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@INPROCEEDINGS{Manos:865117,
      author       = {Manos, Thanos and Diaz, Sandra and Hoffstaedter, Felix and
                      Schreiber, Jan and Peyser, Alexander and Eickhoff, Simon and
                      Popovych, Oleksandr},
      title        = {{I}mpact of brain parcellation on parameter optimization of
                      the whole-brain dynamical models},
      reportid     = {FZJ-2019-04669},
      year         = {2019},
      abstract     = {Recent progress in neuroimaging techniques has advanced our
                      understanding of structural and functional properties of the
                      brain. Resting-state functional connectivity (FC) analysis
                      has brought new insights to the inter-individual variability
                      [1]. Using diffusion-weighted magnetic resonance imaging,
                      one can retrieve the basic features of the anatomical
                      architecture of brain networks, i.e. structural connectivity
                      (SC) [2]. Empirical SC (eSC) and FC (eFC) can be used to
                      build and validate large-scale mathematical models of the
                      brain dynamics being in the focus of research nowadays [3,
                      4]. In this work, we set out to investigate the impact of
                      different brain atlases on the dynamics of the whole-brain
                      computational models and their optimal parameters fitted to
                      the neuroimaging data, resulting in the optimal agreement
                      between empirical and simulated data. We considered a sample
                      of 23 healthy subjects from the Human Connectome Project
                      database [5] and 2 different brain atlases, the
                      Harvard-Oxford structural atlas and the Schaefer functional
                      atlas [6]. The large-scale network model of brain activity
                      is based on an informed by eSC Kuramoto model [8] and is
                      simulated using The Virtual Brain (TVB) platform [7], with
                      an optimized code from TVB-HPC adequate for high-performance
                      clusters computing. We found that the two considered atlases
                      are in good agreement with respect to the optimal parameters
                      (e.g. global coupling strength K) and the corresponding
                      values of the correlation coefficient of the best
                      correspondence between sFC and eSC. Moreover, the considered
                      model can demonstrate relatively strong correlations between
                      eSC and sFC matrices whereas the correspondence between eFC
                      and sFC matrices is, however, weaker for both atlases
                      [9].References[1] Park H. J. and Friston K. J. (2013).
                      Structural and functional brain networks: from connections
                      to cognition. Science 342: 1238411.[2] Maier-Hein K. H.,
                      Neher P. F., Houde J.-C., Côté M.-A., Garyfallidis E.,
                      Zhong J., et al. (2017). The challenge of mapping the human
                      connectome based on diffusion tractography. Nat. Commun. 8:
                      1349.[3] Popovych O. V., Manos T., Hoffstaedter F. and
                      Eickhoff S. B. (2019). What can computational models
                      contribute to neuroimaging data analytics? Frontiers in
                      Systems Neuroscience (in press).[4] Deco G. and Kringelbach
                      M. (2016). Metastability and Coherence: Extending the
                      Communication through Coherence Hypothesis Using a
                      Whole-Brain Computational Perspective. Trends in
                      Neurosciences. 39(6):432[5] McNab J. A., Edlow B. L., Witzel
                      T., Huang S. Y., Bhat H., Heberlein K., Feiweier T., Liu K.,
                      Keil B., Cohen-Adad J., Tisdall M. D., Folkerth R. D.,
                      Kinney H. C., Wald L. L. (2013). The Human Connectome
                      Project and beyond: initial applications of 300 mT/m
                      gradients. NeuroImage 80:234.[6] Schaefer A., Kong R.,
                      Gordon E. M., Laumann T. O., Zuo X. N., Holmes A. J.,
                      Eickhoff S. B., and Yeo B. T. T. (2017). Local-global
                      parcellation of the human cerebral cortex from intrinsic
                      functional connectivity MRI, Cereb. Cortex, 28(9): 3095.[7]
                      Sanz Leon P., Knock S. A., Woodman M. M., Domide L.,
                      Mersmann J., McIntosh A. R. and Jirsa V. (2013). The Virtual
                      Brain: a simulator of primate brain network dynamics. Front.
                      Neuroinform. 7:10 (TVB-HPC:
                      https://gitlab.thevirtualbrain.org/tvb/hpc).[8] Kuramoto Y.
                      (1984). Chemical oscillations, waves, and turbulence,
                      Springer, Berlin.[9] Manos T., Diaz-Pier S., Hoffstaedter
                      F., Schreiber J., Eickhoff S. B. and Popovych O. V. (in
                      preparation).},
      month         = {Jul},
      date          = {2019-07-13},
      organization  = {CNS*2019, Barcelona (Spain), 13 Jul
                       2019 - 17 Jul 2019},
      subtyp        = {Other},
      cin          = {JSC / INM-7 / INM-1},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)INM-7-20090406 /
                      I:(DE-Juel1)INM-1-20090406},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511) / 574 - Theory, modelling and simulation
                      (POF3-574) / VirtualBrainCloud - Personalized
                      Recommendations for Neurodegenerative Disease (826421) / HBP
                      SGA2 - Human Brain Project Specific Grant Agreement 2
                      (785907) / SLNS - SimLab Neuroscience (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF3-511 / G:(DE-HGF)POF3-574 /
                      G:(EU-Grant)826421 / G:(EU-Grant)785907 /
                      G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/865117},
}