  The objective of this paper is to provide a temporal dynamic model for
resting state functional Magnetic Resonance Imaging (fMRI) trajectory to
predict future brain images based on the given sequence. To this end, we came
up with the model that takes advantage of representation learning and Neural
Ordinary Differential Equation (Neural ODE) to compress the fMRI image data
into latent representation and learn to predict the trajectory following
differential equation. Latent space was analyzed by Gaussian Mixture Model. The
learned fMRI trajectory embedding can be used to explain the variance of the
trajectory and predict human traits for each subject. This method achieves
average 0.5 spatial correlation for the whole predicted trajectory, and provide
trained ODE parameter for further analysis.
