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    • ionomycin One other important issue should be noted is that

    2018-11-01

    One other important issue should be noted is that the degree of negative coupling reported previously largely varies. For example, some studies have shown the negative shift from positive to negative values (Gee et al., 2013a,b, 2014), whereas other studies have found relatively reduced or decreased positive connectivity (or near zero) (Cservenka et al., 2014; Fareri et al., 2015; van Duijvenvoorde et al., 2016; Weissman et al., 2015). In our case, we used the terms inverse, negative shift and anti-correlation interchangeably and broadly to describe the maturation of ionomycin development in terms of functional connectivity. However, given the nature of the correlative approach in functional connectivity, the metric per se is not necessarily straightforward in terms of positive and negative value from the absolute zero point (Hutchison et al., 2013) because functional connectivity is more about how much neural signals in certain voxels (or networks) fluctuate together (i.e., positive correlation) or reversely (i.e., negative-, inverse- or anti correlation) with neural signals in other voxels (or networks). Our analyses focused on the limbic network connectivity with the right FPN and its influence on self-control in adolescents. However, other studies have also suggested that functional coupling between other networks such as default-mode network (DMN), central executive network (CEN) and saliency network (SN) plays a role in higher-order attention and control processes in the brain. For example, the SN is involved in the detection of salient stimuli and can initiate the attentional control system of the brain by decreasing the connectivity between DMN and CEN transiently (for a comprehensive review, see Uddin et al., 2010). That is, the moment-by-moment connectivity changes between different networks play an importance role in enhancement for cognitive and attentional control in the human brain system. Therefore, further investigation of transient connectivity switching between networks in terms of attentional control with a link to adolescents’ risk-taking behavior will also increase our understanding of adolescents’ developing brain. Some limitations should be considered in interpreting the current findings. First, we cannot determine whether the functional connectivity patterns preceded and contributed to substance use-behaviors and cognitive control, or vice versa given our correlational analysis and cross-sectional sample. However, it is plausible to interpret that less risky behavior is a result of increased prefrontal inhibitory process in the limbic system (i.e., inverse functional coupling), and our regression model using mediation supported this pathway (i.e., model 1). In our other model using substance-use-onset as the independent variable to predict functional connectivity or self-control, we failed to find any mediation effects, increasing confidence that the observed less substance use was derived from the effect of inverse functional coupling between top-down and bottom-up networks combined with heightened self-control, and not vice versa. Also, because we focused on adolescents using a cross-sectional approach, and adults’ substance-use behavior was not collected in the current study, it is not clear whether the current findings may vary across larger developmental groups and how resting state connectivity patterns change developmentally to predict substance use behaviors. Second, we only used self-report measures of adolescents’ behavior, and thus our assessment might be a suboptimal method to measure self-control and risky behavior compared to experimental based assessments in the laboratory such as the balloon analog risk task (e.g., Qu et al., 2015) and Go-NoGo task (e.g., McCormick et al., 2016), which measure risk-taking behavior and cognitive control, respectively. Future research might employ experimental-based assessments of self-control and risk taking. In summary, consistent with previous development studies (Fareri et al., 2015; Gee et al., 2013a, 2014; Qu et al., 2015; van Duijvenvoorde et al., 2016; Weissman et al., 2015), we showed that the negatively coupled top-down control system (i.e., right fronto-parietal executive network) and bottom-up emotional system (i.e., limbic network) plays an important role in adolescent substance use. Importantly, the present study provides supporting evidence that inverse coupling between right FPN and limbic network could be an index of developmental maturation in the brain by showing that adolescents exhibited greater self-control and later substance use onset with the degree of inverse coupling at the intrinsic brain network level.