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@INPROCEEDINGS{Popovych:888566,
      author       = {Popovych, Oleksandr and Manos, Thanos and Diaz, Sandra and
                      Hoffstaedter, Felix and Schreiber, Jan and Eickhoff, Simon},
      title        = {{O}ptimizing {C}urrent {I}maging {P}ipelines by
                      {W}hole-{B}rain {D}ynamical {M}odels},
      school       = {Heinrich Heine University Düsseldorf},
      reportid     = {FZJ-2020-05030},
      year         = {2020},
      abstract     = {Processing of neuroimaging data and extraction of the
                      structural and functional brain signals relies on plethora
                      of setting and parameters whose values are largely
                      conjectured. This problem is crucial for simulating the
                      whole-brain dynamics by mathematical models derived from and
                      validated against empirical data, which has attracted a
                      great interest during the last decade. For example,
                      considering a certain brain parcellation is essential for
                      defining a model network, but there is practically no
                      empirical evidence for the effect of a particular atlas
                      choice. We address this problem using a computational
                      modeling approach, where the resting-state brain dynamics as
                      reflected by fMRI BOLD is simulated by a dynamical model of
                      coupled oscillators. We considered a system of coupled phase
                      oscillators, where the coupling weights and delays were
                      extracted from empirical structural connectivity (eSC). We
                      compared the modeling outcome for three different brain
                      parcellations as given by the functional Schaefer atlas with
                      100 and 200 cortical parcels (S100 and S200) and anatomical
                      Harvard-Oxford atlas with 96 parcels (HO96). The natural
                      frequencies of the model oscillators were extracted from
                      non-filtered (NF) BOLD signal as well as filtered in
                      different frequency bands including the broad- (BF), low-
                      (LF) and high- (HF) frequency bands. The model was validated
                      by finding the optimal model parameters of the global
                      coupling and delay, where the strongest similarity as
                      revealed by Pearson’s correlation between simulated and
                      empirical functional connectivity (sFC and eFC) or between
                      sFC and eSC is achieved. We found that the maximal
                      similarity between functional data demonstrates a moderate
                      improvement of the model fitting when comparing S200 to
                      S100, and there is a strong enhancement when comparing HO96
                      to S100 and S200. For the structure-function relationship
                      between sFC and eSC, the ordering NF < BF < LF < HF can be
                      observed, but the differences between atlases are less
                      consistent. We found that HF condition can lead to distinct
                      distributions of the optimal model parameters than other
                      frequency bands, and the corresponding fitting results less
                      correlate with those obtained for other frequency bands.
                      Finally, the dynamics of the validated model can result in
                      pronounced bimodal distributions of the order parameter R(t)
                      and other quantities characterizing the model fitting for
                      S100 and S200, but not for HO96. We thus showed that a
                      choice of a particular brain parcellation and variation of
                      BOLD frequency band can cause a significant impact on the
                      quality of the model fitting, dynamics of the validated
                      model and their interrelations. The main impact of the brain
                      atlases can be observed for the fitting of sFC to eFC. The
                      frequency bands of the BOLD filtering mostly affect
                      structure-function relationships and can also influence the
                      reliability of the model validation.},
      month         = {Feb},
      date          = {2020-02-27},
      organization  = {The 10th NIC Symposium of the John von
                       Neumann Institute for Computing (NIC),
                       Jülich (Germany), 27 Feb 2020 - 28 Feb
                       2020},
      subtyp        = {Invited},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) /
                      VirtualBrainCloud - Personalized Recommendations for
                      Neurodegenerative Disease (826421)},
      pid          = {G:(DE-HGF)POF3-574 / G:(EU-Grant)826421},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/888566},
}