Clinical and Computational Studies of Dopamine Function in Schizophrenia

描述（由申请人提供）：精神分裂症是一种高度致残性疾病，影响0.5-1%的美国人。这种疾病的残疾与认知缺陷和阴性症状的联系比与包括幻觉和妄想在内的阳性症状的联系更紧密。阴性症状通常被定义为缺乏正常功能，但产生这种缺乏的实际机制仍然未知，从而扼杀了合理的治疗发展。该应用程序的首要目标是通过严格测试关于精神分裂症患者中意志缺失/快感缺乏起源的高度特异性假设，从根本上改变对阴性症状的理解。这一假设已经在一个计算模型中得到了形式化，该模型表明精神分裂症患者在表现刺激和反应替代品的积极预期值的能力方面存在缺陷，同时还具有从厌恶结果中学习的完整能力。当不确定不同选择的可能回报时，这种代表价值的缺陷也被认为会导致对行为替代方案的探索减少。该项目使用一个行为实验程序来测试这一假设在学习的结果和决策领域。此外，我们将探讨这种缺陷和目前的认知心理模型之间的关系的原因阴性症状，以及这种缺陷的重要性，从行为治疗方法，使用强化塑造行为的成功结果的预测。这种计算方法也导致了一个高度具体的假设，涉及在代表预期值的赤字的神经机制。为了解决这一假设，我们将利用EEG的时间分辨率来测试神经活动的异常是否发生在决策时，正如预测的那样，或者相反地发生在反馈传递时，正如预期的那样，如果患者无法使用多巴胺系统来发出信号，当结果好于预期时，预测错误。行为和神经生理学研究的目标是提供对阴性症状的明确的、机械的理解，并评估这种方法在当前治疗方法中的应用。由于奖励回路在哺乳动物物种中高度保守，因此应该可以“反向翻译”到指导药物开发研究所需的动物模型。

DESCRIPTION (provided by applicant): Schizophrenia is a highly disabling illness that impacts 0.5-1% of Americans. The disability of the illness is more strongly associated with cognitive deficits and negative symptoms than it is with the positive symptoms of the illness including hallucinations and delusions. Negative symptoms are generally defined as the absence of normal function, but the actual mechanisms involved in generating this absence have remained unknown, thereby stifling rational treatment development. The overarching goal of this application is to fundamentally alter the understanding of negative symptoms by rigorously testing a highly specific hypothesis about the origins of avolition/anhedonia in people with schizophrenia. This hypothesis has been formalized in a computational model that suggests that people with schizophrenia have a deficit in the ability to represent the positive expected value of stimuli and response alternatives, coupled with an intact ability to learn from aversive outcomes. This deficit in representing value is also thought to lead to reduced exploration of behavioral alternatives when uncertain about the likely payoffs of different choices. The project uses a program of behavioral experiments to test this hypothesis in the areas of learning from outcomes and decision making. In addition, we will explore the relationship between this deficit and current cognitive psychological models of the causes of negative symptoms, as well as the importance of this deficit for the prediction of successful outcome from a behavioral treatment approach that uses reinforcement to shape behavior. This computational approach also leads to a highly specific hypothesis about the neural mechanisms that are implicated in a deficit in representing expected value. To address this hypothesis, we will take advantage of the temporal resolution of EEG to test whether abnormalities in neural activity occur at the time of decision, as predicted, or instead occur at the time of feedback delivery, as would be expected if patients were unable to use the dopamine system to signal positive prediction errors when outcomes are better than expected. The goal of both the behavioral and neurophysiological studies is to provide an explicit, mechanistic understanding of negative symptoms and evaluate the application of this approach to current treatment approaches. Because reward circuitry is highly conserved across mammalian species, it should be possible to "back translate" to the animal models needed to guide drug development research.