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@ARTICLE{Yeldesbay:861089,
      author       = {Yeldesbay, Azamat and Fink, Gereon R. and Daun, Silvia},
      title        = {{R}econstruction of effective connectivity in the case of
                      asymmetric phase distributions},
      journal      = {Journal of neuroscience methods},
      volume       = {317},
      issn         = {0165-0270},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2019-01654},
      pages        = {94 - 107},
      year         = {2019},
      abstract     = {BackgroundThe interaction of different brain regions is
                      supported by transient synchronization between neural
                      oscillations at different frequencies. Different measures
                      based on synchronization theory are used to assess the
                      strength of the interactions from experimental data. One
                      method of estimating the effective connectivity between
                      brain regions, within the framework of the theory of weakly
                      coupled phase oscillators, was implemented in Dynamic Causal
                      Modelling (DCM) for phase coupling (Penny et al., 2009).
                      However, the results of such an approach strongly depend on
                      the observables used to reconstruct the equations (Kralemann
                      et al., 2008). In particular, an asymmetric distribution of
                      the observables could result in a false estimation of the
                      effective connectivity between the network nodes.New
                      methodIn this work we built a new modelling part into DCM
                      for phase coupling, and extended it with a distortion
                      function that accommodates departures from purely sinusoidal
                      oscillations.ResultsBy analysing numerically generated data
                      sets with an asymmetric phase distribution, we demonstrated
                      that the extended DCM for phase coupling with the additional
                      modelling component, correctly estimates the coupling
                      functions.Comparison with existing methodsThe new method
                      allows for different intrinsic frequencies among coupled
                      neuronal populations and provides results that do not depend
                      on the distribution of the observables.ConclusionsThe
                      proposed method can be used to analyse effective
                      connectivity between brain regions within and between
                      different frequency bands, to characterize m:n phase
                      coupling, and to unravel underlying mechanisms of the
                      transient synchronization.},
      cin          = {INM-3},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-3-20090406},
      pnm          = {572 - (Dys-)function and Plasticity (POF3-572)},
      pid          = {G:(DE-HGF)POF3-572},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30786248},
      UT           = {WOS:000461264000011},
      doi          = {10.1016/j.jneumeth.2019.02.009},
      url          = {https://juser.fz-juelich.de/record/861089},
}