Hierarchical Spatio-Temporal State-Space Modeling for fMRI Analysis

TL;DR

This paper introduces FST-Mamba, a hierarchical spatiotemporal model based on the Mamba architecture, for analyzing fMRI data to discover neurological biomarkers, demonstrating significant improvements in brain classification and regression tasks.

Contribution

The paper presents a novel hierarchical spatiotemporal Mamba-based network with component-wise aggregation and symmetric rotary position encoding for fMRI analysis, enhancing brain connectivity modeling.

Findings

Significant performance improvements on brain classification tasks.

Effective capture of component and network-level connectivity.

Identification of key brain connectivities for prediction.

Abstract

Recent advances in deep learning structured state space models, especially the Mamba architecture, have demonstrated remarkable performance improvements while maintaining linear complexity. In this study, we introduce functional spatiotemporal Mamba (FST-Mamba), a Mamba-based model designed for discovering neurological biomarkers using functional magnetic resonance imaging (fMRI). We focus on dynamic functional network connectivity (dFNC) derived from fMRI and propose a hierarchical spatiotemporal Mamba-based network that processes spatial and temporal information separately using Mamba-based encoders. Leveraging the topological uniqueness of the FNC matrix, we introduce a component-wise varied-scale aggregation (CVA) mechanism to aggregate connectivity across individual components within brain networks, enabling the model to capture component-level and network-level information. Additionally, we propose symmetric rotary position encoding (SymRope) to encode the relative positions of each functional connection while considering the symmetric nature of the FNC matrix. Experimental results demonstrate significant improvements in the proposed FST-Mamba model on various brain-based classification and regression tasks. We further show brain connectivities and dynamics that are crucial for the prediction. Our work reveals the substantial potential of attention-free sequence modeling in brain discovery. The codes are publicly available here: https://github.com/yuxiangwei0808/FunctionalMamba/tree/main.