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| Hauptseite > Publikationsdatenbank > Impact of data processing varieties on DCM estimates of effective connectivity from task‐ fMRI |
| Hauptseite > Publikationsdatenbank > Impact of data processing varieties on DCM estimates of effective connectivity from task‐ fMRI |
| Journal Article | FZJ-2024-03726 |
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2024
Wiley-Liss
New York, NY
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Please use a persistent id in citations: doi:10.1002/hbm.26751 doi:10.34734/FZJ-2024-03726
Abstract: Effective 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 contrastto functional connectivity, the impact of data processing varieties on DCM estimatesof task-evoked EC has hardly ever been addressed. We therefore investigated howtask-evoked EC is affected by choices made for data processing. In particular, weconsidered the impact of global signal regression (GSR), block/event-related designof the general linear model (GLM) used for the first-level task-evoked fMRI analysis,type of activation contrast, and significance thresholding approach. Using DCM, weestimated individual and group-averaged task-evoked EC within a brain networkrelated to spatial conflict processing for all the parameters considered and comparedthe differences in task-evoked EC between any two data processing conditions viabetween-group parametric empirical Bayes (PEB) analysis and Bayesian data comparison (BDC). We observed strongly varying patterns of the group-averaged ECdepending on the data processing choices. In particular, task-evoked EC and parameter certainty were strongly impacted by GLM design and type of activation contrastas revealed by PEB and BDC, respectively, whereas they were little affected by GSRand the type of significance thresholding. The event-related GLM design appears tobe more sensitive to task-evoked modulations of EC, but provides model parameterswith lower certainty than the block-based design, while the latter is more sensitive tothe type of activation contrast than is the event-related design. Our results demonstrate that applying different reasonable data processing choices can substantiallyalter task-evoked EC as estimated by DCM. Such choices should be made with careand, whenever possible, varied across parallel analyses to evaluate their impact andidentify potential convergence for robust outcomes.
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