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@ARTICLE{Zhang:1027284,
author = {Zhang, Shufei and Jung, Kyesam and Langner, Robert and
Florin, Esther and Eickhoff, Simon B. and Popovych,
Oleksandr V.},
title = {{I}mpact of data processing varieties on {DCM} estimates of
effective connectivity from task‐ f{MRI}},
journal = {Human brain mapping},
volume = {45},
number = {8},
issn = {1065-9471},
address = {New York, NY},
publisher = {Wiley-Liss},
reportid = {FZJ-2024-03726},
pages = {e26751},
year = {2024},
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.},
cin = {INM-7},
ddc = {610},
cid = {I:(DE-Juel1)INM-7-20090406},
pnm = {5231 - Neuroscientific Foundations (POF4-523) / 5232 -
Computational Principles (POF4-523)},
pid = {G:(DE-HGF)POF4-5231 / G:(DE-HGF)POF4-5232},
typ = {PUB:(DE-HGF)16},
pubmed = {38864293},
UT = {WOS:001244785000001},
doi = {10.1002/hbm.26751},
url = {https://juser.fz-juelich.de/record/1027284},
}