Imaging Mechanisms of Action in Motor Learning

运动学习中行动的成像机制

• 批准号: 0225711
• 负责人: Jinhu Xiong
• 金额: $ 73.93万
• 依托单位: University of Texas Health Science Center San Antonio
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-01 至 2005-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0225711&HistoricalAwards=false
• 关键词: Imaging; Mechanisms; Action; Motor; Learning

With National Science Foundation support, Dr. Xiong will develop imaging and modeling strategies to study mechanisms underlying adaptive changes of the human brain. The focus of this proposal is to explore the mechanisms underlying motor learning. Learning-induced neural plasticity and functional reorganization are well-established and well-documented, but not well-understood. Current neuroimaging studies investigate neural mechanisms underlying learning by exploring the changes in regional neural activity and inter-regional activity of task-performance. Little effort has been given to studying the more fundamental changes of neural connections and synaptic weighting. On the technical front, human functional imaging research sorely needs more rigorous approaches, as can be provided by mathematical modeling. A modeling framework - Structural Equation Modeling - is now accepted as appropriate for human imaging data. Structural equation modeling however, is currently performed with anatomical constraints based on neuroanatomical studies in non-human species. The performance of structural equation modeling might be greatly enhanced if anatomical constraints are individually optimized using the same subject's task-independent anatomical connectivity data. To date, this strategy has not been reported by any laboratory. The present proposal seeks to develop system-level modeling strategies for neuroimaging and to apply these novel strategies to mechanisms of action of motor learning. The overall goal of this proposal will be accomplished through the following four goals. First, developing and optimizing imaging strategies for detecting anatomical connectivity for each individual subject. Second, developing a structural equation modeling strategy by incorporating individual anatomical constraints to enhance those models' performance. Third, investigating changes in regional neural activity and inter-regional activity of task-performance induced by motor learning using the enhanced modeling strategy. Fourth, investigating synaptic plasticity by applying the enhanced modeling and demonstrating that synaptic plasticity is an underlying mechanism of action of motor learning. When completed, this research project will increase the understanding of mechanisms of adaptive learning and has the potential of defining a new strategy by which functional imaging can be applied to study mechanisms of action and disease pathophysiology.

在国家科学基金会的支持下，熊博士将开发成像和建模策略，以研究人类大脑适应性变化的机制。本研究的重点是探讨运动学习的机制。学习诱导的神经可塑性和功能重组是公认的和有据可查的，但没有得到很好的理解。目前的神经影像学研究通过探索任务执行的区域神经活动和区域间活动的变化来研究学习的神经机制。 很少有人致力于研究神经连接和突触权重的更根本的变化。在技术方面，人体功能成像研究迫切需要更严格的方法，可以通过数学建模提供。 一个建模框架-结构方程建模-现在被接受为适合人类成像数据。 然而，结构方程建模，目前进行的解剖学约束的基础上，在非人类物种的神经解剖学研究。 结构方程建模的性能可能会大大提高，如果解剖约束单独优化使用相同的主题的任务无关的解剖连接数据。 到目前为止，还没有任何实验室报告过这种策略。本提案旨在开发系统级的神经影像学建模策略，并将这些新的策略应用于运动学习的作用机制。本提案的总体目标将通过以下四个目标来实现。首先，开发和优化成像策略，用于检测每个个体受试者的解剖连接。 第二，通过结合个体解剖学约束来开发结构方程建模策略，以提高这些模型的性能。 第三，采用增强的模型策略，研究了运动技能学习引起的区域神经活动和区域间活动的变化。 第四，应用增强的模型研究突触可塑性，证明突触可塑性是运动学习的一种潜在作用机制。 完成后，该研究项目将增加对适应性学习机制的理解，并有可能定义一种新的策略，通过这种策略，功能成像可以应用于研究作用机制和疾病病理生理学。