Multiscale Diffusion for Bio-topological Representation Learning on Multimodal Brain Graphs
Year: 2026
Type: article
Abstract: 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 multiscale 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.
DOI: 10.1145/3821525
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W7165791398