September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
A technique for measurement of ganglion cell and retinal nerve fiber layer thickness change with age in an Alzheimer’s disease (AD) mouse model using human spectral domain optical coherence tomography (OCT)
Author Affiliations & Notes
  • Keegan Harkins
    Ophthalmology, Truhlsen Eye Institute - UNMC, Omaha, Nebraska, United States
  • Mohammad Ali Sadiq
    Ophthalmology, Truhlsen Eye Institute - UNMC, Omaha, Nebraska, United States
  • Vikas Gulati
    Ophthalmology, Truhlsen Eye Institute - UNMC, Omaha, Nebraska, United States
  • Shane Jared Havens
    Ophthalmology, Truhlsen Eye Institute - UNMC, Omaha, Nebraska, United States
  • Shan Fan
    Ophthalmology, Truhlsen Eye Institute - UNMC, Omaha, Nebraska, United States
  • Tara Rudebush
    Ophthalmology, Truhlsen Eye Institute - UNMC, Omaha, Nebraska, United States
  • Deepta Abhay Ghate
    Ophthalmology, Truhlsen Eye Institute - UNMC, Omaha, Nebraska, United States
  • Footnotes
    Commercial Relationships   Keegan Harkins, None; Mohammad Sadiq, None; Vikas Gulati, None; Shane Havens, None; Shan Fan, None; Tara Rudebush, None; Deepta Ghate, None
  • Footnotes
    Support  American glaucoma Society and Research to prevent blindness
Investigative Ophthalmology & Visual Science September 2016, Vol.57, 3374. doi:
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      Keegan Harkins, Mohammad Ali Sadiq, Vikas Gulati, Shane Jared Havens, Shan Fan, Tara Rudebush, Deepta Abhay Ghate; A technique for measurement of ganglion cell and retinal nerve fiber layer thickness change with age in an Alzheimer’s disease (AD) mouse model using human spectral domain optical coherence tomography (OCT). Invest. Ophthalmol. Vis. Sci. 2016;57(12):3374.

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

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Abstract

Purpose : To determine a technique of measuring mouse ganglion cell layer (GCL) + retinal nerve fiber layer (RNFL) thickness with a human OCT machine, to establish a grading protocol for animal studies that was as stringent as human studies, and to look for differences in the GCL + RNFL in young and old Alzheimer mouse models.

Methods : The study population consisted of 9 APPswe,PSEN1dE9 mice including 3 young mice ( average age 10±3 weeks) and 6 old mice (average age 41±2 weeks). Spectralis OCT® machine fitted with a +25 lens with a reference arm adjustment was used to acquire 12 radial optic nerve head images of one dilated eye in the anesthetized mouse. The optic nerve head was separated into 3 sectors (12-2 O clock, 2-4 O clock and 4-6 O clock). The expert grader selected 1 of 4 images from each sector based on image quality. Two primary graders measured GCL + RNFL thickness 500 µm left and right of the bergmeister papillae and internal limiting membrane intersection on the optic nerve head (figure 1) from each image. The mean value of the 2 graders measurements were used. The grading was evaluated by an independent observer. Any discrepancies >20 µm were re-graded by the expert grader. If the expert grader’s measurements were within 20 microns of one of the graders, the median value of the measurement was used. All graders were masked to each other.

Results : Nine eyes for a total of 27 radial OCT images were graded. Of the 54 measurements, only 9 had a discrepancy greater than 20 microns (and needed the 3rd grader) and only 2 had a discrepancy greater than 30 microns. Of the 54 measurements, only 2 were classified as ungradable. The intraclass correlation coefficient was 0.7607 between the two graders. The mean GCL + RNFL thickness was 84.8 µm (SD 1.8 µm) in young mice and 89.0 µm (SD 4.2 µm) in old mice.

Conclusions : Our study demonstrated that it is possible to acquire OCT images of good quality in rodents using a human OCT machine. Our grading protocol was validated by the excellent concordance between graders. OCT studies in animals should follow grading protocols that are as strict as human grading protocols. The mean thickness of GCL + RNFL did not seem to negatively correlate with increasing age in the AD mouse models.

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.

 

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