Neural Characteristics of Reward in Adolescents: Neural Substrates of Risk-taking, Researcher Degrees of Freedom and a Person-Specific Network Approach

Adolescence is a key developmental period that is marked by rises in death and disease predominately from behavioral sources. Contemporary neurodevelopment models associate the changes in risk-taking behaviors with the development of socioemotional and cognitive control processes during adolescence. While these neurodevelopmental models have identified adolescent specific change, their generalizability and reliability in prediction of risk-taking have been mixed. The goal of this dissertation is to evaluate how neurodevelopmental models generalize in a sample of high risk and average/low risk-taking adolescents completing a monetary incentive delay (MID) task during functional magnetic resonance imaging (fMRI), and how methodological techniques may influence the underlying interpretations. To evaluate the generalizability of the neurodevelopmental models as it relates to risk-taking behaviors during late adolescence, Study 1 examined whether differences in neural activity of a priori regions of interest (ROI) and whole brain analyses during a Big Win versus Neutral reward contrast differed across adolescents with high risk versus average/low risk-taking profiles. In the ROI analyses, there were no significant differences in activation in pre-selected regions during the anticipation of Big Win versus Neutral cues in the task between high risk versus average/low risk-taking profiles. While the whole brain analyses during the same contrast did reveal differences in neural activity between the two risk-taking profiles, the differences were in brain regions that were outside of regions hypothesized by the neurodevelopmental models. The methodological issue in these findings may in part relate to the selected contrast, Big Win versus Neutral. Some researchers may operationalize the experimental contrast of reward differently. Study 2 evaluated how the ambiguous operationalization of the reward contrasts during the MID task may impact the underlying brain-behavior associations. In mean-level activation maps, there was evidence for greater similarity in the neural activity of reward regions between Big Win and Big Loss cues than is proposed in the reward literature. Moreover, the magnitude and direction of brain-behavior associations were inconsistent across theoretically related behaviors, such as sensation seeking, externalizing and substance use, and regions, such as ventral striatum and insula. The inconsistency in these mean-based approaches may in part be related to the individual differences among adolescents, which may contribute to unique characterization of the underlying neural connectivity that are important to socioemotional and cognitive control processes. Study 3 evaluated group-, subgroup-, and individual-level characteristics of adolescent neural networks of socioemotional and cognitive networks using a data-driven person-specific network connectivity approach. Using this approach, I evaluated how subgroup features in functional connectivity, and connection magnitude and direction were associated with self-reported sensation seeking. Two distinct subgroups were uncovered for each timeseries from the MID runs that tapped a presumed state of motivational processing. During the first run, subgroups were significantly related to self‐reported sensation seeking. However, this effect was attenuated in the second run and opposite in direction for the combined runs. Some of these differences may relate to habituation or reliability over time and power across methods. These findings highlight the importance that analytic decisions play when mapping brain-behavior associations that are salient to adolescent risk-taking behaviors. This dissertation provides evidence regarding generalizability and variability in researcher decisions that may be of concern when testing key hypotheses of the neurodevelopmental models and considers steps to bridge the gap between developmental neuroscience and measurement to improve our understanding of adolescent motivational processing.PHDPsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/172744/1/demidenm_1.pd