A Graph-Based Generative Adversarial Network Model for Inferring Task-State from Resting-State Functional Connectivity Networks

Abstract

Resting-state functional connectivity networks (rs-FCNs) have been most frequently used for brain network analysis in neuroscience. However, a body of evidence indicates that task-state FCNs (ts-FCNs) are better associated with individual differences in behavior than rs-FCN. Until now there have been no studies of ascertaining to what extent rs-FCNs can account for ts-FCNs. In this paper, we propose a Multiple Graph Autoencoder based Generative Adversarial Network (MGAE-GAN) model to enable the inference of ts-FCNs from rs-FCNs. The generator of MGAE-GAN is built upon several graph autoencoders to learn and adaptively combine multiple implicit relationships between rs-FCNs and ts-FCNs. To ensure the authenticity of the predicted ts-FCNs, we design the discriminator of MGAE-GAN based on graph metric learning. Additionally, we incorporate a correlation loss and a subject-similarity-preserving loss to maintain overall correlation and between-subject similarities before and after the generator, respectively. Experimental results on the Human Connectome Project (HCP) S1200 demonstrate the effectiveness of our MGAE-GAN for predicting ts-FCNs from rs-FCNs.