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@INPROCEEDINGS{Popovych:888565,
      author       = {Popovych, Oleksandr and Manos, Thanos and Diaz, Sandra and
                      Hoffstaedter, Felix and Schreiber, Jan and Eickhoff, Simon},
      title        = {{E}nrichment of data analytics by whole-brain computational
                      models},
      school       = {Heinrich Heine University Düsseldorf},
      reportid     = {FZJ-2020-05029},
      year         = {2020},
      abstract     = {Introduction: Processing of neuroimaging data and
                      extraction of the structural andfunctional brain signals
                      relies on plethora of setting and parameters whose values
                      arelargely conjectured. This problem is crucial for
                      simulating the whole-brain dynamics bymathematical models
                      derived from and validated against empirical data, which has
                      attracteda great interest during the last decade. For
                      example, considering a certain brainparcellation is
                      essential for defining a model network, but there is
                      practically no empiricalevidence for the effect of a
                      particular atlas choice. We address this problem using
                      acomputational modeling approach, where the resting-state
                      brain dynamics as reflected byfMRI BOLD is simulated by a
                      dynamical model of coupled oscillators. We compare
                      themodeling outcome for two brain atlases based on
                      functional and anatomical parcellations.We also consider
                      different frequency bands of the empirical BOLD signals used
                      to extractthe natural frequencies of the model oscillators
                      as another parameter of data processing.Methods: We
                      considered 272 healthy subjects of the Human Connectome
                      Project.The brain was parcellated into regions according to
                      the functional Schaefer atlas with100 and 200 cortical
                      parcels (S100 and S200) and anatomical Harvard-Oxford atlas
                      with 96parcels (HO96). Empirical functional connectivity
                      (eFC) was calculated from the meanBOLD signals extracted for
                      each brain region using FSL. Empirical structural
                      connectivity(eSC) was computed by counting the number of
                      streamlines and evaluating the averagedpath lengths between
                      pairs of brain regions from the whole-brain tractography
                      with 10Mstreamlines obtained by the probabilistic
                      tractography with using MRtrix. The model ofcoupled phase
                      oscillators was simulated, where the coupling weights and
                      delayswere extracted from eSC. The oscillators’ natural
                      frequencies were calculated by peakdetection in the spectral
                      power of the non-filtered (NF) BOLD signals as well filtered
                      in thebroad- (BF, [0.02, 0.07] Hz), low- (LF, [0.02, 0.04]
                      Hz) and high- (HF, [0.04, 0.07] Hz)frequency bands. The
                      model was validated by finding the optimal model parameters
                      of theglobal coupling and delay, where the strongest
                      Pearson’s correlation betweensimulated functional
                      connectivity (sFC) and eFC denoted by Fit(sFC,eFC) orbetween
                      sFC and eSC denoted by Fit(sFC,eSC) is achieved.Results: The
                      maximal similarity Fit(sFC, eFC) does not demonstrate any
                      pronounceddifference (relative changes of $1\%-5\%)$ for
                      varying frequency band for any fixed atlas. An increase of
                      up to $12\%$ can be observed when comparing S200 to S100,
                      and astrong enhancement of the model fitting $(70\%$ and
                      more) is apparent when comparing HO96to S100 and S200. For
                      the structure-function relationship Fit(sFC, eSC),
                      theordering NF < BF < LF < HF can be observed, but the
                      differences between atlases are lessconsistent. We found
                      that HF condition demonstrates distinct distributions of
                      theoptimal model parameters than other frequency bands,
                      especially, for S100 or for Fit(sFC,eSC). Furthermore, the
                      fitting results Fit(sFC, eFC) and Fit(sFC, eSC) for theHF
                      case less correlate with those obtained for other frequency
                      bands across subjects. Finally, the dynamics of the
                      validated model can result in pronounced
                      bimodaldistributions of corr(sFC,eSC) and the order
                      parameter R(t) for S100 and S200, but not for HO96.
                      Conclusion: We showed that a choice of a particular brain
                      parcellation and variation ofBOLD 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 atlasescan be
                      observed for the fitting of sFC to eFC. The frequency bands
                      of the BOLD filteringmostly affect structure-function
                      relationships and can also influence the reliability of
                      themodel validation.},
      month         = {Jun},
      date          = {2020-06-23},
      organization  = {The 2020 Annual Meeting Organization
                       for Human Brain Mapping, Virtual
                       (Canada), 23 Jun 2020 - 3 Jul 2020},
      subtyp        = {After Call},
      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/888565},
}