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@ARTICLE{Domhof:907812,
      author       = {Domhof, Justin W. M. and Eickhoff, Simon B. and Popovych,
                      Oleksandr V.},
      title        = {{R}eliability and subject specificity of personalized
                      whole-brain dynamical models},
      journal      = {NeuroImage},
      volume       = {257},
      issn         = {1053-8119},
      reportid     = {FZJ-2022-02229},
      pages        = {119321},
      year         = {2022},
      abstract     = {Dynamical whole-brain models were developed to link
                      structural (SC) and functional connectivity (FC) together
                      into one framework.Nowadays, they are used to investigate
                      the dynamical regimes of the brain and how these relate to
                      behavioral, clinical and demographic traits.However, there
                      is no comprehensive investigation on how reliable and
                      subject specific the modeling results are given the
                      variability of the empirical FC.In this study, we show that
                      the parameters of these models can be fitted with a "poor"
                      to "good" reliability depending on the exact implementation
                      of the modeling paradigm.We find, as a general rule of
                      thumb, that enhanced model personalization leads to
                      increasingly reliable model parameters.In addition, we
                      observe no clear effect of the model complexity evaluated by
                      separately sampling results for linear, phase oscillator and
                      neural mass network models.In fact, the most complex neural
                      mass model often yields modeling results with "poor"
                      reliability comparable to the simple linear model, but
                      demonstrates an enhanced subject specificity of the model
                      similarity maps.Subsequently, we show that the FC simulated
                      by these models can outperform the empirical FC in terms of
                      both reliability and subject specificity.For the
                      structure-function relationship, simulated FC of individual
                      subjects may be identified from the correlations with the
                      empirical SC with an accuracy up to $70\\%,$ but not vice
                      versa for non-linear models.We sample all our findings for 8
                      distinct brain parcellations and 6 modeling conditions and
                      show that the parcellation-induced effect is much more
                      pronounced for the modeling results than for the empirical
                      data.In sum, this study provides an exploratory account on
                      the reliability and subject specificity of dynamical
                      whole-brain models and may be relevant for their further
                      development and application.In particular, our findings
                      suggest that the application of the dynamical whole-brain
                      modeling should be tightly connected with an estimate of the
                      reliability of the results.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5232 - Computational Principles (POF4-523)},
      pid          = {G:(DE-HGF)POF4-5232},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:35580807},
      UT           = {WOS:000807102900001},
      doi          = {10.1016/j.neuroimage.2022.119321},
      url          = {https://juser.fz-juelich.de/record/907812},
}