June 2015
Volume 56, Issue 7
ARVO Annual Meeting Abstract  |   June 2015
Simulating the Human Pattern Electroretinogram using Flash Stimuli
Author Affiliations & Notes
  • Kate A Godwin
    Psychological & Brain Sciences, University of Louisville, Louisville, KY
  • Thomas J. Roussel
    Bioengineering, University of Louisville, Louisville, KY
  • Paul J DeMarco
    Psychological & Brain Sciences, University of Louisville, Louisville, KY
  • Footnotes
    Commercial Relationships Kate Godwin, None; Thomas Roussel, None; Paul DeMarco, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2015, Vol.56, 464. doi:
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      Kate A Godwin, Thomas J. Roussel, Paul J DeMarco; Simulating the Human Pattern Electroretinogram using Flash Stimuli. Invest. Ophthalmol. Vis. Sci. 2015;56(7 ):464.

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

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Purpose: We tested the validity of a model which predicts that the human pattern electroretinogram (PERG) results from the summation of retinal signals to local light increments and decrements.

Methods: Subjects eyes’ were first dilated and vision corrected with trial frames. The PERG was recorded using counterphase-reversing checkerboard stimuli with 0.8° checks at 100% contrast, with a field size of 14.3° x 14.3°. The flash ERG was also recorded using square-wave, 250 ms increments and decrements of the same field size at both 100% and 50% contrast. The 100% contrast flashes replicated the luminance values of the PERG stimulus checks, whereas the 50% contrast flashes tested the effects of the mean level of retinal adaptation to the PERG stimulus. An additional variable, which involved scaling of flash response amplitude, was incorporated to control for the retinal area stimulated by either the light or dark checks. Offline, the PERG was simulated for each subject via algebraic summation of b-wave and d-wave responses from flash stimuli. The difference between each simulated and obtained waveform was calculated, and the area under this new curve was used to quantify the fit of the simulation for amplitude and waveform morphology. Implicit times of the P1 and N2 responses from the simulated PERG and measured PERG were also recorded and compared.

Results: Simulations resulted in waveforms that resembled the PERG response in morphology, but differed amplitude and/or implicit time for different response components. Best fits to the PERG were obtained from the scaled simulation using 100% contrast stimuli. Implicit times for the P1 component matched those of the P50 component best when 100% contrast was used in the simulation, whereas implicit times for the N2 component matched those of the N95 component best when 50% contrast was used. Across contrast levels, implicit times were more closely matched between P1 and P50 than between N2 and N95.

Conclusions: The best fit was obtained by scaling the amplitude of the responses to higher contrast stimuli, suggesting that both retinal area stimulated and contrast are important parameters in modeling the PERG response. Based on our data, it appears that PERG amplitude was more accurately modeled than was implicit time, suggesting that the phase relationship between the b- and d-wave responses may play an important role in shaping the PERG waveform.


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