% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Wischnewski:904953,
      author       = {Wischnewski, Kevin J. and Eickhoff, Simon B. and Jirsa,
                      Viktor K. and Popovych, Oleksandr V.},
      title        = {{T}owards an {E}fficient {V}alidation of {D}ynamical
                      {W}hole-brain {M}odels},
      reportid     = {FZJ-2022-00267},
      year         = {2021},
      note         = {Planned for publication in Scientific Reports},
      abstract     = {Simulating the resting-state brain dynamics via
                      mathematical whole-brain models requires an optimal
                      selection of parameters, which determine the model’s
                      capability to replicate empirical data. Since the parameter
                      optimization via a grid search (GS) becomes unfeasible for
                      high-dimensional models, we evaluate several alternative
                      approaches to maximize the correspondence between simulated
                      and empirical functional connectivity. A dense GS serves as
                      a benchmark to assess the performance of four optimization
                      schemes: Nelder-Mead Algorithm (NMA), Particle Swarm
                      Optimization (PSO), Covariance Matrix Adaptation Evolution
                      Strategy (CMAES) and Bayesian Optimization (BO). To compare
                      them, we employ an ensemble of coupled phase oscillators
                      built upon individual empirical structural connectivity of
                      105 healthy subjects. We determine optimal model parameters
                      from two- and three-dimensional parameter spaces and show
                      that the overall fitting quality of the tested methods can
                      compete with the GS. There are, however, marked differences
                      in the required computational resources and stability
                      properties, which we also investigate before proposing CMAES
                      and BO as efficient alternatives to a high-dimensional GS.
                      For the three-dimensional case, these methods generated
                      similar results as the GS, but within less than $6\%$ of the
                      computation time. Our results contribute to an efficient
                      validation of models for personalized simulations of brain
                      dynamics.},
      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)25},
      doi          = {10.21203/rs.3.rs-1139051/v1},
      url          = {https://juser.fz-juelich.de/record/904953},
}