Background: Midbrain dopaminergic neurons code a computational quantity, reward prediction error (RPE), which has been causally related to learning. Recently, this insight has been leveraged to link phenomenological and biological levels of understanding in psychiatric disorders, such as schizophrenia. However, results have been mixed, possibly due to small sample sizes. Here we present results from two studies with relatively large Ns to assess VS RPE in schizophrenia.
Methods: In the current study we analyzed data from two independent studies, involving a total of 87 chronic medicated schizophrenia patients and 61 controls. Subjects completed a probabilistic reinforcement-learning task in conjunction with fMRI scanning. We fit each participant's choice behavior to a Q-learning model and derived trial-wise RPEs. We then modeled BOLD signal data with parametric regressor functions using these values to determine whether patient and control groups differed in prediction-error-related BOLD signal modulations.
Results: Both groups demonstrated robust VS RPE BOLD activations. Interestingly, these BOLD activation patterns did not differ between groups in either study. This was true when we included all participants in the analysis, as well as when we excluded participants whose data was not sufficiently fit by the models.
Conclusions: These data demonstrate the utility of computational methods in isolating/testing underlying mechanisms of interest in psychiatric disorders. Importantly, similar VS RPE signal encoding across groups suggests that this mechanism does not drive task deficits in these patients. Deficits may instead stem from aberrant prefrontal/parietal circuits associated with maintenance and selection of goal-relevant information.
Keywords: Computational Psychiatry; Prediction Error; Reward Processing; Schizophrenia; Ventral Striatum; fMRI.