Londei Alessandro, D'Ausilio Alessandro, Basso Demis, Belardinelli Marta Olivetti
Department of Psychology 1, University of Rome "La Sapienza", Via dei Marsi, 78-00185, Rome, Italy.
Cogn Process. 2006 Mar;7(1):42-52. doi: 10.1007/s10339-005-0019-5. Epub 2005 Oct 27.
One of the most important achievements in understanding the brain is that the emergence of complex behavior is guided by the activity of brain networks. To fully apply this theoretical approach fully, a method is needed to extract both the location and time course of the activities from the currently employed techniques. The spatial resolution of fMRI received great attention, and various non-conventional methods of analysis have previously been proposed for the above-named purpose. Here, we briefly outline a new approach to data analysis, in order to extract both spatial and temporal activities from fMRI recordings, as well as the pattern of causality between areas. This paper presents a completely data-driven analysis method that applies both independent components analysis (ICA) and the Granger causality test (GCT), performed in two separate steps. First, ICA is used to extract the independent functional activities. Subsequently the GCT is applied to the independent component (IC) most correlated with the stimuli, to indicate its causal relation with other ICs. We therefore propose this method as a promising data-driven tool for the detection of cognitive causal relationships in neuroimaging data.
在理解大脑方面最重要的成就之一是，复杂行为的出现是由大脑网络的活动所引导的。为了全面应用这一理论方法，需要一种从当前使用的技术中提取活动的位置和时间进程的方法。功能磁共振成像（fMRI）的空间分辨率受到了极大关注，并且之前已经针对上述目的提出了各种非常规分析方法。在此，我们简要概述一种新的数据分析方法，以便从fMRI记录中提取空间和时间活动，以及各区域之间的因果关系模式。本文提出了一种完全数据驱动的分析方法，该方法分两个独立步骤应用独立成分分析（ICA）和格兰杰因果检验（GCT）。首先，使用ICA提取独立的功能活动。随后，将GCT应用于与刺激最相关的独立成分（IC），以表明其与其他IC的因果关系。因此，我们提出这种方法作为一种有前景的数据驱动工具，用于检测神经成像数据中的认知因果关系。
