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@ARTICLE{Jung:910891,
      author       = {Jung, Kyesam and Florin, Esther and Patil, Kaustubh R. and
                      Caspers, Julian and Rubbert, Christian and Eickhoff, Simon
                      B. and Popovych, Oleksandr V.},
      title        = {{W}hole-brain dynamical modeling for classification of
                      {P}arkinson’s disease},
      reportid     = {FZJ-2022-04241},
      year         = {2022},
      abstract     = {Simulated whole-brain connectomes demonstrate an enhanced
                      inter-individual variability depending on data processing
                      and modeling approach. By considering the human brain
                      connectome as an individualized attribute, we investigate
                      how empirical and simulated whole-brain connectome-derived
                      features can be utilized to classify patients with
                      Parkinson’s disease against healthy controls in light of
                      varying data processing and model validation. To this end,
                      we applied simulated blood oxygenation level-dependent
                      signals derived by a whole-brain dynamical model simulating
                      electrical signals of neuronal populations to reveal
                      differences between patients and controls. In addition to
                      the widely used model validation via fitting the dynamical
                      model to empirical neuroimaging data, we invented a model
                      validation against behavioral data, such as subject classes,
                      which we refer to as behavioral model fitting and show that
                      it can be beneficial for Parkinsonian patient
                      classification. Furthermore, the results of machine-learning
                      reported in this study also demonstrated that performance of
                      the patient classification can be improved when the
                      empirical data are complemented by the simulation results.
                      We also showed that temporal filtering of blood oxygenation
                      level-dependent signals influences the prediction results,
                      where the filtering in the low-frequency band is advisable
                      for Parkinsonian patient classification. In addition,
                      composing the feature space of empirical and simulated data
                      from multiple brain parcellation schemes provided
                      complementary features that improve prediction performance.
                      Based on our findings, we suggest including the simulation
                      results with empirical data is effective for
                      inter-individual research and its clinical application.},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5232 - Computational Principles (POF4-523)},
      pid          = {G:(DE-HGF)POF4-5232},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.1101/2022.06.08.495360},
      url          = {https://juser.fz-juelich.de/record/910891},
}