Impaired self-control is a defining feature of addiction. Indeed, self-regulation failures are implicated in many of the most vexing problems facing contemporary society, from drug abuse and addiction, to smoking and cancer, to overeating and obesity, to impulsive sexual behavior and HIV/AIDS. One reason that people may be prone to engaging in unwanted behaviors is because of heightened sensitivity to cues related to those behaviors. Researches findings from the prior award period (R01 DA022582) demonstrate that incidental exposure to drug cues activate brain reward regions (i.e., the nucleus accumbens [NAcc] as well as those involved in motor planning) for subsequent consumption. We also found that heightened NAcc responsively to food and sexual cues is associated with indulgence in overeating and sexual activity, respectively, and provides evidence for a common neural mechanism associated with addictive and appetitive behaviors. The overarching goal of this research is to identify neural predictors of self-regulatory failure and success using a model developed by the investigators that integrates what is known from three decades of social psychological research into the situational and contextual factors under which self-regulation fails with the neuroscience literature on brain mechanisms underlying executive control and reward sensitivity. This model argues that successful self- regulation is dependent on top-down control from cortical brain networks over subcortical regions involved in reward and emotion. This project uses recently developed applications of network analysis to assess resting state connectivity (rs-fcMRI) in brain circuitry and its relation to health-relevant outcomes. Complementary to stimulus-driven fMRI activation studies, network-based rs-fcMRI allows for the examination of functional coupling of brain circuits in a manner that permits assessment of a network's integrity. When individuals are not performing an explicit task, several separable and reproducible brain circuits can be identified and have been demonstrated to predict, for example, brain maturity, body weight and aerobic capacity. The guiding hypothesis of this research is that individual differences in the integrity of these networks can predict success or failure in self-regulation leading to markedly different outcomes when self-regulation is challenged by daily temptations, self-regulatory strength depletion, negative moods, or minor indulgences. The target self- regulatory behaviors in this research are smoking and dieting because they are amenable to functional imaging research and can be manipulated in behavioral laboratory experiments. Studies are proposed to test the specific aims of this project, which include using rs-fcMRI and brain reward activity to predict (1) long-term outcomes in smoking cessation, dietary success, and daily resistance to impulses, (2) functional brain activity following self-regulatory depletion, and (3) to test whether self-regulatory training can strengthen resting-state connectivity and in so doing enhance long-term self-regulatory behavior. Collectively, these studies will provide novel insights into individual differences in sef-regulatory success and failure.
A core feature of addiction is failure of self-regulation. Based on a theoretical model that successful self-regulation depends on domain-general frontal control over context-specific subcortical reward activity, this project uses recently developed applications of network analysis to assess resting-state functional connectivity in control and reward networks and their relation to self-regulatory outcomes (e.g., eating and smoking behavior). Ultimately, this may inform behavioral interventions and psychological treatments for addictive behaviors, obesity, and other self-regulatory failures such as engaging in risky sexual behavior.
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