Effect of initial fMRI data modeling on the connectivity reported between brain areas
Effect of initial fMRI data modeling on the connectivity reported between brain areas
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DOI：
10.1016/j.neuroimage.2006.07.019
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发表时间：
2006-11-01
期刊：
影响因子：
5.7
通讯作者：
Fonlupt, Pierre
中科院分区：
文献类型：
--
作者：
Caclin, Anne;Fonlupt, Pierre
几乎所有的神经影像学数据分析都依赖于某种形式的方差划分。常规分析采用一般线性模型（GLM），将测量的响应变量的方差划分为解释变量的设计矩阵所描述的分区。这种方法也可以在使用数据主导的方法总结功能连接性的研究中的数据初始建模中采用，例如主成分分析，或使用例如结构方程建模的有效连接性研究。本技术说明中提出的一点是，必须准确描述原始时间序列的划分，以限定所考虑的变化来源。对于传统的分析，使用GLM，分区调查对应的子空间的设计矩阵进行测试。然而，在功能和有效连通性的分析中，所考虑的特定子空间并不总是明确指定的。在这里，我们表明，选择不同的子空间，或方差分区，可以有深远的影响，定性和定量的样本协方差和随之而来的推论连接。我们将使用模拟数据来说明这一点，其中包括条件和块相关的影响及其相互作用。我们将使用这三个子空间来展示两个体素之间的相关性如何取决于检查哪些子分区。我们还将展示设计矩阵的划分如何影响在研究误差项之间的相关性时观察到的相关矩阵。最后，我们将证明，定量地，方差分区考虑两个区域之间的相关性使用真实的功能磁共振成像研究的生物运动的效果。(c)2006爱思唯尔公司All rights reserved.
Nearly all neuroimaging data analysis rests upon some form of variance partitioning. Conventional analyses, with a general linear model (GLM), partition the variance in the measured response variable into partitions described by a design matrix of explanatory variables. This approach can also be adopted in the initial modeling of the data in studies using data-led methods to summarize functional connectivity, such as principle component analysis, or studies of effective connectivity, using for example structural equation modeling. The point made in this technical note is that the partition of the original time series has to be precisely described to qualify the sources of variations that are taken into account. For conventional analyses using the GLM, the partition investigated corresponds to the subspaces of the design matrix that are tested. However, in the analyses of functional and effective connectivity, the particular subspaces considered are not always specified explicitly. Here we show that selecting different subspaces, or variance partitions, can have a profound effect, both qualitatively and quantitatively, on the sample covariances and the ensuing inferences about connectivity. We will illustrate this using simulated data that include condition and block-related effects and their interactions. We will use these three subspaces to show how the correlation between two voxels depends on which sub-partitions are examined. We will also show how the partition of the design matrix influences the resulting correlation matrix observed when studying correlations between error terms. We will finally demonstrate, quantitatively, the effect of the variance partitions considered on the correlations between two regions using a real fMRI study of biological motion. (c) 2006 Elsevier Inc. All rights reserved.