Adaptive Multi-Scale Dynamic Graph Representation Learning With Overlapping Community-Awareness for ASD Classification.

Abstract

In recent years, dynamic functional connectivity (dFC) has been widely employed for brain disease diagnosis. By leveraging the inherent topological characteristics of the brain, graph neural networks (GNNs) have emerged as prominent deep learning methods for utilizing dFC in this context. However, existing research has some limitations. Temporally, the conventional fixed-length sliding window approach often fails to capture the multi-scale temporal characteristics inherent in brain activity. Spatially, GNN-derived graph representations usually overlook the multi-network participation of brain regions. To address these limitations, we propose Ada-MST, an adaptive multi-scale spatio-temporal model utilizing multi-scale dFC for brain disease diagnosis. Our framework constructs personalized multi-scale dFC graphs that adapt to subject-specific temporal characteristics. Moreover, we introduce a novel overlapping community-aware readout module that incorporates the participation of brain regions in multiple functional networks, leading to more accurate graph-level representations. Experiments on ABIDE-I and ABIDE-II datasets demonstrate that our method outperforms state-of-the-art approaches. Visualization analysis further confirms the generalizability of the subject-adaptive graphs and their focus on disease-related brain activity. Furthermore, the fuzzy memberships revealed by our readout module indicate distinct patterns across diseases, suggesting the promise of considering functional community membership changes for exploring disease biomarkers.