Department of Human Development and Family Studies, The Pennsylvania State University, University Park, PA 16802, USA.
Neuroimage. 2011 Jan 15;54(2):1151-8. doi: 10.1016/j.neuroimage.2010.08.051. Epub 2010 Sep 15.
There has been increasing emphasis in fMRI research on the examination of how regions covary in a distributed neural network. Event-related data designs present a unique challenge to modeling how couplings among regions change in the presence of experimental manipulations. The present paper presents the extended unified SEM (euSEM), a novel approach for acquiring effective connectivity maps with event-related data. The euSEM adds to the unified SEM, which models both lagged and contemporaneous effects, by estimating the direct effects that experimental manipulations have on blood-oxygen-level dependent activity as well as the modulating effects the manipulations have on couplings among regions. Monte Carlos simulations included in this paper offer support for the model's ability to recover covariance patterns used to estimate data. Next, we apply the model to empirical data to demonstrate feasibility. Finally, the results of the empirical data are compared to those found using dynamic causal modeling. The euSEM provides a flexible approach for modeling event-related data as it may be employed in an exploratory, partially exploratory, or entirely confirmatory manner.
功能磁共振成像（fMRI）研究越来越强调检查分布式神经网络中区域如何协变。事件相关数据设计对建模实验操作存在时区域间耦合如何变化提出了独特的挑战。本文提出了扩展的统一 SEM（euSEM），这是一种使用事件相关数据获取有效连接图的新方法。euSEM 在统一 SEM 的基础上进行了扩展，该模型同时对滞后和同期效应进行建模，估计实验操作对血氧水平依赖活动的直接影响，以及操作对区域间耦合的调节影响。本文包含的蒙特卡罗模拟为模型恢复用于估计数据的协方差模式的能力提供了支持。接下来，我们将模型应用于实证数据以证明其可行性。最后，将实证数据的结果与使用动态因果建模发现的结果进行比较。euSEM 为建模事件相关数据提供了一种灵活的方法，因为它可以以探索性、部分探索性或完全确认性的方式使用。
