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@INPROCEEDINGS{Silchenko:905259,
      author       = {Silchenko, Alexander and Hoffstaedter, Felix and Popovych,
                      Oleksandr and Eickhoff, Simon},
      title        = {{I}mpact of sample size and global confounds removals on
                      estimates of effective connectivity},
      reportid     = {FZJ-2022-00542},
      year         = {2021},
      abstract     = {The interactions within brain networks are inherently
                      directional and can be detected by using thespectral Dynamic
                      Causal Modelling (DCM) for the resting-state functional
                      magnetic resonance imaging (fMRI). The sample size and
                      unavoidable presence of nuisance signals during fMRI
                      measurementare the two important factors influencing
                      stability of the group estimates of connectivity parameters.
                      However, most of the recent studies exploring effective
                      connectivity were conducted for rathersmall and minimally
                      preprocessed datasets. Here, we explore an impact of these
                      two factors by analyzing the cleaned resting-state fMRI data
                      for the group of 330 unrelated subjects from the
                      HumanConnectome Project database. We demonstrate that
                      stability of the model selection procedure andinference of
                      connectivity parameters are both dependent on the sample
                      size. The minimal samplesize required for the stable Dynamic
                      Causal modelling has to be about 50. Our results show
                      thatglobal confounds removals have weak or moderate effect
                      on DCM stability for the datasets properlycleaned from the
                      artifacts.},
      month         = {Oct},
      date          = {2021-10-05},
      organization  = {INM $\&$ IBI Retreat 2021,
                       Forschungszentrum Jülich, Virtual
                       Conference (Germany), 5 Oct 2021 - 6
                       Oct 2021},
      subtyp        = {After Call},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5232 - Computational Principles (POF4-523) / 5231 -
                      Neuroscientific Foundations (POF4-523) / 5254 -
                      Neuroscientific Data Analytics and AI (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)POF4-5231 /
                      G:(DE-HGF)POF4-5254 / G:(EU-Grant)785907 /
                      G:(EU-Grant)945539 / G:(EU-Grant)826421},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/905259},
}