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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},
}