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@ARTICLE{Jung:1043577,
      author       = {Jung, Kyesam and Wischnewski, Kevin J. and Eickhoff, Simon
                      B. and Popovych, Oleksandr V.},
      title        = {{E}ffective workflow from multimodal {MRI} data to
                      model-based prediction},
      journal      = {Scientific reports},
      volume       = {15},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Springer Nature},
      reportid     = {FZJ-2025-02934},
      pages        = {20126},
      year         = {2025},
      note         = {This work was supported by the Portfolio Theme
                      Supercomputing and Modeling for the Human Brain by the
                      Helmholtz association, the Human Brain Project and the
                      European Union’s Horizon 2020 Research and Innovation
                      Programme under Grant Agreements 785907 (HBP SGA2), 945539
                      (HBP SGA3) and 826421 (VirtualBrainCloud). Open-access
                      publication was funded by the Deutsche
                      Forschungsgemeinschaft (German Research Foundation)
                      − 491111487. The funders had no role in the study
                      design, data collection and analysis, decision to publish,
                      or preparation of the manuscript.},
      abstract     = {Predicting human behavior from neuroimaging data remains a
                      complex challenge in neuroscience. To address this, we
                      propose a systematic and multi-faceted framework that
                      incorporates a model-based workflow using dynamical brain
                      models. This approach utilizes multi-modal MRI data for
                      brain modeling and applies the optimized modeling outcome to
                      machine learning. We demonstrate the performance of such an
                      approach through several examples such as sex classification
                      and prediction of cognition or personality traits. We in
                      particular show that incorporating the simulated data into
                      machine learning can significantly improve the prediction
                      performance compared to using empirical features alone.
                      These results suggest considering the output of the
                      dynamical brain models as an additional neuroimaging data
                      modality that complements empirical data by capturing brain
                      features that are difficult to measure directly. The
                      discussed model-based workflow can offer a promising avenue
                      for investigating and understanding inter-individual
                      variability in brain-behavior relationships and enhancing
                      prediction performance in neuroimaging research.},
      cin          = {INM-7},
      ddc          = {600},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / 5254 - Neuroscientific Data Analytics and AI
                      (POF4-525) / HBP - The Human Brain Project (604102)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(DE-HGF)POF4-5254 /
                      G:(EU-Grant)604102},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {40542008},
      UT           = {WOS:001512790500022},
      doi          = {10.1038/s41598-025-04511-5},
      url          = {https://juser.fz-juelich.de/record/1043577},
}