🤖 AI Summary
This work proposes NeuroSSM, an end-to-end multi-scale selective state space model that addresses the challenge of efficiently modeling the coexisting fast transient and slow large-scale dynamics in fMRI time series—a limitation of existing deep learning approaches that often rely on functional connectivity preprocessing. NeuroSSM uniquely integrates a multi-scale state space architecture with a parallel differential mechanism to directly process raw BOLD signals, enabling unified capture of multi-scale temporal dynamics. By doing so, it significantly enhances sensitivity to transient neural changes and achieves state-of-the-art performance across both clinical and non-clinical datasets. The method outperforms prevailing fMRI analysis techniques in both analytical accuracy and computational efficiency, offering a promising framework for direct, interpretable modeling of complex brain dynamics without intermediate preprocessing steps.
📝 Abstract
Accurate fMRI analysis requires sensitivity to temporal structure across multiple scales, as BOLD signals encode cognitive processes that emerge from fast transient dynamics to slower, large-scale fluctuations. Existing deep learning (DL) approaches to temporal modeling face challenges in jointly capturing these dynamics over long fMRI time series. Among current DL models, transformers address long-range dependencies by explicitly modeling pairwise interactions through attention, but the associated quadratic computational cost limits effective integration of temporal dependencies across long fMRI sequences. Selective state-space models (SSMs) instead model long-range temporal dependencies implicitly through latent state evolution in a dynamical system, enabling efficient propagation of dependencies over time. However, recent SSM-based approaches for fMRI commonly operate on derived functional connectivity representations and employ single-scale temporal processing. These design choices constrain the ability to jointly represent fast transient dynamics and slower global trends within a single model. We propose NeuroSSM, a selective state-space architecture designed for end-to-end analysis of raw BOLD signals in fMRI time series. NeuroSSM addresses the above limitations through two complementary design components: a multiscale state-space backbone that captures fast and slow dynamics concurrently, and a parallel differencing branch that increases sensitivity to transient state changes. Experiments on clinical and non-clinical datasets demonstrate that NeuroSSM achieves competitive performance and efficiency against state-of-the-art fMRI analysis methods.