Reflectance decreases before thickness changes in the retinal nerve fiber layer in glaucomatous retinas

Xiang Run Huang, Ye Zhou, Wei Kong, Robert W. Knighton

Research output: Contribution to journalArticlepeer-review

57 Scopus citations


Purpose. Glaucoma damages the retinal never fiber layer (RNFL). RNFL thickness, measured with optical coherence tomography (OCT), is often used in clinical assessment of the damage. In this study the relation between the RNFL reflectance and thickness at early stages of glaucoma was investigated. Methods. A rat model of glaucoma was used that involved laser photocoagulation of the trabecular meshwork. The reflectance of the RNFL in an isolated retina was measured, followed by immunohistochemical staining of the axonal cytoskeleton. RNFL thickness was measured by confocal fluorescence imaging. RNFL reflectance was calculated for bundle areas located at radii of 0.22, 0.33, and 0.44 mm from the optic nerve head (ONH) center. Linear regression was used to study the relation between reflectance and thickness. For glaucomatous eyes, only those bundles with no apparent structural damage were used. Results. Bundles in 11 control retinas and 10 treated retinas were examined. Bundle thickness of both groups at each radius was similar (P = 0.89). The reflectance of the bundles at radii of 0.33 and 0.44 mm was found to be similar in both control and treated retinas (P > 0.5). However, the reflectance of the bundles at the 0.22-mm radius decreased significantly in the treated group (P = 0.005). Conclusions. Elevation of intraocular pressure causes decrease in RNFL reflectance for bundles near the ONH. Change in RNFL reflectance precedes thinning of the RNFL. The results suggest that a decrease in RNFL reflectance near the ONH is an early sign of glaucomatous damage.

Original languageEnglish (US)
Pages (from-to)6737-6742
Number of pages6
JournalInvestigative Ophthalmology and Visual Science
Issue number9
StatePublished - Aug 2011

ASJC Scopus subject areas

  • Ophthalmology
  • Sensory Systems
  • Cellular and Molecular Neuroscience


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