Neurology

Reproducibility of electroencephalography alpha band biomarkers for diagnosis of major depressive disorder

Major depressive disorder (MDD) and other psychiatric diseases can greatly benefit from objective decision support in diagnosis and therapy. Machine learning approaches based on biomarkers extracted from electroencephalography (EEG) have the potential to serve as low-cost decision support systems. Although this approach has shown promise, inconsistent findings regarding the diagnostic value of tho...

Predicting the timing of first sustained cognitive worsening in Alzheimer's disease using real-world clinical data and machine learning

Background: Cognitive assessments are sparsely documented in electronic health records (EHRs), limiting scalable detection of cognitive worsening in real-world clinical settings. Methods: We applied a deep neural network optimized for identifying clinical event timing from sparsely labeled gold-standard data (label-efficient incident phenotyping from longitudinal EHR, LATTE) to predict time-to-fir...

An open-source stereotaxic container with an integrated cutting guide for human brain fixation during magnetic resonance imaging and sectioning for histology

Introduction: Postmortem imaging at ultrahigh field strengths, such as 7 Tesla (7T) magnetic resonance imaging (MRI), enables unprecedented visualizat...

Audited large language model triage for systematic review screening in national clinical guideline production: validation and prospective deployment

Title and abstract screening limit the timeliness of systematic reviews used for clinical guidelines. We evaluated audited large language model (LLM) ...

Medication-Wide Association Study of Alzheimer's Disease and Related Dementias: Identifying Drug Candidates from Electronic Health Records through Explainable AI

Objective: Alzheimer's disease (AD) is a leading cause of death and disability, and treatment options for Alzheimer's disease and related dementias (A...

Conformal Prediction and Ensemble Learning for Uncertainty-Aware ICU Mortality Stratification

Background. Conventional ICU severity scores - SOFA, qSOFA, and APACHE-II - use additive integer weightings that cannot capture non-linear organ failu...

Multiplex Proteomics of Lewy Body Dementia Reveals Cerebrospinal Fluid Biomarkers of Clinical and Neuropathological Heterogeneity

Lewy body dementia (LBD), which encompasses Parkinson's disease dementia (PDD) and Dementia with Lewy bodies (DLB), lacks established biofluid markers...

Developing and Evaluating Deep Learning Approaches for Visual Field Denoising in Glaucoma

Purpose To investigate the relative efficacy of nine distinct visual field (VF) denoising artificial intelligence (AI) methods and a pathology-aware A...

Towards A Foundation Model for Clinical Voice Biomarkers

Vocal biomarkers, encompassing voice and speech, have largely been developed for individual conditions in isolation, limiting their generalizability a...

A Multi-Agent RAG Framework for Biomedical Literature Analysis

Background: The biomedical literature is expanding at an unprecedented rate, with over 4,000 new articles indexed on PubMed each day. Clinicians and r...

MICAFlow: Fast and Robust MRI Preprocessing Bridging Research Neuroimaging and Clinical Practice

MICAFlow is a fully automated MRI preprocessing pipeline designed to translate advanced neuroimaging workflows from research into routine clinical pra...

DINMC: A Deep Learning Framework for Interpretable Normative Model Construction and Pathological Brain Alteration Detection

Background and Objective: Normative modeling is a key tool for understanding brain alterations in neurodegenerative diseases, such as cerebellar-type ...

Supervised Domain Adaptation Mitigates Cross-Ethnicity Prediction Error in Neuroimaging Based Cognitive Prediction

Research has developed machine-learning models to predict cognitive and clinical outcomes from neuroimaging data, yet fairness and generalizability re...

Personalized Brain-Based Analgesia Detection with Portable fNIRS and AI

Neuroimaging based pain decoding faces two underappreciated challenges: between subject variability that prevents classifiers from generalizing across...

Identification of Heterogeneous Cortical Thickness Patterns Associated with Prenatal Gestational Diabetes Exposure: A SuStaIn-Based Subtyping Study

Importance: Prenatal exposure to gestational diabetes mellitus (GDM) has been associated with adverse metabolic, neurodevelopmental, and psychiatric o...

Deriving OCT-Equivalent Retinal Nerve Fiber Layer Thickness Maps from Fundus Photographs with Deep Learning Improves Glaucoma Diagnosis

Purpose: To develop and evaluate a deep learning model that predicts optical coherence tomography (OCT)-equivalent retinal nerve fiber layer thickness...

Comparing Pathway-Informed Polygenic Risk Score Strategies: A multi-cohort evaluation of Amyloid-β

Objective: To systematically evaluate pathway-informed polygenic risk score (PRS) strategies and determine which approaches most effectively leverage ...

Wearable and Interview-based Assessment of Psychological Risk in Alzheimers Caregivers: Machine Learning vs. Large Language Models

Spousal caregivers of individuals with Alzheimers disease and related dementias frequently experience elevated perceived stress, caregiver burden, and...

Deep Learning Prediction of Personalized Peripapillary Retinal Nerve Fiber Layer Thickness Norms from Fundus Images in Glaucoma

Purpose: To predict retinal nerve fiber layer thickness (RNFLT) norms from fundus images. Methods: We selected 18,000 OCT scans and visual fields (VF)...

Investigating Hybrid Deep Learning Architectures for Speech Envelope Reconstruction from EEG

Reconstructing speech envelopes from electroencephalography(EEG) signals is a challenging but valuable task for brain-computer interfaces (BCIs), with...