell carcinoma with spinal metastasis: a retrospective analysis.

OBJECTIVE: Renal cell carcinoma (RCC) is a common malignancy that metastasizes to the spine, leading to complex treatment challenges and reduced survi...

Predicting training outcomes for developmental dyslexia from EEG data.

Developmental dyslexia (DD) is characterised by lower-than-average reading abilities and is diagnosed in approximately 10% of individuals. The societa...

"Early Stratification of Risk for Poor Neurological Outcome After Cardiac Arrest Is Improved with Processed EEG Data".

AIM: To evaluate the impact of adding early processed quantitative EEG biomarkers to health record data for early neurological risk stratification aft...

Identifying candidate therapeutic targets in amyotrophic lateral sclerosis through a transcriptome-wide machine-learning consensus approach for drug repurposing.

Amyotrophic lateral sclerosis (ALS) is a heterogeneous neurodegenerative disease for which effective disease-modifying therapies remain limited. This ...

External Validation of EEG-Based Machine Learning Models for Continuous Pain Prediction.

Machine learning (ML) has been used to predict subjective pain intensity from electroencephalographic (EEG) data. However, few ML models for pain asse...

Histological aging signatures for monitoring tissue-specific aging and disease.

Aging is the primary risk factor for chronic disease and is characterized by profound structural and architectural remodeling of human tissues. Here, ...

Better sleep now, better cognition later? Predicting cognitive function using a machine learning-based sleep EEG brain health score.

STUDY OBJECTIVES: Sleep state electrocortical activity measured using electroencephalograms (EEG) is linked with cognitive function and dementia risk....

A Novel EEG-Based Topographic Brain Map-Driven Deep Learning Method for Autism Spectrum Disorder Detection in Children.

Autism Spectrum Disorder (ASD) is a neurological and developmental condition that affects children's social and cognitive skills, leading to repetitiv...

A task-aligned multimodal machine learning framework for studying working memory dysfunction in Parkinson's disease.

UNLABELLED: Cognitive impairment is one of the most functionally debilitating non-motor symptoms in Parkinson's disease (PD). Yet, current diagnostic ...

Deep Learning-Based Enhancement of Already Diagnostic-Quality MRI for Alzheimer's Disease Classification: Effects on Model Performance and Training Data Requirements.

BACKGROUND: Deep learning (DL)-based image enhancement is widely used to improve suboptimal medical imaging. Whether it also benefits diagnostic-quali...

Development and validation of an amyloid PET dementia of the Alzheimer's type (DAT) score.

BackgroundAlzheimer's disease (AD) can be debilitating if left untreated, but its progression may be altered through early detection.ObjectiveTo devel...

PREDICTIVE OCT BIOMARKERS OF RETINAL CHANGES AND VISUAL OUTCOMES IN SILICONE OIL ENDOTAMPONADE IDENTIFIED BY ARTIFICIAL INTELLIGENCE.

PURPOSE: To quantify retinal layer changes and visual outcomes in eyes with silicone oil (SO) endotamponade for rhegmatogenous retinal detachment usin...

Analyzing Frequency-Space-Time EEG Signatures via Interpretable Neural Networks: A Simulation Study.

OBJECTIVE: Event-related EEG activity is widely investigated to characterize brain functions. Traditional analyses rely on heavy pre-processing and st...

Epistemic Asymmetry Between Abstracts and Discussion Sections in the Orthopaedic Literature: A Corpus Analysis of 201 Publications.

BACKGROUND: Abstracts and Discussion sections serve distinct functions: Abstracts are optimized for brevity and impact, while Discussion sections cont...

Predictive value of the Alzheimer polygenic risk score on cognitive decline in patients with mild cognitive impairment and Alzheimer's disease dementia.

BACKGROUND: Polygenic risk scores for Alzheimer's disease (AD-PRS) are widely used to estimate genetic susceptibility to AD, but their relationship wi...

Integrative machine learning and single-nucleus transcriptomics reveal MAPK-centered neuronal responses underlying the potential Alzheimer's disease risk of GenX.

Hexafluoropropylene oxide-dimer acid (GenX or HFPO-DA) is a novel per- and polyfluoroalkyl substance developed as a replacement for legacy compounds, ...

Integrated population-based evidence, network toxicology, single-cell profiling, and in vitro studies elucidate mechanisms connecting PAHs to stroke.

Environmental exposure to polycyclic aromatic hydrocarbons (PAHs) has been associated with cardiovascular disease, yet the specific metabolites and ce...

Bidirectional cross-modal attention with adaptive gating for multimodal alzheimer's disease classification.

Alzheimer's disease (AD) is a neurodegenerative disorder, and mild cognitive impairment (MCI) represents a transitional stage between AD and cognitive...