Multi-Scale Diffusion for Bio-topological Representation Learning on Multimodal Brain Graphs

Understanding how neurological disorders disrupt brain network topology requires modeling the brain's hierarchical and multiscale organization. However, existing brain graph learning methods predominantly focus on single-scale structures, limiting their ability to capture interdependent topological alterations across modalities and scales. We propose BrainSTORE, a multi-scale diffusion framework for bio-topological representation learning on multimodal brain graphs that jointly models structural and functional connectivity. BrainSTORE introduces a multiscale community-detection strategy that constructs hierarchically consistent topological priors across local, meso-, and global scales while preserving cross-modal dependencies. These priors are integrated into the diffusion process via a scale-specific noise-scheduling mechanism that guides perturbations along biologically meaningful structural pathways. A unified joint denoising architecture further enforces cross-modal consistency, enabling the extraction of both modality-specific and shared bio-topological features. Extensive experiments on two real-world neuroimaging datasets demonstrate that BrainSTORE consistently outperforms state-of-the-art methods in brain disease detection. Beyond improved predictive performance, BrainSTORE provides a principled framework for investigating scale-dependent and modality-coupled topological alterations associated with neurological disorders.