Background: Depression has been associated with abnormalities in neural underpinnings of Reward Learning (RL). However, inconsistencies have emerged, possibly owing to medication effects. Additionally, it remains unclear how neural RL signals relate to real-life behaviour. The current study, therefore, examined neural RL signals in young, mildly to moderately depressed - but non-help-seeking and unmedicated - individuals and how these signals are associated with depressive symptoms and real-life motivated behaviour.

Methods: Individuals with symptoms along the depression continuum (n = 87) were recruited from the community. They performed an RL task during functional Magnetic Resonance Imaging and were assessed with the Experience Sampling Method (ESM), completing short questionnaires on emotions and behaviours up to 10 times/day for 15 days. Q-learning model-derived Reward Prediction Errors (RPEs) were examined in striatal areas, and subsequently associated with depressive symptoms and an ESM measure capturing (non-linearly) how anticipation of reward experience corresponds to actual reward experience later on.

Results: Significant RPE signals were found in the striatum, insula, amygdala, hippocampus, frontal and occipital cortices. Region-of-interest analyses revealed a significant association between RPE signals and (a) self-reported depressive symptoms in the right nucleus accumbens (b = -0.017, p = 0.006) and putamen (b = -0.013, p = .012); and (b) the quadratic ESM variable in the left (b = 0.010, p = .010) and right (b = 0.026, p = 0.011) nucleus accumbens and right putamen (b = 0.047, p < 0.001).

Conclusions: Striatal RPE signals are disrupted along the depression continuum. Moreover, they are associated with reward-related behaviour in real-life, suggesting that real-life coupling of reward anticipation and engagement in rewarding activities might be a relevant target of psychological therapies for depression.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6541542PMC
http://dx.doi.org/10.1017/S0033291718003446DOI Listing

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