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@ARTICLE{Popovych:892819,
      author       = {Popovych, Oleksandr V. and Jung, Kyesam and Manos, Thanos
                      and Diaz-Pier, Sandra and Hoffstaedter, Felix and Schreiber,
                      Jan and Yeo, B. T. Thomas and Eickhoff, Simon B.},
      title        = {{I}nter-subject and inter-parcellation variability of
                      resting-state whole-brain dynamical modeling},
      journal      = {NeuroImage},
      volume       = {236},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {FZJ-2021-02365},
      pages        = {118201 -},
      year         = {2021},
      abstract     = {Modern approaches to investigate complex brain dynamics
                      suggest to represent the brain as a functional network of
                      brain regions defined by a brain atlas, while edges
                      represent the structural or functional connectivity among
                      them. This approach is also utilized for mathematical
                      modeling of the resting-state brain dynamics, where the
                      applied brain parcellation plays an essential role in
                      deriving the model network and governing the modeling
                      results. There is however no consensus and empirical
                      evidence on how a given brain atlas affects the model
                      outcome, and the choice of parcellation is still rather
                      arbitrary. Accordingly, we explore the impact of brain
                      parcellation on inter-subject and inter-parcellation
                      variability of model fitting to empirical data. Our
                      objective is to provide a comprehensive empirical evidence
                      of potential influences of parcellation choice on
                      resting-state whole-brain dynamical modeling. We show that
                      brain atlases strongly influence the quality of model
                      validation and propose several variables calculated from
                      empirical data to account for the observed variability. A
                      few classes of such data variables can be distinguished
                      depending on their inter-subject and inter-parcellation
                      explanatory power.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5232 - Computational Principles (POF4-523) / 5231 -
                      Neuroscientific Foundations (POF4-523) / SLNS - SimLab
                      Neuroscience (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF4-5232 / G:(DE-HGF)POF4-5231 /
                      G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {34033913},
      UT           = {WOS:000670278100013},
      doi          = {10.1016/j.neuroimage.2021.118201},
      url          = {https://juser.fz-juelich.de/record/892819},
}