DyBraSS: Dynamic Brain State Modeling with State-Space Model

ICLR 2026 Conference Submission8714 Authors

Keywords: Resting-state fMRI, State-space model, Brain state dynamics

Abstract:

Brain states, observable through resting-state functional magnetic resonance imaging (rs-fMRI), represent dynamic transitions between recurring connectivity patterns and are closely linked to neurological and psychiatric conditions. Therefore, developing a computational model for dynamic brain state estimation has been a long-lasting research interest. Among existing approaches, state-space models (SSMs) provide a principled framework for modeling these dynamics. However, existing methods face key limitations: they fail to preserve the brain's spatial architecture, and they model temporal dynamics without considering co-evolving spatial patterns.
To address these limitations, we propose $\textbf{DyBraSS}$ ($\textbf{Dy}$namic $\textbf{Bra}$in $\textbf{S}$tate-$\textbf{S}$pace model), a novel structured SSM that unifies spatial and temporal modeling within a single framework, enhancing ROI-level modeling capacity and interpretability through a clustering-based global aggregation module.
This module respects the brain's network topology by integrating information from all regions during each local update, and represents evolving brain states as interpretable clusters.
Comprehensive experiments on multiple fMRI datasets demonstrate that our method consistently outperforms state-of-the-art baselines in diagnostic performance across diverse metrics.
Additionally, individual- and group-level brain state analyses reveal that the learned dynamics align with known neurobiological alterations, providing clinically relevant insights for understanding neural dysfunction and developing diagnostic biomarkers.

Primary Area: applications to neuroscience & cognitive science

Submission Number: 8714