Topological data analysis of task-based fMRI data from experiments on schizophrenia

Abstract We use methods from computational algebraic topology to study functional brain networks in which nodes represent regions and weighted edges encode the similarity of magnetic resonance imaging (fMRI) time series each region. With these tools, allow one characterize topological invariants such as loops high-dimensional data, we are able gain understanding low-dimensional structures a way that complements traditional approaches based on pairwise interactions. In present paper, persistent homology analyze construct task-based fMRI data schizophrenia patients, healthy controls, siblings patients. thereby explore persistence at different scales networks. landscapes images output our persistent-homology calculations, using k -means clustering community detection. Based analysis landscapes, find members sibling cohort have features (specifically, their one-dimensional loops) distinct other two cohorts. From images, distinguish all three subject groups determine (with four or more edges) us make distinctions.