Can subject-specific single-fibre electrically evoked auditory brainstem response data be predicted from a model?

This article investigates whether prediction of subject-specific physiological data is viable through an individualised computational model of a cochlear implant. Subject-specific predictions could be particularly useful to assess and quantify the peripheral factors that cause inter-subject variations in perception. The results of such model predictions could potentially be translated to clinical application through optimisation of mapping parameters for individual users, since parameters that affect perception would be reflected in the model structure and parameters. A method to create a subject-specific computational model of a guinea pig with a cochlear implant is presented. The objectives of the study are to develop a method to construct subject-specific models considering translation of the method to in vivo human models and to assess the effectiveness of subject-specific models to predict peripheral neural excitation on subject level. Neural excitation patterns predicted by the model are compared with single-fibre electrically evoked auditory brainstem responses obtained from the inferior colliculus in the same animal. Results indicate that the model can predict threshold frequency location, spatial spread of bipolar and tripolar stimulation and electrode thresholds relative to one another where electrodes are located in different cochlear structures. Absolute thresholds and spatial spread using monopolar stimulation are not predicted accurately. Improvements to the model should address this.