Masked Momentum Contrastive Dynamic Transformer for Self-Supervised Functional Connectivity Representation Learning

Functional connectivity (FC) derived from functional MRI (fMRI) shows significant promise in predicting behavior and demographics using deep learning techniques. Incorporating vertex-wise FC maps, which capture fine-grained spatial details of neural activity, offers the potential to enhance FC-based prediction accuracy. However, fMRI data is inherently limited and noisy, challenging neural networks to reliably identify patterns within high-dimensional cortical vertices. Therefore, we design a novel Masked Momentum Contrastive Dynamic Transformer, which utilizes masked momentum contrastive pre-training to explore subject-specific features and enhances prediction accuracy by leveraging the temporal dynamics of FCs with a dynamic transformer. Specifically, our framework 1) learns effective subject-specific representations by treating vertex-wise FCs from different runs of an individual as distinct views and maximizing their affinity, and 2) employs a vertex-wise masking strategy to promote learning from limited data. Extensive experiments on gender classification and cognition prediction validate its superior performance on the Human Connectome Project dataset.