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Optical Ultrastructure of Large Mammalian Hearts Recovers Discordant Alternans by In Silico Data Assimilation.

Authors :
Loppini A
Erhardt J
Fenton FH
Filippi S
Hörning M
Gizzi A
Source :
Frontiers in network physiology [Front Netw Physiol] 2022 Apr 13; Vol. 2, pp. 866101. Date of Electronic Publication: 2022 Apr 13 (Print Publication: 2022).
Publication Year :
2022

Abstract

Understanding and predicting the mechanisms promoting the onset and sustainability of cardiac arrhythmias represent a primary concern in the scientific and medical communities still today. Despite the long-lasting effort in clinical and physico-mathematical research, a critical aspect to be fully characterized and unveiled is represented by spatiotemporal alternans patterns of cardiac excitation. The identification of discordant alternans and higher-order alternating rhythms by advanced data analyses as well as their prediction by reliable mathematical models represents a major avenue of research for a broad and multidisciplinary scientific community. Current limitations concern two primary aspects: 1) robust and general-purpose feature extraction techniques and 2) in silico data assimilation within reliable and predictive mathematical models. Here, we address both aspects. At first, we extend our previous works on Fourier transformation imaging (FFI), applying the technique to whole-ventricle fluorescence optical mapping. Overall, we identify complex spatial patterns of voltage alternans and characterize higher-order rhythms by a frequency-series analysis. Then, we integrate the optical ultrastructure obtained by FFI analysis within a fine-tuned electrophysiological mathematical model of the cardiac action potential. We build up a novel data assimilation procedure demonstrating its reliability in reproducing complex alternans patterns in two-dimensional computational domains. Finally, we prove that the FFI approach applied to both experimental and simulated signals recovers the same information, thus closing the loop between the experiment, data analysis, and numerical simulations.<br />Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.<br /> (Copyright © 2022 Loppini, Erhardt, Fenton, Filippi, Hörning and Gizzi.)

Details

Language :
English
ISSN :
2674-0109
Volume :
2
Database :
MEDLINE
Journal :
Frontiers in network physiology
Publication Type :
Academic Journal
Accession number :
36926104
Full Text :
https://doi.org/10.3389/fnetp.2022.866101