Single‐subject morphological brain networks: connectivity mapping, topological characterization and test–retest reliability

• Published in: Brain and behavior Vol. 6; no. 4; pp. e00448 - n/a
• Main Authors: Wang, Hao, Jin, Xiaoqing, Zhang, Ye, Wang, Jinhui
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.04.2016; John Wiley and Sons Inc
• Subjects: Adult; Brain - anatomy & histology; Brain - diagnostic imaging; Brain network; Brain research; Female; Gray Matter - anatomy & histology; Gray Matter - diagnostic imaging; gray matter volume; hub; Humans; Labeling; Magnetic Resonance Imaging - methods; Magnetic Resonance Imaging - standards; Male; Medical imaging; Morphology; Nerve Net - anatomy & histology; Nerve Net - diagnostic imaging; Original Research; Probability distribution; reliability; Reproducibility of Results; structural MRI; Studies; Young Adult; Beijing China; China; gray matter volume; structural MRI; hub; Brain network; reliability
• ISSN: 2162-3279; 2162-3279
• DOI: 10.1002/brb3.448

Introduction 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. Methods 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. Results 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. Conclusions 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. We proposed a method to construct individual‐level morphological brain networks from structural MRI data. We demonstrated that morphological brain networks derived from this method were specifically organized, test–retest reliable and dependent on different analytic strategies of data preprocessing and network construction methods.