Evaluation of effort-based decision making in tobacco use disorder, tobacco and opioid use disorder, and tobacco use cessation - PROJECT SUMMARY Differences in cost-benefit analyses for goal-directed motivation and behavior characterize many substance use disorders (SUD). An important feature of these cost-benefit analyses and decision-making is effort-based decision making (EBDM) which evaluates the reward magnitude and probability in relation to the effort required to obtain the reward. Dopamine (DA) modulates EBDM indicating that increasing or decreasing DA transmission enhances or diminishes (respectively) the willingness to expend effort for rewards. DA has also been shown to be affected by chronic substance use, more specifically tobacco use and opioid use such that over time the production, transmission, absorption, and sensitivity to DA is altered. In line with this, EBDM has been shown to be altered in tobacco use disorder (TUD) but has not been directly investigated in opioid use disorder. For successful recovery, individuals with SUDs must exert effort for non-drug rewards and value non- drug rewards more than drug rewards. But if the endogenous DA system is dysregulated due to substance use, the value of and motivation for non-drug rewards could be hypothetically compromised. This is why it is critical to understand how EBDM might be differentiated in the ability or inability to successfully quit smoking. It is also critical whether these differences in smoking status are unique to the pharmacology of nicotine and tobacco use, or if they are related more broadly to other SUDs such as opioid use. The primary goal of this fellowship proposal is to investigate the neurocomputational basis of EBDM in individuals with TUD, tobacco and opioid use disorder (TOUD), and controls (Aim 1) and characterize the neurocomputational basis of EBDM in a smoking cessation attempt (Aim 2). In both aims I will computationally model effort-based choice behavior to assess the underlying processes that potentially differ between groups such as the systematic use of reward and effort information and learning from the outcomes of previous actions. In Aim 1, I will set the foundation by understanding how EBDM differs between TUD, TOUD, and controls; this will further the understanding of not only how EBDM in TUD but also how EBDM could differ in polysubstance use (TOUD). In Aim 2, I will then further this work by understanding how EBDM is implicated in a smoking cessation attempt to potentially create new pathways for therapies. This 3-year fellowship will be analyzed by leveraging a previously completed dataset and an existing ongoing study. In all, this fellowship will provide training in contemporary computational modeling methods and experimental design that address fundamental questions in computational neuroscience regarding the neurocomputational basis of EBDM. The central findings of this research will advance our understanding of the principles of EBDM in SUDs and a smoking cessation attempt.
