How is Performance Evaluation Encoded in the Brain?

大脑中的绩效评估是如何编码的？

• 批准号: 10248575
• 负责人: Vikram Gadagkar
• 金额: $ 24.9万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10248575
• 关键词: Animals; Auditory; Basal Ganglia; Behavior; Behavioral; Benchmarking; Birds; Brain; Calcium; Chronic; Communities; Complex; Computational Technique; Corpus striatum structure; Courtship; Cues; Custom; Data; Disease; Dopamine; Dystonia; Electrophysiology (science); Endoscopy; Evaluation; Exhibits; Feedback; Female; Fiber; Food; Goals; Huntington Disease; Image; Juice; Learning; Mammals; Mediating; Melissa; Memory; Mentors; Modeling; Monitor; Motor; Motor Skills; Motor output; Mus; Neurons; Neurosciences; Optical Methods; Optics; Outcome; Parkinson Disease; Pathway interactions; Performance; Phase; Photometry; Play; Process; Research; Resolution; Rewards; Role; Self-Examination; Signal Transduction; Social Environment; Social Interaction; Songbirds; Sports; Stereotyping; Symptoms; System; Techniques; Testing; Time; Update; Virus; auditory feedback; bird song; dopaminergic neuron; innovation; instrument; learned behavior; motor behavior; motor control; motor learning; multidisciplinary; nervous system disorder; neuromechanism; novel; optogenetics; outcome prediction; programs; sensory input; sequence learning; skills; social; tutoring; virtual

Dopamine (DA)-basal ganglia (BG) circuits are critical for motor control and learning. Our current understanding of these circuits comes largely from studies of animals learning for external rewards such as food or juice. Yet symptoms of diseases such as Parkinson’s, Huntington’s and dystonia include degradation of motor behaviors unrelated to reward seeking. In fact most of our behaviors, such as learning a sport or an instrument, are not simple actions in pursuit of rewards but are instead complex motor sequences learned by matching performance to internal goals. Mechanisms of this type of motor learning are poorly understood. The songbird model offers a unique opportunity to study how internally guided motor sequences are constructed. First, birdsong is learned by matching a complex vocal sequence to the memory of a tutor song, or ‘template’. Second, song learning requires a DA-BG circuit that is part of a tractable ‘song system.’ We will leverage these advantages to decipher how motor sequences are learned during ‘natural’ trial and error. To test if DA encodes error during internally-guided performance evaluation, I will record BG-projecting VTA neurons as I induce auditory error in specific song syllables using distorted auditory feedback (Aim 1, K99 phase). Preliminary results suggest DA encodes performance error, the difference between actual and predicted performance. DA activity was phasically suppressed after distorted syllables, consistent with a worse-than- predicted outcome, and was phasically activated at the precise moment of the song when a predicted distortion did not occur, consistent with a better-than-predicted outcome. Next I will resolve the paradox (Aim 2, K99 phase) of how DA activity both evaluates past behavior for learning and also modulates ongoing motor variability by recoding BG-projecting VTA neurons as birds transition from singing alone (variable ‘practice mode’) to singing to a female (stereotyped ‘performance mode’). Finally I will develop optical techniques (Aim 3.1) to chronically monitor VTA neurons over learning-relevant timescales to determine the origins and consequences of DA performance error (Aims 3.2 and 3.3, R00 phase). My mentor, Dr. Jesse Goldberg, co-mentor Dr. Joseph Fetcho, and collaborators Drs. Melissa Warden, Chris Schaffer, and Nozomi Nishimura all have extensive expertise in calcium imaging and optogenetics. Developing these techniques, along with frequent data presentations, attendance of seminars and professional courses, and close interactions with the strong collaborative Cornell neuroscience community, will equip me with the necessary skills for transitioning to independence. In the independent R00 phase, I will use these acquired skills and innovative behavioral, optical, and computational techniques to complete the proposed aims (Aims 3.2 and 3.3) and establish an independent research program focused on the neural mechanisms of natural motor sequence learning.

多巴胺（DA）-基底神经节（BG）回路对于运动控制和学习至关重要，目前我们对这些回路的理解主要来自对动物学习食物或果汁等疾病症状的研究。事实上，我们的大多数行为，例如学习一项运动或一种乐器，并不是追求奖励的简单行为，而是通过将表现与内部目标相匹配来学习的复杂运动序列。人们对这种类型的运动学习机制知之甚少，鸣禽模型提供了一个独特的机会来研究内部引导的运动序列是如何构建的。首先，鸟鸣是通过将复杂的声音序列与导师歌曲的记忆相匹配来学习的。其次，歌曲学习需要 DA-BG 电路，它是易于处理的“歌曲系统”的一部分。我们将利用这些优势来破译在“自然”试错过程中如何学习运动序列，以测试 DA 编码过程中是否存在错误。在内部引导的性能评估中，当我使用扭曲的听觉反馈（目标 1，K99 阶段）在特定歌曲音节中诱发听觉错误时，我将记录 BG 投射的 VTA 神经元。初步结果表明 DA 编码了性能误差，即实际性能与预测性能之间的差异。 DA 活动在音节扭曲后被阶段性抑制，这与比预测更糟糕的结果一致，并且在歌曲的精确时刻被阶段性激活。扭曲没有发生，与比预测更好的结果一致 接下来，我将解决 DA 活动如何评估过去的学习行为并通过记录 BG 投影 VTA 来调节持续的运动变异性的悖论（目标 2，K99 阶段）。最后，我将开发光学技术（目标 3.1）来长期监测 VTA 神经元。与学习相关的时间尺度来确定 DA 表现错误的根源和后果（目标 3.2 和 3.3，R00 阶段）。和 Nozomi Nishimura 都在钙成像和光遗传学开发方面拥有丰富的专业知识，并且经常进行数据演示、参加研讨会和专业课程以及密切互动。与康奈尔神经科学界的强大合作，将为我提供过渡到独立所需的技能。在独立 R00 阶段，我将使用这些已获得的技能和创新的行为、光学和计算技术来完成目标（目标 3.2 和）。 3.3）并建立一个独立的研究计划，重点关注自然运动序列学习的神经机制。