Unraveling constraints on motor cortical activity exploration and shaping during structural skill learning using large-scale 2-photon imaging and holographic optogenetic stimulation

使用大规模 2 光子成像和全息光遗传学刺激，揭示结构技能学习过程中运动皮层活动探索和塑造的限制

• 批准号: 9788757
• 负责人: Vivek Athalye
• 金额: $ 6.62万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-16 至 2021-09-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9788757
• 关键词: Algorithms; Auditory; BRAIN initiative; Base of the Brain; Behavior; Behavioral; Brain; Calcium; Chronic; Communication; Data Analyses; Dimensions; Etiology; Factor Analysis; Feedback; Forelimb; Goals; Head; Image; Knowledge; Learning; Learning Skill; Link; Maps; Memory; Microscope; Motion; Motor; Motor Cortex; Movement; Mus; Muscle; Neurons; Outcome; Paralysed; Patients; Pattern; Performance; Play; Population; Role; Series; Shapes; Site; Source; Specific qualifier value; Structure; System; Testing; Training; Work; auditory feedback; base; brain machine interface; design; experience; high dimensionality; motor disorder; motor learning; multidimensional data; neural patterning; neuroprosthesis; novel; optogenetics; relating to nervous system; skills; two-photon

Project Summary When learning new skills, experience with previously-learned skills can facilitate faster learning by constraining behavioral exploration and shaping, a concept known as “structural learning”. The motor cortex plays an essential role in learning new skills, and its initially variable activity is shaped and consolidated over learning. However, how previous experience modulates exploration and shaping of cortical network activity to facilitate new skill learning is not well understood. When the brain learns to control a brain-machine interface (BMI), cortical network activity exploration and shaping is broad (high-dimensional) in BMI-naïve subjects and constrained (low-dimensional) in BMI-experienced subjects, suggesting the following hypothesis. Hypothesis: Previous experience facilitates faster learning of new, related skills by constraining how motor cortical network activity is explored and shaped, effectively reducing the number of neural parameters to learn. The hypothesis’ prediction is that during faster learning of related skills, neural dimensionality will be decreased and aligned with previously learned neural patterns. This project tests the prediction by leveraging novel closed-loop paradigms, chronic large-scale 2-photon calcium imaging, high-dimensional data analysis, and holographic optogenetic stimulation to study and manipulate the neural basis of structural skill learning. First, the correspondence between structural learning of muscle patterns and cortical network activity exploration and shaping will be studied using large-scale 2-photon calcium imaging. Second, to causally link neural variance to learning neural patterns, a high-performance, calcium imaging-based BMI will be developed, and the relationship between structural neuroprosthetic learning and neural exploration and shaping will be analyzed. Finally, the structure of cortical network activity will be artificially shaped using holographic optogenetic stimulation and tested on neuroprosthetic skill learning. The long-term objective of this proposal integrates several core goals of the BRAIN initiative. The proposal will produce a dynamic picture of the learning brain and demonstrate causality using BMIs and holographic optogenetic stimulation. This work’s outcome will contribute conceptual principles underlying skill learning and memory and guide the design of BMI systems to restore movement and assist learning. Aim 1: Investigate the relationship between structural motor learning and cortical network activity exploration and shaping using a novel motor task and large-scale 2-photon calcium imaging. Aim 2: Investigate the relationship between structural neuroprosthetic learning and cortical network activity exploration and shaping using a high-performance, calcium imaging-based BMI. Aim 3: Artificially shape structure of cortical network activity using closed-loop holographic optogenetic stimulation and test effect on neuroprosthetic learning.

项目摘要 当学习新技能时，以前学过的技能的经验可以通过限制 行为探索和塑造，一个被称为“结构学习”的概念。运动皮层扮演着 在学习新技能中的重要作用，其最初的可变活动是在 学习然而，先前的经验如何调节皮层网络活动的探索和塑造， 促进新技能的学习还没有得到很好的理解。当大脑学会控制脑机接口时 (BMI)，皮质网络活动探索和塑造在BMI初治受试者中是广泛的（高维） 和约束（低维）的BMI经验的受试者，提出了以下假设。 假设：以前的经验通过限制运动的方式，促进了新的相关技能的更快学习。 探索和塑造皮层网络活动，有效地减少了要学习的神经参数的数量。 该假说的预测是，在快速学习相关技能的过程中，神经维度会降低 并与先前学习的神经模式相匹配。该项目通过利用新的 闭环范例，慢性大规模双光子钙成像，高维数据分析， 和全息光遗传学刺激来研究和操纵结构技能的神经基础 学习第一，肌肉模式的结构学习和皮层网络活动之间的对应关系 将使用大规模双光子钙成像来研究探测和成形。第二，因果联系 神经变异学习神经模式，将开发一种高性能的、基于钙成像的BMI， 结构神经假体学习与神经探索和塑造之间的关系将是 分析了最后，皮质网络活动的结构将使用全息技术人工塑造。 光遗传学刺激并在神经假体技能学习上进行测试。本提案的长期目标 整合了BRAIN倡议的几个核心目标。该提案将提供一个动态的图片， 学习大脑和证明因果关系使用BMI和全息光遗传学刺激。这件作品 结果将有助于技能学习和记忆的概念原则，并指导BMI的设计 恢复运动和帮助学习的系统。 目的1：探讨结构运动学习与皮层网络活动探索的关系 以及使用新的运动任务和大规模双光子钙成像进行塑形。 目的2：探讨结构神经修复学习与皮层网络活动的关系 探索和塑造使用高性能，钙成像为基础的BMI。 目的3：利用闭环全息光遗传学方法初步构建大脑皮层网络活动的结构 刺激和测试对神经假体学习影响。