Neurology

Single-cell multiomics connects 3D genome and transcriptome alterations in Alzheimer's disease.

Alzheimer's disease (AD) disrupts brain function through cell type-specific transcriptomic and epigenomic alterations, yet the contribution of three-dimensional (3D) genome organization to AD remains poorly understood. We applied GAGE-seq (genome architecture and gene expression by sequencing) to jointly profile gene expression and 3D chromatin structure in single cells from postmortem brain tissu...

Multimodal foundation model assistance for differentiating primary open-angle glaucoma in highly myopic eyes.

PURPOSE: To develop and evaluate a multimodal foundation-model-assisted system for differentiating primary open-angle glaucoma (POAG) from non-glaucoma in highly myopic eyes and assessing whether artificial intelligence (AI) assistance changes ophthalmologist diagnostic performance. DESIGN: Retrospective diagnostic model-development study with internal validation and paired sequential multi-reader...

A large-scale comparative study of YOLO-based detectors for ischemic stroke lesion localization on diffusion-weighted imaging.

Ischemic stroke lesion localization on diffusion-weighted imaging (DWI) remains challenging because acute lesions may be small, faint, irregular, or m...

A deep learning model for speech-based prediction of clinical scores in people with Huntington's disease: a longitudinal study with cross-sectional replication.

BACKGROUND: Sensitive monitoring tools are needed to track progression in neurodegenerative diseases and assess interventions before overt brain damag...

Diagnosis of Parkinson's Disease Based on Voice and Speech Using Machine Learning and Deep Learning: A Systematic Umbrella Review.

OBJECTIVES: Early diagnosis of Parkinson's disease (PD) is complicated. Speech impairment, as an early symptom of PD, offers a noninvasive, scalable b...

Clinically aligned artificial intelligence for glaucoma diagnosis: enhancing retinal nerve fibre layer interpretation from fundus images.

BACKGROUND/OBJECTIVES: This study aimed to develop an interpretable artificial intelligence (AI) screening system that replicates a specialist's evalu...

Gut Microbiome in Depression with and without REM Sleep Behavior Disorder.

Major depressive disorder (MDD) is a risk factor for neurodegeneration, yet its heterogeneity makes identifying at-risk subtype challenging. Notably, ...

Broad-coverage targeted lipidomics reveals stage-specific lipid metabolic profiles and diagnostic and prognostic marker panels in ischemic stroke.

BACKGROUND: Ischemic stroke (IS) is a major cause of mortality and disability globally, with challenges in early diagnosis and prognosis prediction. D...

Crowdsourcing and machine learning contests in Parkinson's disease research - when do they work?

ObjectiveTo review the application of crowdsourcing and machine learning contests in Parkinson's disease (PD) research, identify best practices for su...

Fast Submillimeter Whole-Brain T2*-Weighted Imaging Using 3D-EPI With CAIPIRINHA and Deep-Learning Denoising at 3T.

PURPOSE: Recent updates to the diagnostic criteria of multiple sclerosis (MS) require whole-brain T2*-weighted (T2*w) imaging with submillimeter resol...

Emerging applications of artificial intelligence for early diagnosis and assessments for people with cerebral palsy.

Artificial intelligence (AI) promises to rapidly transform healthcare, but as of 2026, the direct implications for clinical care supporting individual...

AI-based Synthetic CT Angiography from Non-contrast CT for Cerebrovascular Disease Screening.

BACKGROUND: CT angiography (CTA) is a key investigation in cerebrovascular disease. However, CTA is not always available and it also requires intraven...

Mathematical mapping of circadian genes in Alzheimer's disease.

Sleep-wake disturbances commonly occur in Alzheimer's disease (AD). However, the precise mechanisms underlying the breakdown of the circadian gene net...

Domain-specific functional outcome prediction in stroke rehabilitation: A multicenter artificial intelligence study.

BACKGROUND: Stroke is a leading cause of long-term disability worldwide, yet existing clinical decision support tools rely on global disability metric...

Machine learning approaches for prediction of epilepsy risk across clinical pathways: a systematic review.

Machine learning (ML) and deep learning (DL) models are increasingly being explored for individualized epilepsy risk prediction after a first unpr...

Diagnosis of Neurological Dysfunction from EEG Signals Using DuelQ-SeizureNet and Meta Black Ant Optimization for Epileptic Seizure Detection.

BACKGROUND: Early diagnosis of neurological dysfunctions, particularly epilepsy, is vital for early intervention and improvement of patients' quality ...

A comparative evaluation of multiple enlarged perivascular space segmentation tools.

BACKGROUND: Enlarged perivascular spaces (ePVS) are a marker of cerebral small vessel disease, potentially reflecting reduced waste clearance. Because...

Post-thrombectomy models for outcome prediction in ischemic stroke: systematic review and external validation.

BACKGROUND: Outcome prediction models for patients with ischemic stroke after endovascular thrombectomy (EVT) demonstrated the value of including post...

A multimodal machine learning model integrating plasma biomarkers and MRI metrics for non-invasive prediction of amyloid-β pathology in mild cognitive impairment.

BackgroundAccurate, non-invasive prediction of cerebral amyloid-β (Aβ) pathology in mild cognitive impairment (MCI) remains challenging yet critical f...

Early Detection of Cognitive Impairment Using Time-Frequency Analysis of Fine Motor Accelerometry.

PURPOSE: Early detection of cognitive decline is essential for timely diagnosis and treatment. This study aimed to evaluate whether upper-limb movemen...