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@ARTICLE{Jung:892609,
      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      = {NeuroImage},
      volume       = {237},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {FZJ-2021-02198},
      pages        = {118176 -},
      year         = {2021},
      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 informed by SC. 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. The main
                      objective of this study is to explore how the quality of the
                      model validation can vary across the considered simulation
                      conditions. 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 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 WBT density such that different
                      recommendations can be made with respect to the data
                      processing for individual subjects and brain parcellations.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5231 - Neuroscientific Foundations (POF4-523) / 5232 -
                      Computational Principles (POF4-523)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(DE-HGF)POF4-5232},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {34000399},
      UT           = {WOS:000671132300003},
      doi          = {10.1016/j.neuroimage.2021.118176},
      url          = {https://juser.fz-juelich.de/record/892609},
}