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@ARTICLE{Jung:916631,
      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 modelling for classification of
                      {P}arkinson’s disease},
      journal      = {Brain communications},
      volume       = {5},
      number       = {1},
      issn         = {2632-1297},
      address      = {[Großbritannien]},
      publisher    = {Guarantors of Brain},
      reportid     = {FZJ-2022-06382},
      pages        = {fcac331},
      year         = {2023},
      abstract     = {Simulated whole-brain connectomes demonstrate enhanced
                      inter-individual variability depending on the data
                      processing and modelling 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 behavioural data, such as subject
                      classes, which we refer to as behavioural 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 the
                      performance of the patient classification can be improved
                      when the empirical data are complemented by the simulation
                      results. We also showed that the temporal filtering of blood
                      oxygenation level-dependent signals influences the
                      prediction results, where 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 improved prediction
                      performance. Based on our findings, we suggest that
                      combining the simulation results with empirical data is
                      effective for inter-individual research and its clinical
                      application.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5232 - Computational Principles (POF4-523)},
      pid          = {G:(DE-HGF)POF4-5232},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {36601625},
      UT           = {WOS:000905773200004},
      doi          = {10.1093/braincomms/fcac331},
      url          = {https://juser.fz-juelich.de/record/916631},
}