Intact Ventral Striatal Prediction Error Signaling in Medicated Schizophrenia Patients

ABSTRACTBackgroundMidbrain 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 sample sizes to assess ventral striatum (VS) RPE in schizophrenia.MethodsIn the current study we analyzed data from two independent studies, involving a total of 87 chronic medicated schizophrenia patients and 61 control subjects. Subjects completed a probabilistic reinforcement-learning task in conjunction with functional magnetic resonance imaging scanning. We fit each participant's choice behavior to a Q-learning model and derived trialwise RPEs. We then modeled blood oxygen level-dependent (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.ResultsBoth 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.ConclusionsThese data demonstrate the utility of computational methods in isolating or 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 or parietal circuits associated with maintenance and selection of goal-relevant information.