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@INPROCEEDINGS{Domhof:893375,
      author       = {Domhof, Justin and Jung, Kyesam and Eickhoff, Simon and
                      Popovych, Oleksandr},
      title        = {{P}arcellation-induced {V}ariation of {E}mpirical and
                      {S}imulated {F}unctional {B}rain {C}onnectivity},
      school       = {Heinrich-Heine University Düsseldorf},
      reportid     = {FZJ-2021-02716},
      year         = {2021},
      abstract     = {Recent developments of large-scale whole-brain network
                      models have demonstrated their great potential when
                      investigating resting-state brain activity which is believed
                      to be highly personalised (Bansal 2018, Popovych 2019). So
                      far, however, it has not been systematically investigated
                      how alternating derivations of the empirical brain
                      connectivity from MRI data, which serves as the input to
                      these models, influence the modelling results. Here, we
                      study the influence from an element indispensable in such
                      calculations: the brain parcellation scheme that reduces the
                      dimensionality of investigated brain networks by grouping
                      thousands of voxels together into a few hundred brain
                      regions.MethodWe extracted empirical structural and
                      functional connectivities from the dwMRI and fMRI data of
                      200 subjects (96 males, age 28.5 ± 3.5 years) included in
                      the Human Connectome Project dataset (Van Essen, 2012, 2013)
                      using 19 different freely available brain parcellations.
                      Subsequently, for each combination of parcellation and
                      subject, we constructed two dynamical whole-brain models on
                      the basis of the empirical structural connectivity. The
                      first is a model of coupled phase oscillators, and the
                      second is a network of Wilson-Cowan neural mass models
                      (Wilson, 1972). Both models were used to simulate the
                      resting-state functional connectivity, which was correlated
                      to its empirical counterpart to obtain an index
                      characterising their similarity. By varying the free
                      parameters included in both models, we obtained the
                      maximised similarity index or goodness-of-fit for every pair
                      of parcellation and subject. To find an explanation for any
                      differences observed in the goodness-of-fit for varying
                      parcellations, we derived 9 graph-theoretical network
                      statistics characterising the degree and closeness
                      centrality distribution, the modularity and the global
                      efficiency from both the structural and functional
                      connectomes (Rubinov, 2010). Finally, through the use of
                      principal component analysis combined with linear
                      regressions, we related the goodness-of-fits to the
                      extracted networks statistics on both the level of the group
                      and individual subjects.ResultsOur study revealed large
                      deviations in the goodness-of-fit across parcellations (Fig.
                      1A). By regressing the group-averages corresponding to both
                      models, we showed that this heterogeneity does not depend on
                      the considered models. Next, we conducted a principal
                      component analysis of the parcellation-based group-averaged
                      network statistics and structure-function relationship of
                      the empirical connectomes. The scores of the first principal
                      component were regressed with the group-averaged
                      goodness-of-fits, which explained approximately $75\%$ and
                      $86\%$ of the parcellation-induced variation in the
                      goodness-of-fits of the phase oscillator and neural mass
                      model, respectively (Fig. 1B). Including more PCs in the
                      regression model only marginally increased this proportion
                      (Fig. 1C). However, applying the same approach to the
                      interindividual variation in the goodness-of-fit for every
                      parcellation in isolation resulted in low explanatory power
                      and its high variability across parcellations and models
                      (Fig. 2A). Finally, a multivariate linear model was used to
                      regress the network statistics directly with the
                      goodness-of-fits of the individuals. This change in strategy
                      led to an increase in explained variance, which was
                      nevertheless still smaller than $30\%$ and highly variable
                      across parcellations (Fig. 2B).ConclusionOur results
                      demonstrated that the goodness-of-fit of the model to
                      empirical data was influenced greatly by the parcellation
                      both at the level of the entire cohort and the individual
                      subjects. We furthermore provided evidence that
                      graph-theoretical network properties derived from the
                      empirical data can explain group-averaged but not
                      interindividual variations in this respect. These findings
                      may contribute to mechanisms explaining how the validation
                      of the whole-brain models works, and how the fitting results
                      depend on the choice of parcellation.},
      month         = {Jun},
      date          = {2021-06-21},
      organization  = {The 27th Annual Meeting of the
                       Organization for Human Brain Mapping,
                       Virtual (Virtual), 21 Jun 2021 - 25 Jun
                       2021},
      subtyp        = {After Call},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5231 - Neuroscientific Foundations (POF4-523) / HBP SGA2 -
                      Human Brain Project Specific Grant Agreement 2 (785907) /
                      HBP SGA3 - Human Brain Project Specific Grant Agreement 3
                      (945539)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(EU-Grant)785907 /
                      G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/893375},
}