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@ARTICLE{Wischnewski:1042365,
      author       = {Wischnewski, Kevin J. and Jarre, Florian and Eickhoff,
                      Simon B. and Popovych, Oleksandr V.},
      title        = {{E}xploring dynamical whole-brain models in
                      high-dimensional parameter spaces},
      journal      = {PLOS ONE},
      volume       = {20},
      number       = {5},
      issn         = {1932-6203},
      address      = {San Francisco, California, US},
      publisher    = {PLOS},
      reportid     = {FZJ-2025-02547},
      pages        = {e0322983 -},
      year         = {2025},
      abstract     = {Personalized modeling of the resting-state brain activity
                      implies the usage of dynamical whole-brain models with
                      high-dimensional model parameter spaces. However, the
                      practical benefits and mathematical challenges originating
                      from such approaches have not been thoroughly documented,
                      leaving the question of the value and utility of
                      high-dimensional approaches unanswered. Studying a
                      whole-brain model of coupled phase oscillators, we proceeded
                      from low-dimensional scenarios featuring 2–3 global model
                      parameters only to high-dimensional cases, where we
                      additionally equipped every brain region with a specific
                      local model parameter. To enable the parameter optimizations
                      for the high-dimensional model fitting to empirical data, we
                      applied two dedicated mathematical optimization algorithms
                      (Bayesian Optimization, Covariance Matrix Adaptation
                      Evolution Strategy). We thereby optimized up to 103
                      parameters simultaneously with the aim to maximize the
                      correlation between simulated and empirical functional
                      connectivity separately for 272 subjects. The obtained model
                      parameters demonstrated increased variability within
                      subjects and reduced reliability across repeated
                      optimization runs in high-dimensional spaces. Nevertheless,
                      the quality of the model validation (goodness-of-fit, GoF)
                      improved considerably and remained very stable and reliable
                      together with the simulated functional connectivity.
                      Applying the modeling results to phenotypical data, we found
                      significantly higher prediction accuracies for sex
                      classification when the GoF or coupling parameter values
                      optimized in the high-dimensional spaces were considered as
                      features. Our results elucidate the model fitting in
                      high-dimensional parameter spaces and can contribute to an
                      improved dynamical brain modeling as well as its application
                      to the frameworks of inter-individual variability and
                      brain-behavior relationships.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {40354445},
      UT           = {WOS:001488716700004},
      doi          = {10.1371/journal.pone.0322983},
      url          = {https://juser.fz-juelich.de/record/1042365},
}