LERD: Latent Event-Relational Dynamics for Neurodegenerative Classification

2026-02-20

AI Summary

This work proposes an end-to-end Bayesian neural dynamics system for Alzheimer’s disease (AD) diagnosis from EEG, addressing the interpretability limitations of existing black-box approaches. The method uniquely enables unsupervised inference of latent neural events, their temporal patterns, and relational structures directly from multichannel EEG data without event-level annotations. It integrates continuous-time event reasoning, stochastic event generation, and electrophysiologically informed dynamic priors, and comes with theoretical stability guarantees. Experiments on synthetic data and two real-world AD EEG cohorts demonstrate that the approach outperforms strong baselines, yielding latent representations that align with known neurophysiological mechanisms and effectively capture dynamic differences between patient groups.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Bayesian Learning
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Alzheimer's disease (AD) alters brain electrophysiology and disrupts multichannel EEG dynamics, making accurate and clinically useful EEG-based diagnosis increasingly important for screening and disease monitoring. However, many existing approaches rely on black-box classifiers and do not explicitly model the underlying dynamics that generate observed signals. To address these limitations, we propose LERD, an end-to-end Bayesian electrophysiological neural dynamical system that infers latent neural events and their relational structure directly from multichannel EEG without event or interaction annotations. LERD combines a continuous-time event inference module with a stochastic event-generation process to capture flexible temporal patterns, while incorporating an electrophysiology-inspired dynamical prior to guide learning in a principled way. We further provide theoretical analysis that yields a tractable bound for training and stability guarantees for the inferred relational dynamics. Extensive experiments on synthetic benchmarks and two real-world AD EEG cohorts demonstrate that LERD consistently outperforms strong baselines and yields physiology-aligned latent summaries that help characterize group-level dynamical differences.

Problem

• Alzheimer's disease
• EEG dynamics
• latent neural events
• neurodegenerative classification
• electrophysiology

Innovation

• latent event inference
• relational dynamics
• Bayesian neural dynamical system
• electrophysiological prior
• multichannel EEG