Impact of data processing varieties on DCM estimates of effective connectivity from task‐fMRI

Abstract

AbstractEffective connectivity (EC) refers to directional or causal influences between interacting neuronal populations or brain regions and can be estimated from functional magnetic resonance imaging (fMRI) data via dynamic causal modeling (DCM). In contrast to functional connectivity, the impact of data processing varieties on DCM estimates of task‐evoked EC has hardly ever been addressed. We therefore investigated how task‐evoked EC is affected by choices made for data processing. In particular, we considered the impact of global signal regression (GSR), block/event‐related design of the general linear model (GLM) used for the first‐level task‐evoked fMRI analysis, type of activation contrast, and significance thresholding approach. Using DCM, we estimated individual and group‐averaged task‐evoked EC within a brain network related to spatial conflict processing for all the parameters considered and compared the differences in task‐evoked EC between any two data processing conditions via between‐group parametric empirical Bayes (PEB) analysis and Bayesian data comparison (BDC). We observed strongly varying patterns of the group‐averaged EC depending on the data processing choices. In particular, task‐evoked EC and parameter certainty were strongly impacted by GLM design and type of activation contrast as revealed by PEB and BDC, respectively, whereas they were little affected by GSR and the type of significance thresholding. The event‐related GLM design appears to be more sensitive to task‐evoked modulations of EC, but provides model parameters with lower certainty than the block‐based design, while the latter is more sensitive to the type of activation contrast than is the event‐related design. Our results demonstrate that applying different reasonable data processing choices can substantially alter task‐evoked EC as estimated by DCM. Such choices should be made with care and, whenever possible, varied across parallel analyses to evaluate their impact and identify potential convergence for robust outcomes.