Functional magnetic resonance imaging (fMRI) technology can be implemented to infer
brain activities consistently shared by a population or to identify their differences between populations.
There are numerous studies that investigated the anatomical and functional connectivity of the network
based on group-analysis. Here, the virtual-typical-subject (VTS) group-analysis methods of restingstate
fMRI based on Bayesian network (BN) were investigated. There are three types of VTS-based
methods: principal component analysis method (PCA-based), average method (AVE-based) and concatenate method
(CAT-based). Sets of simulated data were constructed to test the robustness and performance of these VTS methods. The
results from simulation demonstrated that the performance and robustness of AVE-based and CAT-based method was superior
to that of the PCA-based method. Compared with CAT-based method, AVE-based method has higher stability,
while the CAT-based method showed a better performance for different coefficient matrix of simulated data. Finally, the
BN group-analysis (AVE-based and CAT-based) methods were applied to infer effective brain connectivity from real
fMRI data. The results were consistent to previous study and demonstrated the validity of group-analysis method.
Keywords: Bayesian network, effective connectivity, fMRI, group-analysis, resting-state, virtual typical subject (VTS).
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