Meeting Banner
Abstract #3127

Spatiotemporal Trajectories in Resting-state FMRI Revealed by Convolutional Variational Autoencoder

Xiaodi Zhang1, Eric Maltbie1, and Shella Dawn Keilholz1
1BME, Emory University/Georgia Tech, Atlanta, GA, United States

We trained a novel convolutional variational autoencoder to extract intrinsic spatial temporal patterns from short segments of resting-state fMRI data. The network was trained in an unsupervised manner using data from the Human Connectome Project. The extracted latent dimensions not only show clear clusters in the spatial domain that were in agreement with DMN/TPN anticorrelations and principal gradients, but also provide temporal information as well. The method provides a way to extract orthogonal spatial temporal patterns within fMRI data in a short time window, among which many patterns were not previously discovered and are worth investigating in the future.

This abstract and the presentation materials are available to members only; a login is required.

Join Here