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@INPROCEEDINGS{Domhof:905261,
      author       = {Domhof, Justin and Jung, Kyesam and Eickhoff, Simon and
                      Popovych, Oleksandr},
      title        = {{P}arcellation-induced variation of empirical and simulated
                      functional brain connectivity},
      reportid     = {FZJ-2022-00544},
      year         = {2021},
      abstract     = {Recent developments of whole-brain models have demonstrated
                      their potential when investigatingresting-state brain
                      activity. However, it has not been systematically
                      investigated how alternatingderivations of the empirical
                      structural and functional connectivity, serving as the model
                      input, fromMRI data influence modelling results. Here, we
                      study the influence from one major element: thebrain
                      parcellation scheme that reduces the dimensionality of brain
                      networks by grouping thousandsof voxels into a few hundred
                      brain regions. We show graph-theoretical statistics derived
                      from theempirical data and modelling results exhibiting a
                      high heterogeneity across parcellations. Furthermore, the
                      network properties of empirical brain connectomes explain
                      the lion’s share of the variancein the modelling results
                      with respect to the parcellation variation. Such a clear-cut
                      relationship isnot observed at the subject-resolved level
                      per parcellation. Finally, the graph-theoretical
                      statisticsof the simulated connectome correlate with those
                      of the empirical functional connectivity
                      acrossparcellations. 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 but not a
                      single-subject level, whichprovides further insights into
                      the personalisation of whole-brain models.},
      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/905261},
}