Brain is found to have a small-world network using network analysis¹, favoring both integrated and segregated information processing. Using the classical ACE model (Fig. 1), twins share 100% of common environmental influences and at least 50% of their segregating genes regardless of zygosity², the effects of genes and environment on their brains can be studied using network analysis. The objective of this study was to investigate the importance of education, genetic and environmental influences on the brain at a global level.

Participants 23 twin pairs (age = 215 ± 14 months) and 24 non-twin term peers (216 ± 12 months) were recruited.

Image acquisition DWIs were acquired using single-shot EPI with b values of 1000 and 2000 s/mm² along 32 gradient directions using a 3T scanner (Achieva TX scanner, Philips Healthcare).

Network analysis 3D-MPRAGE images were segmented into 90 brain regions according to AAL atlas. Whole-brain white matter tractography was obtained using Diffusion Toolkit (trackvis.org/dtk/). Fiber tracts traversing two regions were counted, resulting in a connectivity matrix. Brain network topology, including clustering coefficient, characteristic path length, normalized clustering coefficient (γ), normalized characteristic path length (λ), global and local efficiency were computed using the Brain Connectivity Toolbox³.

Statistical analysis Non-twin subjects were paired according to their age, gender and education level. Differences between twin pairs were studied using paired t-tests. Pearson correlation was performed to determine the association between network metrics and education for all subjects.

Nodal characteristics Consistent amongst all subjects, precuneus, insular, putamen, hippocampus, caudate, superior frontal cortex, thalamus, middle cingulate cortex and globus pallidus were found as hubs. Significant correlations between twin pairs for the local clustering coefficient of insula (r = 0.52, p = 0.011) and hippocampus (r = 0.50, p = 0.015); and for the nodal degree of insula (r = 0.55, p = 0.007), caudate (r = 0.59, p = 0.003), putamen (r = 0.53, p = 0.01) and thalamus (r = 0.57, p = 0.005) were found. For correlations between network metrics and the starting age of primary school of all subjects, significant correlations for the nodal degree of precentral gyrus (r = -0.32, p = 0.007); for the local clustering coefficient of caudate (r = -0.240, p = 0.045); for the betweenness centrality of insula (r = -0.272, p = 0.023); and for the nodal efficiency of globus pallidus (r = -0.326, p = 0.006) were found. For correlations between network metrics and the number of months of education of all subjects, significant correlations for the nodal degree of precentral gyrus (r = 0.29, p = 0.016); for the betweenness centrality of insula (r = 0.250, p = 0.037); and for the nodal efficiency of globus pallidus (r = 0.253, p = 0.035) were found.

Global network All subjects demonstrated small-world structural brain network. Significant correlations between twin pairs were found for characteristic path length (r = 0.57, p < 0.001) and (r = 0.56, p = 0.006) (Fig. 2), but not non-twin pairs.

Previous studies have focused on the heritability of the topology of functional⁴ and structural⁵ brain networks. Few have studied the effects of genetic and common environmental influences on the hubs of the brain, which play essential role in integrating and distributing information across the brain for the normal functioning of cognition⁶. Our current results showed significant correlations between twin pairs for various characteristics of different hubs, suggesting that cognitive function is heritable. Indeed, the hubs found in the current study are consistent with the brain regions (except globus pallidus) that showed heritability of working memory brain activation in a previous task-based fMRI study⁷, further supporting the notion that brain hubs are associated with cognition such as working memory and that the hubs’ characteristics are subject to genetic and environmental influences. We also demonstrated significant correlation between nodal characteristics and months of education as well as primary school starting age, suggesting that hubs played a more dominant role in regulating information processing in adolescents whom received longer years of education or started education earlier. This is consistent with a prior study that investigated the association between structural brain network and high cognitive ability⁸. We have successfully demonstrated that the cognitive capacity and brain hubs are under the influences of shared genes and common environment, and that the dominant roles of hubs were correlated with education level. These results suggested that the topology of brain and cognitive capacity is heritable.