Predictive Coding as a Framework for Understanding Psychosis

7.项目总结 此应用程序使用NIMH研究领域标准(RDoC)响应NIMH PAR-16-136 了解精神病的方法。“精神病症状，如妄想和幻觉，是 许多患者耐受治疗，并与高水平的痛苦和损害有关。治疗 由于缺乏这些症状如何产生和持续的模型，进展缓慢。采用 RDoC方法，我们认为这些症状可能是神经和认知的异常所致。 认知、行动和信念形成的基础过程。分层预测编码代表一种 将感知、行动和信念形成中的功能和功能障碍统一起来的解释框架。我们 根据我们以前的经验感知、行动和相信，我们根据新的数据和 它们引发的预测误差。我们认为幻觉和妄想的形成和维持是通过 异常的预测编码机制，破坏感知、行动和信念。 我们将在大样本中使用一套预测编码措施来测试这些假设，捕获 症状严重程度和持续时间的变异性。我们将在治疗期间使用功能磁共振成像(FMRI) 感知、行动和信念的任务，测量失配负波(MMN)的脑电图学 意想不到的知觉刺激和磁共振波谱(MRS)来测量谷氨酸 浓度，这可能是精神病患者MMN和fMRI信号扰动的基础。我们会 将行为和大脑数据与正式的计算模型结合在一起，使我们能够从 每个个体受试者的数据，他们的前科的强度和跨层次的预测误差 从简单的刺激到更复杂的感知、行动选择和信念的表征丰富性。 我们提出了四个具体目标：(1)检验不适当强烈的自上而下知觉先验是否导致 幻觉；(2)测试妄想是否由异常的预测误差信号引起；(3)检查 精神病症状是由于未能恰当地将结果归因于自己的行为而产生的； 评估谷氨酸水平是否与目标1-3中检测的预测编码现象有关。五分之一 探索性目的，我们将检查预测性编码异常是否会随着病程的变化而改变。 我们的总体目标是对妄想症的预测编码进行严格的计算测试 还有幻觉。根据结果，我们要么放弃理论，要么用它来设计和测试 治疗方法更多地是针对精神病患者的特定需求而定制的，而这些需求目前还没有得到满足。

7. Project Summary This application responds to the NIMH PAR-16-136, “Using the NIMH Research Domain Criteria (RDoC) Approach to Understand Psychosis.” Psychotic symptoms, such as delusions and hallucinations, are treatment-resistant in many patients and are associated with high levels of distress and impairment. Treatment advances have been slowed by the lack of a model of how these symptoms arise and persist. Adopting the RDoC approach, we suggest that these symptoms may result from abnormalities in the neural and cognitive processes that underlie perception, action, and belief formation. Hierarchical predictive coding represents an explanatory framework that unites function and dysfunction in perception action and belief formation. We perceive, act, and believe based on our prior experiences, and we update those priors in light of new data and the prediction errors they elicit. We suggest that hallucinations and delusions form, and are maintained, via aberrant predictive coding mechanisms that vitiate perception, action and belief. We will test these hypotheses with a suite of predictive coding measures in a large sample, capturing variability in symptom severity and duration. We will use functional magnetic resonance imaging (fMRI) during tasks of perception, action, and belief, electroencephalography to measure mismatch negativity (MMN) to unexpected perceptual stimuli, and magnetic resonance spectroscopy (MRS) to measure glutamate concentrations, which may underlie the perturbed MMN and fMRI signals in people with psychosis. We will bring together behavioral and brain data with formal computational modeling that will allow us to estimate, from each individual subject's data, the strength of their priors and prediction errors across a hierarchy of representational richness from simple stimuli through more complex percepts, action choices, and beliefs. We propose four specific aims: (1) testing whether inappropriately strong top-down perceptual priors cause hallucinations; (2) testing if delusions are caused by aberrant prediction error signaling; (3) examining whether psychotic symptoms result from a failure to attribute outcomes to one's own actions appropriately; (4) and assessing whether glutamate levels are related to predictive coding phenomena assayed in Aims 1-3. In a fifth exploratory aim, we will examine whether predictive coding abnormalities change over course of illness. Our overall goal is to provide a computationally rigorous test of the predictive coding account of delusions and hallucinations. Depending on the outcome, we will either discard the theory, or use it to design and test treatment approaches more tailored to the specific, and this far unmet, needs of individuals with psychosis.