Neuroscience of Reward-Related Learning and Memory in Depression

抑郁症中奖励相关学习和记忆的神经科学

• 批准号: 8850636
• 负责人: DANIEL G DILLON
• 金额: $ 24.83万
• 依托单位: MCLEAN HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-04-01 至 2017-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8850636
• 关键词: Accounting; Affect; Algorithms; Anhedonia; Behavior; Brain region; Collection; Computer Simulation; Controlled Study; Corpus striatum structure; Cues; Data; Delayed Memory; Depressed mood; Diagnostic; Diffusion Magnetic Resonance Imaging; Dopamine; Dorsal; Faculty; Failure; Foundations; Functional Magnetic Resonance Imaging; Functional disorder; Funding; Goals; Heterogeneity; Hippocampus (Brain); Hour; Human; Image Analysis; Interview; Laboratories; Learning; Literature; Magnetic Resonance Imaging; Major Depressive Disorder; Mediating; Memory; Memory impairment; Mental Depression; Mentors; Modeling; Molecular; Neurocognitive; Neurosciences; Occupations; Operant Conditioning; Outcome; Participant; Pathway interactions; Patient Self-Report; Phase; Prefrontal Cortex; Probability; Psychological reinforcement; Public Health; Relative (related person); Reporting; Research; Research Training; Rest; Rewards; Role; Stimulus; Symptoms; Testing; Time; Training; Ventral Striatum; Work; animal data; classical conditioning; cognitive neuroscience; depressive symptoms; dopaminergic neuron; endophenotype; experience; hedonic; infancy; memory encoding; memory process; neural circuit; patient oriented research; pleasure; positive emotional state; psychologic; response; reward processing; skills

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.

重度抑郁症（MDD）是一个昂贵的公共卫生问题，并且是诊断性异质性 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 离散神经回路功能障碍。 Anhedonia（失去愉悦）是一种有希望的内表型，但 MDD这种核心症状的基础神经认知机制尚不清楚。当前申请将 检验一个假设，即刺激奖励和动作奖励学习的失败会导致Anhedonia。 在为期两年的K99阶段，申请人将追求四个目标。首先，为了开发定量 关于MDD如何影响强化学习的假设，他将从博士那里学习计算建模。 迈克尔·弗兰克（K99）。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帕夫洛维亚的控件中收集fMRI数据 调理任务。这将为在R00阶段与MDD受试者进行研究奠定基础，同时也是 提供有价值的数据，这些数据将用于测试增强学习的时间差异算法。到 了解R00阶段的其他技能，申请人还将完成一个长期的计算 弗兰克博士提供的认知神经科学课程。其次，尼古拉斯·兰格（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 区域与记忆和奖励处理有关，并且可能会在MDD中降级。第三，申请人 将在诊断面试中进行专注的培训，当他过渡到 独立，开始指导一个专注于以患者为导向的研究的实验室。第四，输入 申请人Pizzagalli博士和Frank博士将在 为了建立专注于与奖励相关的学习和记忆的实验室。 在独立阶段，奖励的三个功能磁共振成像（fMRI）研究 将进行控制和MDD主题中的相关学习和记忆。第一个研究将重点放在 腹侧纹状体，将使用帕夫洛维亚调节来检查MDD对提示奖励的影响 学习。第二项研究将重点放在背纹状体上，并将使用工具条件检查 MDD中的动作奖励学习。第三项研究将涉及刺激 - 奖励关联的明确编码， 然后在两个时间点延迟召回，以研究MDD如何影响海马 - 纹状体相互作用 在编码和合并奖励信息期间。最后，这些研究的MRI数据将是 合并以确定Anhedonia是否反映了纹状体之间的弱功能或结构连接， 海马和前额叶皮层的区域以前涉及奖励处理的不同方面。 严格的范式，计算模型和尖端连通性的组合组合 分析有可能显着提高对MDD病理生理学的理解。