Neurology

DualMyo: Multi-Channel Dual-Stream Transformer Architecture for EMG-to-Digit Classification

Surface electromyography (sEMG) is a robust non-invasive modality for human-machine interaction, yet its application remains largely limited to coarse motor tasks such as grasping or rotation. The decoding of fine motor skills, specifically handwriting, remains a challenging problem with potential relevance for prosthetic control and natural communication interfaces. In this work, we explore a Tra...

Mapping Alzheimer's neuropathology signatures to the whole brain transcriptome using machine learning data fusion

In Alzheimer's disease (AD), misfolded proteins emerge across the entire brain in structured, yet not rigid, spatiotemporal patterns. Yet, a systematic bias of single-cell genomics toward sampling mostly cortical tissue limits our understanding of the whole-brain transcriptomic vulnerability to AD. Here, we develop a machine learning method to extrapolate local AD neuropathology signatures to the ...

Reliability and disease sensitivity are dissociable properties of EEG foundation-model representations

Abstract EEG foundation models (EEG-FMs) are evaluated almost entirely on disease-discrimination accuracy. A clinical biomarker additionally requires ...

Progressive Loosening of a Dual Autoinhibitory Interface Activates PP2A-B56δ

Protein phosphatase 2A containing the B56{delta} regulatory subunit (PP2A-B56{delta}) is a critical signaling enzyme whose dysregulation is associated...

EEG Microstate Sequences as Potential Brain-Computer Interface Triggers Derived from Motor Imagery Classification

EEG microstates are a distinct number of quasi-stable spatial distributions of brain activity. Microstate trajectories are strongly suspected to refle...

Towards Investigating Residual Hearing Loss: Quantification of Fibrosis in a Novel Cochlear OCT Dataset

Objective: Cochlear implants (CIs) are bionic prostheses that restores hearing via electrical stimulation of the auditory nerve. Hybrid CIs, which use...

Event-Wise Stability of Patient-Specific EEG-MEG Deep Learning Spike Detection in Clinical MEG

Objective: Computational magnetoencephalography (MEG) interictal epileptiform discharge (IED) detectors have mainly used generalized MEG-only models, ...

Brain-Language Alignment During Naturalistic Reading and Its Disruption by Mind-Wandering

Encoding models offer a principled framework for linking computational representations of language to neural activity, but most electroencephalography...

Aggregate, Don't Adapt: Subject-Level Posterior Aggregation and Transductive Calibration for Cross-Site Parkinsonian Gait Severity

We describe the winning entry to the MoCha 2026 Benchmark and Challenge on Parkinsonian Gait, which predicts MDS-UPDRS gait severity from canonicalize...

Biomarker Fidelity Score - A Quantitative Framework for Individual-Level Validation of Explainability Methods in 3D Alzheimer's Disease MRI Classification

Explainability methods applied to deep learning models for Alzheimer's disease neuroimaging produce attribution maps that vary substantially across me...

State-dependent aperiodic EEG dynamics track cortical network reorganization in chronic epilepsy

Aperiodic (1/f-like) EEG activity has rapidly become a popular noninvasive marker of cortical network state, proposed to index excitation-inhibition (...

AsymFeX: A Symmetry-Driven Framework for Ischemic Stroke Segmentation Across Imaging Modalities and Stroke Stages

Fast and accurate segmentation of Acute Ischemic Stroke (AIS) lesions is essential for stroke prognosis and treatment planning. Non-contrast CT (NCCT)...

A Resource-Efficient CNN-Based EEG Auditory Attention Decoding ASIC

Following a target speaker in a noisy environment, commonly known as the cocktail party problem, remains particularly challenging for cochlear implant...

Longitudinal Bayesian Learning of Continuous Disease Position across the Alzheimer's Disease Continuum

Alzheimer's disease (AD) progresses as a continuous biological process, whereas most existing neuroimaging-based artificial intelligence methods remai...

Atrial Fibrillation Detection with Arbitrary Leads via a Codebook-Based Reconstruction-Classification Framework

\textbf{Background and Objective}: Reliable atrial fibrillation (AF) detection from electrocardiogram (ECG) signals remains challenging in real-world ...

X-LMC: Cross-View Spatiotemporal Collateral Circulation Scoring from DSA

Digital subtraction angiography (DSA) is the reference standard for leptomeningeal collateral (LMC) assessment, providing critical prognostic insights...

SCORE: Subject Coordinate Recovery for Label-Free Cross-Subject EEG-to-Image Retrieval

Accurate visual decoding can reveal how the brain represents visual information and recover perceived content from neural signals such as electroencep...

Delta2Gamma: Band-Wise Adaptive Contrastive Learning of EEG for Alzheimer's Disease Detection

Low-cost, scalable screening for dementia remains an open problem. Imaging-based diagnosis is costly and hard to deploy widely. Electroencephalography...

BrainNorm: A Foundation Model that knows Normal via Semantic Atlas Pretraining

We introduce BrainNorm, a normative foundation model, trained and tested on ~66,000 T1-weighted structural MRI (T1w sMRI) scans. By leveraging languag...

OOD Detection for EEG-based Machine Learning in High-Risk Environments

Machine learning models for electroencephalography (EEG) analysis show great promise across a wide range of applications, but their deployment in high...