Individualized texture similarity network in schizophrenia

BACKGROUND:Structural covariance network disruption has been considered an important pathophysiological indicator for schizophrenia. Here, we introduced a novel individualized structural covariance (ISCN) measure named texture similarity network (TSN) and hypothesized that TSN could reliably reveal unique inter-subject heterogeneity and complex dysconnectivity patterns in schizophrenia. METHODS:The TSN was constructed by measuring the covariance of 180 3D voxel-wise gray-level co-occurrence matrix feature maps between brain areas in each subject. We first tested the validity and reproducibility of TSN in characterizing the inter-subject variability in two longitudinal test-retest healthy cohorts. Then, the TSN was further applied to elucidate the inter-subject variability and dysconnectivity patterns in ten schizophrenia case-control datasets (609 schizophrenias vs. 579 controls), as well as in a first-episode depressive dataset (69 patients vs. 69 controls). RESULTS:The test-retest analysis demonstrated higher TSN inter-subject than intra-subject variability. Moreover, TSN reliably revealed higher inter-subject variability in both chronic and first-episode schizophrenia but not in depressive patients. TSN also replicabally detected coexisted increased and decreased TSN strength in widespread brain areas, increased global small-worldness, and the coexistence of both structural hypo-synchronization in the central networks and hyper-synchronization in peripheral networks in schizophrenia but not in depressive patients. Finally, the aberrant inter-subject variability and covariance strength patterns revealed by TSN showed a missing or weak correlation with other ISCN measures, functional connectivity, and regional volume changes. CONCLUSIONS:These findings support the reliability of TSN in revealing unique structural heterogeneity and complex dysconnectivity in schizophrenia patients.