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                                Chapter 4
 on rather large anatomical regions (i.e., the whole frontal cortex) and also report genetic influences (Jansen et al., 2015). One developmental study that specifically investigated heritability of the DLPFC showed heritability estimates of around 40% for cortical thickness (age range 5-19, Lenroot et al. (2009)). Only a handful of studies have addressed heritability in task-based fMRI (for an overview, see Jansen et al. (2015)). Blokland and colleagues (2011) investigated brain activation during a working memory task in young adults (aged 20-30) and showed heritability of brain function in (amongst others) DLPFC, ranging from 20-65%. To our best knowledge, our study is the first to investigate the heritability of task- based fMRI in middle childhood, so direct comparisons to previous studies cannot be made. However, test-retest reliability studies on task-based fMRI in developmental samples have shown higher interclass correlation coefficients (ICCs) for lateral PFC regions than for subcortical regions (van den Bulk et al., 2013; Peters et al., 2016), indicating that the DLPFC might indeed reflect trait-like genetic influences. An important next step would be to reveal which environmental and genetic factors play a role in explaining the variance in brain activation and aggression following social evaluation, and test whether specific environmental influences (e.g. supportive parenting) might moderate the influence of specific genetic factors (for example, see the study protocol of Euser et al. (2016).
Several limitations of the current study may be addressed in future research. First, the cover story of the SNAT task explicitly stated that the peers would not hear the noise blast. This decision was based on previous studies using a similar design (Konijn et al., 2007). Therefore the aggression measure reflects imagined aggression. Future studies may separate real aggression from imagined aggression to test any neural differences between these two types of aggression. Second, although our sample size can be considered large with regards to fMRI, it is rather small for behavioral genetic modeling. The statistical power of genetic studies is influenced by, amongst others, the sample size and the ratio MZ:DZ (Visscher, 2004; Verhulst, 2017). Our genetic analyses of neural responses resulted in high estimates for the E component (and specifically E- models, see supplementary materials), reflecting influences from the unique environment and measurement error. However, our sample size may have been insufficient to detect significant contributions of A (genetics) and C (shared environment). Fortunately, our sample did have an approximately equal numbers of MZ and DZ twins, which is considered optimal (Visscher, 2004). Moreover, prior studies have showed that the E component was also the primary determinant of variance in structural brain measures (Lenroot et al., 2009), highlighting the urgent need to disentangle unique environmental influences from measurement error. Last, we used several ROIs to investigate brain-behavior associations and twin correlations. Significant results did not survive Bonferroni correction for multiple
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