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Subcortical-PFC resting state connectivity
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that variance in amygdala-OFC functional connectivity in childhood is explained by genetic factors. This finding has important implications for intervention research: Certain genetic profiles might be more susceptible to environmental influences than others, as is proposed by the differential susceptibility theory (Bakermans-Kranenburg and van Ijzendoorn, 2007; Ellis et al., 2011). A next step could be to examine whether children with specific genetic profiles are more susceptible to both the adverse effects of unsupportive environments and the beneficial effects of supportive rearing (see the study protocol of Euser et al. (2016)). Important aspects to take into account in those studies are the developmental differences in heritability estimates for brain anatomy and connectivity (Lenroot et al., 2009; van den Heuvel et al., 2013). That is, previous studies have found lower heritability estimates in children than in adults (van den Heuvel et al., 2013). However, the literature on heritability of functional brain connectivity is still relatively sparse, and most studies have examined whole brain RS and/or used different RS methods (Glahn et al., 2010; Richmond et al., 2016; Yang et al., 2016; Colclough et al., 2017; Ge et al., 2017), making comparisons between studies difficult. Studying differences in heritability estimates between children and adults, nevertheless, is an important issue for future studies, providing important insights in the developmental phase during which connections might be most sensitive to environmental influences.
Overall, the patterns of genetic and environmental influences for ventral striatum and amygdala were distinct: Long-range PFC connectivity with the ventral striatum was genetically influenced, whereas long-range amygdala connectivity was mostly environmentally influenced. These results may be the starting point for a better understanding of how brain development is both biologically based and environmentally driven.
Methodological considerations
Some methodological considerations should be noted. First, due to excessive motion, we had to exclude almost half of our initial sample. Nevertheless, due to our large sample size we could still perform analyses on a relatively large group of children, thereby increasing the statistical power of our analyses. It should be noted that the current standard of remaining motion in (adult) RS studies is even stricter, often using a cutoff of 0.3 mm FD. However, in terms of motion, the current results are based on a very clean dataset compared to earlier developmental studies. After exclusion of participants with excessive motion the gender distribution was significantly different from chance in the MZ and DZ twin samples, with more girls than boys included. Although there were no significant differences in gender between the MZ and DZ samples, and therefore this gender distribution is unlikely to have influenced our results, future studies on heritability of brain measures in childhood should opt to oversample young boys, since our results show the highest attrition rate in boys. Secondly, even
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