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@ARTICLE{Domhof:894888,
      author       = {Domhof, Justin and Jung, Kyesam and Eickhoff, Simon B. and
                      Popovych, Oleksandr},
      title        = {{P}arcellation-induced variation of empirical and simulated
                      brain connectomes at group and subject levels},
      journal      = {Network neuroscience},
      volume       = {5},
      number       = {3},
      issn         = {2472-1751},
      address      = {Cambridge, MA},
      publisher    = {The MIT Press},
      reportid     = {FZJ-2021-03454},
      pages        = {798 - 830},
      year         = {2021},
      abstract     = {Recent developments of whole-brain models have demonstrated
                      their potential when investigating resting-state brain
                      activity. However, it has not been systematically
                      investigated how alternating derivations of the empirical
                      structural and functional connectivity, serving as the model
                      input, from MRI data influence modeling results. Here, we
                      study the influence from one major element: the brain
                      parcellation scheme that reduces the dimensionality of brain
                      networks by grouping thousands of voxels into a few hundred
                      brain regions. We show graph-theoretical statistics derived
                      from the empirical data and modeling results exhibiting a
                      high heterogeneity across parcellations. Furthermore, the
                      network properties of empirical brain connectomes explain
                      the lion’s share of the variance in the modeling results
                      with respect to the parcellation variation. Such a clear-cut
                      relationship is not observed at the subject-resolved level
                      per parcellation. Finally, the graph-theoretical statistics
                      of the simulated connectome correlate with those of the
                      empirical functional connectivity across parcellations.
                      However, this relation is not one-to-one, and its precision
                      can vary between models. Our results imply that network
                      properties of both empirical connectomes can explain the
                      goodness-of-fit of whole-brain models to empirical data at a
                      global group level but not at a single-subject level, which
                      provides further insights into the personalization of
                      whole-brain models.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5231 - Neuroscientific Foundations (POF4-523) / HBP SGA2 -
                      Human Brain Project Specific Grant Agreement 2 (785907) /
                      HBP SGA3 - Human Brain Project Specific Grant Agreement 3
                      (945539)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(EU-Grant)785907 /
                      G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {34746628},
      UT           = {WOS:000711092200009},
      doi          = {10.1162/netn_a_00202},
      url          = {https://juser.fz-juelich.de/record/894888},
}