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Cited by 190Functional Brain Connectivity StudiesAdvanced Neuroimaging Techniques and ApplicationsComplex Network Analysis Techniques

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Development Trends of White Matter Connectivity in the First Years of Life

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Yap, Pew-Thian, Fan, Yong, Chen, Yasheng, Gilmore, John H., Lin, Weili, Shen, Dinggang·2011·PLoS ONE
DOI 10.1371/journal.pone.0024678PMC PMC3179462
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Abstract

The human brain is organized into a collection of interacting networks with specialized functions to support various cognitive functions. Recent research has reached a consensus that the brain manifests small-world topology, which implicates both global and local efficiency at minimal wiring costs, and also modular organization, which indicates functional segregation and specialization. However, the important questions of how and when the small-world topology and modular organization come into existence remain largely unanswered. Taking a graph theoretic approach, we attempt to shed light on this matter by an in vivo study, using diffusion tensor imaging based fiber tractography, on 39 healthy pediatric subjects with longitudinal data collected at average ages of 2 weeks, 1 year, and 2 years. Our results indicate that the small-world architecture exists at birth with efficiency that increases in later stages of development. In addition, we found that the networks are broad scale in nature, signifying the existence of pivotal connection hubs and resilience of the brain network to random and targeted attacks. We also observed, with development, that the brain network seems to evolve progressively from a local, predominantly proximity based, connectivity pattern to a more distributed, predominantly functional based, connectivity pattern. These observations suggest that the brain in the early years of life has relatively efficient systems that may solve similar information processing problems, but in divergent ways.

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Figure 1 Obtaining the Connectivity Matrix. A schematic digram illustrating the major processes involved in generating the final connectivity maps. Streamline fiber tractography was performed on each diffusion tensor image and a connectivity matrix was computed based on the AAL [23] ROIs. The fiber count matrices were constructed by enumerating the number of fibers connecting each region pair. The connectivity matrix, indicating consistent connections, was generated by thresholding the fiber count statistics.
Figure 1 Obtaining the Connectivity Matrix. A schematic digram illustrating the major processes involved in generating the final connectivity maps. Streamline fiber tractography was performed on each diffusion tensor image and a connectivity matrix was computed based on the AAL [23] ROIs. The fiber count matrices were constructed by enumerating the number of fibers connecting each region pair. The connectivity matrix, indicating consistent connections, was generated by thresholding the fiber count statistics.CC BY · PubMed Central
Figure 2 Connectograms. Connectivity matrices characterizing the backbone connections. The network cost is 0.21, which ensures that all nodes were full connected (see Fig. 3).
Figure 2 Connectograms. Connectivity matrices characterizing the backbone connections. The network cost is 0.21, which ensures that all nodes were full connected (see Fig. 3).CC BY · PubMed Central
Figure 3 Largest Connected Component. The number of nodes of the largest connected component in all networks stabilize and reach the maximum possible value (78) at the network cost of 0.21, a value which we used for our analysis, unless otherwise stated.
Figure 3 Largest Connected Component. The number of nodes of the largest connected component in all networks stabilize and reach the maximum possible value (78) at the network cost of 0.21, a value which we used for our analysis, unless otherwise stated.CC BY · PubMed Central
Figure 4 Network Efficiency. Local and global efficiency of pediatric brain networks of (a) 2-weeks-olds, (b) 1-year-olds and (c) 2-year-olds. All networks exhibit small-world nature, which is characterized by local efficiency greater than comparable random networks, and global efficiency greater than regular lattices [5], [6]. There is a general trend of efficiency increase with age. The neonatal brain network shows significantly lower efficiency compared to the other two age groups.
Figure 4 Network Efficiency. Local and global efficiency of pediatric brain networks of (a) 2-weeks-olds, (b) 1-year-olds and (c) 2-year-olds. All networks exhibit small-world nature, which is characterized by local efficiency greater than comparable random networks, and global efficiency greater than regular lattices [5], [6]. There is a general trend of efficiency increase with age. The neonatal brain network shows significantly lower efficiency compared to the other two age groups.CC BY · PubMed Central
Figure 5 Inter-Region Connection Fiber Length Distribution. Cumulative distribution plots of the inter-region connection fiber lengths indicate that there is a progressive maturation of long fibers with growth. The dashed horizontal line marks the 0.90 frequency point and indicates that only a small fraction of the fibers are long fibers.
Figure 5 Inter-Region Connection Fiber Length Distribution. Cumulative distribution plots of the inter-region connection fiber lengths indicate that there is a progressive maturation of long fibers with growth. The dashed horizontal line marks the 0.90 frequency point and indicates that only a small fraction of the fibers are long fibers.CC BY · PubMed Central
Figure 6 Node Degree Distributions. Single-scale, scale-free and broad-scale [39] are characterized by Gaussian/exponential decay, power law decay, and truncated power law decay, respectively. The node degree distributions give good indication that the pediatric brain networks are broad-scale in nature. In the double logarithmic plots, the degree distribution decays linearly before a sharp cutoff. The gradient magnitudes of the fitted lines are 3.921, 2.784 and 2.764 for (a), (b) and (c), respectively.
Figure 6 Node Degree Distributions. Single-scale, scale-free and broad-scale [39] are characterized by Gaussian/exponential decay, power law decay, and truncated power law decay, respectively. The node degree distributions give good indication that the pediatric brain networks are broad-scale in nature. In the double logarithmic plots, the degree distribution decays linearly before a sharp cutoff. The gradient magnitudes of the fitted lines are 3.921, 2.784 and 2.764 for (a), (b) and (c), respectively.CC BY · PubMed Central
Figure 7 Nonrandom Modularity. Comparing the modularities [15], [16] of the brain networks with comparable random networks indicates non-random network modularity.
Figure 7 Nonrandom Modularity. Comparing the modularities [15], [16] of the brain networks with comparable random networks indicates non-random network modularity.CC BY · PubMed Central
Figure 8 Network Communities. The spring-embedding visualization of networks is implemented with Kamada-Kawai layout algorithm using the Pajek [57] software package (pajek.imfm.si/doku.php). The nodes and intra-modular connections are colored-coded by the communities detected by the algorithm described in [16], while inter-modular connections are colored-coded with light-gray. The sizes of the vertices are weighted by the (logarithmically scaled) node betweenness [29]. Descriptions of the abbreviated region labels can be found in Table 1. See Table 3 for the constituent regions in each community.
Figure 8 Network Communities. The spring-embedding visualization of networks is implemented with Kamada-Kawai layout algorithm using the Pajek [57] software package (pajek.imfm.si/doku.php). The nodes and intra-modular connections are colored-coded by the communities detected by the algorithm described in [16], while inter-modular connections are colored-coded with light-gray. The sizes of the vertices are weighted by the (logarithmically scaled) node betweenness [29]. Descriptions of the abbreviated region labels can be found in Table 1. See Table 3 for the constituent regions in each community.CC BY · PubMed Central
Figure 9 Betweenness Centrality, Intra-Modular Degree, and Participation Coefficient. The values are sorted based those of the 2-year-olds. The role of each node, as defined in [26], is specified above the respective bar: (A) non-hub ultra-peripheral node; (B) non-hub peripheral node; (C) non-hub connector nodes; and (D) non-hub kinless nodes; (E) provincial hubs; (F) connector hubs; and (G) kinless hubs. No node was found to satisfy the conditions required by (F) and (G).
Figure 9 Betweenness Centrality, Intra-Modular Degree, and Participation Coefficient. The values are sorted based those of the 2-year-olds. The role of each node, as defined in [26], is specified above the respective bar: (A) non-hub ultra-peripheral node; (B) non-hub peripheral node; (C) non-hub connector nodes; and (D) non-hub kinless nodes; (E) provincial hubs; (F) connector hubs; and (G) kinless hubs. No node was found to satisfy the conditions required by (F) and (G).CC BY · PubMed Central
Figure 10 Betweenness Centrality and Vulnerability. Removal of a node with high betweenness generally results in a significant disruption of information flow in the brain network as indicated by a higher vulnerability value. The dashed lines indicate 95% confidence interval. The betweenness centrality value is normalized by division by the total number of possible connections .
Figure 10 Betweenness Centrality and Vulnerability. Removal of a node with high betweenness generally results in a significant disruption of information flow in the brain network as indicated by a higher vulnerability value. The dashed lines indicate 95% confidence interval. The betweenness centrality value is normalized by division by the total number of possible connections .CC BY · PubMed Central
Figure 11 Inter-Hemispheric Correlation of Node Betweenness. Each circle gives the left and right betweenness value for each node. Each age group shows a rightward assymetry - indicated by the slope values 0.2843, 0.6202, and 0.4738, respectively (1 indicates perfect symmetry). The dashed lines indicate 95% confidence interval. The betweenness centrality value is normalized by division by .
Figure 11 Inter-Hemispheric Correlation of Node Betweenness. Each circle gives the left and right betweenness value for each node. Each age group shows a rightward assymetry - indicated by the slope values 0.2843, 0.6202, and 0.4738, respectively (1 indicates perfect symmetry). The dashed lines indicate 95% confidence interval. The betweenness centrality value is normalized by division by .CC BY · PubMed Central

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Metadata

Authors
Yap, P.-T., Fan, Y., Chen, Y., Gilmore, J. H., Lin, W., & Shen, D.
Affiliations
University of North Carolina at Chapel Hill (US)
Corresponding author
Shen, Dinggang
ORCID
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2011-09-23
Volume
6
Issue
9
Pages
e24678
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0024678
PMC
PMC3179462
OpenAlex
W2171775073
Cited by
190
References
60
Citations by year
2026: 7 · 2025: 5 · 2024: 6 · 2023: 5 · 2022: 13 · 2021: 11 · 2020: 9 · 2019: 9 · 2018: 18 · 2017: 19 · 2016: 15 · 2015: 11 · 2014: 26 · 2013: 25 · 2012: 11
FWCI
10.07
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • UNC Libraries
Funders
  • National Institutes of Health (MH064065)
  • National Institute of Mental Health (MH064065, P50MH064065, R03MH076970, MH076970)
  • National Institute of Biomedical Imaging and Bioengineering (EB009634, R01EB009634)
  • Eunice Kennedy Shriver National Institute of Child Health and Human Development (HD05300)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
Keywords
white matter connectivity, brain network development, small-world networks, brain network efficiency, network modularity, network hubs, network resilience, early infancy, functional segregation, graph theory, diffusion tensor imaging, fiber tractography, child brain development
MeSH terms
Algorithms, Brain, Brain Mapping, Child, Preschool, Female, Humans, Infant, Infant, Newborn, Longitudinal Studies, Male, Models, Neurological, Nerve Net, Neural Pathways, Diffusion Tensor Imaging

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