Elimination of reentry spiral waves using adaptive optogenetical illumination based on dynamic learning techniques

Qianming Ding, Yipeng Hu, Yong Wu, Xueyan Hu, Ya Jia and Lijian Yang

Chaos, Solitons & Fractals, 2025, vol. 191, issue C

Abstract: Differences in excitability within cardiac tissues can undermine the effectiveness of electric field-induced gradient terms, which can potentially be addressed through optical feedback control in optogenetically modified tissues. In this paper, we introduces a novel technique of dynamic learning of synchronization (DLS) for excitable media, where the ideal LEDs array feedback adaptive optogenetical illumination (AOI) by training nodal membrane potentials in photosensitive media. Then, the AOI induces ionic currents for energy-efficient elimination of the spiral waves. We discuss global, local, and discrete AOIs (modeling the finite resolution) within the generalized cardiac model, and validate the applicability of our technique using a detailed biophysical model. The results show that global depolarizing light-induced currents are more effective for elimination of spiral waves, whereas hyperpolarizing light-induced currents can be applied locally to drive linear drift of wave tips thereby eliminating spiral waves. Although the latter approach consumes less energy, it requires a higher resolution of the LEDs array. For biophysical photosensitive media, AOI modulates Channelrhodopsin-2 (ChR2), creating the “negative current traps” at resting nodes that inhibits spiral wave diffusion. In our simulation, the energy consumption per unit area of AOI is below 1 mJ/cm2, which is significantly lower than that of conventional electric shock methods. Our AOI technology may offer a new solution for the control and elimination of cardiac spiral waves.

Keywords: Dynamic learning of synchronization; Adaptive optogenetical illumination; Spiral waves elimination; Energy-efficient defibrillation (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:191:y:2025:i:c:s0960077924013985

DOI: 10.1016/j.chaos.2024.115846

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