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@MISC{Domhof:908132,
      author       = {Domhof, Justin W. M. and Eickhoff, Simon B. and Popovych,
                      Oleksandr V.},
      title        = {{P}arcellation-based functional connectivity simulated by
                      personalized whole-brain dynamical models (1.0)},
      publisher    = {EBRAINS},
      reportid     = {FZJ-2022-02396},
      year         = {2022},
      abstract     = {This dataset contains functional connectomes generated by
                      whole-brain dynamical models for a healthy cohort and 19
                      brain parcellations. The models were derived from and
                      validated against the parcellation-based empirical
                      structural (SC) and functional connectivities (FC) of
                      individuals, respectively, which have been published as a
                      separate dataset ([DOI:
                      10.25493/81EV-ZVT](https://doi.org/10.25493/81EV-ZVT)). In
                      the current dataset, two particular models for local
                      dynamics were considered for modeling the mean-field
                      activities of the brain regions, in particular, the
                      resting-state electrical and ultra-slow
                      blood-oxygen-level-dependent dynamics of neuronal
                      populations. Subsequently, the constructed models were
                      simulated, which yielded the simulated activity time series
                      for each brain region. From these time series, the
                      corresponding simulated FC was calculated and compared with
                      the empirical FC of the subject. Finally, the model
                      parameters were optimized via a grid search so that the
                      similarity between the empirical and simulated FC was
                      maximized. The procedure was repeated for 200 subjects, the
                      two models and 19 parcellations, and this dataset includes
                      the corresponding optimal model parameter settings as well
                      as the respective simulated FCs.},
      keywords     = {Neuroscience (Other)},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5232 - Computational Principles (POF4-523) / 5254 -
                      Neuroscientific Data Analytics and AI (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5232 / G:(DE-HGF)POF4-5254},
      typ          = {PUB:(DE-HGF)32},
      doi          = {10.25493/CBE0-EQV},
      url          = {https://juser.fz-juelich.de/record/908132},
}