  Resting-state functional MRI (rs-fMRI) is a rich imaging modality that
captures spontaneous brain activity patterns, revealing clues about the
connectomic organization of the human brain. While many rs-fMRI studies have
focused on static measures of functional connectivity, there has been a recent
surge in examining the temporal patterns in these data. In this paper, we
explore two strategies for capturing the normal variability in resting-state
activity across a healthy population: (a) an autoencoder approach on the
rs-fMRI sequence, and (b) a next frame prediction strategy. We show that both
approaches can learn useful representations of rs-fMRI data and demonstrate
their novel application for abnormality detection in the context of
discriminating autism patients from healthy controls.
