Computational models for predicting outcomes of neuroprosthesis implantation: the case of cochlear implants

  • Authors
  • Ceresa, Mario; Mangado, Nerea; Andrews, Russell J.; González Ballester, Miguel Ángel
  • UPF authors
  • CERESA ., MARIO; MANGADO LOPEZ, NEREA; GONZALEZ BALLESTER, MIGUEL ANGEL;
  • Type
  • Scholarly articles
  • Journal títle
  • Molecular Neurobiology
  • Publication year
  • 2015
  • Volume
  • 52
  • Number
  • 2
  • Pages
  • 934-941
  • ISSN
  • 0893-7648
  • Publication State
  • Published
  • 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.
  • Complete citation
  • Ceresa, Mario; Mangado, Nerea; Andrews, Russell J.; González Ballester, Miguel Ángel. Computational models for predicting outcomes of neuroprosthesis implantation: the case of cochlear implants. Molecular Neurobiology 2015; 52(2): 934-941.
Bibliometric indicators
  • 11 times cited Scopus
  • 12 times cited WOS
  • Índex Scimago de 2.039(2015)