Neuroscience of Reward-Related Learning and Memory in Depression

重度抑郁症（MDD）是一个代价高昂的公共卫生问题， MDD使治疗复杂化。一种方法是研究内表型，MDD的关键方面，可能涉及 离散神经回路的功能障碍快感缺失（丧失快乐）是一种有希望的内在表型，但 MDD核心症状的神经认知机制尚不清楚。目前的应用程序将 测试刺激奖励和行动奖励学习失败导致快感缺乏的假设。 在为期两年的K99阶段，申请人将追求四个目标。第一，为了发展量化 关于MDD如何影响强化学习的假设，他将从Dr. 迈克尔·弗兰克（K99共同导师）。在Frank博士和Diego Pizzagalli博士（K99导师）的指导下，K99 研究基金将支持从执行奖励巴甫洛夫的控制中收集功能磁共振成像数据， 条件反射任务这将为R00期MDD受试者的研究奠定基础，同时 提供将用于测试强化学习的时间差算法的有价值的数据。到 学习R00阶段的额外技能，申请人还将完成一个学期的课程， 计算 认知神经科学 弗兰克博士提供的课程第二，Nicholas Lange博士将培训申请人， 进行扩散张量成像分析，以探测大脑的结构完整性和连通性 涉及记忆和奖励处理的区域，并且可能在MDD中退化。三、申请人 我将在诊断面试方面进行重点培训，这将是非常宝贵的，当他过渡到 独立，并开始指导一个实验室专注于以病人为导向的研究。第四，根据来自 博士Pizzagalli和Frank博士，申请人将进行有效的求职谈话并进行教师求职 目的：建立一个专门研究抑郁症患者奖励相关学习记忆的实验室。 在独立阶段，三个功能性磁共振成像（fMRI）研究的奖励- 将在对照和MDD受试者中进行相关学习和记忆。第一项研究将侧重于 腹侧纹状体，并将使用巴甫洛夫条件反射来检查MDD对线索奖励应急的影响 学习第二项研究将集中在背侧纹状体，并将使用工具性条件反射来检查 MDD中的行动奖励学习。第三项研究将涉及刺激-奖励关联的外显编码， 随后在两个时间点进行延迟回忆，以研究MDD如何影响海马-纹状体相互作用 在奖励信息的编码和整合过程中。最后，这些研究的MRI数据将 汇集以确定快感缺乏是否反映纹状体之间的弱功能或结构连接， 海马体和前额叶皮层的区域，这些区域以前与奖励处理的不同方面有关。 严格的范例、计算模型和尖端连接的拟议组合 这些分析有可能显著促进对MDD病理生理学的理解。

Major Depressive Disorder (MDD) is a costly public health problem, and the diagnostic heterogeneity of MDD complicates treatment. One approach is to study endophenotypes, key facets of MDD that may involve dysfunction in discrete neural circuits. Anhedonia (loss of pleasure) is a promising endophenotype, but the neurocognitive mechanisms underlying this core symptom of MDD are unclear. The current application would test the hypothesis that failures of stimulus-reward and action-reward learning contribute to anhedonia. During the two-year K99 phase, the applicant will pursue four aims. First, in order to develop quantitative hypotheses about how MDD affects reinforcement learning, he will learn computational modeling from Dr. Michael Frank (K99 co-mentor). With guidance from Dr. Frank and Dr. Diego Pizzagalli (K99 mentor), the K99 research funds will support collection of fMRI data from controls performing a rewarded Pavlovian conditioning task. This will lay the foundation for a study with MDD subjects in the R00 phase, while also providing valuable data that will be used to test temporal difference algorithms of reinforcement learning. To learn additional skills for the R00 phase, the applicant will also complete a semester-long Computational Cognitive Neuroscience course offered by Dr. Frank. Second, Dr. Nicholas Lange will train the applicant to conduct diffusion tensor imaging analyses in order to probe the structural integrity and connectivity of brain regions implicated in memory and reward processing, and that may be degraded in MDD. Third, the applicant will pursue focused training in diagnostic interviewing, which will be invaluable when he transitions to independence and begins directing a laboratory focused on patient-oriented research. Fourth, with input from Dr. Pizzagalli and Dr. Frank, the applicant will develop an effective job talk and conduct a faculty job search in order to establish a laboratory focused on reward-related learning and memory in MDD. During the independent phase, three functional magnetic resonance imaging (fMRI) studies of reward- related learning and memory in controls and MDD subjects will be conducted. The first study will focus on the ventral striatum and will use Pavlovian conditioning to examine effects of MDD on cue-reward contingency learning. The second study will focus on the dorsal striatum and will use instrumental conditioning to examine action-reward learning in MDD. The third study will involve explicit encoding of stimulus-reward associations, followed by delayed recall at two time-points, to investigate how MDD affects hippocampal-striatal interactions during encoding and consolidation of rewarding information. Finally, the MRI data from these studies will be pooled to determine whether anhedonia reflects weak functional or structural connections among the striatum, hippocampus, and regions of prefrontal cortex previously implicated in different facets of reward processing. The proposed combination of rigorous paradigms, computational models, and cutting-edge connectivity analyses has the potential to significantly advance understanding of the pathophysiology of MDD.