Testing competing models of the computational role of dopamine in hallucinations

项目总结： 幻觉在临床和非临床人群中很常见，很难治疗，而且经常预示着病情恶化。 功能性。目前治疗效果不佳和严重副作用的部分原因是我们的 对导致幻觉的机制的理解还不成熟。纹状体多巴胺的过量释放 与幻觉的发展和严重程度有因果关系，但精确的回路和认知 将这种神经化学变化与错误感知联系起来的过程尚不清楚。来自基础的证据 神经科学文献激发了关于纹状体中过量的多巴胺如何驱动 幻觉。具体地说，幻觉的奖赏和知觉假说已经出现，但它们 还需要在一个可证伪的框架中进行直接测试。确定这些假设的机制中的哪一种驱动 幻觉是至关重要的，因为奖励和知觉学习是由不同的 多巴胺能基础回路中的每一个都可以提供单独的治疗靶点。我们已经开发出一种 用生物基础的计算模型来形式化这些假设的数学框架 产生了关于知觉或奖励学习的改变如何驱动的可证伪预测 幻觉。 在这里，我们将使用一种与fMRI兼容的新技术严格测试这些模型的神经和行为预测 听觉信号检测任务和中脑多巴胺功能的有效替代测量。在目标1中，我们将 评估参与者知觉和奖赏学习以及与幻觉倾向的关系。在目标2中， 我们将确定在任务过程中支持奖励和知觉学习的神经回路。在《目标3》中，我们将 使用经验证的多巴胺功能的代理测量来分离驱动脑电变化的特定子电路 学习。总括而言，建议的研究旨在弥合我们对 神经化学和幻觉现象学。关键的是，这可能会促进对 个体化的治疗目标，不仅更有效，而且副作用更有限。这项建议 我还将支持我在最先进的计算建模和神经成像方法方面的培训，以及 促进我作为计算精神病学领域的独立研究人员的发展。

Project Summary: Hallucinations are common in clinical and nonclinical groups, can be difficult to treat, and often predict worsening functionality. The poor efficacy and severe side effects of current treatments are in part a consequence of our immature understanding of the mechanisms that cause hallucinations. Excess striatal dopamine release has been causally implicated in the development and severity of hallucinations but the precise circuits and cognitive processes that link this neurochemical alteration to false perception remain unclear. Evidence from the basic neuroscience literature has inspired competing theories about how excess striatal dopamine drives hallucinations. Specifically, reward and perceptual hypotheses of hallucinations have emerged, but they have yet to be directly tested in a falsifiable framework. Identifying which of these hypothesized mechanisms drives hallucinations is critically important given that reward and perceptual learning are facilitated by distinct dopaminergic basal circuits each of which may provide a separate treatment target. We have developed a mathematical framework that formalizes these hypotheses with biologically grounded computational models and generated falsifiable predictions about how alterations in either perceptual or reward learning could drive hallucinations. Here, we will rigorously test the neural and behavioral predictions of these models using a novel fMRI-compatible auditory signal-detection task and a validated proxy measure for midbrain dopamine function. In Aim 1, we will evaluate participant perceptual and reward learning and the relationship with hallucination proneness. In Aim 2, we will identify the neural circuits that support reward and perceptual learning during the task. In Aim 3, we will use a validated proxy measure of dopamine function to dissociate the specific subcircuits driving alterations in learning. Overall, the proposed study aims to bridge the explanatory gap between our understanding of the neurochemistry and phenomenology of hallucinations. Critically, this could promote the identification of individualized treatment targets that are not only more effective but have more limited side effects. This proposal will also support my training in state-of-the-art computational modeling and neuroimaging approaches and promote my development as an independent researcher in the field of computational psychiatry.