Predicting Resting-state Functional Connectivity With Efficient Structural Connectivity  

作　　者：Xue Chen Yanjiang Wang 

机构地区：[1]College of Information and Control Engineering,China University of Petroleum (East China)

出　　处：《IEEE/CAA Journal of Automatica Sinica》2018年第6期1079-1088,共10页自动化学报（英文版）

基　　金：supported by China Scholarship Council(201306455001);the National Natural Science Foundation of China(61271407);the Fundamental Research Funds for the Central Universities(16CX06050A)

摘　　要：The complex relationship between structural connectivity(SC) and functional connectivity(FC) of human brain networks is still a critical problem in neuroscience. In order to investigate the role of SC in shaping resting-state FC, numerous models have been proposed. Here, we use a simple dynamic model based on the susceptible-infected-susceptible(SIS) model along the shortest paths to predict FC from SC. Unlike the previous dynamic model based on SIS theory, we focus on the shortest paths as the principal routes to transmit signals rather than the empirical structural brain network. We first simplify the structurally connected network into an efficient propagation network according to the shortest paths and then combine SIS infection theory with the efficient network to simulate the dynamic process of human brain activity. Finally, we perform an extensive comparison study between the dynamic models embedded in the efficient network, the dynamic model embedded in the structurally connected network and dynamic mean field(DMF) model predicting FC from SC. Extensive experiments on two different resolution datasets indicate that i) the dynamic model simulated on the shortest paths can predict FC among both structurally connected and unconnected node pairs; ii) though there are fewer links in the efficient propagation network, the predictive power of FC derived from the efficient propagation network is better than the dynamic model simulated on a structural brain network; iii) in comparison with the DMF model,the dynamic model embedded in the shortest paths is found to perform better to predict FC.The complex relationship between structural connectivity（SC） and functional connectivity（FC） of human brain networks is still a critical problem in neuroscience. In order to investigate the role of SC in shaping resting-state FC, numerous models have been proposed. Here, we use a simple dynamic model based on the susceptible-infected-susceptible（SIS） model along the shortest paths to predict FC from SC. Unlike the previous dynamic model based on SIS theory, we focus on the shortest paths as the principal routes to transmit signals rather than the empirical structural brain network. We first simplify the structurally connected network into an efficient propagation network according to the shortest paths and then combine SIS infection theory with the efficient network to simulate the dynamic process of human brain activity. Finally, we perform an extensive comparison study between the dynamic models embedded in the efficient network, the dynamic model embedded in the structurally connected network and dynamic mean field（DMF） model predicting FC from SC. Extensive experiments on two different resolution datasets indicate that i） the dynamic model simulated on the shortest paths can predict FC among both structurally connected and unconnected node pairs; ii） though there are fewer links in the efficient propagation network, the predictive power of FC derived from the efficient propagation network is better than the dynamic model simulated on a structural brain network; iii） in comparison with the DMF model,the dynamic model embedded in the shortest paths is found to perform better to predict FC.

关 键 词：Brain connectivity structure-function relationship susceptible-infected-susceptible （SIS）model the shortest paths 

分 类 号：TP393[自动化与计算机技术—计算机应用技术] TP2[自动化与计算机技术—计算机科学与技术]