IMAGING MECHANISMS OF ACTION IN MOTOR LEARNING

运动学习中作用机制的成像

• 批准号: 7627564
• 负责人: JINHU XIONG
• 金额: $ 1.95万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCIENCE CENTER
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-04-01 至 2008-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7627564
• 关键词: Computer Retrieval of Information on Scientific Projects Database; Data; Equation; Functional Magnetic Resonance Imaging; Funding; Grant; Image; Institution; Learning; Maps; Measurement; Measures; Modeling; Motor; Positron-Emission Tomography; Research; Research Personnel; Resources; Rest; Source; Synapses; System; Task Performances; Training; Transcranial magnetic stimulation; United States National Institutes of Health; Weight; Weights and Measures; motor learning; neuroimaging; novel strategies; relating to nervous system

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. OBJECTIVE: The focus of this proposal is to explore the mechanisms of action underlying motor learning. Current neuroimaging studies investigate mechanisms of action of 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. 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. RESEARCH PLAN AND METHODS: Imaging studies will be performed to map task-performance strategy (measured as regional neural activity of task performance), information flow (measured as effective inter-regional connectivity during task performance), synaptic weighting (measured as task-independent effective inter-regional connectivity during the resting state), and changes in these measures before and after motor training. Task-performance strategy will be determined using functional magnetic resonance imaging (fMRI). Task-independent connectivity will be measured with concurrent transcranial magnetic stimulation/positron emission tomography (TMS/PET) and with resting-state fMRI. Structural Equation Modeling, using anatomical constraints individually defined by the TMS/PET and resting-state fMRI measurements, will be performed to investigate the effective inter-regional connectivity during task performance. Synaptic weighting measurements will be performed on the resting-state fMRI data using SEM.

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 目的：本研究的重点是探索运动学习的作用机制。 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作用机制。 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。 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。 研究方法：将进行成像研究，以绘制任务执行策略（测量为任务执行的区域神经活动）、信息流（测量为任务执行期间的有效区域间连接）、突触加权（测量为静息状态期间的任务无关有效区域间连接）以及运动训练前后这些测量的变化。 将使用功能性磁共振成像（fMRI）确定任务绩效策略。 任务无关的连接将测量并行经颅磁刺激/正电子发射断层扫描（TMS/PET）和静息态功能磁共振成像。 结构方程模型，使用TMS/PET和静息状态功能磁共振成像测量单独定义的解剖约束，将进行调查的有效区域间的连接在任务执行。 将使用SEM对静息状态fMRI数据进行突触加权测量。