Towards an efficient validation of dynamical whole-brain models

Autor: Kevin J. Wischnewski, Simon B. Eickhoff, Viktor K. Jirsa, Oleksandr V. Popovych
Přispěvatelé: Institute of Neuroscience and Medicine, Brain and Behaviour [Jülich, Germany] (INM-7), Jülich Research Centre, Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf], Institut de Neurosciences des Systèmes (INS), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), The authors thank F. Jarre and C. Helzel for helpful ideas and comments on the study design and K. Jung and J. Domhof for many practical advices. This study was supported by the Portfolio Theme Supercomputing and Modeling for the Human Brain of the Helmholtz Association (https://www.helmholtz.de/en), and the European Union's Horizon 2020 Research and Innovation Program under Grant Agreement No. 785907 (HBP SGA2), 945539 (HBP SGA3) and 826421 (VirtualBrainCloud). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors gratefully acknowledge the computing time granted through JARA on the supercomputer JURECA at Forschungszentrum Julich. Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: D. Van Essen and K. Ugurbil, 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research, and by the McDonnell Center for Systems Neuroscience at Washington University., European Project: 785907,H2020,HBP SGA2(2018), European Project: 945539,H2020,H2020-SGA-FETFLAG-HBP-2019,HBP SGA3(2020), European Project: 826421,TVB-Cloud (2018)
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Scientific Reports
Scientific Reports, 2022, 12 (1), pp.4331. ⟨10.1038/s41598-022-07860-7⟩
Scientific reports 12(1), 4331 (2022). doi:10.1038/s41598-022-07860-7
ISSN: 2045-2322
DOI: 10.1038/s41598-022-07860-7⟩
Popis: Simulating 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.
Databáze: OpenAIRE
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