CPD-NSL: A Two-Stage Brain Effective Connectivity Network Construction Method Based on Dynamic Bayesian Network

Current brain science reveals that the connectivity patterns of the human brain are constantly changing when performing different tasks. Thus, brain effective connectivity networks based on non-stationary assumption can describe such neurodynamics better than the ones based on stationary assumption. However, existing methods for inferring non-stationary brain effective connectivity networks are committed to estimating the change points and network structures simultaneously. It is even worse that these methods will inevitably focus on one part of the estimation process and lead to the deviation of the results obtained by the other part. Then, the construction results of non-stationary brain effective connectivity networks cannot accurately reflect the real brain dynamics. In this paper, a novel approach to constructing non-stationary brain effective connectivity networks is proposed, namely CPD-NSL. It involves two stages including change point detection and network structure learning. In the first stage, the latent block model is used, and then the improved forward-backward search method is used to construct the stationary networks between adjacent change points in the network structure learning part. Finally, the constructed stationary networks are arranged in chronological order to obtain the final time-varying brain effective connectivity network. CPD-NSL is validated using simulated data as well as real fMRI data from HCP public datasets. The results show that CPD-NSL can restore the real network more accurately and consume less time. Experimental results on both simulated and real data demonstrate the effectiveness of the proposed method in constructing non-stationary state brain effective connectivity networks.