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@INPROCEEDINGS{Jung:1010417,
      author       = {Jung, Kyesam and Florin, Esther and Patil, Kaustubh and
                      Caspers, Julian and Rubbert, Christian and Eickhoff, Simon
                      and Popovych, Oleksandr},
      title        = {{W}hole-brain dynamical modeling for classification of
                      {P}arkinson's disease},
      reportid     = {FZJ-2023-03046},
      year         = {2023},
      note         = {This work was supported by the Portfolio Theme
                      Supercomputing and Modeling for the Human Brain by the
                      Helmholtz association (https://www.helmholtz.de/en), the
                      Human Brain Project, and the European Union’s Horizon 2020
                      Research and Innovation Programme (https://cordis.europa.eu)
                      under Grant Agreements 785907 (HBP SGA2), 945539 (HBP SGA3),
                      and 826421 (VirtualBrainCloud). Open access publication was
                      funded by the Deutsche Forschungsgemeinschaft (DFG, German
                      Research Foundation) - 491111487. The funders had no role in
                      study design, data collection and analysis, decision to
                      publish, or preparation of the manuscript.},
      abstract     = {Introduction:Simulated whole-brain connectomes demonstrate
                      disparate inter-individual variability depending on
                      dataprocessing and modeling approach (Domhof et al., 2021;
                      Jung et al., 2021; Popovych et al., 2021). Byconsidering the
                      human brain connectome as an individualized attribute, we
                      investigate how empirical andsimulated whole-brain
                      connectome-derived features can be utilized to classify
                      patients with Parkinson'sdisease against healthy controls in
                      light of varying data processing and model
                      validation.Methods:This study included 51 (30 males) healthy
                      controls and 65 (45 males) patients with Parkinson's
                      disease.Diffusion-weighted MRI (dMRI), T1-weighted MRI and
                      resting-state functional MRI (rsfMRI) were acquired inthe
                      subjects. Empirical functional connectivity (eFC) was
                      calculated by Pearson correlation between regionsof a
                      whole-brain parcellation using blood oxygenation
                      level-dependent (BOLD) signals extracted from thersfMRI.
                      Empirical structural connectivity (eSC) was reconstructed
                      using extracted streamlines connectingregions of the
                      parcellation from whole-brain tractography that was
                      calculated using dMRI. We used twodifferent parcellation
                      schemes based on functional and structural brain properties
                      for calculation of the eFCand eSC. We also simulated BOLD
                      signals by a whole-brain dynamical model of Jansen-Rit type
                      (Jansen etal., 1995) derived from the eSC serving as a
                      network backbone. The simulated data was used to
                      calculatethe simulated FC that was employed together with
                      eFC and eSC to reveal differences between patients
                      andcontrols. In this study, we applied four temporal
                      filtering conditions in four frequency bands to empirical
                      andsimulated BOLD signals. In addition to the widely used
                      model validation via fitting the dynamical model toempirical
                      neuroimaging data (Deco et al., 2015; Honey et al., 2009;
                      Naskar et al., 2021), we invented amodel validation against
                      behavioral data, such as subject classes, which we refer to
                      as behavioral modelfitting and applied it to a
                      machine-learning (ML) classification of Parkinsonian
                      patients.Results:The results of ML investigation
                      demonstrated that performance of the patient classification
                      can besignificantly improved when the empirical data are
                      complemented by the simulation results. This issupported by
                      both integrative performance measures (Fig. 1) and predicted
                      probabilities for individualsubjects (Fig. 2). We also
                      showed that temporal filtering of empirical and simulated
                      BOLD signals influencesthe prediction results, where the
                      filtering in the low-frequency band is advisable for
                      Parkinsonian patientclassification. In addition, composing
                      the feature space of empirical and simulated data from
                      multiple brainparcellation schemes provided complementary
                      features that further improved the prediction
                      performance.The best performance (median of balanced
                      accuracies) was 0.65 for unseen subjects (cf. 0.61 using
                      theempirical data).},
      month         = {Jul},
      date          = {2023-07-22},
      organization  = {Organization for Human Brain Mapping
                       (OHBM), Montreal (Canada), 22 Jul 2023
                       - 26 Jul 2023},
      subtyp        = {After Call},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5232 - Computational Principles (POF4-523) / HBP SGA2 -
                      Human Brain Project Specific Grant Agreement 2 (785907) /
                      HBP SGA3 - Human Brain Project Specific Grant Agreement 3
                      (945539) / VirtualBrainCloud - Personalized Recommendations
                      for Neurodegenerative Disease (826421)},
      pid          = {G:(DE-HGF)POF4-5232 / G:(EU-Grant)785907 /
                      G:(EU-Grant)945539 / G:(EU-Grant)826421},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2023-03046},
      url          = {https://juser.fz-juelich.de/record/1010417},
}