Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data
Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data
复制标题
DOI：
10.1002/hbm.20259
复制
发表时间：
2007-02-01
影响因子：
4.8
通讯作者：
Ernst, Thomas
中科院分区：
文献类型：
--
作者：
Kim, Jieun;Zhu, Wei;Ernst, Thomas
脑连通性研究的最终目标是提出、测试、修改和比较某些定向脑通路。路径分析或结构方程模型（SEM）是这类研究的理想统计方法。在这项工作中，我们提出了一种两阶段统一的SEM加GLM（一般线性模型）方法，用于分析具有受试者水平协变量的多受试者、多变量功能磁共振成像（fMRI）时间序列数据。在第一阶段，我们通过统一的扫描电镜模型分析了每个受试者的fMRI多变量时间序列，该模型结合了由多变量自回归（MAR）模型表示的纵向路径和由传统扫描电镜表示的同期路径。在第二阶段，将所得的主体水平路径系数与主体水平协变量（如性别、年龄、智商等）合并，通过GLM检查这些协变量对有效连通性的影响。我们的方法通过fMRI视觉注意实验的分析得到了例证。此外，将统一扫描电镜分析的重要路径网络与不包含纵向信息的传统扫描电镜分析以及动态因果建模（DCM）方法的重要路径网络进行了比较。
The ultimate goal of brain connectivity studies is to propose, test, modify, and compare certain directional brain pathways. Path analysis or structural equation modeling (SEM) is an ideal statistical method for such studies. In this work, we propose a two-stage unified SEM plus GLM (General Linear Model) approach for the analysis of multisubject, multivariate functional magnetic resonance imaging (fMRI) time series data with subject-level covariates. In Stage 1, we analyze the fMRI multivariate time series for each subject individually via a unified SEM model by combining longitudinal pathways represented by a multivariate autoregressive (MAR) model, and contemporaneous pathways represented by a conventional SEM. In Stage 2, the resulting subject-level path coefficients are merged with subject-level covariates such as gender, age, IQ, etc., to examine the impact of these covariates on effective connectivity via a GLM. Our approach is exemplified via the analysis of an fMRI visual attention experiment. Furthermore, the significant path network from the unified SEM analysis is compared to that from a conventional SEM analysis without incorporating the longitudinal information as well as that from a Dynamic Causal Modeling (DCM) approach.