Simultaneous cluster structure learning and estimation of heterogeneous graphs for matrix-variate fMRI data.

ABSTRACT: Graphical models play an important role in neuroscience studies, particularly in brain connectivity analysis. Typically, observations/samples are from several heterogenous groups and the group membership of each observation/sample is unavailable, which poses a great challenge for graph structure learning. In this paper, we propose a method which can achieve Simultaneous Clustering and Estimation of Heterogeneous Graphs (briefly denoted as SCEHG) for matrix-variate functional magnetic resonance imaging (fMRI) data. Unlike the conventional clustering methods which rely on the mean differences of various groups, the proposed SCEHG method fully exploits the group differences of conditional dependence relationships among brain regions for learning cluster structure. In essence, by constructing

SUBMITTER: Liu D 

PROVIDER: S-EPMC10309652 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature