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Chapter 9
Future directions
Based on the main scientific outcomes of this thesis, and taking into account the methodological considerations that arose from the different studies, I have formulated three objectives that are important for future research.
Combined forces: Multimodal brain imaging
In order to use experimental neuroimaging to its full potential, while taking into account the limitations that it entails, it is important to combine different MRI metrics. Aggressive behavior and emotion regulation have been studied using different MRI methodologies, such as structural anatomy (Bos et al., 2018), experimental fMRI (Ochsner et al., 2012), functional connectivity (Fulwiler et al., 2012) and structural connectivity (Olson et al., 2009; Peper et al., 2015), but the number of studies that combined different metrics is limited. Nevertheless, most theoretical frameworks suggest that behaviors and emotions are regulated through communication between specific brain regions that are part of a large and complex brain network (Casey, 2015). To empirically examine the complex features of the developing brain and its association with behavioral outcomes, a multimodal brain imaging approach is needed.
Individual differences in developmental trajectories
The single time-point studies in this thesis (chapters 2, 3, 4, 6, 7) provide starting points for understanding social emotion regulation in the childhood brain. To understand the developmental trajectories of social emotion regulation, however, we need longitudinal studies (Crone and Elzinga, 2015; Telzer et al., 2018). Although I made a start with this approach in chapter 5 and 8, it should be noted that two measures are only slightly better than one. Three or more measures are needed to capture complex developmental trajectories, as this allows investigating both linear and non-linear individual growth trajectories (Madhyastha et al., 2018). Both behavioral outcomes (such as reward sensitivity or emotional reactivity) and brain development have shown non-linear development across childhood, adolescence and adulthood (Galvan, 2010; Silvers et al., 2012; Wierenga et al., 2018a). Most of these studies had an underrepresentation of children, resulting in more uncertainty (larger confidence intervals) in developmental trajectories across childhood. The L-CID sample consists of a unique twin sample that will be followed for a total of six years (Euser et al., 2016), including three MRI measures. This will allow for examination of individual differences in developmental trajectories across childhood and emerging adolescence. Additionally, due to the large sample size and therefore excellent statistical power, we can examine how childhood brain development
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