Prediction Error Signaling and Reinforcement Learning in Schizophrenia

精神分裂症的预测误差信号和强化学习

• 批准号: 7998936
• 负责人: Erin Connor Dowd
• 金额: $ 2.59万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7998936
• 关键词: Activities of Daily Living; Address; Anhedonia; Antipsychotic Agents; Area; Attenuated; Behavior; Clinical; Code; Computer Simulation; Corpus striatum structure; Data; Development; Dopamine; Dopamine Receptor; Electrophysiology (science); Functional Magnetic Resonance Imaging; Future; Goals; Impairment; Individual; Individual Differences; Learning; Motivation; Operant Conditioning; Outcome; Patients; Pharmaceutical Preparations; Psychological reinforcement; Quality of life; Resistance; Rewards; Schizophrenia; Signal Transduction; Symptoms; System; Task Performances; Translating; Work; behavioral impairment; dosage; experience; hedonic; improved; interest; mesolimbic system; neuroimaging; pleasure; public health relevance; relating to nervous system; reward processing

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.

描述(由申请人提供)：这项提案的长期目标是了解精神分裂症患者的奖赏处理改变与快感缺失症状(快感减少)和运动障碍之间的关系。动机障碍是精神分裂症的重要特征，显著影响功能能力，并对治疗产生抵抗。越来越多的数据表明，虽然精神分裂症患者的瞬间享乐体验完好无损，但患者将有益体验转化为未来目标导向行为的能力可能会受到损害。这种能力的关键是强化学习的能力，这种能力加强了那些能带来奖励结果的行动，而抑制了那些不能产生奖励结果的行动。这项建议旨在确定精神分裂症患者是否在强化学习及其相关神经活动方面表现出障碍，以及这些障碍的个体差异是否与快感缺失和运动障碍的症状有关。来自电生理学、神经成像和计算建模的数据融合表明，中脑边缘多巴胺系统参与强化学习，表明它编码奖励预测错误，逐渐整合几个试验的结果。这一系统对精神分裂症特别感兴趣，因为有证据表明，这种疾病患者的纹状体多巴胺释放发生了变化。这里提出的工作使用功能磁共振成像结合强化学习的计算模型来检查在患者和对照的工具性学习范例中与预测误差相关的神经活动。如果精神分裂症患者的预测错误信号被扰乱，患者将表现出纹状体等中边缘区域与预测错误相关的神经活动减少，以及强化学习中的行为障碍。如果这些预测错误信号的中断导致快感缺乏和缺乏动力的症状，那么这些症状较高的患者将会在预测错误活动和较差的任务表现方面表现出更大的减少。此外，由于阻断纹状体多巴胺受体的抗精神病药物可能会扰乱精神分裂症的强化学习，这项建议的另一个目的是检查预测错误信号/任务表现的个体差异与抗精神病药物类型和剂量的关系。如果多巴胺受体拮抗干扰强化学习，经历更高水平的多巴胺受体拮抗的患者将表现出预测错误信号减弱和任务表现受损。更好地理解精神分裂症中多巴胺能预测错误和强化学习之间的关系，以及它们与临床症状和潜在药物疗效的关系，可能有助于开发有针对性的治疗方法，以解决这些临床上重要但目前治疗不足的症状。 公共卫生相关性：这项建议旨在确定强化学习的障碍是否会导致动机和目标导向行为的缺陷，从而显著降低精神分裂症患者的生活质量。目前的药物对这些症状的处理很差，而且对这些症状背后的神经异常也知之甚少。这项工作旨在提高我们对精神分裂症从积极和消极结果中学习的能力如何被干扰，以及这些干扰如何导致动机缺陷的理解，为开发有针对性的治疗方法来改善精神分裂症患者的这些症状铺平道路。