% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Zhang:1022114,
      author       = {Zhang, Shufei and Jung, Kyesam and Langner, Robert and
                      Florin, Esther and Eickhoff, Simon and Popovych, Oleksandr},
      title        = {{I}mpact of data processing on {DCM} estimates of effective
                      connectivity from task-evoked f{MRI}},
      reportid     = {FZJ-2024-01235},
      year         = {2023},
      note         = {This study was supported by the Portfolio Theme
                      Supercomputing and Modeling for the Human Brain by the
                      Helmholtz association, the Human Brain Project (HBP 785907
                      SGA2), (HBP 945539 SGA3), and VirtualBrainCloud (826421).
                      The computing time was granted through the Jülich–Aachen
                      Research Alliance (JARA) on the supercomputer JURECA at
                      Forschungszentrum Jülich. Open access publication was
                      funded by the Deutsche Forschungsgemeinschaft (DFG, German
                      Research Foundation) - 491111487.},
      abstract     = {Introduction. Effective connectivity (EC) refers to
                      directional or causal influences among interacting neuronal
                      populations or brain regions and can be estimated from
                      functional magnetic resonance imaging (fMRI) data via
                      dynamic causal modeling (DCM) (Friston et al., 2003).
                      However, in contrast to functional connectivity, the impact
                      of data processing varieties (Carp, 2012) on DCM estimates
                      of task-evoked EC has hardly ever been systematically
                      addressed. We therefore investigated how task-evoked EC is
                      affected by reasonable choices made for processing task fMRI
                      data. Methods. Task-evoked EC was investigated for a spatial
                      stimulus-response compatibility (SRC) task (Fitts $\&$
                      Deininger, 1954) in 271 subjects (123 females, 18-85 years
                      old, mean age: 52.6 ± 16.5 years) recruited from the
                      subject pool of the 1000BRAINS project (Caspers et al.,
                      2014). We considered the impact of the following data
                      processing conditions on the modulatory component of
                      task-evoked EC: Global signal regression (Almgren et al.,
                      2020; Power et al., 2017), block vs. event-related general
                      linear model (GLM) design (Daunizeau et al., 2011; Petersen
                      $\&$ Dubis, 2012), type of activation task contrast
                      (Zeidman, Jafarian, Corbin, et al., 2019), and significance
                      thresholdingapproach (Roels et al., 2015). Using DCM
                      designed in accordance with the considered parameters of the
                      data processing, we estimated individual and group-averaged
                      task-evoked EC within the SRC brain network of 9 nodes
                      related to spatial conflict processing [Fig. 1]. Using the
                      Parametric Empirical Bayes (PEB) analysis (Zeidman,
                      Jafarian, Seghier, et al., 2019), we evaluated and compared
                      the group-mean task-evoked EC patterns and between-group
                      differencesin the task-evoked EC for any two of the
                      considered conditions of the data processing (with vs.
                      without GSR, event-related vs. block designs, corrected vs.
                      uncorrected thresholding, and incompatible+compatible vs.
                      incompatible contrasts). Results. We observed strongly
                      varying patterns of the group-averaged EC depending on data
                      processing choices. In particular, task-evoked EC was
                      significantly impacted by GLM design (event-related or
                      block) and type of activation contrast (incompatible task
                      contrast vs. incompatible + compatible task contrast) [Fig.
                      2]. On the other hand, EC was little affected by
                      globalsignal regression and the type of significance
                      thresholding. The PEB analyses showed that more EC edges
                      were significantly modulated by the task conditionsfor the
                      event-related GLM than for the block one. Furthermore, the
                      variation of the activation contrast induced more changes to
                      the task-evoked EC for the block GLM than for the
                      event-related one [Fig. 2].Conclusions. Our results
                      demonstrate that different reasonable data processing
                      choices can substantially alter the task-evoked EC as
                      estimated by DCM. In particular, the event-related GLM
                      design appears to be more responsive to task-evoked
                      modulations of EC than the block design. On the other hand,
                      the latter GLM design is more sensitive to the type of
                      activation contrast than the event-related design. These
                      choices should thus be made with care and, whenever
                      possible, varied across parallel analyses to evaluate their
                      impact and identify potential convergence for robust
                      outcomes of the data analysis and neuroscientific
                      interpretation of the estimated connectivity patterns.},
      month         = {Jul},
      date          = {2023-07-22},
      organization  = {The 29th Annual Meeting of the
                       Organization for Human Brain Mapping,
                       Montreal (Canada), 22 Jul 2023 - 26 Jul
                       2023},
      subtyp        = {After Call},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5232 - Computational Principles (POF4-523) / 5251 -
                      Multilevel Brain Organization and Variability (POF4-525) /
                      HBP SGA2 - Human Brain Project Specific Grant Agreement 2
                      (785907) / HBP SGA3 - Human Brain Project Specific Grant
                      Agreement 3 (945539) / VirtualBrainCloud - Personalized
                      Recommendations for Neurodegenerative Disease (826421)},
      pid          = {G:(DE-HGF)POF4-5232 / G:(DE-HGF)POF4-5251 /
                      G:(EU-Grant)785907 / G:(EU-Grant)945539 /
                      G:(EU-Grant)826421},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2024-01235},
      url          = {https://juser.fz-juelich.de/record/1022114},
}