Bipartite Organization of Cerebellar-Cortical Networks Identified based on Intrinsic Functional Connectivity

In order to identify brain networks with homogenous cerebellar-cortical functional connectivity, a novel orthogonal nonnegative matrix tri-factorization (ONMTF) based parcellation method was proposed. Specifically, the cerebellum and cerebrum were jointly parcellated into disjoint networks with homogenous cerebellar-cortical functional connectivity based on resting-state functional MRI (rs-fMRI) data. Firstly, similarity measures among voxels of the rs-fMRI data were calculated based on their cerebellar-cortical functional connectivity measures, yielding a matrix of similarity measures with rows and columns characterizing voxels of the cerebellum and cerebrum respectively. Then, the ONMTF algorithm was applied to the similarity matrix to extract the mutually connected cerebellar-cortical functional networks. The proposed method was utilized to identify a bipartite organization of the cerebellar-cortical networks, and our results have demonstrated that the proposed method could identify two cerebellar subsystems with distinctive functional connectivity patterns with the default-mode and task-positive networks, respectively. Moreover, the proposed method had better performance than state-of-the-art brain parcellation methods in terms of functional homogeneity.