MODEL FREE ANALYSIS OF FUNCTIONAL IMAGING OF ACUPUNCTURE

针灸功能成像的无模型分析

• 批准号: 7723355
• 负责人: TODD B PARRISH
• 金额: $ 0.05万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7723355
• 关键词: Acupuncture procedure; Attention; Brain; Computer Retrieval of Information on Scientific Projects Database; Fingers; Functional Imaging; Functional Magnetic Resonance Imaging; Funding; Grant; Institution; Language; Measures; Memory; Modeling; National Center for Complementary and Alternative Medicine; Needles; Numbers; Pain; Population; Principal Component Analysis; Research; Research Personnel; Resources; Sensory; Source; Standards of Weights and Measures; System; Time; United States National Institutes of Health; Visual system structure; base; cognitive function; computing resources; independent component analysis; memory process; research study; response; visual motor

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. The use of functional MRI has been applied to a number of brain systems such as language, sensory (motor, visual), pain, and higher level cognitive functions such as memory or spatial attention. Typically, there is a known model of the experiment (i.e. the subject moves their fingers at the appropriate time); however, in the case of acupuncture this may not hold true. There is known model of the presence of the needling but the brains response may differ. This response may be variable from subject to subject. We are proposing to use a combination of principal components analysis followed by independent component analysis to identify the primary brain responses to acupuncture of the visual system. These experiments have a large number of voxels (64x64x32) for a large number of time points (500) making this analysis difficult on standard computational hardware. Furthermore, we plan to investigate how the independent components interact across a population of subjects to identify the population-based time course of acupuncture. We seek the use of the super-computing resources (storage, processing and memory) to conduct our large scale analysis of within and across subject responses to acupuncture as measured by functional MRI collected using NIH funds from the National Center for Complementary and Alternative Medicine.

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。 功能性MRI的使用已被应用于许多脑系统，如语言，感觉（运动，视觉），疼痛和更高层次的认知功能，如记忆或空间注意力。通常，有一个已知的实验模型（即受试者在适当的时间移动手指）;然而，在针灸的情况下，这可能不成立。针刺存在的已知模型，但大脑的反应可能不同。这种反应可能因受试者而异。我们建议使用主成分分析的组合，其次是独立成分分析，以确定主要的大脑反应，针灸的视觉系统。这些实验对于大量时间点（500）具有大量体素（64 × 64 × 32），使得在标准计算硬件上难以进行该分析。此外，我们计划调查如何在一个受试者的人口相互作用的独立组件，以确定基于人口的针灸的时间过程。我们寻求使用超级计算资源（存储，处理和内存）进行我们的大规模分析内和跨主题的反应，针灸测量功能磁共振成像收集使用国家补充和替代医学中心的资金。