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BackgroundMajor depressive disorder (MDD) is associated with abnormalities in financial reward processing. Previous research suggests that patients with MDD show reduced sensitivity to frequency of financial rewards. However, there is a lack of conclusive evidence from studies investigating the evaluation of financial rewards over time, an important aspect of reward processing that influences the way people plan long-term investments. Beck's cognitive model posits that patients with MDD hold a negative view of the future that may influence the amount of resources patients are willing to invest into their future selves.MethodWe administered a delay discounting task to 82 participants: 29 healthy controls, 29 unmedicated participants with fully remitted MDD (rMDD) and 24 participants with current MDD (11 on medication).ResultsPatients with current MDD, relative to remitted patients and healthy subjects, discounted large-sized future rewards at a significantly higher rate and were insensitive to changes in reward size from medium to large. There was a main effect of clinical group on discounting rates for large-sized rewards, and discounting rates for large-sized rewards correlated with severity of depressive symptoms, particularly hopelessness.ConclusionsHigher discounting of delayed rewards in MDD seems to be state dependent and may be a reflection of depressive symptoms, specifically hopelessness. Discounting distant rewards at a higher rate means that patients are more likely to choose immediate financial options. Such impairments related to long-term investment planning may be important for understanding value-based decision making in MDD, and contribute to ongoing functional impairment.

Affective bias, the tendency to differentially prioritise the processing of negative relative to positive events, is commonly observed in clinical and non-clinical populations. However, why such biases develop is not known. Using a computational framework, we investigated whether affective biases may reflect individuals’ estimates of the information content of negative relative to positive events. During a reinforcement learning task, the information content of positive and negative outcomes was manipulated independently by varying the volatility of their occurrence. Human participants altered the learning rates used for the outcomes selectively, preferentially learning from the most informative. This behaviour was associated with activity of the central norepinephrine system, estimated using pupilometry, for loss outcomes. Humans maintain independent estimates of the information content of distinct positive and negative outcomes which may bias their processing of affective events. Normalising affective biases using computationally inspired interventions may represent a novel approach to treatment development.

Background: Exposure therapy is a first-line treatment for anxiety disorders but remains ineffective in a large proportion of patients. A proposed mechanism of exposure involves inhibitory learning where the association between a stimulus and an aversive outcome is suppressed by a new association with an appetitive or neutral outcome. The blood pressure medication losartan augments fear extinction in rodents and might have similar synergistic effects on human exposure therapy, but the exact cognitive mechanisms underlying these effects remain unknown.Methods: We used a reinforcement learning paradigm with compound rewards and punishments to test the prediction that losartan augments learning from appetitive relative to aversive outcomes. In a double-blind parallel design, healthy volunteers were randomly assigned to single-dose losartan (50mg) (N=28) versus placebo (N=25). Participants then performed a reinforcement learning task which simultaneously probes appetitive and aversive learning. Participant choice behaviour was analysed using both a standard reinforcement learning model and analysis of choice switching behaviour.Results: Losartan significantly reduced learning rates from aversive events (losses) when participants were first exposed to the novel task environment, while preserving learning from positive outcomes. The same effect was seen in choice switching behaviour. Conclusion:This study shows that losartan enhances learning from positive relative to negative events. This effect may represent a computationally defined neurocognitive mechanism by which the drug could enhance the effect of exposure in clinical populations.

Currently depressed individuals were less altruistic in both a charitable donation and an interpersonal cooperation task. Taken together, our results challenge the guilt-driven pathological hyper-altruism hypothesis in depression. There were also differences in both current and remitted patients in the relationship between altruistic behaviour and pathological self-blaming, suggesting an important role for these emotions in moral and social decision-making abnormalities in depression.

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Affective bias, the tendency to prioritise the processing of negative relative to positive events, is causally linked to clinical depression. However, why such biases develop or how they may best be ameliorated is not known. Using a computational framework, we investigated whether affective biases may reflect an individual’s estimates of the information content of negative and positive events. During a reinforcement learning task, the information content of positive and negative outcomes was manipulated independently by varying the volatility of their occurrence. Human participants altered the learning rates used for the outcomes selectively, preferentially learning from the most informative. This behaviour was associated with activity of the central norepinephrine system, estimated using pupilometry, for loss outcomes. Humans maintain independent estimates of the information content of positive and negative outcomes which bias their processing of affective events. Normalising affective biases using computationally inspired interventions may represent a novel treatment approach for depression.

Proneness to self-blaming moral emotions such as shame and guilt is increased in major depressive disorder (MDD), and may play an important role in vulnerability even after symptoms have subsided. Social psychologists have argued that shame-proneness is relevant for depression vulnerability and is distinct from guilt. Shame depends on the imagined critical perception of others, whereas guilt results from one’s own judgement. The neuroanatomy of shame in MDD is unknown. Using fMRI, we compared 21 participants with MDD remitted from symptoms with no current co-morbid axis-I disorders, and 18 control participants with no personal or family history of MDD. The MDD group exhibited higher activation of the right amygdala and posterior insula for shame relative to guilt (SPM8). This neural difference was observed despite equal levels of rated negative emotional valence and frequencies of induced shame and guilt experience across groups. These same results were found in the medication-free MDD subgroup (N = 15). Increased amygdala and posterior insula activations, known to be related to sensory perception of emotional stimuli, distinguish shame from guilt responses in remitted MDD. People with MDD thus exhibit changes in the neural response to shame after symptoms have subsided. This supports the hypothesis that shame and guilt play at least partly distinct roles in vulnerability to MDD. Shame-induction may be a more sensitive probe of residual amygdala hypersensitivity in MDD compared with facial emotion-evoked responses previously found to normalize on remission.