Introduction: Traditional extraocular electrical stimulation typically produces diffuse electric fields across the retina, limiting the precision of targeted therapy. Temporally interfering (TI) electrical stimulation, an emerging approach, can generate convergent electric fields, providing advantages for targeted treatment of various eye conditions.
Objective: Understanding how detailed structures of the retina, especially the optic nerve, affects electric fields can enhance the application of TI approach in retinal neurodegenerative and vascular diseases, an essential aspect that has been frequently neglected in previous researches.
Methods: We developed an anatomically accurate multi-layer human eye model, incorporating the optic nerve segment and setting it apart from current research endeavors. Based on this model, we conducted in silico investigations to predict the influence of the optic nerve on spatial characteristics of the temporally interfering electric field (TIEF) generated by diverse electrode configurations.
Results: Optic nerve directly influenced spatial distributions and modulation rules of TIEFs. It caused convergent areas to shift nasally or temporally in relation to return electrode positions, and further increased the axial anisotropy within the convergent TIEF. Furthermore, alterations in electrode positions and adjustments to current ratios among channels induced diverse spatial patterns of TIEFs within the macular region, the area surrounding the optic nerve, as well as peripheral retina.
Conclusion: Our findings suggested that presence of the optic nerve necessitated the utilization of different modulating paradigms when employing TI strategy for targeted treatment of various retinal lesions. And also provided theoretical references for developing a novel retinal electrical stimulation therapeutic device based on TI technology.
Keywords: computational modeling; extraocular electrical stimulation; optic nerve; retinal diseases therapy; temporal interference.
Copyright © 2024 Zhou, Su, Guo, Meng, Wu, Di, Li, Li and Chai.