June 2017
Volume 58, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2017
Monte Carlo Simulation of Propagation of Axonal Loss Within the Optic Nerve In Leber Hereditary Optic Neuropathy
Author Affiliations & Notes
  • Razek Georges Coussa
    Opthalmology, McGill University, Montreal, Quebec, Canada
  • Pooya Merat
    Department of Electrical and Computer Engineering, McGill University, Montreal, Quebec, Canada
  • Leonard A Levin
    Opthalmology, McGill University, Montreal, Quebec, Canada
    Ophthalomology, University of Wisconsin, Madison, Wisconsin, United States
  • Footnotes
    Commercial Relationships   Razek Georges Coussa, None; Pooya Merat, None; Leonard Levin, Aerie (C), GSK (C), Inotek (C), Patents Assigned to Wisconsin Alumni Research Foundation (P), Quark (C), Regenera (C)
  • Footnotes
    Support  NIH R21EY025074 and P30EY016665; Canada Research Chairs; Canadian Foundation for Innovation
Investigative Ophthalmology & Visual Science June 2017, Vol.58, 3862. doi:
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    • Get Citation

      Razek Georges Coussa, Pooya Merat, Leonard A Levin; Monte Carlo Simulation of Propagation of Axonal Loss Within the Optic Nerve In Leber Hereditary Optic Neuropathy. Invest. Ophthalmol. Vis. Sci. 2017;58(8):3862.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Leber hereditary optic neuropathy (LHON) results in acute/subacute loss of central vision due to mutations in mitochondrial DNA coding for components of complex I. The preferential pattern of axonal loss preferentially involves small fibers. The present study seeks to simulate the spread of injury in LHON optic nerve axons.

Methods : The optic nerve was modeled in C#/C++ (Microsoft Visual Studio 2016) and optimized for speed by parallel processing on a NVIDIA® GeForce® graphics card. The data was then imported into MATLAB for analysis. The software generated models of randomly placed axons, based on previously reported axon size distributions. Model parameters included: optic nerve radius, minimum distance between two axons, and axon radii. The propagation of LHON injury was simulated using an initial injury that then spread as a diffusible substance from one axon to adjacent axons, resulting in their death when concentrations reached a toxic threshold. The following parameters were varied: toxic threshold, diffusion rate within and between the axons, and injury location. The simulation steady state was considered achieved once the overall axon loss did not vary more than 2%. One-way ANOVA was used to analyze the effects of propagation surface area, speed, and proportion of dead axons based on size (small, medium and large).

Results : Simulations based on 50 different axon size distributions demonstrated that 97%, 93% and 50% of small, medium and large axons, respectively, died by the time steady-state was reached. Varying the initial injury location affected time to steady state, with more temporal and peripheral injuries reaching steady state faster. Sensitivity analyses on various insult showed robust reproducibility when simulations were repeated under varying conditions.

Conclusions : A Monte Carlo simulation of spread of axonal injury using a theoretical axotoxin results in preferential loss of small fibers within the optic nerve. These results could provide insight into the specific nature of axonal loss in LHON.

This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.

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