Computational Substrates of Addiction and Reward

描述(由申请人提供)：滥用药物部分通过颠覆通常用于选择行动和做出决定的适应机制(算法)来运作。否则，为什么一个正常人会放弃食物、性、住所和一般的幸福，而把白色粉末放进鼻子里，或者把海洛因放进血管里？显然，吸毒者的决策算法出现了故障，从这个意义上说，吸毒代表了一套病态的奖励学习和依赖奖励的决策算法。众所周知，许多(如果不是大多数)滥用药物影响中脑多巴胺系统，或者在多巴胺受体水平上，或者直接作用于向靶结构输送多巴胺的神经元。因此，重要的是要了解这些系统执行的算法、算法正常工作的方式以及它们与潜在的细胞和分子事件相联系的方式。这项提议的总体目标是通过提供对中脑多巴胺系统构建和传播的信息种类以及这些信号对下游神经靶标的影响的计算理解，来建立一种这样的联系。利用人类受试者的功能磁共振成像(FMRI)，这项工作将对中脑多巴胺能系统的一个计算模型：预测误差模型进行详细测试。这个模型已经被证明可以解释非人类灵长类动物在依赖奖励的学习和决策任务中神经活动的快速变化。这个计算模型的一个核心思想是，腹侧被盖区和黑质中的一大部分多巴胺神经元的活动变化代表着对未来奖励事件预测的错误。这个模型的细节做出了许多从未在人类受试者身上测试过的重要预测。这项工作将确定那些涉及人类奖赏条件反射的神经通路，并将用于检验和扩展多巴胺能功能的预测误差模型。此外，通过研究人类奖赏期望的基本神经生物学过程，这一建议对理解学习和其他认知过程的各个方面具有广泛的意义。

DESCRIPTION (provided by applicant): Drugs of abuse operate in part by subverting the adaptive mechanisms (algorithms) that are normally used to choose actions and make decisions. Otherwise, why would a normal human give up food, sex, shelter, and general well being to put white powder in their nose or heroin in their veins? Clearly an, addict's decision making algorithms are malfunctioning, and in this sense, drug taking represents a set of diseased algorithms for reward-learning and reward-dependent decision-making. It is well known that many if not most drugs of abuse affect midbrain dopamine systems either at the level of dopamine receptors or directly on the neurons that deliver dopamine to target structures. It is therefore important to understand the algorithms that these systems carry out, the way that the algorithms normally function, and the way they are linked to underlying cellular and molecular events. The broad objective of this proposal is to establish one such link by providing computational understanding of the kinds of information constructed and broadcast by midbrain dopamine systems and the influence of these signals on downstream neural targets. Using functional magnetic resonance imaging (fMRI) in human subjects, this work will carry out detailed tests of a computational model of midbrain dopaminergic systems: the prediction error model. This model has been shown to account for rapid changes in neural activity in non-human primates during reward-dependent learning and decision-making tasks. One central idea of this computational model is that activity changes in a large subset of dopamine neurons of the ventral tegmental area and substantia nigra represent errors in the predictions of future rewarding events. The details of this model make numerous important predictions that have never been tested in human subjects. This work will identify those neural pathways involved in reward conditioning in humans and will serve to test and extend the prediction error model of dopaminergic function. Moreover; by studying a fundamental neurobiological process of reward expectancy in humans, this proposal has broad implications for understanding aspects of learning and other cognitive processes.