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.

项目概要 学习新技能时，以前学过的技能的经验可以通过限制来促进更快的学习 行为探索和塑造，一个被称为“结构学习”的概念。 在学习新技能中发挥着至关重要的作用，其最初的可变活动是在不同时期形成和巩固的 然而，先前的经验如何调节皮质网络活动的探索和塑造。 当大脑学习控制脑机接口时，促进新技能学习的方式还没有被很好地理解。 (BMI)，皮质网络活动探索和塑造在 BMI 幼稚受试者中是广泛的（高维度） 并且在有 BMI 经历的受试者中受到限制（低维），提出以下假设。 假设：以前的经验通过限制运动方式来促进更快地学习新的相关技能 探索和塑造皮质网络活动，有效减少需要学习的神经参数数量。 该假设的预测是，在更快地学习相关技能的过程中，神经维度将会降低 并与之前学习的神经模式保持一致，该项目通过利用新颖的方法来测试预测。 闭环范式、慢性大规模2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 首先，肌肉模式的结构学习与皮质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索和塑造第二，因果关系。 学习神经模式的神经差异，将开发基于钙成像的高性能 BMI， 结构神经假体学习与神经探索和塑造之间的关系将是 最后，将利用全息技术人工塑造皮质网络活动的结构。 光遗传学刺激和神经修复技能学习测试是该提案的长期目标。 该提案整合了 BRAIN 计划的几个核心目标，将产生一个动态的图景。 这项工作的目的是利用 BMI 和全息光遗传学刺激来学习大脑并证明因果关系。 结果将贡献技能学习和记忆的概念原则，并指导 BMI 的设计 恢复运动和协助学习的系统。 目标 1：研究结构运动学习与皮质网络活动探索之间的关系 使用新颖的运动任务和大规模 2 光子钙成像进行整形。 目标 2：研究结构神经假体学习与皮质网络活动之间的关系 使用基于钙成像的高性能 BMI 进行探索和塑造。 目标 3：利用闭环全息光遗传学人工塑造皮质网络活动的结构 对神经假体学习的刺激和测试效果。