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@ARTICLE{Jung:888477,
      author       = {Jung, Kyesam and Eickhoff, Simon B. and Popovych, Oleksandr
                      V.},
      title        = {{T}ractography density affects whole-brain structural
                      architecture and resting-state dynamical modeling},
      journal      = {bioRxiv beta},
      address      = {Cold Spring Harbor},
      publisher    = {Cold Spring Harbor Laboratory, NY},
      reportid     = {FZJ-2020-04941},
      year         = {2020},
      abstract     = {Dynamical modeling of the resting-state brain dynamics
                      essentially relies on the empirical neuroimaging data
                      utilized for the model derivation and validation. There is
                      however still no standardized data processing for magnetic
                      resonance imaging pipelines and the structural and
                      functional connectomes involved in the models. In this
                      study, we thus address how the parameters of
                      diffusion-weighted data processing for structural
                      connectivity (SC) can influence the validation results of
                      the whole-brain mathematical models and search for the
                      optimal parameter settings. On this way, we simulate the
                      functional connectivity by systems of coupled oscillators,
                      where the underlying network is constructed from the
                      empirical SC and evaluate the performance of the models for
                      varying parameters of data processing. For this, we
                      introduce a set of simulation conditions including the
                      varying number of total streamlines of the whole-brain
                      tractography (WBT) used for extraction of SC, cortical
                      parcellations based on functional and anatomical brain
                      properties and distinct model fitting modalities. We
                      observed that the graph-theoretical network properties of
                      structural connectome can be affected by varying
                      tractography density and strongly relate to the model
                      performance. We explored free parameters of the considered
                      models and found the optimal parameter configurations, where
                      the model dynamics closely replicates the empirical data. We
                      also found that the optimal number of the total streamlines
                      of WBT can vary for different brain atlases. Consequently,
                      we suggest a way how to improve the model performance based
                      on the network properties and the optimal parameter
                      configurations from multiple WBT conditions. Furthermore,
                      the population of subjects can be stratified into subgroups
                      with divergent behaviors induced by the varying number of
                      WBT streamlines such that different recommendations can be
                      made with respect to the data processing for individual
                      subjects and brain parcellations.},
      cin          = {INM-7},
      ddc          = {570},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {574 - Theory, modelling and simulation (POF3-574)},
      pid          = {G:(DE-HGF)POF3-574},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.1101/2020.12.03.410688},
      url          = {https://juser.fz-juelich.de/record/888477},
}