McKeown M J, Jung T P, Makeig S, Brown G, Kindermann S S, Lee T W, Sejnowski T J
Howard Hughes Medical Institute, Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92186-5800, USA.
Proc Natl Acad Sci U S A. 1998 Feb 3;95(3):803-10. doi: 10.1073/pnas.95.3.803.
A method is given for determining the time course and spatial extent of consistently and transiently task-related activations from other physiological and artifactual components that contribute to functional MRI (fMRI) recordings. Independent component analysis (ICA) was used to analyze two fMRI data sets from a subject performing 6-min trials composed of alternating 40-sec Stroop color-naming and control task blocks. Each component consisted of a fixed three-dimensional spatial distribution of brain voxel values (a "map") and an associated time course of activation. For each trial, the algorithm detected, without a priori knowledge of their spatial or temporal structure, one consistently task-related component activated during each Stroop task block, plus several transiently task-related components activated at the onset of one or two of the Stroop task blocks only. Activation patterns occurring during only part of the fMRI trial are not observed with other techniques, because their time courses cannot easily be known in advance. Other ICA components were related to physiological pulsations, head movements, or machine noise. By using higher-order statistics to specify stricter criteria for spatial independence between component maps, ICA produced improved estimates of the temporal and spatial extent of task-related activation in our data compared with principal component analysis (PCA). ICA appears to be a promising tool for exploratory analysis of fMRI data, particularly when the time courses of activation are not known in advance.
本文给出了一种方法，用于从功能磁共振成像（fMRI）记录中区分出与任务持续相关和瞬时相关的激活的时间进程和空间范围，这些激活来自其他生理成分和人为因素。独立成分分析（ICA）被用于分析来自一名受试者的两个fMRI数据集，该受试者进行了6分钟的试验，试验由40秒的斯特鲁普颜色命名任务和控制任务块交替组成。每个成分由脑体素值的固定三维空间分布（一个“图谱”）和相关的激活时间进程组成。对于每次试验，该算法在无需事先了解其空间或时间结构的情况下，检测到在每个斯特鲁普任务块期间激活的一个与任务持续相关的成分，以及仅在一两个斯特鲁普任务块开始时激活的几个与任务瞬时相关的成分。其他技术无法观察到仅在fMRI试验部分时间出现的激活模式，因为其时间进程难以预先得知。其他ICA成分与生理脉动、头部运动或机器噪声有关。通过使用高阶统计量来为成分图谱之间的空间独立性指定更严格的标准，与主成分分析（PCA）相比，ICA对我们数据中与任务相关的激活的时间和空间范围产生了更好的估计。ICA似乎是一种用于fMRI数据探索性分析的有前途的工具，特别是当激活的时间进程事先未知时。
