Impaired Learning From Negative Feedback in Stimulant Use Disorder: Dopaminergic Modulation

A Cognitive Pathway to Persistent, Maladaptive Choice

BACKGROUND

Correctly recognising that alcohol or other substances are causing problems is a necessary condition for those problems to spur beneficial behaviour change. Yet such recognition is neither immediate nor straightforward. Recognition that one's alcohol or drug use is causing negative consequences often occurs gradually. Contemporary addiction neuroscience has yet to make progress in understanding and addressing these recognition barriers, despite evidence that a lack of problem recognition is a primary impediment to seeking treatment.

SUMMARY

Based on our recent empirical work, this article shows how recognition barriers can emerge from dual constraints on how we learn about the negative consequences of our actions. One constraint is imposed by the characteristics of negative consequences themselves. A second constraint is imposed by the characteristics of human cognition and information processing. In some people, the joint action of these constraints causes a lack of correct awareness of the consequences of their behaviour and reduced willingness to update that knowledge and behaviour when confronted with counterevidence.

KEY MESSAGES

This "cognitive pathway" can drive persistent, maladaptive choice.

Medial prefrontal cortex suppresses reward-seeking behavior with risk of punishment by reducing sensitivity to reward

Reward-seeking behavior is frequently associated with risk of punishment. There are two types of punishment: positive punishment, which is defined as addition of an aversive stimulus, and negative punishment, involves the omission of a rewarding outcome. Although the medial prefrontal cortex (mPFC) is important in avoiding punishment, whether it is important for avoiding both positive and negative punishment and how it contributes to such avoidance are not clear. In this study, we trained male mice to perform decision-making tasks under the risks of positive (air-puff stimulus) and negative (reward omission) punishment, and modeled their behavior with reinforcement learning. Following the training, we pharmacologically inhibited the mPFC. We found that pharmacological inactivation of mPFC enhanced the reward-seeking choice under the risk of positive, but not negative, punishment. In reinforcement learning models, this behavioral change was well-explained as an increase in sensitivity to reward, rather than a decrease in the strength of aversion to punishment. Our results suggest that mPFC suppresses reward-seeking behavior by reducing sensitivity to reward under the risk of positive punishment.

Dysfunctional feedback processing in male methamphetamine abusers: Evidence from neurophysiological and computational approaches

Methamphetamine use disorder (MUD) as a major public health risk is associated with dysfunctional neural feedback processing. Although dysfunctional feedback processing in people who are substance dependent has been explored in several behavioral, computational, and electrocortical studies, this mechanism in MUDs requires to be well understood. Furthermore, the current understanding of latent components of their behavior such as learning speed and exploration-exploitation dilemma is still limited. In addition, the association between the latent cognitive components and the related neural mechanisms also needs to be explored. Therefore, in this study, the underlying neurocognitive mechanisms of feedback processing of such impairment, and age/gender-matched healthy controls are evaluated within a probabilistic learning task with rewards and punishments. Mathematical modeling results based on the Q-learning paradigm suggested that MUDs show less sensitivity in distinguishing optimal options. Additionally, it may be worth noting that MUDs exhibited a slight decrease in their ability to learn from negative feedback compared to healthy controls. Also through the lens of underlying neural mechanisms, MUDs showed lower theta power at the medial-frontal areas while responding to negative feedback. However, other EEG measures of reinforcement learning including feedback-related negativity, parietal-P300, and activity flow from the medial frontal to lateral prefrontal regions, remained intact in MUDs. On the other hand, the elimination of the linkage between value sensitivity and medial-frontal theta activity in MUDs was observed. The observed dysfunction could be due to the adverse effects of methamphetamine on the cortico-striatal dopamine circuit, which is reflected in the anterior cingulate cortex activity as the most likely region responsible for efficient behavior adjustment. These findings could help us to pave the way toward tailored therapeutic approaches.

Computational modelling of reinforcement learning and functional neuroimaging of probabilistic reversal for dissociating compulsive behaviours in gambling and cocaine use disorders

BACKGROUND

Individuals with cocaine use disorder or gambling disorder demonstrate impairments in cognitive flexibility: the ability to adapt to changes in the environment. Flexibility is commonly assessed in a laboratory setting using probabilistic reversal learning, which involves reinforcement learning, the process by which feedback from the environment is used to adjust behavior.

AIMS

It is poorly understood whether impairments in flexibility differ between individuals with cocaine use and gambling disorders, and how this is instantiated by the brain. We applied computational modelling methods to gain a deeper mechanistic explanation of the latent processes underlying cognitive flexibility across two disorders of compulsivity.

METHOD

We present a re-analysis of probabilistic reversal data from individuals with either gambling disorder (n = 18) or cocaine use disorder (n = 20) and control participants (n = 18), using a hierarchical Bayesian approach. Furthermore, we relate behavioural findings to their underlying neural substrates through an analysis of task-based functional magnetic resonanceimaging (fMRI) data.

