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@ARTICLE{Jung:1033617,
      author       = {Jung, Kyesam and Wischnewski, Kevin J. and Eickhoff, Simon
                      B and Popovych, Oleksandr V.},
      title        = {{E}ffective workflow from multi-modal {MRI} data to
                      model-based prediction},
      reportid     = {FZJ-2024-06497},
      year         = {2024},
      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},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      HBP SGA3 - Human Brain Project Specific Grant Agreement 3
                      (945539) / HBP SGA2 - Human Brain Project Specific Grant
                      Agreement 2 (785907) / VirtualBrainCloud - Personalized
                      Recommendations for Neurodegenerative Disease (826421) / DFG
                      project G:(GEPRIS)491111487 - Open-Access-Publikationskosten
                      / 2025 - 2027 / Forschungszentrum Jülich (OAPKFZJ)
                      (491111487) / 5232 - Computational Principles (POF4-523) /
                      5231 - Neuroscientific Foundations (POF4-523)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(EU-Grant)945539 /
                      G:(EU-Grant)785907 / G:(EU-Grant)826421 /
                      G:(GEPRIS)491111487 / G:(DE-HGF)POF4-5232 /
                      G:(DE-HGF)POF4-5231},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.31219/osf.io/67zxe},
      url          = {https://juser.fz-juelich.de/record/1033617},
}