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.
['Kathleen M. Gates', 'Peter C. M. Molenaar', 'Frank G. Hillary', 'Semyon Slobounov']
Extended unified SEM approach for modeling event-related fMRI data