title: Single-subject morphological brain networks: connectivity mapping, topological characterization and test-retest reliability.
abstract: Structural MRI has long been used to characterize local morphological features of the human brain. Coordination patterns of the local morphological features among regions, however, are not well understood. Here, we constructed individual-level morphological brain networks and systematically examined their topological organization and long-term test-retest reliability under different analytical schemes of spatial smoothing, brain parcellation, and network type.
  
  
  
  This study included 57 healthy participants and all participants completed two MRI scan sessions. Individual morphological brain networks were constructed by estimating interregional similarity in the distribution of regional gray matter volume in terms of the Kullback-Leibler divergence measure. Graph-based global and nodal network measures were then calculated, followed by the statistical comparison and intra-class correlation analysis.
  
  
  
  The morphological brain networks were highly reproducible between sessions with significantly larger similarities for interhemispheric connections linking bilaterally homotopic regions. Further graph-based analyses revealed that the morphological brain networks exhibited nonrandom topological organization of small-worldness, high parallel efficiency and modular architecture regardless of the analytical choices of spatial smoothing, brain parcellation and network type. Moreover, several paralimbic and association regions were consistently revealed to be potential hubs. Nonetheless, the three studied factors particularly spatial smoothing significantly affected quantitative characterization of morphological brain networks. Further examination 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. Interestingly, nodal centralities were positively correlated with their reliabilities, and nodal degree and efficiency outperformed nodal betweenness with respect to reliability.
  
  
  
  Our findings support single-subject morphological 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 intersubject variability in behavior and function and establishing morphological network biomarkers in brain disorders.
authors.affiliation: Department of Psychology Hangzhou Normal University Hangzhou 311121 China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou 311121 China.
authors.forename: Hao
authors.initials: H
authors.lastname: Wang
authors.affiliation: Department of Acupuncture and Moxibustion Zhejiang Hospital Hangzhou 310030 China.
authors.forename: Xiaoqing
authors.initials: X
authors.lastname: Jin
authors.affiliation: Department of Psychology Hangzhou Normal University Hangzhou 311121 China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou 311121 China.
authors.forename: Ye
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Department of Psychology Hangzhou Normal University Hangzhou 311121 China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou 311121 China.
authors.forename: Jinhui
authors.initials: J
authors.lastname: Wang
mesh_terms: D000328:Adult; D001921:Brain / Q000033:anatomy & histology* / Q000000981:diagnostic imaging; D005260:Female; D066128:Gray Matter / Q000033:anatomy & histology* / Q000000981:diagnostic imaging; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods / Q000592:standards*; D008297:Male; D009415:Nerve Net / Q000033:anatomy & histology* / Q000000981:diagnostic imaging; D015203:Reproducibility of Results; D055815:Young Adult
keywords: Brain network; gray matter volume; hub; reliability; structural MRI