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Computational Models for Predicting Outcomes of Neuroprosthesis Implantation: the Case of Cochlear Implants

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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.

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Acknowledgments

The research leading to these results received funding from the European Union Seventh Frame Programme (FP7/2007-2013) under grant agreement 304857.

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Authors and Affiliations

  1. Simbiosys Group, Universitat Pompeu Fabra, Barcelona, Spain

    Mario Ceresa, Nerea Mangado & Miguel A. Gonzalez Ballester

  2. Smart Systems and Nanotechnology, NASA Ames Research Center, Moffett Field, CA, USA

    Russell J. Andrews

  3. ICREA, Barcelona, Spain

    Miguel A. Gonzalez Ballester

Authors
  1. Mario Ceresa
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  2. Nerea Mangado
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  3. Russell J. Andrews
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  4. Miguel A. Gonzalez Ballester
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Correspondence to Mario Ceresa.

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Ceresa, M., Mangado, N., Andrews, R.J. et al. Computational Models for Predicting Outcomes of Neuroprosthesis Implantation: the Case of Cochlear Implants. Mol Neurobiol 52, 934–941 (2015). https://doi.org/10.1007/s12035-015-9257-4

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  • DOI: https://doi.org/10.1007/s12035-015-9257-4

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