QUANTITATIVE PHYSIOLOGY & FUNCTIONAL MRI

本子项目是利用由NIH/NCRR资助的中心赠款提供的资源的众多研究子项目之一。子项目和研究者（PI）可能已经从另一个NIH来源获得了主要资金，因此可以在其他CRISP条目中表示。列出的机构是中心的，不一定是研究者的机构。功能磁共振成像的独立分量分析方法。ICA是一种数据驱动技术，可用于分析包含“激活”的fMRI数据集，这些数据集可能无法从范式时间过程中简单地预测。我们开发了有史以来第一个有用的ICA方法，用于研究来自人群的数据，而不是单个受试者。这项研究发表在《人类大脑图谱》上。我们已经应用了这种群体ICA方法来分析模拟汽车驾驶的fMRI数据报告，作为一个丰富的自然行为的例子。虽然传统的fMRI分析需要详细的大脑激活的先验模型，但我们能够使用群体ICA来研究一组参与模拟驾驶范式的受试者的大脑活动（见“人脑图谱”）。我们已经开发了ICA方法来分析复杂的fMRI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，每个体素都有幅度和相位），尽管BOLD对比可以诱导幅度和相位的动态变化，但大多数fMRI分析方法都是从计算幅度开始的，并“丢弃”相位信息。复杂MRI图像的ICA分析似乎增加了ICA的灵敏度（参见，医学磁共振）。用于fMRI分析的ICA方法对于揭示无法通过简单模型提前预测的大脑活动具有重要价值，并且允许将fMRI方法扩展到丰富的自然行为。

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.