Single Subject Morphological Brain Networks:Connectivity Mapping,Topological Characterization And Test–retest Reliability

Introduction: Structural MRI was usually used to depicted the brain's local morphological features.However,organization patterns of the local morphological features among brain regions are still largely uncharacterized.Hence,we constructed individual-level morphological brain networks and thoroughly scrutinized their topological organization and long-term test–retest reliability under different analytical arrangements.Methods: 57 healthy participants were enrolled in our study and all of them completed two MRI scan sessions.We constructed individual morphological brain networks by assessing interregional distribution's similarity of regional gray matter volume.Connectome-based global and nodal network measures were then calculated,followed by the statistical comparison and intra-class correlation analysis.Results: The morphological brain networks were highly reproducible between sessions and exhibited high efficiency,modular,and small-worldness architecture regardless of the analytical choices of spatial smoothing,brain parcellation and network type.Besides,several association and paralimbic regions were identified as potential Hubs.Nonetheless,among the three studied factors,spatial smoothing significantly affected quantifiable description of morphological brain networks.Further investigation of long-term reliability revealed that all the examined network topological properties showed fair to excellent reliability irrespective of the analytical strategies,but performing spatial smoothing significantly improved reliability.Attractively,nodal centralities were positively correlated with their reliabilities,and nodal degree and efficiency outperformed nodal betweenness regarding reliability.Conclusions: Our findings support single-subject morphological brain network analysis as a meaningful and reliable method to characterize structural organization of the human brain;this method thus opens a new avenue toward understanding the substrate of inter-subject variability in behavior and function and establishing morphological network biomarkers in brain disorders.