Dynamic Effective Connectivity Learning Based on Nonparametric State Estimation and GAN

Dynamic effective connectivity (DEC) contains abundant temporal and spatial dynamic information, which can characterize the formation and dissolution of distributed directional functional patterns over time. Recently, learning DEC from functional magnetic resonance imaging (fMRI) time-series data has become a new hot spot in the field of neuroinformatics. However, current DEC learning methods are hard to effectively estimate the transition of brain states, and accurately learn the network structure of DEC. In this paper, we propose a novel dynamic effective connectivity learning method based on non-parametric state estimation and generative adversarial network, named nPSE-GAN. The nPSE-GAN first employs non-parametric state estimation (nPSE) to automatically infer the number of brain states and transition time. In detail, the nPSE uses dual extended Kalman filtering (dEKF) to obtain state features, and employs hierarchical clustering to estimate the transition of brain states. Then, the proposed method uses generative adversarial network (GAN) to learn the network structure of DEC. Specifically, GAN takes the transition information and original fMRI time-series data as input, which trains the generator and discriminator simultaneously. The experimental results on simulated data sets show that nPSE-GAN can effectively estimate the transition of brain states and is superior to other state-of-art methods in learning the network structure of DEC. The experimental results on real data sets show that nPSE-GAN can better reveal abnormal patterns of brain activity and has a good application potential in brain network analysis.