NeuroSSM: Multiscale Differential State-Space Modeling for Context-Aware fMRI Analysis

• URL: http://arxiv.org/abs/2601.01229v1
• Date: Sat, 03 Jan 2026 16:35:45 GMT
• Title: NeuroSSM: Multiscale Differential State-Space Modeling for Context-Aware fMRI Analysis
• Authors: Furkan Genç, Boran İsmet Macun, Sait Sarper Özaslan, Emine U. Saritas, Tolga Çukur,
• Abstract summary: We propose NeuroSSM, a selective state-space architecture designed for end-to-end analysis of raw BOLD signals in fMRI time series.<n>NeuroSSM addresses the above limitations through two complementary design components.<n> Experiments on clinical and non-clinical datasets demonstrate that NeuroSSM achieves competitive performance and efficiency against state-of-the-art fMRI analysis methods.
• Score: 4.753690672619091
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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