Key result
Fourier transformation imaging combined with in silico data assimilation successfully recovered complex spatial patterns of voltage alternans and higher-order rhythms in canine ventricular models.
Why the study?
Spatiotemporal alternans patterns remain incompletely characterized, limited by lack of robust feature extraction techniques and reliable in silico data assimilation within predictive mathematical models.
A novel data assimilation procedure using Fourier transformation imaging successfully reproduces complex cardiac alternans patterns in silico, providing a new framework for studying arrhythmia mechanisms.
No immediate clinical implications; leaves open validation of Fourier assimilation for human arrhythmia modeling.
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.
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Loppini et al. (2022) studied Cardiac alternans. Fourier transformation imaging and in silico data assimilation vs. Homogeneous mathematical models was evaluated on Reproduction of complex discordant alternans patterns. Fourier transformation imaging combined with in silico data assimilation successfully recovered complex spatial patterns of voltage alternans and higher-order rhythms in canine ventricular models.
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