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

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          Abstract

          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.

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          Most cited references39

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          Rich-club organization of the human connectome.

          The human brain is a complex network of interlinked regions. Recent studies have demonstrated the existence of a number of highly connected and highly central neocortical hub regions, regions that play a key role in global information integration between different parts of the network. The potential functional importance of these "brain hubs" is underscored by recent studies showing that disturbances of their structural and functional connectivity profile are linked to neuropathology. This study aims to map out both the subcortical and neocortical hubs of the brain and examine their mutual relationship, particularly their structural linkages. Here, we demonstrate that brain hubs form a so-called "rich club," characterized by a tendency for high-degree nodes to be more densely connected among themselves than nodes of a lower degree, providing important information on the higher-level topology of the brain network. Whole-brain structural networks of 21 subjects were reconstructed using diffusion tensor imaging data. Examining the connectivity profile of these networks revealed a group of 12 strongly interconnected bihemispheric hub regions, comprising the precuneus, superior frontal and superior parietal cortex, as well as the subcortical hippocampus, putamen, and thalamus. Importantly, these hub regions were found to be more densely interconnected than would be expected based solely on their degree, together forming a rich club. We discuss the potential functional implications of the rich-club organization of the human connectome, particularly in light of its role in information integration and in conferring robustness to its structural core.
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            Specificity and stability in topology of protein networks

            Molecular networks guide the biochemistry of a living cell on multiple levels: its metabolic and signalling pathways are shaped by the network of interacting proteins, whose production, in turn, is controlled by the genetic regulatory network. To address topological properties of these two networks we quantify correlations between connectivities of interacting nodes and compare them to a null model of a network, in which al links were randomly rewired. We find that for both interaction and regulatory networks, links between highly connected proteins are systematically suppressed, while those between a highly-connected and low-connected pairs of proteins are favored. This effect decreases the likelihood of cross talk between different functional modules of the cell, and increases the overall robustness of a network by localizing effects of deleterious perturbations.
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              A tension-based theory of morphogenesis and compact wiring in the central nervous system.

              Many structural features of the mammalian central nervous system can be explained by a morphogenetic mechanism that involves mechanical tension along axons, dendrites and glial processes. In the cerebral cortex, for example, tension along axons in the white matter can explain how and why the cortex folds in a characteristic species-specific pattern. In the cerebellum, tension along parallel fibres can explain why the cortex is highly elongated but folded like an accordion. By keeping the aggregate length of axonal and dendritic wiring low, tension should contribute to the compactness of neural circuitry throughout the adult brain.
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                Author and article information

                Journal
                Brain Behav
                Brain Behav
                10.1002/(ISSN)2157-9032
                BRB3
                Brain and Behavior
                John Wiley and Sons Inc. (Hoboken )
                2162-3279
                03 March 2016
                April 2016
                : 6
                : 4 ( doiID: 10.1002/brb3.2016.6.issue-4 )
                : e00448
                Affiliations
                [ 1 ] Department of PsychologyHangzhou Normal University Hangzhou 311121China
                [ 2 ]Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou 311121China
                [ 3 ] Department of Acupuncture and MoxibustionZhejiang Hospital Hangzhou 310030China
                Author notes
                [*] [* ] Correspondence

                Jinhui Wang, Department of Psychology, Hangzhou Normal University, Hangzhou 311121, China. Tel/Fax: 86 571 2886 7717; E‐mail: jinhui.wang.1982@ 123456hznu.edu.cn

                Article
                BRB3448
                10.1002/brb3.448
                4782249
                27088054
                166e1e71-2df7-4027-a202-bbbf10774164
                © 2016 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.

                This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 07 October 2015
                : 20 January 2016
                : 22 January 2016
                Page count
                Pages: 21
                Funding
                Funded by: National Natural Science Foundation of China
                Award ID: 81301284
                Funded by: Zhejiang Provincial Natural Science Foundation of China
                Award ID: LZ13C090001
                Categories
                Original Research
                Original Research
                Custom metadata
                2.0
                brb3448
                April 2016
                Converter:WILEY_ML3GV2_TO_NLMPMC version:4.8.8 mode:remove_FC converted:03.05.2016

                Neurosciences
                brain network,gray matter volume,hub,reliability,structural mri
                Neurosciences
                brain network, gray matter volume, hub, reliability, structural mri

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