title: Computational Models for Predicting Outcomes of Neuroprosthesis Implantation: the Case of Cochlear Implants.
abstract: Electrical stimulation of the brain has resulted in the most successful neuroprosthetic techniques to date: deep brain stimulation (DBS) and cochlear implants (CI). In both cases, there is a lack of pre-operative measures to predict the outcomes after implantation. We argue that highly detailed computational models that are specifically tailored for a patient can provide useful information to improve the precision of the nervous system electrode interface. We apply our framework to the case of CI, showing how we can predict nerve response for patients with both intact and degenerated nerve fibers. Then, using the predicted response, we calculate a metric for the usefulness of the stimulation protocol and use this information to rerun the simulations with better parameters.
authors.affiliation: Simbiosys Group, Universitat Pompeu Fabra, Barcelona, Spain, mario.ceresa@upf.edu.
authors.forename: Mario
authors.initials: M
authors.lastname: Ceresa
authors.forename: Nerea
authors.initials: N
authors.lastname: Mangado
authors.forename: Russell J
authors.initials: RJ
authors.lastname: Andrews
authors.forename: Miguel A
authors.initials: MA
authors.lastname: Gonzalez Ballester
mesh_terms: D003051:Cochlea / Q000000981:diagnostic imaging; D019929:Cochlear Implantation; D003054:Cochlear Implants; D003198:Computer Simulation; D046690:Deep Brain Stimulation; D004553:Electric Conductivity; D004567:Electrodes, Implanted; D006801:Humans; D008959:Models, Neurological; D009410:Nerve Degeneration; D009412:Nerve Fibers / Q000502:physiology; D057285:Precision Medicine / Q000379:methods; D016896:Treatment Outcome; D055114:X-Ray Microtomography