Functional Brain Network Estimation with Human-Guided Modularity Representation

Abstract

Abstract Functional brain network (FBN) has been demonstrated with remarkable advancements in understanding the human brain organization architectures and diagnosis disorders. Thus, it is crucial to accurately estimate both biologically meaningful and discriminative FBNs. Although several FBN estimation approaches have been proposed, the accurate estimation of FBN is still an open field due to the high complexity of human brains and the poor quality of the observed data. Moreover, most existing works fail in incorporating domain expert knowledge. In this paper, we stress the importance of both modular topology prior and domain expert knowledge for FBN estimation, and a human-guided modular representation (MR) FBN estimation framework is proposed. Specifically, we depict the intra- and intermodular structures of FBNs under domain expert knowledge guidance and characterize them with an adversarial low-rank constraint. An efficient ConCave-Convex Procedure (CCCP) is applied to estimate FBN, which is then verified on the Chronic Tinnitus Identification task. The proposed methods achieves a 92.11% classification accuracy, significantly outperformed the state-of-the-art methods. Our method also tends to provide more biologically meaningful functional connections, which benefit for both basic and clinical neuroscience studies.