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@ARTICLE{Popovych:859356,
      author       = {Popovych, Oleksandr and Manos, Thanos and Hoffstaedter,
                      Felix and Eickhoff, Simon},
      title        = {{W}hat {C}an {C}omputational {M}odels {C}ontribute to
                      {N}euroimaging {D}ata {A}nalytics?},
      journal      = {Frontiers in systems neuroscience},
      volume       = {12},
      issn         = {1662-5137},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2019-00224},
      pages        = {68},
      year         = {2019},
      note         = {The authors gratefully acknowledge helpful discussions
                      withViktor Jirsa and Gustavo Deco. This work was supportedby
                      the Deutsche Forschungsgemeinschaft (DFG, EI 816/11-1),the
                      National Institute of Mental Health (R01-MH074457),
                      theHelmholtz Portfolio Theme Supercomputing and Modeling
                      forthe Human Brain and the European Union’s Horizon
                      2020Research and Innovation Programme under Grant
                      Agreement720270 (HBP SGA1) and 785907 (HBP SGA2).},
      abstract     = {Over the past years, nonlinear dynamical models have
                      significantly contributed to the general understanding of
                      brain activity as well as brain disorders. Appropriately
                      validated and optimized mathematical models can be used to
                      mechanistically explain properties of brain structure and
                      neuronal dynamics observed from neuroimaging data. A
                      thorough exploration of the model parameter space and
                      hypothesis testing with the methods of nonlinear dynamical
                      systems and statistical physics can assist in classification
                      and prediction of brain states. On the one hand, such a
                      detailed investigation and systematic parameter variation
                      are hardly feasible in experiments and data analysis. On the
                      other hand, the model-based approach can establish a link
                      between empirically discovered phenomena and more abstract
                      concepts of attractors, multistability, bifurcations,
                      synchronization, noise-induced dynamics, etc. Such a
                      mathematical description allows to compare and differentiate
                      brain structure and dynamics in health and disease, such
                      that model parameters and dynamical regimes may serve as
                      additional biomarkers of brain states and behavioral modes.
                      In this perspective paper we first provide very brief
                      overview of the recent progress and some open problems in
                      neuroimaging data analytics with emphasis on the resting
                      state brain activity. We then focus on a few recent
                      contributions of mathematical modeling to our understanding
                      of the brain dynamics and model-based approaches in
                      medicine. Finally, we discuss the question stated in the
                      title. We conclude that incorporating computational models
                      in neuroimaging data analytics as well as in translational
                      medicine could significantly contribute to the progress in
                      these fields.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) / HBP
                      SGA1 - Human Brain Project Specific Grant Agreement 1
                      (720270) / HBP SGA2 - Human Brain Project Specific Grant
                      Agreement 2 (785907)},
      pid          = {G:(DE-HGF)POF3-574 / G:(EU-Grant)720270 /
                      G:(EU-Grant)785907},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30687028},
      UT           = {WOS:000460573600001},
      doi          = {10.3389/fnsys.2018.00068},
      url          = {https://juser.fz-juelich.de/record/859356},
}