HAHN-SGCL: Hierarchical Attention and Hard Negatives-aware State Graph Contrastive Learning for functional connectome fingerprinting.

Functional connectome (FC) fingerprinting is crucial for understanding individual cognitive patterns and advancing personalized medicine for neuro/psychiatric disorders by developing individual-specific biomarkers. However, existing FC fingerprinting methods oversimplify the complex and nonlinear nature of FC patterns. As a result, they fail to effectively extract individual-specific information from variability across different brain states, thereby limiting individual identification performance. To address this issue, we propose Hierarchical Attention and Hard Negatives-aware State Graph Contrastive Learning (HAHN-SGCL) model. HAHN-SGCL directly leverages brain states to generate intra- and inter-individual contrasts, effectively extracting individual-specific connectivity patterns for accurate identification across diverse states. Specifically, to fully extract individual-specific information across multiple topological levels of the FC, we designed a Hierarchical Graph Attention Network (HGAT) encoder. HGAT constructs a hierarchical graph with diverse topological perspectives and employs level-specific attention mechanisms to capture distinctive individual features. Additionally, to overcome the severe sample imbalance that hampers effective gradient propagation, we introduce a Hard Negatives-aware Strategy (HNS). HNS focuses on challenging negatives through Hard Negative Mining (HNM) and incorporating a corrective term, effectively avoiding early convergence plateaus. Extensive experiments demonstrate that our HAHN-SGCL model outperforms state-of-the-art methods. It also exhibits strong cross-task transferability, as evidenced by its robust performance in psychiatric disorder classification. The code of HAHN-SGCL is at https://anonymous.4open.science/r/HAHN-SGCL.