Brain anatomical networks in early human brain development - PubMed (original) (raw)

Brain anatomical networks in early human brain development

Yong Fan et al. Neuroimage. 2011.

Abstract

Recent neuroimaging studies have demonstrated that human brain networks have economic small-world topology and modular organization, enabling efficient information transfer among brain regions. However, it remains largely unknown how the small-world topology and modular organization of human brain networks emerge and develop. Using longitudinal MRI data of 28 healthy pediatric subjects, collected at their ages of 1 month, 1 year, and 2 years, we analyzed development patterns of brain anatomical networks derived from morphological correlations of brain regional volumes. The results show that the brain network of 1-month-olds has the characteristically economic small-world topology and nonrandom modular organization. The network's cost efficiency increases with the brain development to 1 year and 2 years, so does the modularity, providing supportive evidence for the hypothesis that the small-world topology and the modular organization of brain networks are established during early brain development to support rapid synchronization and information transfer with minimal rewiring cost, as well as to balance between local processing and global integration of information.

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Figures

Fig. 1

Global efficiency (A, B), local efficiency (C, D), and cost efficiency (E, F), as functions of network cost (x-axis) for brain networks. Global and local efficiency of random graphs and regular lattices are also shown in the plots of global efficiency and local efficiency for testing the small-worldness of brain networks. The plots in panels of A, C, and E show the results derived directly from the imaging data, while the plots in panels of B, D, and F show the distribution (mean ± standard deviation) of network parameters derived from bootstrap sampling. In these plots, significant difference (p<0.05) is indicated for comparisons: 1yr>1mon, 2yr>1yr, and adult>2yr, respectively.

Fig. 2

Modularity (A, B) and size of the largest connected component (C, D) as a function of cost (x-axis) for brain networks. Modularity of the comparable random graphs and regular lattices is also shown in the modularity plots for testing nonrandomness of the modular organization of brain networks. The plots of A and C show the results derived directly from the image data, while the plots of B and D show the distribution (mean ± standard deviation) of network parameters derived from bootstrap sampling. In the plots of right column, significant difference (p<0.05) is indicated for comparisons: 1yr>1mon, 2yr>1yr, and adult>2yr, respectively.

Fig. 3

Surface rendering and spring-embedding visualization of brain networks of 1-month-olds (A), 1-year-olds (B), 2-year-olds (C), and adults (D). For the surface rendering visualization, each network node (brain region) is projected onto the brain surface of its corresponding hemisphere by minimizing its distance to the brain surface. The brain surfaces were generated with the Freesurfer image analysis suite, which is documented and freely available for download online (

surfer.nmr.mgh.harvard.edu

). Nodes are color-coded by modules and larger nodes are connector hubs or provincial hubs. Lateral, medial, and top views of both hemi-spheres are shown. The spring-embedding visualization of networks is implemented with Kamada-Kawai layout algorithm using Pajek software package (pajek.imfm.si/doku.php). The nodes and intra-modular connections are colored-coded by modules, while inter-modular connections are colored-coded with light-gray. The abbreviation of region labels can be found in Table S1.

Fig. 4

Bar-plots of regional measures related to the regional role: participation coefficient (a), intra-modular degree (b), and normalized betweenness centrality (c). In each bar-plot, the brain regions are ranked by the measures obtained for the adult network. The bars in color of red, green, blue, and cyan are corresponding to measures of one-month-olds, one-year-olds, two-year-olds, and adults, respectively. The same color-coding is used for the indicators of connector hub (H), provincial hub (P), and connector non-hub (C). The abbreviation of region labels can be found in Table S1.

References

1. Achard S, Bullmore ET. Efficiency and cost of economical brain functional networks. PLoS Comput. Biol. 2007;3:e17. -PMC -PubMed
1. Achard S, Salvador R, Whitcher B, Suckling J, Bullmore E. A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J Neurosci. 2006;26:63–72. -PMC -PubMed
1. Bartolomei F, Bosma I, Klein M, Baayen JC, Reijneveld JC, Postma TJ, Heimans JJ, van Dijk BW, de Munck JC, de Jongh A, Cover KS, Stam CJ. Disturbed functional connectivity in brain tumour patients: evaluation by graph analysis of synchronization matrices. Clinical Neurophysiology. 2006;117:2039–2049. -PubMed
1. Bassett DS, Bullmore E, Verchinski BA, Mattay VS, Weinberger DR, Meyer-Lindenberg A. Hierarchical Organization of Human Cortical Networks in Health and Schizophrenia. The Journal of Neuroscience. 2008;28:9239–9248. -PMC -PubMed
1. Bassett DS, Bullmore ET. Small-world brain networks. Neuroscientist. 2006;12:512–523. -PubMed

Brain anatomical networks in early human brain development - PubMed (original) (raw)