Modeling Transient Brain Coactivity Patterns in Latent Space with FMRI Data

Abstract

The brain is a complex dynamic system that constantly evolves. Characterization of the spatiotemporal dynamics of brain activity is fundamental to understanding how brain works. Current studies with functional connectivity and linear models are limited by low temporal resolution and insufficient model capacity. With a generative variational auto encoder (VAE), the present study mapped the high-dimensional transient co-activity patterns (CAPs) of functional magnetic resonance imaging data to a low-dimensional latent representation that followed a multivariate gaussian distribution. We demonstrated with multiple datasets that the VAE model could effectively represent the transient CAPs in the latent space. Transient CAPs from high-intensity and low-intensity values reflected the same functional structure of brain and could be reconstructed from the same distribution in the latent space. With the reconstructed latent time courses, preceding CAPs successful predicted the following transient CAP with a long short-term memory recurrent neural network. Our methods provide a new avenue to characterize the brains transient co-activity maps and model the complex dynamics between them in a framewise manner.