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Volume 5 Issue 6
Nov.  2018

IEEE/CAA Journal of Automatica Sinica

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Xue Chen and Yanjiang Wang, "Predicting Resting-state Functional Connectivity With Efficient Structural Connectivity," IEEE/CAA J. Autom. Sinica, vol. 5, no. 6, pp. 1079-1088, Nov. 2018. doi: 10.1109/JAS.2017.7510880
Citation: Xue Chen and Yanjiang Wang, "Predicting Resting-state Functional Connectivity With Efficient Structural Connectivity," IEEE/CAA J. Autom. Sinica, vol. 5, no. 6, pp. 1079-1088, Nov. 2018. doi: 10.1109/JAS.2017.7510880

Predicting Resting-state Functional Connectivity With Efficient Structural Connectivity

doi: 10.1109/JAS.2017.7510880

China Scholarship Council 201306455001

the National Natural Science Foundation of China 61271407

the Fundamental Research Funds for the Central Universities 16CX06050A

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  • 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.


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