Comparison of conventional and rapid-acting antidepressants in a rodent probabilistic reversal learning task

Abstract

Deficits in reward processing are a central feature of major depressive disorder with patients exhibiting decreased reward learning and altered feedback sensitivity in probabilistic reversal learning tasks. Methods to quantify probabilistic learning in both rodents and humans have been developed, providing translational paradigms for depression research. We have utilised a probabilistic reversal learning task to investigate potential differences between conventional and rapid-acting antidepressants on reward learning and feedback sensitivity. We trained 12 rats in a touchscreen probabilistic reversal learning task before investigating the effect of acute administration of citalopram, venlafaxine, reboxetine, ketamine or scopolamine. Data were also analysed using a Q-learning reinforcement learning model to understand the effects of antidepressant treatment on underlying reward processing parameters. Citalopram administration decreased trials taken to learn the first rule and increased win-stay probability. Reboxetine decreased win-stay behaviour while also decreasing the number of rule changes animals performed in a session. Venlafaxine had no effect. Ketamine and scopolamine both decreased win-stay probability, number of rule changes performed and motivation in the task. Insights from the reinforcement learning model suggested that reboxetine led animals to choose a less optimal strategy, while ketamine decreased the model-free learning rate. These results suggest that reward learning and feedback sensitivity are not differentially modulated by conventional and rapid-acting antidepressant treatment in the probabilistic reversal learning task.

Investigation of reward learning and feedback sensitivity in non-clinical participants with a history of early life stress

“…Participants who did not pass the practice phase were excluded from analysis. Data were analysed as previously described [39]. Win-stay probability was defined as the probability that if a participant was rewarded for selecting a stimulus they would select the same stimulus for the next trial.…”

“…The number of rule changes, how many times participants were able to meet criterion for a rule change, accuracy and response latency per block were additionally analysed. A Qlearn reinforcement learning model was applied to data as previously described [39,40] to give estimates of model free learning rate, accuracy compared to a model predicted perfect strategy (subjective accuracy) and beta, a measure of choice variability (low values indicate essentially random choices while high values show a deterministic choice strategy). Additionally, data per phase (practice, acquisition of the first rule in block 1 and the following two reversals) was analysed consisting of participant accuracy, errors to criterion and win-stay / lose-shift probability.…”

“…Participants who did not pass the practice phase were excluded from analysis. Data was analysed as previously described (Wilkinson, Grogan, Mellor, & Robinson, 2020) with win-stay and lose-shift probabilities being calculated as measures of positive and negative feedback sensitivity respectively. These were subdivided into either true, feedback that matches with the underlying task rules, or misleading feedback, that which is opposite to the underlying task rule.…”

Applying Reinforcement Learning to Rodent Stress Research

“… 11 Furthermore, conventional and fast-acting antidepressants induce notable effects on performance of wild type rats in a probabilistic reward learning task. 19 …”

“…11 Furthermore, conventional and fast-acting antidepressants induce notable effects on performance of wild type rats in a probabilistic reward learning task. 19 It is crucial to recognize that reward processing deficits represent only one dimension of human depression. Given the heterogeneity and range of impairments associated with depression, modeling the disorder in rodents is challenging and unlikely to recapitulate the full extent of symptoms in humans.…”