Estimating Brain Effective Connectivity In Fmri Data By Non-Stationary Dynamic Bayesian Networks

Estimating brain effective connectivity (EC) from neuroimaging data has recently received wide interest and become a new topic in the neuroinformatics field. Currently, dynamic Bayesian networks (DBN) have been successfully applied to estimating EC from functional magnetic resonance imaging (fMRI) time-series data as they can capture the temporal characteristics of connectivity among brain regions. However, DBN methods assume that activations of brain regions are stationary and follow a Markovian condition, which are strong assumptions that may not be valid in many cases. In this paper, we introduce a novel method to estimate brain effective connectivity networks from fMRI data using non-stationary dynamic Bayesian networks, named as EC-nsDBN. EC-nsDBN can not only capture the non-stationary temporal information from fMRI time-series data but also estimate how interactions among brain regions change dynamically over the fMRI experiments. Systematic experiments on simulated data show that EC-nsDBN has better direction identification ability compared with other state-ofthe-art algorithms, and can accurately capture the temporal characteristics of connectivity. Experiments on real-world data sets are also provided to support our analysis.