利用功能磁共振成像分析有效连接性。
Analyzing effective connectivity with functional magnetic resonance imaging.
作者信息
Stephan Klaas Enno, Friston Karl J
机构信息
Laboratory for Social and Neural Systems Research, Institute for Empirical Research in Economics, University of Zurich, 8006 Zurich, Switzerland.
Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK.
出版信息
Wiley Interdiscip Rev Cogn Sci. 2010 May;1(3):446-459. doi: 10.1002/wcs.58. Epub 2010 Apr 2.
Functional neuroimaging techniques are used widely in cognitive neuroscience to investigate aspects of functional specialization and functional integration in the human brain. Functional integration can be characterized in two ways, functional connectivity and effective connectivity. While functional connectivity describes statistical dependencies between data, effective connectivity rests on a mechanistic model of the causal effects that generated the data. This review addresses the conceptual and methodological basis of established techniques for characterizing effective connectivity using functional magnetic resonance imaging (fMRI) data. In particular, we focus on dynamic causal modeling (DCM) of fMRI data and emphasize the importance of model selection procedures and nonlinear mechanisms for context-dependent changes in connection strengths. Copyright © 2010 John Wiley & Sons, Ltd. For further resources related to this article, please visit the WIREs website.
功能神经成像技术在认知神经科学中被广泛应用，以研究人类大脑功能特化和功能整合的各个方面。功能整合可以通过两种方式来表征，即功能连接和有效连接。功能连接描述数据之间的统计依赖性，而有效连接则基于生成数据的因果效应的机制模型。本综述探讨了使用功能磁共振成像（fMRI）数据表征有效连接的既定技术的概念和方法基础。特别是，我们专注于fMRI数据的动态因果建模（DCM），并强调模型选择程序和非线性机制对于连接强度上下文相关变化的重要性。版权所有© 2010 John Wiley & Sons, Ltd。有关本文的更多资源，请访问WIREs网站。
Zotero 插件