Page 182 - Like me, or else... - Michelle Achterberg

Chapter 6
after controlling for motion and including additional regressors with CSF and WM signals, our whole brain analyses show minimal but potentially artefactual correlations with non grey matter tissue. Future studies could include additional analytic steps to further minimize these effects, for example by controlling for cortical signal bleeding, i.e., regressing out signal from surrounding voxels (Buckner et al., 2011; Choi et al., 2012).
Third, we included the global signal as nuisance signals to reduce artifacts of cardiac and respiratory fluctuations and scanner drifts (Birn et al., 2006; Fox and Raichle, 2007), however, inclusion of global signal regression can introduce negative correlations between regions (Murphy et al., 2009) and therefore the intepretation of these negative connectivities should be done with caution.
Fourth, some of our genetic analyses of neural responses resulted in high estimates for the E component (up to 92%), reflecting influences from the unique environment and measurement error. The statistical power of genetic studies is influenced by, amongst others, the sample size (Visscher, 2004; Verhulst, 2017). Although our sample size can be considered relatively large for a developmental RS-fMRI study, it is modest for behavioral genetic modeling. Our sample size may have been insufficient to detect significant contributions of A (genetics) and C (shared environment), resulting in inflated estimates of the E component. Future studies should try to discriminate between the influence of unique environment and measurement error, for example by accounting for intra-subject fluctuations using repeated measures, as has recently been described by Ge et al. (2017).
Lastly, the current study made use of post hoc ROI analyses to further investigate limbic/subcortical-cortical connectivity, based on structural brain atlases. Although recent studies have provided functional atlases of the brain (Yeo et al., 2011; Choi et al., 2012), these are based on adults. To our best knowledge, there are no functional atlases based on developmental samples, and the vast majority of developmental studies have used anatomical regions to mask and/or extract functional connectivity (Gabard-Durnam et al., 2014; Fareri et al., 2015; van Duijvenvoorde et al., 2016a). By using these structural ROIs our results can be compared or combined with previously published studies. Nevertheless, we acknowledge that the functional architecture of the brain does not follow structural subdivisions, and this may be considered as a limitation of the current design.
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