Reward and punishment learning deficits among bipolar disorder subtypes

Objectives: Bipolar disorder (BD) is defined by alternation of depressive and (hypo)manic states. An essential dimension related to mood fluctuations is reward sensitivity. The expression of mood disorders can be modulated by environmental factors and life events, as well as by their subsequent learning related to reward or punishment sensitivity. According to the dimensional focus of the Research Domain Criteria, BD subtypes may be conceptualized as a spectrum in which reward sensitivity is a key dimension of pathology. Here, we examine reward maximizations vs. punishment avoidance learning in patients with BD during intercritical phase to test this hypothesis. Methods: Patients with BD-I (n=45), BD-II (n=34) and age and gender matched (n=30) healthy controls (HC) participated to the study. They performed an instrumental learning task designed to dissociate reward-based from punishment-based reinforcement learning. Computational modeling was used to identify the mechanisms underlying reinforcement learning performance. Results: Behavioral results showed a significant reward learning deficit across BD subtypes compared to HC. Conversely, BD-I patients performed better during punishment avoidance learning than BD-II patients. Computational analysis indicated that the observed reward-based learning deficit was captured by a lower reinforcement magnitude in both BD subtypes, when compared to HC. The punishment-based learning deficit was captured by a higher choice randomness in the BD-II compared to BD-I patients’ group. Conclusions: Our results are consistent with the reward hyposensitivity theory in BD. Furthermore, our results also suggest that studying punishment avoidance learning across BD subtypes could be useful for classification and consequent treatment tailoring in BD

Neural interactions in the human frontal cortex dissociate reward and punishment learning

How human prefrontal and insular regions interact while maximizing rewards and minimizing punishments is unknown. Capitalizing on human intracranial recordings, we demonstrate that the functional specificity toward reward or punishment learning is better disentangled by interactions compared to local representations. Prefrontal and insular cortices display non-selective neural populations to reward and punishment. The non-selective responses, however, give rise to context-specific interareal interactions. We identify a reward subsystem with redundant interactions between the orbitofrontal and ventromedial prefrontal cortices, with a driving role of the latter. In addition, we find a punishment subsystem with redundant interactions between the insular and dorsolateral cortices, with a driving role of the insula. Finally, switching between reward and punishment learning is mediated by synergistic interactions between the two subsystems. These results provide a unifying explanation of distributed cortical representations and interactions supporting reward and punishment learning

Neural interactions in the human frontal cortex dissociate reward and punishment learning

International audienceHow human prefrontal and insular regions interact while maximizing rewards and minimizing punishments is unknown. Capitalizing on human intracranial recordings, we demonstrate that the functional specificity toward reward or punishment learning is better disentangled by interactions compared to local representations. Prefrontal and insular cortices display non-selective neural populations to rewards and punishments. Non-selective responses, however, give rise to context-specific interareal interactions. We identify a reward subsystem with redundant interactions between the orbitofrontal and ventromedial prefrontal cortices, with a driving role of the latter. In addition, we find a punishment subsystem with redundant interactions between the insular and dorsolateral cortices, with a driving role of the insula. Finally, switching between reward and punishment learning is mediated by synergistic interactions between the two subsystems. These results provide a unifying explanation of distributed cortical representations and interactions supporting reward and punishment learning