1. Use of a Computational Electro Anatomical Model to Evaluate Intracochlear Electrode Arrays Student: Joseph Giorgio (gior0025@flinders.edu.au) Masters of Engineering (Biomedical) Industry Supervisor: Dr. Nick Pawsey (Cochlear Ltd.) Academic Supervisor: Professor Karen Reynolds Introduction: Cochlear implants (CI) have become prolific as a treatment for severe to profound sensorineural hearing loss. Since the first implantation in 1978 over 300,000 people have been implanted worldwide with CI, producing varying results in speech perception. CI bypass the transduction of sound vibrations into electrical signals within the cochlea and directly stimulate the neural tissue of auditory afferents using electrical pulses. CI aim to retain the natural frequency filtering within the cochlea (tonotopic specificity) through sound encoding strategies and the generation of controlled electrical pulses incident on localized regions of neural tissues. There are three main values that are compared when determining intracochlear electrode array performance, they are; threshold current level, which is the amount of supplied current through the electrode to generate a perceivable response. Maximum comfort level, which is the maximum supplied current to maintain a comfortable response in recipients. Spread of excitation (tonotopic specificity), which is the amount of spread of excited neural fibers in response to a maximum comfort level supplied current. The performance of intracochlear electrode arrays is investigated through clinical studies, however as there are many contributing factors to performance it can be hard to isolate direct causal relationships. One method of removing these confounding effects on performance is through the use of computer models. The current state of the art Electro-Anatomical-Models are based on complex 3 dimensional representations of the human cochlear and allow the potential field to be calculated anywhere within the geometry represented. Method: To determine the effect of modiolar positioning on tonotopic specificity and threshold values, 5 different intracochlear electrode array positions were investigated, within an Electro-Anatomical-Model (EAM). These positions related to arrays with an Electrode to Modiolus, Medial-Lateral (EMML) position of 0,10,20,40 and 60% total Medial- Lateral distance. The EAM is run with stimulation conditions to predict the potential field in response to a monopolar stimulation on a basal electrode (EL1), Middle electrode (EL11) and Apical electrode (EL22). From these fields the second spatial derivative of the potential field in the direction of the nerve fiber, at the location of the spiral ganglion cells (J SGC ) was calculated. This is taken to be the activating function of a SGC region (Rattay, Leao et al. 2001)that is sufficiently large to evoke a perceivable response (Goldwyn, Bierer et al. 2010)(Whiten 2007). Figure 1. Components of the Cochlear Nucleus Implant Figure 2. Basic cochlear anatomy Figure 1 shows the components of the Cochlear Ltd Nucleus implant system, with the intracochlear electrode array seen within the cochlea. Image adapted from: Cochlear Ltd, Sydney, Australia. www.cochlear.com Figure 2. shows a simplified cochlear mid section slice, where the central bony axis encasing the cochlear nerve is the modiolus. Image adapted from: Medical Dictionary, 2011. Modiolus, www.medicine.academic.ru Aim: To investigate the effect of intracochlear electrode array position on tonotopic specificity, threshold current level and power consumption. Cochlear EAM: The Cochlear EAM (EAM) is a finite difference solver and a volume conduction model based on a simplified cochlear spiral, the model is run using MATLAB and the potential field is predicted for every location within the volume conduction model, in response to a user defined current source and grounding conditions. Figure 3 shows the volume conduction model of the cochlea, where the blue region is fluid, the red region is nervous tissue and the entire model is encased in highly resistive bone. The black regions are the nerve fiber bundles. Peri-Modiolar Range (0-10%) Mid-Scala Range (20-40%) Lateral wall array (60%) Figure 4. Three electrode arrays within the cochlea Results: Current Spread, Thresholds and Power Consumption. Fig 5. Current Spread Figure 5 shows the current spread at the SGC for stimulation on EL1,EL11 and EL22. Using the voltage and threshold current for each stimulation a relative approximation of the power required to generate a perceivable response is calculated as seen in figure 4 c. A comparison of lateral wall and peri-modiolar electrodes shows; a decrease in power consumption of 9, 6.2 and 16.6 times reduction in spread of excitation of 12.3, 4.4 and 11.5 times for basal, middle and apical stimulation respectively as seen in figure 4 a. Generally it was found that the majority of the improvements occurred when the array was brought the last 20 - 30% of the way to the modiolus. Figure 4. Variation of Current Focus, Q (a), Relative Thresholds(b) and Relative Power (c) with EMML position. a. b. Conclusion : The results of this study are consistent with the clinical data obtained by Long et al.(2014) and Holden et al.(2013) who found a correlation between reduced EMML position and improved speech understanding. The results of this study further predict the expected benefits of designing an electrode that achieves an even more consistent and closer modiolar position. From the current density spread seen in Fig 5, three values are calculated. The relative Peak Current density, the relative Threshold Current Level and Q. The Q value is a measure of focus, calculated as the width of activated SGC in response to a stimulation at maximum comfort level, where a higher Q corresponds to a reduced spread of excitation. The results show that peri-modiolar positioned arrays require considerably less current to reach threshold compared to lateral wall arrays, with 3.2, 3.1 and 4.4 times higher thresholds for lateral arrays stimulated on basal, middle and apical electrodes respectively. c. Other Work: The finite difference solver was initially verified by replicating a bench set up of a scale cochlear spiral. A 22 channel intracochlear electrode array was used for stimulation and recording, where one contact was a stimulating electrode and the remaining 21 electrodes were returns, connected to a common ground. The volume conduction model was created using CAD software, the geometry is then imported into MATLAB. From here the stimulation and grounding conditions were input. It was determined that the computational model was able to accurately predict the current flowing through each electrode and thus the finite difference solver can accurately predict electrical stimulation scenarios. During intracochlear electrode insertion surgery there are several complications that can occur. The EAM was used to predict and compare specific voltage profiles specific to these complications, these profiles were captured in a human temporal bone study using an intracochclear electrode array. It was determined that the EAM can be used for designing software to diagnose electrode insertion complications. References: Long, C.J., et al., Examining the electro-neural interface of cochlear implant users using psychophysics, CT scans, and speech understanding. J Assoc Res Otolaryngol, 2014. 15(2): p. 293-304. Holden, L.K., et al., Factors affecting open-set word recognition in adults with cochlear implants. Ear Hear, 2013. 34(3): p. 342-60. Rattay, F., R.N. Leao, and H. Felix, A model of the electrically excited human cochlear neuron. II. Influence of the three-dimensional cochlear structure on neural excitability. Hear Res, 2001. 153(1-2): p. 64-79. Goldwyn, J.H., S.M. Bierer, and J.A. Bierer, Modeling the electrode-neuron interface of cochlear implants: effects of neural survival, electrode placement, and the partial tripolar configuration. Hear Res, 2010. 268(1-2): p. 93-104. Whiten, D.M., Electro-Anatomical Models of the Cochlear Implant, in Harvard-MIT Division of Health Sciences and Technology. 2007, Massachusetts Institute of Technology. Figure 3. Volume Conduction Model