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@INPROCEEDINGS{Wischnewski:1018096,
      author       = {Wischnewski, Kevin and Eickhoff, Simon and Popovych,
                      Oleksandr},
      title        = {{V}alidation of dynamical whole-brain models in
                      high-dimensional parameter spaces},
      school       = {HHU Düsseldorf},
      reportid     = {FZJ-2023-04546},
      year         = {2023},
      abstract     = {INTRODUCTIONSimulating the resting-state brain dynamics via
                      mathematical whole-brain models allows for describing a
                      subject’s brain activity by a set of interpretable model
                      parameters. In this setting, increased efforts are currently
                      devoted to personalized simulations, which require a rising
                      number of optimally selected model parameters [1]. However,
                      a dense grid search is computationally unfeasible and
                      constrains the studies of high-dimensional models. In our
                      work, we apply mathematical optimization algorithms to
                      explore low- and high-dimensional parameter spaces at
                      moderate computational costs. We analyze the results in
                      terms of fit to empirical data and required computation time
                      as well as the impact of the number of optimized model
                      parameters on the differentiability between males and
                      females based on simulated data.METHODSWe used neuroimaging
                      data of 272 healthy subjects (128 males) of the Human
                      Connectome Project [2]. Working with an ensemble of coupled
                      phase oscillators [3] built upon individual empirical
                      structural connectivity (SC), we aimed at replicating the
                      empirical functional connectivity (FC) of every subject. We
                      considered Schaefer’s functional brain atlas (100 regions,
                      [4]) and the anatomical Harvard-Oxford parcellation (96
                      regions, [5]) as well as 2 optimization schemes based on
                      Covariance Matrix Adaptation Evolution Strategy (CMAES, [6])
                      and Bayesian Optimization (BO, [7]). Both algorithms
                      represent global stochastic search methods and were applied
                      to detect the optimal model parameters which maximize the
                      correlation between empirical FC (eFC) and simulated FC
                      (sFC). The optimized parameters included the global coupling
                      and delay (2D parameter space), the noise intensity (3D
                      parameter space), and the oscillation frequencies of
                      individual brain regions (99D or 103D parameter space). By
                      optimizing between 2 and 103 free parameters simultaneously,
                      we generated whole-brain models of an improved fitting to
                      empirical data of individual subjects and thus enhanced
                      model personalization, which we compared across subjects and
                      brain atlases. Particular focus was set on the optimal model
                      parameters and sFC as well as on their reliability and
                      subject-specificity.RESULTSWhen increasing the number of
                      optimized model parameters, we observed an enhancement of
                      the quality of the model validation, i.e. the similarity
                      between eFC and sFC (goodness-of-fit, GoF), for both atlases
                      and optimization algorithms. While the improvement from 2 to
                      3 variables ranges around $12\%,$ the transition to the
                      high-dimensional cases is sharp and can double the GoF. Also
                      the computational demands increased, but remain within a
                      tractable extent: For CMAES, the requirements were doubled,
                      and for BO around 12 times more resources were needed. The
                      reliability of the optimal parameters drops in higher
                      dimensions, and several constellations of parameter values
                      appear to generate comparably high GoF values. However, we
                      also observed high correlations (positive and negative)
                      between individual solutions. This hints at the presence of
                      a manifold in the model parameter space, where the optimal
                      values may be located. Additionally, we observed higher GoF
                      values for males than for females, with the differentiation
                      between these subject groups being enhanced for the model
                      optimization in high-dimensional parameter
                      spaces.CONCLUSIONSOur results provide an insight into the
                      model validation in high-dimensional parameter spaces, which
                      has been made possible by mathematical optimization schemes.
                      In particular, we have shown that more optimized parameters
                      lead to a much higher GoF that can be obtained for several
                      configurations of model parameters. The fact that
                      phenotypical differences were more pronounced in the data
                      derived from high-dimensional simulations implies a great
                      potential for model-based, personalized studies with
                      application to the investigation of inter-individual
                      variability.REFERENCES[1] Hashemi, M., Vattikonda, A. N.,
                      Sip, V., Diaz-Pier, S., Peyser, A., Wang, H., Guye, M.,
                      Bartolomei, F., Woodman, M. M., $\&$ Jirsa, V. K. (2021). On
                      the influence of prior information evaluated by fully
                      Bayesian criteria in a personalized whole-brain model of
                      epilepsy spread. PLoS computational biology, 17(7),
                      e1009129. https://doi.org/10.1371/journal.pcbi.1009129[2]
                      Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T.
                      E., Yacoub, E., Ugurbil, K., $\&$ WU-Minn HCP Consortium
                      (2013). The WU-Minn Human Connectome Project: an overview.
                      NeuroImage, 80, 62–79.
                      https://doi.org/10.1016/j.neuroimage.2013.05.041[3] Cabral,
                      J., Hugues, E., Sporns, O., $\&$ Deco, G. (2011). Role of
                      local network oscillations in resting-state functional
                      connectivity. NeuroImage, 57(1), 130–139.
                      https://doi.org/10.1016/j.neuroimage.2011.04.010[4]
                      Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo,
                      X. N., Holmes, A. J., Eickhoff, S. B., $\&$ Yeo, B. T. T.
                      (2018). Local-Global Parcellation of the Human Cerebral
                      Cortex from Intrinsic Functional Connectivity MRI. Cerebral
                      cortex (New York, N.Y. : 1991), 28(9), 3095–3114.
                      https://doi.org/10.1093/cercor/bhx179[5] Desikan, R. S.,
                      Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C.,
                      Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P.,
                      Hyman, B. T., Albert, M. S., $\&$ Killiany, R. J. (2006). An
                      automated labeling system for subdividing the human cerebral
                      cortex on MRI scans into gyral based regions of interest.
                      NeuroImage, 31(3), 968–980.
                      https://doi.org/10.1016/j.neuroimage.2006.01.021[6] Hansen,
                      N. (2006). The CMA Evolution Strategy: A Comparing Review.
                      In: Lozano, J.A., Larrañaga, P., Inza, I., Bengoetxea, E.
                      (eds) Towards a New Evolutionary Computation. Studies in
                      Fuzziness and Soft Computing, vol 192. Springer, Berlin,
                      Heidelberg. $https://doi.org/10.1007/3-540-32494-1_4[7]$
                      Martinez-Cantin, R. (2014). BayesOpt: A Bayesian
                      Optimization Library for Nonlinear Optimization,
                      Experimental Design and Bandits. Journal of Machine Learning
                      Research, 15, 3735-3739.[8] Rosenthal, R., $\&$ Rosnow, R.
                      L. (1991). Essentials of behavioral research: Methods and
                      data analysis (2nd ed.). New York: McGraw Hill.},
      month         = {Jul},
      date          = {2023-07-22},
      organization  = {The 29th Annual Meeting of the
                       Organization for Human Brain Mapping
                       (OHBM2023), Montréal (Canada), 22 Jul
                       2023 - 26 Jul 2023},
      subtyp        = {After Call},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5232 - Computational Principles (POF4-523) / HBP SGA2 -
                      Human Brain Project Specific Grant Agreement 2 (785907) /
                      HBP SGA3 - Human Brain Project Specific Grant Agreement 3
                      (945539) / VirtualBrainCloud - Personalized Recommendations
                      for Neurodegenerative Disease (826421)},
      pid          = {G:(DE-HGF)POF4-5232 / G:(EU-Grant)785907 /
                      G:(EU-Grant)945539 / G:(EU-Grant)826421},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2023-04546},
      url          = {https://juser.fz-juelich.de/record/1018096},
}