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Model-Based Recommendations for Optimal Surgical Placement of Epiretinal Implants

  • Michael BeyelerEmail author
  • Geoffrey M. Boynton
  • Ione Fine
  • Ariel Rokem
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11768)

Abstract

A major limitation of current electronic retinal implants is that in addition to stimulating the intended retinal ganglion cells, they also stimulate passing axon fibers, producing perceptual ‘streaks’ that limit the quality of the generated visual experience. Recent evidence suggests a dependence between the shape of the elicited visual percept and the retinal location of the stimulating electrode. However, this knowledge has yet to be incorporated into the surgical placement of retinal implants. Here we systematically explored the space of possible implant configurations to make recommendations for optimal intraocular positioning of the electrode array. Using a psychophysically validated computational model, we demonstrate that better implant placement has the potential to reduce the spatial extent of axonal activation in existing implant users by up to \(\sim \)55%. Importantly, the best implant location, as inferred from a population of simulated virtual patients, is both surgically feasible and is relatively stable across individuals. This study is a first step towards the use of computer simulations in patient-specific planning of retinal implant surgery.

Keywords

Retinal implant surgery Axonal stimulation Computational model 

Notes

Acknowledgments

Supported by the Washington Research Foundation Funds for Innovation in Neuroengineering and Data-Intensive Discovery (M.B.), by an award from the Gordon and Betty Moore Foundation (Award #2013-10-29) and the Alfred P. Sloan Foundation (Award #3835) to the University of Washington eScience Institute (A.R.), and by the National Institutes of Health (NIH K99 EY-029329 to M.B., EY-12925 to G.M.B., and EY-014645 to I.F.).Research credits for cloud computing were provided by Amazon Web Services.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Michael Beyeler
    • 1
    • 2
    • 3
    Email author
  • Geoffrey M. Boynton
    • 1
  • Ione Fine
    • 1
    • 2
  • Ariel Rokem
    • 2
    • 3
  1. 1.Department of PsychologyUniversity of WashingtonSeattleUSA
  2. 2.Institute for Neuroengineering (UWIN)University of WashingtonSeattleUSA
  3. 3.eScience InstituteUniversity of WashingtonSeattleUSA

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