Frontal Striatal Systems and Dopamine in Normal and Pathological Behavior

我们的实验室使用神经生理学，药理学和成像的组合来了解杏仁核，纹状体，前额皮质和多巴胺组成的一组大脑区域如何成为学习的基础。我们专门研究学习预测选择是否会导致好的或坏的结果的过程。我们与临床合作者密切合作，在动物中实施任务，这些任务显示了患者和对照组之间的差异，并从动物工作中获得发现，以帮助了解临床人群。随着我们对这些大脑系统如何支持学习有了更好的理解，我们可以更好地理解这些系统中的病理学如何成为各种精神疾病的基础，包括焦虑、抑郁和成瘾。我们最近的工作重点是扩大我们对这些学习过程的重要领域网络的理解。目前关于这种学习的基础电路的概念集中在多巴胺和腹侧纹状体，而我们最近的工作也指出了杏仁核的重要作用。正在进行的工作研究不同的神经系统是否是从积极与消极结果（奖励与惩罚）中学习的基础，以及重叠或不同的神经系统是否是有益的学习行为与学习选择有益的对象的基础。我们最近也研究了越来越复杂的学习机制，发现前额叶皮层代表了利用环境中的上下文关系的学习过程。我们开发了计算模型，可以预测当我们学习将行为与奖励联系起来时，前额叶皮层和纹状体的神经活动如何随时间变化。 最后，我们使用计算建模来剖析焦虑症患者的威胁学习，将其分解为与我们研究的神经系统相关的机制。

Our lab uses a combination of neurophysiology, pharmacology and imaging to understand how a set of brain areas composed of the amygdala, striatum, prefrontal cortex and dopamine underlie learning. We specifically study the process of learning to predict whether choices will lead to good or bad outcomes. We work closely with clinical collaborators, implementing tasks in animals that have shown differences between patients and controls, and taking findings from the animal work to help understand clinical populations. As we develop a better understanding of how these brain systems support learning, we can develop a better understanding of how pathology in these systems can underlie various psychiatric disorders including anxiety, depression and addiction. Our recent work has focused on broadening our understanding of the network of areas important for these learning processes. Current conceptions of the circuitry that underlies this learning focuses on dopamine and the ventral-striatum, whereas our recent work also points to an important role for the amygdala. Ongoing work examines whether different neural systems underlie learning from positive vs. negative outcomes (rewards vs. punishments), and whether overlapping or distinct neural systems underlie learning actions that are beneficial vs. learning to choose objects that are beneficial. We have also recently studied increasingly sophisticated learning mechanisms and found that prefrontal cortex represents learning processes that take advantage of contextual regularities in the environment. We developed computational models that predict how neural activity changes over time in prefrontal cortex and the striatum when we learn to associate actions with rewards. Finally, we have used computational modeling to dissect threat learning in patients with anxiety disorders into mechanisms related to the neural systems we study.