HiBrain: Hierarchical Prototype Learning on Multimodal Brain Graphs for Stage-Aware Biomarker Discovery

Abstract

Neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) are characterised by progressive, stage-dependent disruptions in brain connectivity. Multimodal neuroimaging data, particularly functional MRI (fMRI) and diffusion tensor imaging (DTI), provide complementary perspectives on functional and structural brain organisation. However, most existing graph-based approaches compress whole-brain networks into a single global representation, limiting their capacity to model hierarchical connectivity patterns and to deliver interpretable insights for biomarker discovery. In this work, we propose HiBrain, a hierarchical prototype-based framework for multimodal brain network analysis. HiBrain explicitly represents brain graphs at node-, graph-, and stage-level abstractions, while preserving the distinct structural and functional connectivity characteristics throughout the hierarchy. The proposed framework progressively abstracts representative local connectivity patterns into global network representations and disease-stage–specific prototypes, enabling both accurate stage-aware classification and principled interpretability. Experiments on multimodal AD and PD datasets demonstrate consistent improvements over state-of-the-art baselines in multi-stage classification tasks. Moreover, prototype-driven visualisations of connectivity difference matrices and biomarker subgraphs reveal clear and stage-specific brain network signatures, highlighting the interpretability and scientific utility of the proposed framework for neurodegenerative disease analysis. The source code is available at https://github.com/yangkf825/HiBrain.