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

• Published in: Journal of physic, complexity Vol. 2; no. 3; pp. 35006 - 35031
• Main Authors: Stolz, Bernadette J, Emerson, Tegan, Nahkuri, Satu, Porter, Mason A, Harrington, Heather A
• Format: Journal Article
• Language: English
• Published: United States IOP Publishing 01.09.2021
• Subjects: 60 APPLIED LIFE SCIENCES; fMRI; functional brain networks; networks; persistence images; persistence landscapes; persistent homology
• ISSN: 2632-072X; 2632-072X
• DOI: 10.1088/2632-072X/abb4c6

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