Brain Disease Detection Based on Bottom-Up Hierarchical Graph Representation Learning

Current research on brain disorders focuses on the functional connectivity networks of regions of interest (ROIs), but less attention has been paid to explicit brain functional cluster recognition, which neuroscience has identified as playing a crucial role in brain disease detection. In this paper, we propose a bottom-up hierarchical graph representation learning method for brain disease detection. Bottom-level brain graph representation with ROI node importance is designed to suppress noise for effective recognition of top-level brain functional clusters. Top-level brain graph representation with clustering consistency constraints explicitly characterizes functional clusters and interaction patterns. We demonstrate through theoretical analysis that bottom-level gating conditionally bounds perturbations in spectral alignment, while top-level clustering consistency constraints yield a conditional upper bound on the continuous normalized cut objective. Our model is validated on the publicly available neuroimaging datasets ADHD-200, OpenNeuro-ds002424, and OpenNeuro-ds000030, demonstrating significant advantages in brain disease detection. Code is available at https://anonymous.4open.science/r/Bottom-Up-Hierarchical-Graph-Representation-Learning-0670.