  Graph Neural Networks (GNNs) have shown promise in learning dynamic
functional connectivity for distinguishing phenotypes from human brain
networks. However, obtaining extensive labeled clinical data for training is
often resource-intensive, making practical application difficult. Leveraging
unlabeled data thus becomes crucial for representation learning in a
label-scarce setting. Although generative self-supervised learning techniques,
especially masked autoencoders, have shown promising results in representation
learning in various domains, their application to dynamic graphs for dynamic
functional connectivity remains underexplored, facing challenges in capturing
high-level semantic representations. Here, we introduce the Spatio-Temporal
Joint Embedding Masked Autoencoder (ST-JEMA), drawing inspiration from the
Joint Embedding Predictive Architecture (JEPA) in computer vision. ST-JEMA
employs a JEPA-inspired strategy for reconstructing dynamic graphs, which
enables the learning of higher-level semantic representations considering
temporal perspectives, addressing the challenges in fMRI data representation
learning. Utilizing the large-scale UK Biobank dataset for self-supervised
learning, ST-JEMA shows exceptional representation learning performance on
dynamic functional connectivity demonstrating superiority over previous methods
in predicting phenotypes and psychiatric diagnoses across eight benchmark fMRI
datasets even with limited samples and effectiveness of temporal reconstruction
on missing data scenarios. These findings highlight the potential of our
approach as a robust representation learning method for leveraging label-scarce
fMRI data.
