Revealing representational content with pattern-information fMRIan introductory guide
Revealing representational content with pattern-information fMRIan introductory guide
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DOI：
10.1093/scan/nsn044
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发表时间：
2009-03-01
影响因子：
4.2
通讯作者：
Kriegeskorte, Nikolaus
中科院分区：
文献类型：
--
作者：
Mur, Marieke;Bandettini, Peter A.;Kriegeskorte, Nikolaus
传统的功能性磁共振成像（fMRI）数据的统计分析方法在检测特定心理活动期间作为整体被激活的大脑区域方面非常成功。一个区域的整体激活通常被用来指示该区域参与任务。然而，这种激活分析没有考虑大脑区域内的多体素活动模式。这些被认为反映神经元群体编码的活动模式可以通过模式信息分析来研究。在这个框架中，一个区域的多元模式信息被用来表示代表性的内容。本教程介绍了模式信息分析的动机，解释了其基本假设，以直观的方式介绍了最常用的方法，并概述了分析步骤的基本顺序。
Conventional statistical analysis methods for functional magnetic resonance imaging (fMRI) data are very successful at detecting brain regions that are activated as a whole during specific mental activities. The overall activation of a region is usually taken to indicate involvement of the region in the task. However, such activation analysis does not consider the multivoxel patterns of activity within a brain region. These patterns of activity, which are thought to reflect neuronal population codes, can be investigated by pattern-information analysis. In this framework, a regions multivariate pattern information is taken to indicate representational content. This tutorial introduction motivates pattern-information analysis, explains its underlying assumptions, introduces the most widespread methods in an intuitive way, and outlines the basic sequence of analysis steps.