Clique Network-Based Statistics for Detecting Altered Topological Structures in the Brain Network

Networks can model structural and functional connection of human brain. Clique as a fully connected structure reveals valuable interaction pattern of network. Some statistical methods, such as network-based statistics (NBS), are frequently utilized to detect discrepancy connections and connected components of networks. The existing NBS family methods focus on the identification of different expressed connected component and fail to extract special topological structure. In this paper, a clique network-based statistics (clique-NBS) method is proposed as an extension of NBS to detect discrepant cliques of networks, and its effect has been validated by both simulated and clinical data. The clinical data is the resting state BOLD-fMRI of 29 multiple system atrophy (MSA) patients and 27 healthy controls for detecting altered functional modules after MSA. The proposed methods successfully identified 28 altered functional modules containing cerebellar parts. By comparing with edge-wise FDR correction method, the proposed method shows an increased performance on true positive rate. As a result, the proposed clique-NBS extends the existing NBS family, and it can be used to find and investigate the discrepant subnetworks with clique topological structure in human brain.