Prediction Error Signaling and Reinforcement Learning in Schizophrenia

描述（由申请人提供）：本提案的长期目标是了解精神分裂症患者奖励加工改变与快感缺乏症状（快感体验减少）和动机之间的关系。动机障碍是精神分裂症的关键特征，它显著影响功能能力，并且对治疗有抵抗力。越来越多的数据表明，虽然精神分裂症患者当下的享乐体验完好无损，但他们将有益体验转化为未来目标导向行为的能力可能受损。这种能力的关键是强化学习的能力，它加强了那些产生有益结果的行为，抑制了那些没有结果的行为。本研究旨在确定精神分裂症患者是否表现出强化学习及其相关神经活动的障碍，以及这些障碍的个体差异是否与快感缺乏和动机症状有关。来自电生理学、神经成像和计算建模的数据表明，中边缘多巴胺系统参与强化学习，表明它编码奖励预测错误，并在几次试验中逐渐整合结果。这个系统在精神分裂症中特别有趣，因为有证据表明患有这种疾病的患者纹状体中多巴胺释放发生了改变。本文提出的工作使用fMRI结合强化学习的计算模型来检查患者和对照组在工具性学习范式期间与预测误差相关的神经活动。如果预测错误信号在精神分裂症中被破坏，那么预计患者在中边缘区域（如纹状体）表现出与预测错误相关的神经活动减少，同时在强化学习中表现出行为障碍。如果这些预测错误信号的中断导致了快感缺乏症和动机症状，那么这些症状较高的患者预计会表现出预测错误活动的更大减少和更差的任务表现。此外，由于阻断纹状体多巴胺受体的抗精神病药物可能会破坏精神分裂症患者的强化学习，因此本研究的另一个目的是研究预测错误信号/任务表现的个体差异与抗精神病药物类型和剂量之间的关系。如果多巴胺受体拮抗剂干扰强化学习，那么多巴胺受体拮抗剂水平较高的患者预计会表现出预测错误信号减弱和任务表现受损。对精神分裂症中多巴胺能预测误差和强化学习之间关系的进一步了解，以及它们与临床症状和潜在药物效应的关系，可能有助于开发靶向治疗方法，以解决这些临床重要但目前治疗不足的症状。

DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: 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.