Self-supervised Learning of Dynamic Functional Connectivity from Human Brain

The author introduces the Spatio-Temporal Joint Embedding Masked Autoencoder (ST-JEMA) to address challenges in representation learning for dynamic functional connectivity in fMRI data. By leveraging generative self-supervised learning techniques, ST-JEMA shows exceptional performance in predicting phenotypes and psychiatric diagnoses.