Mesoscale differences in brain organization in schizophrenia revealed by topological data analysis
We uncover a novel, mesoscale perspective of the differences in the white-matter connectome between healthy controls (HC) and subjects with schizophrenia (SCH) us- ing a method developed from computational algebraic topology: persistent homology (PH) via clique topology. We extract and compare topological motifs found in the structural connectomes of the subjects in the two groups and find significant differ- ences. We compare our results with those obtained from easy-to-interpret null models to build an understanding of the connectivity patterns found in the data, and we explore the overlap of mesoscale structures found in two different datasets, COBRE (Center of Biomedical Research Excellence) and HCP (Human Connectome Project). Differences in acquisition usually render experiments recorded on different scanners incomparable, but here we see that there are shared structures. Our method offers a way to estab- lish connectomic fingerprinting that could lead to a neuroimaging-based diagnosis of schizophrenia and other psychiatric and neurological conditions as well as the develop- ment of new treatments.