Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 May 17;12(5):e0178004.
doi: 10.1371/journal.pone.0178004. eCollection 2017.

Measurement of macular structure-function relationships using spectral domain-optical coherence tomography (SD-OCT) and pattern electroretinograms (PERG)

Keunheung Park  1 , Jinmi Kim  2 , Jiwoong Lee  1   3
Affiliations

Measurement of macular structure-function relationships using spectral domain-optical coherence tomography (SD-OCT) and pattern electroretinograms (PERG)

Keunheung Park et al. PLoS One. .

Erratum in

Abstract

Background: Retinal ganglion cell (RGC) death is a common cause of loss of vision during glaucoma. Pattern electroretinogram (PERG) is an objective measure of the central retinal function that correlates with macular GCL thickness. The aim of this study is to determine possible relationships between the N95 amplitude of pattern electroretinogram (PERGamp) and macular ganglion cell/inner plexiform layer thickness (GCIPLT).

Methods and findings: This was a retrospective and comparative study including 74 glaucoma patients (44 early stage and 30 advanced stage cases) and 66 normal control subjects. Macular GCIPLT was measured using Cirrus spectral domain-optical coherence tomography. Standard automated perimetry and pattern ERGs were used in all patient examinations. Three types of regression analysis (broken stick, linear regression, and quadratic regression) were used to evaluate possible relationships between PERGamp and GCIPLT. Correlations between visual field parameters and GCIPLT were evaluated according to glaucoma severity. The best fit model for the relationship between PERGamp and GCIPLT was the linear regression model (r2 = 0.22; P < 0.001). The best-fit model for the relationship between visual field parameters and GCIPLT was the broken stick model. During early glaucoma, macular GCIPLT was positively correlated with PERGamp, but not with visual field loss. In advanced glaucoma, macular GCIPLT was positively correlated with both PERGamp and visual field loss.

Conclusions: PERGamp was significantly correlated with macular GCIPT in early glaucoma patients, while visual field performance showed no correlation with GCIPLT. PERGamp can therefore assist clinicians in making an early decision regarding the most suitable treatment plan, especially when GCIPLT is thinning with no change in visual field performance.

PubMed Disclaimer

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Calculation of central visual field sensitivity.
Total deviation values (TDn) of central 12 points (out of 54 points) are unlogged, averaged, and finally logged again to transform back to the decibel scale.
Fig 2
Fig 2. Scatter plots showing relationship between ganglion cell/inner plexiform layer (GCIPL) thickness and pattern electroretinogram N95 amplitude (PERGamp) in entire study sample.
Three different regression models applied; (A) broken stick model (B) linear regression model (C) quadratic regression model. The best fit model for GCIPL thickness and PERGamp was linear regression model (r2 = 0.220, P < <0.001, AIC = 588.7).
Fig 3
Fig 3
Scatter plots showing relationship of central visual field sensitivities (VFcenter) (A, B, C) or visual field mean deviation (VFMD) (D, E, F) with ganglion cell/inner plexiform layer (GCIPL) thickness in entire study sample. Three regression models were applied (broken stick model / linear regression mode / quadratic regression model respectively from left to right). For the GCIPL thickness and VFcenter, broken stick model was best fitted model with significant tipping point (Davies’ test P < 0.001) where the location was 71.9 μm. The broken stick model was best fitting model for the GCIPL thickness and VFMD among the three different regression models. The tipping point was significant (Davies’ test P < 0.001) where the location was 72.1 μm. VFcenter visual field centeral sensitivity, VFMD visual field mean deviation, GCIPL ganglion cell/ inner plexiform layer.
See this image and copyright information in PMC

References

    1. Bobak P, Bodis-Wollner I, Podos S, Harnois C, Mylin L, Maffei L, et al. Pattern electroretinograms and visual-evoked potentials in glaucoma and multiple sclerosis. Am. J. Ophthalmol. 1983;96:72–83. - PubMed
    1. Marx MS, Bodis-Wollner I, Lustgarten JS, Podos SM. Electrophysiological evidence that early glaucoma affects foveal vision. Doc. Ophthalmol. 1987;67:281–301. - PubMed
    1. Falsini B, Colotto A, Porciatti V, Buzzonetti L, Coppe A, De Luca LA. Macular flicker-and pattern-ERGs are differently affected in ocular hypertension and glaucoma. Clin. Vis. Sci. 1991;6:423–429.
    1. Siliprandi R, Bucci MG, Canella R, Carmignoto G. Flash and pattern electroretinograms during and after acute intraocular pressure elevation in cats. Invest. Ophthalmol. Vis. Sci. 1988;29:558–565. - PubMed
    1. Glovinsky Y, Quigley HA, Pease ME. Foveal ganglion cell loss is size dependent in experimental glaucoma. Invest. Ophthalmol. Vis. Sci. 1993;34:395–400. - PubMed