QUANTITATIVE PHYSIOLOGY & FUNCTIONAL MRI

这个子项目是利用由NIH/NCRR资助的中心拨款提供的资源的许多研究子项目之一。子项目和调查员(PI)可能从另一个NIH来源获得了主要资金，因此可能会出现在其他CRISE条目中。列出的机构是针对中心的，而不一定是针对调查员的机构。功能磁共振成像的独立成分分析(ICA)方法。ICA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“激活”可能不是简单地从范式时间过程中预测的。我们开发了有史以来第一种ICA方法，用于研究来自群体的数据，而不是单一的受试者。这项研究发表在《人脑图谱》上。我们应用了这组ICA方法来分析关于模拟汽车驾驶的fMRI数据报告，作为丰富的自然主义行为的一个例子。虽然常规的fMRI分析需要一个详细的大脑激活先前模型，但我们能够使用组ICA来研究一组参与模拟驾驶范例的受试者的大脑活动(见《人脑图谱》。我们开发了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。虽然MRI图像本质上是复杂的(即每个体素都有很大的幅度和相位)，而且尽管强烈的对比度会引起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和“丢弃”相位信息开始的。对复杂的MRI图像进行ICA分析似乎可以提高ICA(医学中的磁共振)的灵敏度。用于功能磁共振成像分析的ICA方法对于揭示简单模型无法预先预测的大脑活动以及允许将功能磁共振成像方法扩展到丰富的自然行为是有价值的。

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.