The long-term objective of this proposal is to understand the relationship between altered processing of rewards and symptoms of anhedonia (a reduced experience of pleasure) and amotivation in individuals with schizophrenia. Motivational impairments are critical features of schizophrenia that significantly impact functional capacity and are resistant to treatment. A growing body of data suggests that while in-the-moment hedonic experience is intact in schizophrenia, patients may be impaired in their ability to translate rewarding experiences into future goal-directed behavior. Essential to this ability is a capacity for reinforcement learning, which strengthens actions that result in rewarding outcomes and suppresses those that do not. This proposal aims to determine whether individuals with schizophrenia show impairments in reinforcement learning and its related neural activity, and whether individual differences in these impairments are related to symptoms of anhedonia and amotivation. Converging data from electrophysiology, neuroimaging, and computational modeling implicates the mesolimbic dopamine system in reinforcement learning, suggesting that it codes reward prediction errors that gradually integrate outcomes over several trials. This system is of particular interest in schizophrenia, given evidence of altered dopamine release in the striatum in patients with this illness. The work proposed here uses fMRI in conjunction with a computational model of reinforcement learning to examine prediction error-related neural activity during an instrumental learning paradigm in patients and controls. If prediction error signaling is disrupted in schizophrenia, patients would be expected to show reduced prediction error-related neural activity in mesolimbic areas such as the striatum, as well as behavioral impairments in reinforcement learning. If these disruptions in prediction error signaling contribute to symptoms of anhedonia and amotivation, patients who are higher in these symptoms would be expected to show larger reductions in prediction error activity and poorer task performance. Furthermore, because antipsychotic medications that block dopamine receptors in the striatum may disrupt reinforcement learning in schizophrenia, an additional aim of this proposal is to examine the relationship between individual differences in prediction error signaling/task performance and antipsychotic type and dosage. If dopamine receptor antagonism interferes with reinforcement learning, patients experiencing higher levels of dopamine receptor antagonism would be expected to show attenuated prediction error signaling and impaired task performance. An improved understanding of the relationship between dopaminergic prediction errors and reinforcement learning in schizophrenia, as well as their relationship to clinical symptoms and potential medication effects, may contribute to the development of targeted therapies to address these clinically important but currently under- treated symptoms.
This proposal aims to determine whether impairments in reinforcement learning contribute to the deficits in motivation and goal-directed behavior that significantly reduce quality of life in people with schizophrenia. These symptoms are poorly addressed by current medications, and the neural abnormalities that underlie them are poorly understood. This work seeks to improve our understanding of how the ability to learn from positive and negative outcomes may be disrupted in schizophrenia, and how these disruptions may contribute to motivational deficits, paving the way for the development of targeted therapies to improve these symptoms in individuals with schizophrenia.
|Dowd, Erin C; Frank, Michael J; Collins, Anne et al. (2016) Probabilistic Reinforcement Learning in Patients With Schizophrenia: Relationships to Anhedonia and Avolition. Biol Psychiatry Cogn Neurosci Neuroimaging 1:460-473|