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@INPROCEEDINGS{Jung:888478,
      author       = {Jung, Kyesam and Florin, Esther and Eickhoff, Simon and
                      Popovych, Oleksandr},
      title        = {{E}ffects of structural connectivity for the whole-brain
                      resting-state dynamical models},
      school       = {Heinrich Heine University Düsseldorf},
      reportid     = {FZJ-2020-04942},
      year         = {2020},
      abstract     = {[INTRODUCTION]The primary goal of this study is to
                      investigate the impact of MRI data processing on the
                      modeling of the resting-state brain dynamics, in particular,
                      the number of streamlines for the whole-brain tractography
                      (WBT) that affects structural connectivity (the number of
                      streamlines between two regions; SC) and path-length (mean
                      length of streamlines; PL). We consider a system of phase
                      oscillators (as brain regions) coupled with delay, where SC
                      is used for coupling between two oscillators and PL is used
                      as proxy for the delay of the signal propagation. We
                      systematically explore the parameter space (coupling and
                      delay) of the considered model and find optimal parameter
                      configurations, where the model dynamics closely replicates
                      the empirical data. Finally, we discuss the optimal number
                      of streamlines and the difference between two distinct brain
                      parcellations for modeling.[METHODS]T1-weighted images and
                      diffusion-weighted images from 105 subjects from the Human
                      Connectome Project (HCP) dataset were preprocessed for the
                      WBT. The developed pipeline has three parts of
                      preprocessing, tractography and reconstructing, where FSL,
                      FreeSurfer, MRtrix, and ANTs were used. After preprocessing,
                      the WBT was extracted for six different number of
                      streamlines (10M, 2M, 500K, 100K, 50K, and 10K), and SC and
                      PL were reconstructed for Schaefer's and Harvard-Oxford
                      atlases with 100 and 96 cortical areas, respectively, by
                      MRtrix. Furthermore, BOLD signals were extracted from the
                      minimally preprocessed fMRI with these two atlases, and
                      functional connectivity (FC) was calculated as Pearson
                      cross-correlation. Finally, the whole-brain mathematical
                      model of coupled phase oscillators, where the coupling
                      weights, delays and natural frequencies were extracted from
                      the empirical SC, PL and BOLD, respectively, generated
                      simulated FC for 12 conditions (6 WBTs and 2
                      atlases).[RESULTS]Simulated FC was compared to the empirical
                      FC and SC, and similarity between simulated and empirical
                      data was calculated by Pearson correlation for varying
                      parameters of the global coupling and delay. We found that
                      the shapes of the corresponding model parameter planes were
                      consistent across all WBTs. However, the distributions of
                      the maximal similarity between simulated and empirical FCs
                      and between simulated FC and empirical SC showed differences
                      through varying WBTs. On the one hand, the case of 10M
                      streamlines showed the highest similarity for Schaefer's
                      atlas. On the other hand, WBT of 100K streamlines can be
                      optimal for fitting simulated and empirical FC for
                      Harvard-Oxford atlas. The optimal parameters for the
                      strongest correlation between simulated and empirical FCs
                      are unimodaly distributed for Harvard-Oxford atlas, whereas
                      those for Schaefer's atlas can exhibit clustering. In
                      contrast, the optimal parameter sets for the correlation
                      between simulated FC and empirical SC showed a similar
                      two-cluster pattern for both atlases. Figure 2 illustrates
                      that clusters undergo a reshaping, where the optimal
                      parameters redistribute between clusters as WBT varies for
                      the Schaefer's atlas, whereas the clusters for
                      Harvard-Oxford atlas remain stable for any considered number
                      of streamlines.[CONCLUSIONS]In this study, we described the
                      impact of WBT streamlines used for whole-brain models on the
                      results of model validation. We showed that variation of WBT
                      has an impact on the maximal similarity between simulated
                      and empirical data as well as on the distribution of the
                      optimal model parameters for the considered Schaefer's and
                      Harvard-Oxford brain atlases. Still there are unclear
                      structural-functional relationships for modeling, e.g., an
                      effect of different types of atlases on the modeling
                      outcome. Further systematic modeling studies on the effect
                      of data-processing parameters are necessary for designing an
                      optimal approach for dynamical models and neuroimaging data
                      analytics.},
      month         = {Jun},
      date          = {2020-06-23},
      organization  = {2020 Organization for Human Brain
                       Mapping, Virtual (Canada), 23 Jun 2020
                       - 3 Jul 2020},
      subtyp        = {Other},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {574 - Theory, modelling and simulation (POF3-574)},
      pid          = {G:(DE-HGF)POF3-574},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/888478},
}