Neuroscience of Reward-Related Learning and Memory in Depression

严重抑郁障碍(MDD)是一个代价高昂的公共卫生问题，而诊断的异质性 MDD使治疗复杂化。一种方法是研究MDD的内表型，即可能涉及的关键方面 离散神经回路中的功能障碍。快感缺乏(丧失快感)是一种很有前途的内表型，但 MDD这一核心症状背后的神经认知机制尚不清楚。当前应用程序将 检验这样一种假设，即刺激奖赏和行动奖赏学习的失败会导致快感缺失。 在为期两年的K99阶段，申请者将追求四个目标。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试验的控制组收集功能磁共振数据 条件反射任务。这将为R00阶段的MDD受试者研究奠定基础，同时 提供了有价值的数据，将用于测试强化学习的时间差分算法。至 学习R00阶段的其他技能，申请者还将完成为期一学期的计算 弗兰克博士开设的认知神经科学课程。其次，尼古拉斯·兰格博士将培训申请者 进行扩散张量成像分析，以探索脑的结构完整性和连通性 涉及记忆和奖赏处理的区域，可能会在MDD中退化。第三，申请人 将在诊断面谈方面进行重点培训，这将是非常宝贵的，当他过渡到 独立，并开始领导一个专注于以患者为中心的研究的实验室。第四，有了来自 Pizzagalli博士和Frank博士，申请者将发展有效的工作谈话并在 建立一个专注于MDD奖赏相关学习和记忆的实验室。 在独立阶段，三项关于奖励的功能磁共振成像(FMRI)研究- 将对对照组和MDD受试者进行相关学习和记忆。第一项研究将集中在 腹侧纹状体，并将使用巴甫洛夫条件反射来检验MDD对线索-奖赏偶合的影响 学习。第二项研究将集中在背侧纹状体，并将使用仪器条件反射来检查 MDD中的行动奖赏学习。第三项研究将涉及对刺激-奖励关联的明确编码， 然后在两个时间点进行延迟回忆，以研究MDD如何影响海马-纹状体的相互作用 在对奖励信息进行编码和合并期间。最后，来自这些研究的MRI数据将是 共同确定快感缺失是否反映了纹状体之间功能或结构上的弱联系， 海马区和前额叶皮质区域先前与奖赏处理的不同方面有关。 建议将严谨的范例、计算模型和尖端连接相结合 分析有可能极大地促进对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.