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@PHDTHESIS{Jung:1006719,
      author       = {Jung, Kyesam},
      title        = {{I}mpact of data processing parameters on whole-brain
                      dynamical models},
      school       = {Heinrich-Heine-Universität Düsseldorf},
      type         = {Dissertation},
      reportid     = {FZJ-2023-01798},
      pages        = {121},
      year         = {2023},
      note         = {Dissertation, Heinrich-Heine-Universität Düsseldorf,
                      2023},
      abstract     = {Magnetic resonance imaging (MRI) in neuroscience is one of
                      the most powerful non-invasivemethods to measure the human
                      brain. Neuroimaging studies have been using MRI to
                      extractstructural and functional properties from the brain.
                      In computational neuroscience, whole-brainmodeling employs
                      MRI data as a backbone and allows researchers to scrutinize
                      simulatedwhole-brain dynamics in silico by exploring free
                      parameters of whole-brain models. However,MRI data
                      processing has no standardized method because of the lack of
                      ground truth of thehuman brain. Thus, using different
                      softwares and data processing parameters can
                      induceinconsistent results and lead to different conclusions
                      across studies. Besides, the impact of dataprocessing on
                      whole-brain models has not been clearly understood.
                      Therefore, I performedthree studies considering conditions
                      of MRI data processing for whole-brain modeling
                      andinvestigated the impact of data processing parameters on
                      whole-brain models. In study 1, varieddata processing was
                      used to calculate the structural connectome, which can
                      directly influencewhole-brain models. Subsequently, these
                      different whole-brain models strongly influencedsimulated
                      results and the subjects were stratified based on empirical
                      and simulated data. Instudy 2, different brain parcellation
                      schemes were used for data processing. Empirical
                      andsimulated results from different parcellation schemes
                      showed inter-individual variability viadata variables. In
                      these respects, in study 3, varied functional data
                      processing was used forwhole-brain dynamical modeling.
                      Afterwards, the empirical and simulated results
                      withdifferent conditions were used for the classification of
                      patients with Parkinson’s disease againsthealthy subjects.
                      The classification performance was affected by the
                      functional data processingconditions. Furthermore,
                      whole-brain modeling improved the performance when the
                      empiricaldata are complemented by the simulation results.
                      From these studies in the thesis, varying MRIdata processing
                      parameters does not only impact empirical data but also
                      leads to differentsimulation results in whole-brain
                      dynamical modeling and its application.},
      cin          = {INM-7},
      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)11},
      doi          = {10.34734/FZJ-2023-01798},
      url          = {https://juser.fz-juelich.de/record/1006719},
}