Global sensitivity analysis informed model reduction and selection applied to a Valsalva maneuver model. (7th October 2021)

• Record Type: Journal Article
• Authors: Randall, E. Benjamin; Randolph, Nicholas Z.; Alexanderian, Alen; Olufsen, Mette S.
• Is Part Of: Journal of theoretical biology. Volume 526(2021)
• Publication Date: 2021-10-07
• Subjects: AICc Akaike information criterion with correction -- BIC Bayesian information criterion -- DDE Delay differential equations -- ECG Electrocardiogram -- GSIs Generalized Sobol' indices -- GSA Global sensitivity analysis -- ICs Initial conditions -- ITP Intrathoracic pressure -- LMSIs Limited-memory Sobol' indices -- LSA Local sensitivity analysis -- ODEs Ordinary differential equations -- PCHIP Piecewise cubic Hermite interpolating polynomial -- PTSIs Pointwise-in-time Sobol' indices -- QoI Quantity of interest -- RSA Respiratory sinus arrhythmia -- SIs Sobol' indices -- SBP Systolic blood pressure -- VM Valsalva maneuver; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2021.110759
• Languages: English
• ISSNs: 0022-5193

Abstract

Graphical abstract: Highlights: GSA determines noninfluential parameters informing model reduction. We introduce a windowed approach, limited-memory Sobol' indices (LMSIs), for GSA. LMSIs capture transient changes in time-varying parameter influence. Statistical and qualitative model selection is conducted on reduced VM models. Results support modeling both the aortic and carotid baroreceptors to model the VM. Abstract: In this study, we develop a methodology for model reduction and selection informed by global sensitivity analysis (GSA) methods. We apply these techniques to a control model that takes systolic blood pressure and thoracic tissue pressure data as inputs and predicts heart rate in response to the Valsalva maneuver (VM). The study compares four GSA methods based on Sobol' indices (SIs) quantifying the parameter influence on the difference between the model output and the heart rate data. The GSA methods include standard scalar SIs determining the average parameter influence over the time interval studied and three time-varying methods analyzing how parameter influence changes over time. The time-varying methods include a new technique, termed limited-memory SIs, predicting parameter influence using a moving window approach. Using the limited-memory SIs, we perform model reduction and selection to analyze the necessity of modeling both the aortic and carotid baroreceptor regions in response to the VM. We compare the original model to systematically reduced models including (i) the aortic and carotid regions, (ii) the aortic region only, and (iii) the carotid region only. Model selection is done quantitatively using the Akaike and Bayesian Information Criteria and qualitatively by comparing the neurological predictions. Results show that it is necessary to incorporate both the aortic and carotid regions to model the VM.