RESULTS

We observed lower 'stimulus stickiness' in gambling disorder, and report differences in tracking expected values in individuals with gambling disorder compared to controls, with greater activity during reward expected value tracking in the cingulate gyrus and amygdala. In cocaine use disorder, we observed lower responses to positive punishment prediction errors and greater activity following negative punishment prediction errors in the superior frontal gyrus compared to controls.

CONCLUSIONS

Using a computational approach, we show that individuals with gambling disorder and cocaine use disorder differed in their perseverative tendencies and in how they tracked value neurally, which has implications for psychiatric classification.

Tonic dopamine and biases in value learning linked through a biologically inspired reinforcement learning model

A hallmark of various psychiatric disorders is biased future predictions. Here we examined the mechanisms for biased value learning using reinforcement learning models incorporating recent findings on synaptic plasticity and opponent circuit mechanisms in the basal ganglia. We show that variations in tonic dopamine can alter the balance between learning from positive and negative reward prediction errors, leading to biased value predictions. This bias arises from the sigmoidal shapes of the dose-occupancy curves and distinct affinities of D1- and D2-type dopamine receptors: changes in tonic dopamine differentially alters the slope of the dose-occupancy curves of these receptors, thus sensitivities, at baseline dopamine concentrations. We show that this mechanism can explain biased value learning in both mice and humans and may also contribute to symptoms observed in psychiatric disorders. Our model provides a foundation for understanding the basal ganglia circuit and underscores the significance of tonic dopamine in modulating learning processes.

Resting-State Functional Connectivity of the Dorsal and Ventral Striatum, Impulsivity, and Severity of Use in Recently Abstinent Cocaine-Dependent Individuals

BACKGROUND

Previous studies have focused on both ventral striatum (VS) and dorsal striatum (DS) in characterizing dopaminergic deficits in addiction. Animal studies suggest VS and DS dysfunction each in association with impulsive and compulsive cocaine use during early and later stages of addiction. However, few human studies have aimed to distinguish the roles of VS and DS dysfunction in cocaine misuse.

METHODS

We examined VS and DS resting-state functional connectivity (rsFC) of 122 recently abstinent cocaine-dependent individuals (CDs) and 122 healthy controls (HCs) in 2 separate cohorts. We followed published routines in imaging data analyses and evaluated the results at a corrected threshold with age, sex, years of drinking, and smoking accounted for.

RESULTS

CDs relative to HCs showed higher VS rsFC with the left inferior frontal cortex (IFC), lower VS rsFC with the hippocampus, and higher DS rsFC with the left orbitofrontal cortex. Region-of-interest analyses confirmed the findings in the 2 cohorts examined separately. In CDs, VS-left IFC and VS-hippocampus connectivity was positively and negatively correlated with average monthly cocaine use in the prior year, respectively. In the second cohort where participants were assessed with the Barratt Impulsivity Scale (BIS-11), VS-left IFC and VS-hippocampus connectivity was also positively and negatively correlated with BIS-11 scores in CDs. In contrast, DS-orbitofrontal cortex connectivity did not relate significantly to cocaine use metrics or BIS-11 scores.

CONCLUSION

These findings associate VS rsFC with impulsivity and the severity of recent cocaine use. How DS connectivity partakes in cocaine misuse remains to be investigated.

Reinforcement learning detuned in addiction: integrative and translational approaches

Suboptimal decision-making strategies have been proposed to contribute to the pathophysiology of addiction. Decision-making, however, arises from a collection of computational components that can independently influence behavior. Disruptions in these different components can lead to decision-making deficits that appear similar behaviorally, but differ at the computational, and likely the neurobiological, level. Here, we discuss recent studies that have used computational approaches to investigate the decision-making processes underlying addiction. Studies in animal models have found that value updating following positive, but not negative, outcomes is predictive of drug use, whereas value updating following negative, but not positive, outcomes is disrupted following drug self-administration. We contextualize these findings with studies on the circuit and biological mechanisms of decision-making to develop a framework for revealing the biobehavioral mechanisms of addiction.

Neuromolecular Underpinnings of Negative Cognitive Bias in Depression

This selective review aims to summarize the recent advances in understanding the neuromolecular underpinnings of biased cognition in depressive disorder. We begin by considering the cognitive correlates of depressed mood and the key brain systems implicated in its development. We then review the core findings across two domains of biased cognitive function in depression: pessimistic judgment bias and abnormal response to negative feedback. In considering their underlying substrates, we focus on the neurochemical mechanisms identified by genetic, molecular and pharmacological challenge studies. We conclude by discussing experimental approaches to the treatment of depression, which are derived largely from an improved understanding of its cognitive substrates.