Department of Human Development and Family Studies, The Pennsylvania State University, University Park, PA 16802, USA. address:
Neuroimage. 2010 Apr 15;50(3):1118-25. doi: 10.1016/j.neuroimage.2009.12.117. Epub 2010 Jan 6.
Modeling the relationships among brain regions of interest (ROIs) carries unique potential to explicate how the brain orchestrates information processing. However, hurdles arise when using functional MRI data. Variation in ROI activity contains sequential dependencies and shared influences on synchronized activation. Consequently, both lagged and contemporaneous relationships must be considered for unbiased statistical parameter estimation. Identifying these relationships using a data-driven approach could guide theory-building regarding integrated processing. The present paper demonstrates how the unified SEM attends to both lagged and contemporaneous influences on ROI activity. Additionally, this paper offers an approach akin to Granger causality testing, Lagrange multiplier testing, for statistically identifying directional influence among ROIs and employs this approach using an automatic search procedure to arrive at the optimal model. Rationale for this equivalence is offered by explicating the formal relationships among path modeling, vector autoregression, and unified SEM. When applied to simulated data, biases in estimates which do not consider both lagged and contemporaneous paths become apparent. Finally, the use of unified SEM with the automatic search procedure is applied to an empirical data example.
对感兴趣脑区（ROI）之间的关系进行建模具有独特的潜力，可以阐明大脑如何协调信息处理。然而，在使用功能磁共振成像数据时会遇到障碍。ROI 活动的变化包含了序列依赖性和对同步激活的共享影响。因此，为了进行无偏的统计参数估计，必须同时考虑滞后和同期关系。使用数据驱动的方法识别这些关系可以指导关于集成处理的理论构建。本文展示了统一 SEM 如何同时考虑 ROI 活动的滞后和同期影响。此外，本文提供了一种类似于格兰杰因果检验、拉格朗日乘子检验的方法，用于在 ROI 之间统计识别方向影响，并使用自动搜索程序采用该方法得出最优模型。通过阐明路径建模、向量自回归和统一 SEM 之间的形式关系，给出了这种等价性的原理。当应用于模拟数据时，不考虑滞后和同期路径的估计中的偏差变得明显。最后，将自动搜索程序与统一 SEM 一起应用于实证数据示例。
