Despite an increasing interest in the use of light for neural stimulation there is little information on how it interacts with neural tissue. The choice of wavelength in most of the optical stimulation literature is based on already available light sources designed for other applications. This paper is the first one to report the complex refractive index of the Sciatic nerve of Xenopus laevis, which is a crucial parameter for identifying the optimal wavelength of optical stimuli. The Xenopus laevis neural tissue is the most widely used tissue type in peripheral neurostimulation studies. In this work, the Reflectance (R) and the Transmittance (T ) of the Sciatic nerve were measured over a wavelength range of 860 nm to 2250 nm, and the corresponding real (n) and the imaginary (k) refractive indices were calculated using appropriate formulae in a novel way. The reported n values were between 1.3-1.44 and the k values are of the order of 10−5 over the full wavelength range. The absorption coefficient, α was found to be 100-500 cm−1. Several localised wavelength ranges were identified that can offer a maximised power coupling between potential optical stimuli and the neural tissue (1150- 1200 nm, 1500-1700 nm and 1900-2050 nm). The narrower regions of 1400-1600 nm and 1850-2150 nm were found to exhibit maximised absorbance. Separately, three regions were identified, where the penetration depths are the greatest (950-1000 nm, 1050-1350 nm and 1600-1900 nm). This paper provides, for the first time, the fundamental specifications for optimising the parameters of optical neurostimulation systems.
|Journal||IEEE Transactions on Neural Systems and Rehabilitation Engineering|
|Publication status||Published - 25 Oct 2018|
- Complex refractive index,
- Xenopus laevis.
- Optical Stimulation,
- Sciatic nerve,
Rahman, E., Powner, M., Kyriacou , P. A., & Triantis, I. (2018). Assessment of the Complex Refractive Indices of Xenopus leaves Sciatic Nerve for the Optimisation of Optical (NIR) Neurostimulation. IEEE Transactions on Neural Systems and Rehabilitation Engineering. https://doi.org/10.1109/TNSRE.2018.2878107