Spatiotemporal trajectories in resting-state FMRI revealed by convolutional variational autoencoder.

ABSTRACT: Recent resting-state fMRI studies have shown that brain activity exhibits temporal variations in functional connectivity by using various approaches including sliding window correlation, co-activation patterns, independent component analysis, quasi-periodic patterns, and hidden Markov models. These methods often model the brain activity as a discretized hopping among several brain states that are defined by the spatial configurations of network activity. However, the discretized states are merely a simplification of what is likely to be a continuous process, where each network evolves over time following its unique path. To model these characteristic spatiotemporal trajectories, we trained a variational autoencoder using rs-fMRI data and evaluated the spatiotemporal features of the latent

SUBMITTER: Zhang X 

PROVIDER: S-EPMC8637345 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature