Toward Developmental Connectomics of the Human Brain

Abstract

Imaging connectomics based on graph theory has become an effective and unique methodological framework for studying structural and functional connectivity patterns of the developing brain. Normal brain development is characterized by continuous and significant network evolution throughout infancy, childhood, and adolescence, following specific maturational patterns. Disruption of these normal changes is associated with neuropsychiatric developmental disorders, such as autism spectrum disorders or attention-deficit hyperactivity disorder. In this review, we focused on the recent progresses regarding typical and atypical development of human brain networks from birth to early adulthood, using a connectomic approach. Specifically, by the time of birth, structural networks already exhibit adult-like organization, with global efficient small-world and modular structures, as well as hub regions and rich-clubs acting as communication backbones. During development, the structure networks are fine-tuned, with increased global integration and robustness and decreased local segregation, as well as the strengthening of the hubs. In parallel, functional networks undergo more dramatic changes during maturation, with both increased integration and segregation during development, as brain hubs shift from primary regions to high order functioning regions, and the organization of modules transitions from a local anatomical emphasis to a more distributed architecture. These findings suggest that structural networks develop earlier than functional networks; meanwhile functional networks demonstrate more dramatic maturational changes with the evolution of structural networks serving as the anatomical backbone. In this review, we also highlighted topologically disorganized characteristics in structural and functional brain networks in several major developmental neuropsychiatric disorders (e.g., autism spectrum disorders, attention-deficit hyperactivity disorder and developmental dyslexia). Collectively, we showed that delineation of the brain network from a connectomics perspective offers a unique and refreshing view of both normal development and neuropsychiatric disorders.

Keywords: ADHD; autism; brain development; connectomics; dyslexia; hub; network; rich club.

Figure 1

Summary of the main measures with graph theoretical analysis. (A) Metrics regarding the segregation of a network. Local clustering describes the tendency of nodes to form local triangles, providing insight into the local organization of the network. There are four modules in the graph in which connections within modules are much denser than connections between them. (B) Metrics about the integration of a network. The shortest path length describes the minimum number of steps needed to travel between two nodes (dots in yellow) and provides insight into the capacity of the network to communicate between remote regions. (C) The existence of a small set of high-degree nodes with a central position in the network may suggest the existence of hub nodes. High-level connectivity (lines in red) between hub nodes (dots in red) may suggest the existence of a central so-called rich club within the overall network structure.

Figure 2

Development of white-matter connectomes. (A) Structural connectivity matrices of the neonates, toddlers, pre-adolescents, and adults group-averaged connectome. Adapted from Huang et al. (2015). (B) Late adolescent developmental changes in structural connectivity, with the thickness of each connection weighted by its associated one-tailed t-test statistic (FWE corrected, p < 0.05). Edge color represents connection type: non-hub to non-hub (yellow), hub to non-hub (orange), and hub to hub (red), with larger nodes corresponding to hub regions. Node color represents the assignment of each region of interest to one of five broad anatomical divisions: frontal (cyan), parietal (lime), temporal (magenta), occipital (orange-red), or subcortical (blue). The center panel illustrates the anatomical distribution of developmental decreases (lower triangular matrix) and increases (upper triangular matrix) in connectivity based on the classification of edges according to the anatomical divisions they interconnected. The values in these matrices represent relative proportions, calculated as the ratio between the frequency of edges linking each pair of divisions and the total number of edges belonging to the two categories. Adapted from Baker et al. (2015). (C) Distributions of hub regions in different age groups based on nodal efficiency centrality. PCG, precentral gyrus; PCUN, precuneus; CUN, cuneus; DCG, dorsal cingulate gyrus; INS, insular; ACG, anterior cingulate gyrus; SOG, superior occipital gyrus; ORBinf, inferior frontal gyrus; ROL, rolandic operculum; HES, Heschl's gyrus. Adapted from Huang et al. (2015). (D) Topological robustness of the structural networks in each group. The graphs show the AUC of the largest connected component (LCC) as a function of the removed node number by targeted attacks. The brain networks in the preadolescents (red line) were approximately as robust as those in toddlers (blue line) in response to both target failures. However, the neonates (green line) displayed remarkably reduced stability against both targeted attack and random failure compared with the other two groups. Adapted from Huang et al. (2015).

Figure 3

Development of functional connectomes. (A) Distribution of hub regions in the functional networks of infants and adults based on degree centrality. In infants, the majority of cortical hubs were located in the homomodal cortex, mostly in the auditory, visual, and sensorimotor areas, and to a lesser extent in the PFC. Prominent locations for hubs in adults included the precuneus/posterior cingulate cortex, medial PFC, anterior cingulate cortex, bilateral parietal lobule, and bilateral insula. Adapted from Fransson et al. (2011). (B) The figure showed the dynamic development of the default network, and cerebellar network using spring embedding. The figure highlights the segregation of local, anatomically clustered regions, and the integration of functional networks over development. Nodes are color coded by their adult network profile (core of the nodes) and by their anatomical location (node outlines). Connections with r > 0.1 were considered connected. Adapted from Fair et al. (2009). (C) The functional rich-club organizations in children and adults. Although many regions overlap (red arrows, for example), there are bilateral regions that appear only in adults (blue arrows, for example). Adapted from Grayson et al. (2014). (D) Modularity and SC–FC correlation. Cortical SC and FC matrices averaged over the younger (<4 years) and older (>13 years) age group. Structural modules are delineated by the superimposed white grid. Eleven modules (M1–M6 in the right hemisphere, M7–M11 in the left hemisphere) were identified, and the two sets of SC and FC matrices are displayed such that modules correspondence across age is maximized. Although modules are highly conserved (normalized mutual information = 0.82), there is a notable increase in SC–FC correspondence from younger to older brains. There is an increasing statistically significant relationship between SC and FC across age (R = 0.74, p < 0.005). Adapted from Hagmann et al. (2010).

Figure 4

(A) Brain network alterations in ADHD. (i) Small-world models for ADHD and healthy brain networks. The ADHD networks showed a regular tendency compared with healthy controls. Adapted from Cao et al. (2014a). (ii) Decreased or increased functional connectivity density (FCD) in ADHD patients compared with healthy controls. Adapted from Tomasi and Volkow (2012). (iii) Decreased or increased white matter connections in ADHD participants compared with healthy controls and their relationships with the clinical characteristics of the patients. Blue curve: the significantly decreased network-based statistic (NBS) component; red curve: the significantly increased NBS component. Adapted from Cao et al. (2013). (B) Brain regions displaying disrupted functional connectivity in autism. (i) Voxels displaying altered functional connectivity in autism. Voxels that displayed weaker functional connectivity in the autistic population than in the controls are shown in blue, and the voxels that displayed stronger functional connectivity in the autistic population are shown in red. The color bar represents the degree of connectivity according to the number of significantly affected edges relating to a given voxel. Adapted from Eilam-Stock et al. (2014). (ii) White matter tracts of the socio-emotional processing system. Left: white matter tracts of the limbic system; middle: white matter tracts linking the mirror neuron system; right: white matter tracts of the face processing system. Adapted from Ameis and Catani (2015). (C) Brain network alterations in dyslexia. (i) Whole-brain functional connectivity differences between groups. Three-dimensional representation of healthy controls readers (NC) > dyslexic readers (DYS) and DYS > NC edge components (p < 0.01 after NBS correction). Adapted from Finn et al. (2014). (ii) Between-group differences in regional nodal characteristics in cortical thickness networks. Group differences of and nodal degree in cortical thickness networks. Blue represents the brain areas with significantly lower nodal properties in DYS than in NC, whereas red represents the brain areas with significantly higher nodal properties in NC than in DYS. Adapted from Qi et al. (2016).

Figure 5

Sketch plot showing the development of structural and functional brain networks in infancy and childhood and adolescence relative to healthy adults.