Neural Substrates of Reinforcement Learning and its Training in Major Depression

 描述(申请人提供)：严重抑郁障碍是世界上导致死亡和残疾的最重要原因之一。来自我们小组和其他人的最新数据强调，奖赏和损失学习的障碍是抑郁症的核心，有不同的神经基础，并随着成功的治疗而改善。总而言之，这些发现表明，迫切需要描述神经行为奖赏和丧失学习障碍与抑郁之间的关系。同样重要的是，这些见解提出了新的干预目标，这样操纵奖赏和损失学习的神经行为底物可能会促进抑郁症症状的改变。为了解决这些问题，我们使用功能神经成像和定量强化学习框架来i)系统地表征导致抑郁症中奖赏和损失学习障碍的神经和行为底物(目标1)，以及ii)评估这些损害对从不同角度直接针对奖赏和损失学习的两种训练方法的反应程度(目标2和3)。在目标2和目标3中，我们利用了来自我们团队和其他人的现有数据，这些数据分别表明，外显和隐性任务修改分别导致控制组中适应性神经和行为学习的变化。在这里，我们将这项工作扩展到抑郁症患者，并测试了广泛的假设，即i)抑郁症可能以奖赏和损失学习中明显的神经行为障碍为特征，以及ii)这些缺陷可以通过有针对性的行为训练来正常化。基于计算模型的强化学习分析的最新进展提供了一个稳健的神经机制框架，在该框架内描述了所建议的奖赏和损失学习障碍的性质和轨迹及其改善。

 DESCRIPTION (provided by applicant): Major depressive disorder ranks among the most significant causes of mortality and disability in the world. Recent data from our group and others highlight that impairments in reward and loss learning are central to depression, have distinct neural substrates, and improve with successful treatment. Together, these findings suggest an urgent need to delineate the relationships among neurobehavioral reward and loss learning impairments and depression. Equally important, these insights suggest novel targets for intervention such that manipulating the neurobehavioral substrates of reward and loss learning may facilitate symptom change in depression. To address these issues, we use functional neuroimaging and a quantitative reinforcement learning framework to i) systematically characterize the neural and behavioral substrates that attend reward- and loss- learning impairments in depression (Aim 1), and ii) assess the degree to which these impairments respond to two methods of training that directly target reward and loss learning from different angles (Aims 2 and 3). In Aims 2 and 3, we capitalize on extant data from our group and others showing that explicit and covert task modifications, respectively, lead to adaptive neural and behavioral learning changes in controls. Here we extend this work to individuals with depression and test the broad hypotheses that i) that depression may be characterized by distinct neurobehavioral impairments in reward- and loss- learning, and ii) these deficits may be normalized through targeted behavioral training. Recent advances in computational model-based analyses of reinforcement learning provide a robust neuromechanistic framework within which to delineate the nature and trajectory of the suggested reward- and loss- learning impairments and their amelioration.