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@INPROCEEDINGS{Popovych:905236,
      author       = {Popovych, Oleksandr and Jung, Kyesam and Manos, Thanos and
                      Diaz, Sandra and Hoffstaedter, Felix and Schreiber, Jan and
                      Thomas Yeo, B. T. and Eickhoff, Simon},
      title        = {{I}mpact of {B}rain {P}arcellation and {E}mpirical {D}ata
                      on {M}odeling of the {R}esting-{S}tate {B}rain {D}ynamics},
      reportid     = {FZJ-2022-00519},
      year         = {2021},
      abstract     = {Modern approaches to investigation of complex brain
                      dynamics suggest to represent the brain as a functional
                      network where nodes encapsulate brain-region-specific
                      function while edges consolidate the structural or
                      functional connectivity among these regions. Brain regions
                      can be delimited using a parcellation, i.e., brain atlas.
                      There is however no consensus on which brain atlas is more
                      adequate for one or another analysis. We address this
                      problem by a dynamical modeling approach, where the
                      resting-state brain dynamics is simulated by the whole-brain
                      personalized models derived from and validated against
                      empirical neuroimaging structural and functional data. We
                      investigate the impact of the fitting modalities and brain
                      atlases based on distinct anatomical and functional
                      parcellation techniques. We show that these simulation
                      conditions may strongly influence the modeling results
                      including the quality of the model fitting and structure of
                      the model parameter space. We also assess the variation of
                      the fitting results across subjects and parcellations and
                      observe that variation of selected data indices extracted
                      from the experimental data may greatly account for the
                      variations in the fitting results. At this, a few
                      correlative types of the data variables can be distinguished
                      depending on their intra- and inter-parcellation explanatory
                      power. The obtained results can contribute to the
                      improvement of the resting-state brain modeling and data
                      analytics.},
      month         = {May},
      date          = {2021-05-23},
      organization  = {SIAM Conference on Applications of
                       Dynamical Systems (DS21), Virtual
                       Conference (USA), 23 May 2021 - 27 May
                       2021},
      subtyp        = {Invited},
      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)6},
      url          = {https://juser.fz-juelich.de/record/905236},
}