ANALYSIS OF LARGE-SCALE BRAIN NETWORK IN HUMANS

人类大规模大脑网络分析

• 批准号: 7956211
• 负责人: PATRICK C M WONG
• 金额: $ 0.08万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7956211
• 关键词: Biomedical Research; Brain; Brain region; Computer Retrieval of Information on Scientific Projects Database; Data; Equation; Functional Magnetic Resonance Imaging; Funding; Grant; Graph; High Performance Computing; Human; Institution; Linear Models; Methods; Modeling; Nature; Pilot Projects; Research; Research Personnel; Resources; Source; Time; United States National Institutes of Health; computing resources; falls; neuroimaging; research study; theories

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. Traditional methods for the analysis of human functional neuroimaging data (e.g., fMRI) focus on isolated brain regions (e.g., by using General Linear Model) or a small set of brain regions (e.g., by using Structural Equation Modeling). While informative, these methods fall short of informing us about the interactive nature of regions across the entire brain. In this pilot study, we aim at examining large-scale human brain networks by using methods that are conducive to such analysis (e.g., methods that are used in conjunction with graph theory). Typically, 30,000 regions (in the form of voxels) are collected from one subject at one time point of the experiment (a typical experiment consists of hundreds of time points). The high computational demands make the analysis extremely difficult in our current facility. We seek to use the super-computing resources provided by the CyberInfrastructure Partnership to perform such large-scale analyses on our data collected using NIH funds.

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目和 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 对于中心来说，它不一定是研究者的机构。 分析人类功能神经影像数据的传统方法（例如，功能磁共振成像）侧重于孤立的大脑区域（例如，通过使用通用线性模型）或一小组大脑区域（例如，通过使用结构方程模型）。虽然这些方法提供了丰富的信息，但不足以让我们了解整个大脑区域的交互性质。在这项试点研究中，我们的目标是通过使用有利于此类分析的方法（例如与图论结合使用的方法）来检查大规模人脑网络。通常，在实验的一个时间点从一名受试者收集 30,000 个区域（以体素的形式）（典型的实验由数百个时间点组成）。高计算要求使得我们当前的设施中的分析变得极其困难。我们寻求利用网络基础设施合作伙伴提供的超级计算资源，对使用 NIH 资金收集的数据进行大规模分析。