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@ARTICLE{Dimulescu:280905,
      author       = {Dimulescu, Cristiana and Strömsdörfer, Ronja and Flöel,
                      Agnes and Obermayer, Klaus},
      title        = {{O}n the robustness of the emergent spatiotemporal dynamics
                      in biophysically realistic and phenomenological whole-brain
                      models at multiple network resolutions.},
      journal      = {Frontiers in network physiology},
      volume       = {5},
      issn         = {2674-0109},
      address      = {Lausanne},
      publisher    = {Frontiers Media},
      reportid     = {DZNE-2025-00989},
      pages        = {1589566},
      year         = {2025},
      abstract     = {The human brain is a complex dynamical system which
                      displays a wide range of macroscopic and mesoscopic patterns
                      of neural activity, whose mechanistic origin remains poorly
                      understood. Whole-brain modelling allows us to explore
                      candidate mechanisms causing the observed patterns. However,
                      it is not fully established how the choice of model type and
                      the networks' spatial resolution influence the simulation
                      results, hence, it remains unclear, to which extent
                      conclusions drawn from these results are limited by
                      modelling artefacts. Here, we compare the dynamics of a
                      biophysically realistic, linear-nonlinear cascade model of
                      whole-brain activity with a phenomenological Wilson-Cowan
                      model using three structural connectomes based on the
                      Schaefer parcellation scheme with 100, 200, and 500 nodes.
                      Both neural mass models implement the same mechanistic
                      hypotheses, which specifically address the interaction
                      between excitation, inhibition, and a slow adaptation
                      current which affects the excitatory populations. We
                      quantify the emerging dynamical states in detail and
                      investigate how consistent results are across the different
                      model variants. Then we apply both model types to the
                      specific phenomenon of slow oscillations, which are a
                      prevalent brain rhythm during deep sleep. We investigate the
                      consistency of model predictions when exploring specific
                      mechanistic hypotheses about the effects of both short- and
                      long-range connections and of the antero-posterior
                      structural connectivity gradient on key properties of these
                      oscillations. Overall, our results demonstrate that the
                      coarse-grained dynamics is robust to changes in both model
                      type and network resolution. In some cases, however, model
                      predictions do not generalize. Thus, some care must be taken
                      when interpreting model results.},
      keywords     = {network physiology (Other) / network resolution (Other) /
                      neural mass modeling (Other) / slow oscillations (Other) /
                      spatiotemporal dynamics (Other) / whole-brain modeling
                      (Other)},
      cin          = {AG Flöel},
      ddc          = {610},
      cid          = {I:(DE-2719)5000081},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40861379},
      pmc          = {pmc:PMC12371574},
      doi          = {10.3389/fnetp.2025.1589566},
      url          = {https://pub.dzne.de/record/280905},
}