Neurology

Deep Learning for Individual-Level Classification of Schizophrenia Versus Healthy Controls from Trial-Level Auditory Oddball ERP Waveforms

Machine learning approaches may support individual-level classification in psychiatry, but many EEG-based schizophrenia studies have relied on small samples or conventional summary features. We evaluated whether trial-level auditory oddball event-related potential (ERP) waveforms could support schizophrenia versus healthy-control classification using deep learning. The study included 258 patients ...

Edge-Based ADL Recognition Using Room-Specialized Mixture-of-Experts

Activities of daily living (ADLs) provide important indicators of functional decline in people living with dementia, motivating the need for continuous in-home monitoring. However, deploying transformer-based activity recognition models on resource-constrained edge devices remains challenging because of limited computational resources and the need to preserve participant privacy by avoiding cloud-...

Interpretable gene networks from single-cell foundation models reveal conserved neurogenic dysfunction in Parkinson's disease

Interpreting large-scale singlecell transcriptomic data remains a major challenge for understanding disease mechanisms. Recent single-cell foundation ...

Disentangling Acoustic Cues in Alzheimer's Pathology and Perception: The Roles of Language and Gender

Acoustic biomarkers show promise for detecting Alzheimer's Disease (AD), yet whether the cues driving diagnostic AI align with those salient to human ...

Bayesian Feature Extraction using Gaussian and Diffused-gamma Priors for High Dimensional Spatio-Temporal Data

High-dimensional data with sparse structure and spatio-temporal dependence arise in many scientific domains. We develop a Bayesian feature-extraction ...

Stress-Testing EEG Foundation Models for Clinical Decoding: Dataset Identity and Targeted Negative Controls

Pretrained EEG foundation models are increasingly proposed for clinical decoding, but their transfer across populations and robustness to negative con...

EMG-BIDS: an extension to the Brain Imaging Data Structure for electromyography

Electromyography (EMG) is fundamental to clinical assessment, rehabilitation, neuromuscular research, and human-machine interfaces. Despite decades of...

Interpretable EEG biomarkers with bag-of-waves: Spatial and temporal waveform dictionaries for low-data regimes

Electroencephalography (EEG) is widely used to diagnose neurological conditions, but its analysis usually relies on either predefined spectral feature...

An Explainable AI-Driven Classification Framework for Parkinson's Disease Detection via Acoustic Speech Features: A Comparative Machine Learning Study

Parkinsons Disease (PD) is a progressive neurodegenerative disorder which significantly affects motor function, daily coordination and verbal communic...

Localizing epileptogenic zones using interictal intracranial electroencephalography and deep learning

Introduction: Approximately 25% of the 51.7 million people with epilepsy globally develop drug-resistant disease, for whom surgical resection offers a...

Machine learning-based neuroimaging for prediction of deep brain stimulation outcomes in movement disorders: Systematic review and meta-analysis

Abstract Background: Deep Brain Stimulation (DBS) surgery is a treatment of choice for movement disorders, and utilizes an implanted electrical pulse ...

Decoding Agency-Related Neural States During Human-AI Interaction in Autonomous Driving Using EEG and Deep Learning

The sense of agency, the experience of controlling one's actions and their consequences, is a fundamental component of human interaction with autonomo...

FSB-Net: Frequency-Spatial Boundary Network for Brain Stroke Lesion Segmentation in Non-Contrast CT

Accurate segmentation of brain stroke lesions in non-contrast computed tomography (NCCT) scans is critical for rapid clinical decision-making, yet rem...

Validation of clinical diagnosis and machine learning classification of cognitive impairment

INTRODUCTION: Cognitive syndrome diagnosis (Normal, Mild Cognitive Impairment (MCI), Dementia) is important for summarizing disease status and predict...

A Multimodal Multiomics Machine Learning (MMM) approach for biomarker discovery and acceleration of clinical trial readiness for childhood-onset neurological disorders

Background Childhood neurodegenerative disorders are usually rare, genetic, and life-limiting. Whilst targeted approaches present huge potential, sign...

SIINR: Structurally Informed Implicit Neural Representations for super-resolution with uncertainty quantification of clinical quality diffusion MRI datasets

Diffusion Magnetic Resonance Imaging (dMRI) is a powerful tool for probing brain microstructure, but clinical acquisitions are often limited by low ou...

Real-Time EEG Cap Electrode Detection for Guided Point-of-Care Placement

We present a two-stage vision system that detects EEG cap electrodes in a live webcam stream and validates their anatomical placement in real time. A ...

Encoding Discordance in the Alzheimer's Disease A/T/N Framework

INTRODUCTION: The biomarker-based amyloid/ tau/ neurodegeneration (A/T/N) framework has become a popular staging method for Alzheimer's disease (AD) r...

Multi-model Segmentation and Morphometric Quantification of Cerebral Amyloid Angiopathy in Alzheimer's Disease Whole Slide Histopathology Images

Introduction: Cerebral amyloid angiopathy (CAA) is characterized by amyloid-beta deposition in cortical and leptomeningeal vessels and associated with...

When does more data help? Spectral Geometry and Scaling Laws in MRI Transformers

Scaling laws describe how model performance improves as the amount of training data increases, and recent theories such as the zeta law suggest that s...