Marrelec Guillaume, Horwitz Barry, Kim Jieun, Pélégrini-Issac Mélanie, Benali Habib, Doyon Julien
Inserm, U678, F-75013 Paris, France.
Magn Reson Imaging. 2007 Oct;25(8):1181-9. doi: 10.1016/j.mri.2007.02.012. Epub 2007 May 1.
In functional magnetic resonance imaging (fMRI) data analysis, effective connectivity investigates the influence that brain regions exert on one another. Structural equation modeling (SEM) has been the main approach to examine effective connectivity. In this paper, we propose a method that, given a set of regions, performs partial correlation analysis. This method provides an approach to effective connectivity that is data driven, in the sense that it does not require any prior information regarding the anatomical or functional connections. To demonstrate the practical relevance of partial correlation analysis for effective connectivity investigation, we reanalyzed data previously published [Bullmore, Horwitz, Honey, Brammer, Williams, Sharma, 2000. How good is good enough in path analysis of fMRI data? NeuroImage 11, 289-301]. Specifically, we show that partial correlation analysis can serve several purposes. In a pre-processing step, it can hint at which effective connections are structuring the interactions and which have little influence on the pattern of connectivity. As a post-processing step, it can be used both as a simple and visual way to check the validity of SEM optimization algorithms and to show which assumptions made by the model are valid, and which ones should be further modified to better fit the data.
在功能磁共振成像（fMRI）数据分析中，有效连接性研究大脑区域之间相互施加的影响。结构方程建模（SEM）一直是检验有效连接性的主要方法。在本文中，我们提出了一种方法，该方法在给定一组区域的情况下进行偏相关分析。这种方法提供了一种数据驱动的有效连接性研究方法，即它不需要任何关于解剖或功能连接的先验信息。为了证明偏相关分析在有效连接性研究中的实际相关性，我们重新分析了先前发表的数据[布尔莫尔、霍维茨、霍尼、布拉默、威廉姆斯、夏尔马，2000年。fMRI数据路径分析中多好才算足够好？《神经影像学》11卷，289 - 301页]。具体而言，我们表明偏相关分析可以有多种用途。在预处理步骤中，它可以提示哪些有效连接正在构建相互作用，哪些对连接模式影响不大。作为后处理步骤，它既可以作为一种简单直观的方式来检查SEM优化算法的有效性，又可以展示模型所做的哪些假设是有效的，哪些需要进一步修改以更好地拟合数据。
