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Methamphetamine use disorder associated with a dysfunctional neural feedback (reward and punishment) processing system and is considered a public health risk. Although several behavioral, computational, and electrocortical studies have explored feedback processing in other groups of individuals, the precise mechanisms of feedback processing dysfunction in methamphetamine use dependent (MUD) individuals remain unclear. Furthermore, our recent knowledge about the underlying feedback related connectivity patterns and intertwining latent components of behavior with electrocortical signals in MUDs remained quite poor. The present study intended to fill these gaps by exploring the behavioral and electrocortical responses of abstained MUDs during a feedback based learning paradigm. As mathematical models revealed, MUDs have less sensitivity to distinguishing optimal options (less sensitivity to options value) and learned less from negative feedback, compared with healthy controls. The MUDs also presented smaller medial-frontal theta (5 to 8 Hz) oscillations in response to negative feedback (300 to 550 ms post feedback) while other measures responsible for learning including, feedback-related negativity (FRN), parietal-P300, and a flux originated from medial frontal to lateral prefrontal remained intact for them. Further, in contrast to healthy controls, the observed association between feedback sensitivity and medialfrontal theta activity is eliminated in MUDs. We suggested that these results in MUDs may be due to the adverse effect of methamphetamine on the cortico-striatal dopamine circuit, reflected in anterior cingulate cortex (ACC) activity as the best candidate region responsible for efficient behavior adjustment. This study unveils the underlying neural mechanism of feedback processing in individuals with methamphetamine use history and could offer individual therapeutic approaches.

Background: The prevalence of methamphetamine use disorder (MUD) as a major public health problem has increased dramatically over the last two decades, reaching epidemic levels, which poses high costs to the health care systems worldwide, and is commonly associated with an experience-based decision-making (EDM) aberrant. However, precise mechanisms underlying such non-optimally in choice patterns still remain poorly understood.Methods: In this study, to uncover the latent neurobiological and psychological meaningful processes of such impairment, we apply a reinforcement learning diffusion decision model (RL-DDM) while methamphetamine abuser participants (n = 18, all men; mean (±SD) age: 27.3±5) and age/sex-matched healthy controls (n = 25, all men; mean (±SD) age: 26.8.0±3.63) perform choices to resolve uncertainty within a simple probabilistic learning task with rewards and punishments.Results: Preliminary behavior results indicated that addicts made maladaptive patterns of learning that mirrored in both choices and response times (RTs). Furthermore, modeling results revealed that such EDM impairment (maladaptive pattern in optimal selection) in addicts was more imputable to both increased learning rates (more sensitive to outcome fluctuations) and decreased drift rate (less reward sensitivity) compared to healthy. In addition, addicts also showed substantially longer non-decision times (attributed to slower RTs), as well as lower decision boundary criteria (reflection of impulsive choice).Conclusion: Taken together, our findings reveal precise mechanisms associated with EDM impairments in methamphetamine use disorder and confirm the debility of the options values assignment system as the main hub in learning-based decision making.