Uncovering personal circadian responses to light through particle swarm optimization

Vicente-Martínez, Jesús; Madrid, Juan Antonio; Rol, María Ángeles; Bonmatí Carrión, María de los Ángeles

Publication:
Uncovering personal circadian responses to light through particle swarm optimization

dc.contributor.author	Vicente-Martínez, Jesús
dc.contributor.author	Madrid, Juan Antonio
dc.contributor.author	Rol, María Ángeles
dc.contributor.author	Bonmatí Carrión, María de los Ángeles
dc.contributor.department	Anatomía Humana y Psicobiología
dc.date.accessioned	2025-02-01T23:03:21Z
dc.date.available	2025-02-01T23:03:21Z
dc.date.issued	2023-11-25
dc.description	© 2023 The Authors. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Published Manuscript, version of a Published Work that appeared in final form in Computer Methods and Programs in Biomedicine. To access the final edited and published work see https://doi.org/10.1016/j.cmpb.2023.107933
dc.description.abstract	Background and objectives: Kronauer’s oscillator model of the human central pacemaker is one of the most commonly used approaches to study the human circadian response to light. Two sources of error when applying it to a personal light exposure have been identified: (1) as a populational model, it does not consider interindividual variability, and (2) the initial conditions needed to integrate the model are usually unknown, and thus subjectively estimated. In this work, we evaluate the ability of particle swarm optimization (PSO) algorithms to simultaneously uncover the optimal initial conditions and individual parameters of a pre-defined Kronauer’s oscillator model. Methods: A Canonical PSO, a Dynamic Multi-Swarm PSO and a novel modification of the latter, namely Hierarchical Dynamic Multi-Swarm PSO, are evaluated. Two different target models (under a regular and an irregular schedule) are defined, and the same realistic light profile is fed to them. Based on their output, a fitness function is proposed, which is minimized by the algorithms to find the optimum set of parameters and initial conditions of the model. Results: We demonstrate that Dynamic Multi-Swarm and Hierarchical Dynamic Multi-Swarm algorithms can accurately uncover personal circadian parameters under both regular and irregular schedules, but as expected, optimization is easier under a regular schedule. Circadian parameters play the most important role in the optimization process and should be prioritized over initial conditions, although assessment of the impact of misestimating the latter is recommended. The log-log linear relationship between mean absolute error and computational cost shows that the number of particles to use is at the discretion of the user. Conclusions: The robustness and low errors achieved by the algorithms support their further testing, validation and systematic application to empirical data under a regular or irregular schedule. Uncovering personal circadian parameters can improve the assessment of the circadian status of a person and the applicability of personalized light therapies, as well as help to discover other factors that may lie behind the interindividual variability in the circadian response to light.
dc.format	application/pdf	es
dc.identifier.citation	Computer Methods and Programs in Biomedicine, Vol. 243, 2024 : 107933
dc.identifier.doi	https://doi.org/10.1016/j.cmpb.2023.107933
dc.identifier.issn	Print : 0169-2607
dc.identifier.issn	Electronic: 1872-7565
dc.identifier.uri	http://hdl.handle.net/10201/149945
dc.language	eng	es
dc.publisher	Elsevier
dc.relation	This paper has been funded by the Ministry of Economy and Competitiveness, the Instituto de Salud Carlos III through a CIBERFES grant (CB16/10/00239) and Diabfrail LatAm (European Union Horizon 2020 research and innovation program No. 825546) awarded to MAR (all co-financed by FEDER). Grant RTI2018-093528-B-I00, funded by MCIN/AEI/ 10.13039/ 501100011033 and by “ERDF A way of making Europe”, by the “European Union” and by the “European Union NextGenerationEU/ PRTR”. JVM predoctoral contract has been funded by grant ’21610/FPI/21’. Fundación Séneca. Región de Murcia (Spain).	es
dc.relation.publisherversion	https://www.sciencedirect.com/science/article/pii/S0169260723005990
dc.rights	info:eu-repo/semantics/openAccess	es
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 Internacional	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject	Particle swarm optimization
dc.subject	Kronauer’s oscillator model
dc.subject	Circadian personalization
dc.subject	Circadian response to light
dc.subject	Parameter optimization of ordinary differential equations
dc.subject	Heuristic algorithms
dc.title	Uncovering personal circadian responses to light through particle swarm optimization	es
dc.type	info:eu-repo/semantics/article	es
dspace.entity.type	Publication	es
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