Substance abuse is a widespread problem that is associated with a predisposition to increased risk-taking in general. Longitudinal and cross-sectional studies indicate that those who are more risk averse, or high harm avoidant, are less likely to use and abuse drugs (Masse &Tremblay 1997;Finn 2002). Such individuals appear to be strongly drug avoidant. What brain mechanisms drive aversion to risky behavior such as illicit drug taking? The long-term goal of this research is to clarify the brain and cognitive mechanisms that detect the risk associated with certain behaviors, including illicit drug use, and how these brain mechanisms are activated to avoid risky behavior. The proposed research goes beyond descriptive theories to leverage our separately developed new and integrative computational neural models. The medial prefrontal cortex (mPFC) and connected regions such as anterior insula are critically involved in the prediction and avoidance of risky outcomes and behaviors. Our computational model studies predict that a region of mPFC, the anterior cingulate cortex (ACC), becomes active in proportion to both the likelihood that an individual will make a mistake and the severity of the potential consequences, a prediction borne out by our subsequent fMRI studies. Of note, individuals who abuse drugs show reduced or absent ACC effects.
The specific aims of this project are twofold and will use methods of stop-signal, gambling and task-switching behavioral tasks, individual trait difference measures, and fMRI in healthy individuals. First, we will use predictions of a new computational neural model to test the hypothesis that mPFC forms temporally-structured expectations of the outcome of a subject's planned actions, including both desirable and undesirable outcomes. Second, we will build on our existing computational neural model predictions to determine how implicit environmental manipulations of available reward as well as explicit positively and negatively-framed persuasive messages may interact with ACC sensitivity to risk. This will clarify how a network including ACC and anterior insula contributes to subjects'ability to avoid risky behavior. In turn, the results will lay a theoretical foundation that will help unify and reinterpret diverse effects found in mPFC, including effects of error and response conflict.
This project will use fMRI to study the cognitive and brain mechanisms of risk avoidance. Impairments in these mechanisms are associated with substance use and abuse. Knowledge of such mechanisms can inform the prevention and treatment of substance abuse problems.
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