QUANTITATIVE PHYSIOLOGY & FUNCTIONAL MRI

定量生理学

• 批准号: 7420409
• 负责人: JAMES J. PEKAR
• 金额: $ 16.18万
• 依托单位: HUGO W. MOSER RES INST KENNEDY KRIEGER
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2007-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7420409
• 关键词: behavior; brain; brain mapping; functional magnetic resonance imaging; human; magnetism; medicine; model; motivation; physiology

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. Independent Component Analysis (ICA) Methods for fMRI. ICA is a data-driven technique useful for analysis of fMRI data sets containing "activations" which might not be simply predictable from the paradigm timecourse. We developed the first ever ICA method useful for studying data from groups of people, as opposed to single subjects. This was published in "Human Brain Mapping". We have applied this group ICA approach to allow analysis of fMRI data reporting on simulated automobile driving, as an example of a rich naturalistic behavior. While conventional fMRI analysis requires a detailed prior model of brain activation, we were able to use group ICA to study brain activity in a group of subjects participating in a simulated driving paradigm (in press "Human Brain Mapping". We have developed ICA approaches for analysis of complex fMRI data, as opposed to the magnitude images which most approaches use. Although MRI images are intrinsically complex (i.e., each voxel has bot h amplitude and phase), and although BOLD contrast can induce dynamic change in both amplitude and phase, most fMRI analysis methods begin by computing the magnitude and "throwing away" the phase information. ICA analysis of the complex MRI images appears to increase the sensitivity of ICA (in press, Magnetic Resonance in Medicine). ICA approaches for fMRI analysis are valuable for revealing brain activity that would not be predicted in advance by a simple model, and for allowing the extension of fMRI methods to rich naturalistic behaviors.

该子项目是利用NIH/NCRR资助的中心赠款提供的资源的许多研究子项目之一。子项目和研究者（PI）可能从另一个NIH来源获得主要资金，因此可以在其他CRISP条目中表示。所列机构为中心，不一定是研究者所在机构。功能磁共振成像的独立成分分析（伊卡）方法伊卡是一种数据驱动的技术，可用于分析包含“激活”的fMRI数据集，这些数据集可能无法简单地从范式时间过程中预测。我们开发了有史以来第一个伊卡方法，用于研究来自人群的数据，而不是单个受试者。这篇文章发表在《Human Brain Mapping》。我们已经应用了这个组伊卡的方法，允许分析功能磁共振成像数据报告模拟汽车驾驶，作为一个例子，丰富的自然行为。虽然传统的功能磁共振成像分析需要一个详细的大脑激活的先验模型，我们能够使用组伊卡来研究参与模拟驾驶范例的一组受试者的大脑活动（在出版“人脑映射”。我们已经开发了伊卡的方法来分析复杂的功能磁共振成像数据，而不是大多数方法使用的幅度图像。尽管MRI图像本质上是复杂的（即，每个体素都具有幅度和相位），尽管BOLD对比度可以引起幅度和相位的动态变化，但是大多数fMRI分析方法开始是通过计算幅度并“丢弃”相位信息来开始的。复杂MRI图像的伊卡分析似乎增加了伊卡的灵敏度（出版中，医学磁共振）。伊卡方法的功能磁共振成像分析是有价值的，揭示大脑活动，不会提前预测一个简单的模型，并允许扩展功能磁共振成像方法，以丰富的自然行为。