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@ARTICLE{Silchenko:1015000,
      author       = {Silchenko, Alexander N. and Hoffstaedter, Felix and
                      Eickhoff, Simon B.},
      title        = {{I}mpact of sample size and regression of tissue‐specific
                      signals on effective connectivity within the core default
                      mode network},
      journal      = {Human brain mapping},
      volume       = {44},
      number       = {17},
      issn         = {1065-9471},
      address      = {New York, NY},
      publisher    = {Wiley-Liss},
      reportid     = {FZJ-2023-03536},
      pages        = {5858-5870},
      year         = {2023},
      note         = {ACKNOWLEDGMENTSThis work was supported by the
                      Forschungzentrum Jülich GmbH (Alexander Silchenko), Simon
                      B. Eickhoff acknowledges funding by the European Union's
                      Horizon 2020 Research and Innovation Program (grant
                      agreements 945539 [HBP SGA3] and 826421 [VBC]), the Deutsche
                      Forschungsgemeinschaft (DFG, SFB 1451 and IRTG 2150) and the
                      National Institute of Health (R01 MH074457). Open Access
                      funding enabled and organized by Projekt DEAL.},
      abstract     = {Interactions within brain networks are inherently
                      directional, which are inaccessible to classical functional
                      connectivity estimates from resting-state functional
                      magnetic resonance imaging (fMRI) but can be detected using
                      spectral dynamic causal modeling (DCM). The sample size and
                      unavoidable presence of nuisance signals during fMRI
                      measurement are the two important factors influencing the
                      stability of group estimates of connectivity parameters.
                      However, most recent studies exploring effective
                      connectivity (EC) have been conducted with small sample
                      sizes and minimally pre-processed datasets. We explore the
                      impact of these two factors by analyzing clean resting-state
                      fMRI data from 330 unrelated subjects from the Human
                      Connectome Project database. We demonstrate that both the
                      stability of the model selection procedures and the
                      inference of connectivity parameters are highly dependent on
                      the sample size. The minimum sample size required for stable
                      DCM is approximately 50, which may explain the variability
                      of the DCM results reported so far. We reveal a stable
                      pattern of EC within the core default mode network computed
                      for large sample sizes and demonstrate that the use of
                      subject-specific thresholded whole-brain masks for
                      tissue-specific signals regression enhances the detection of
                      weak connections.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5252},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37713540},
      UT           = {WOS:001068502700001},
      doi          = {10.1002/hbm.26481},
      url          = {https://juser.fz-juelich.de/record/1015000},
}