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000904953 0247_ $$2doi$$a10.21203/rs.3.rs-1139051/v1
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000904953 037__ $$aFZJ-2022-00267
000904953 1001_ $$0P:(DE-Juel1)178756$$aWischnewski, Kevin J.$$b0$$ufzj
000904953 245__ $$aTowards an Efficient Validation of Dynamical Whole-brain Models
000904953 260__ $$c2021
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000904953 500__ $$aPlanned for publication in Scientific Reports
000904953 520__ $$aSimulating the resting-state brain dynamics via mathematical whole-brain models requires an optimal selection of parameters, which determine the model’s capability to replicate empirical data. Since the parameter optimization via a grid search (GS) becomes unfeasible for high-dimensional models, we evaluate several alternative approaches to maximize the correspondence between simulated and empirical functional connectivity. A dense GS serves as a benchmark to assess the performance of four optimization schemes: Nelder-Mead Algorithm (NMA), Particle Swarm Optimization (PSO), Covariance Matrix Adaptation Evolution Strategy (CMAES) and Bayesian Optimization (BO). To compare them, we employ an ensemble of coupled phase oscillators built upon individual empirical structural connectivity of 105 healthy subjects. We determine optimal model parameters from two- and three-dimensional parameter spaces and show that the overall fitting quality of the tested methods can compete with the GS. There are, however, marked differences in the required computational resources and stability properties, which we also investigate before proposing CMAES and BO as efficient alternatives to a high-dimensional GS. For the three-dimensional case, these methods generated similar results as the GS, but within less than 6% of the computation time. Our results contribute to an efficient validation of models for personalized simulations of brain dynamics.
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000904953 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x1
000904953 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x2
000904953 536__ $$0G:(EU-Grant)826421$$aVirtualBrainCloud - Personalized Recommendations for Neurodegenerative Disease (826421)$$c826421$$fH2020-SC1-DTH-2018-1$$x3
000904953 588__ $$aDataset connected to CrossRef
000904953 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon B.$$b1$$ufzj
000904953 7001_ $$0P:(DE-HGF)0$$aJirsa, Viktor K.$$b2
000904953 7001_ $$0P:(DE-Juel1)131880$$aPopovych, Oleksandr V.$$b3$$eCorresponding author
000904953 773__ $$a10.21203/rs.3.rs-1139051/v1
000904953 8564_ $$uhttps://juser.fz-juelich.de/record/904953/files/Preprint%20Wischnewski%20et%20al..pdf$$yOpenAccess
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