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| Hauptseite > Publikationsdatenbank > Efficient validation of dynamical whole-brain models via mathematical optimization algorithms > print |
| Hauptseite > Publikationsdatenbank > Efficient validation of dynamical whole-brain models via mathematical optimization algorithms > print |
| 001 | 905258 | ||
| 005 | 20230110132134.0 | ||
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| 037 | _ | _ | |a FZJ-2022-00541 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Wischnewski, Kevin |0 P:(DE-Juel1)178756 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a INM & IBI Retreat 2021, Forschungszentrum Jülich |c Virtual Conference |d 2021-10-05 - 2021-10-06 |w Germany |
| 245 | _ | _ | |a Efficient validation of dynamical whole-brain models via mathematical optimization algorithms |
| 260 | _ | _ | |c 2021 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
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| 520 | _ | _ | |a Investigating the resting-state brain dynamics involves its simulation via mathematical whole-brainmodels. The quality of the models’ output and its benefit for further studies, however, depend onoptimally selected input parameters, whose detection via systematic parameter space scans becomespractically unfeasible for high-dimensional models. In our work, we thus test and analyze severalalternative approaches to solve the so-called inverse problem that consists of a parameter-dependentmaximization of the models’ goodness-of-fit to empirical data. An exhaustive parameter variationon a dense grid serves as a benchmark to assess the performance of four optimization schemes:Nelder-Mead Algorithm (NMA), Particle Swarm Optimization (PSO), Covariance Matrix AdaptationEvolution Strategy (CMAES) and Bayesian Optimization (BO). To compare the methods, we employa dynamical model of coupled phase oscillators built upon the individual empirical structural connectivities of a cohort of 105 healthy subjects. For each subject, we determine the optimal modelparameters from two- and three-dimensional parameter spaces to maximize the correspondencebetween simulated and empirical functional connectivity. We show that the overall fitting quality of the tested methods can compete with the extensive parameter sweep exploration. There are,however, marked differences in the required computational resources and stability properties ofthe investigated techniques. By considering a trade-off between enhanced global convergence andthe economy of computation time, we propose two approaches, CMAES and BO, as effective andresource-saving alternatives to a high-dimensional parameter search on a dense grid. For the three-dimensional parameter optimization, they generated similar results as the grid search, but withinless than 6% of the required computation time. Our results can contribute to an efficient validationof mathematical models for personalized simulations of brain dynamics. |
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| 856 | 4 | _ | |u https://events.hifis.net/event/161/ |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/905258/files/Poster_INM_IBI_Retreat_2021.pdf |y OpenAccess |
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