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Updated: Sep 13, 2025

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

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Novel Structure-Function Models for Estimating Retinal Ganglion Cell Count Using Pattern Electroretinography in

Andrew Tirsi^1,2, Isabella Tello¹, Timothy Foster²

¹Department of Ophthalmology, Manhattan Eye, Ear, and Throat Hospital, Northwell Health, 210 E 64th St., 8th Floor, New York, NY 10065, USA.

Diagnostics (Basel, Switzerland)

|July 29, 2025

Summary

This summary is machine-generated.

Steady-state pattern electroretinogram (ssPERG) estimates, specifically eRGCC_MagD, show greater promise in predicting early structural changes in glaucoma suspects compared to the combined structure-function index (CSFI). This ssPERG-based approach offers a more sensitive tool for early glaucoma detection and risk stratification.

Keywords:

glaucoma suspects optical coherence tomography pattern electroretinogram retinal ganglion cells

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Area of Science:

Ophthalmology
Neuroscience
Biomedical Engineering

Background:

Early detection of retinal ganglion cell (RGC) dysfunction is crucial for managing glaucoma suspects (GSs).
The combined structure-function index (CSFI) may have limitations in assessing early RGC changes in GSs.
Evaluating alternative biomarkers for predicting structural damage in early glaucoma is essential.

Purpose of the Study:

To compare the predictive ability of steady-state pattern electroretinogram (ssPERG)-derived estimates against CSFI for early structural changes in glaucoma suspects.
To determine if ssPERG parameters can more accurately predict retinal nerve fiber layer thickness (RNFLT) and ganglion cell layer-inner plexiform layer thickness (GCL-IPLT).

Main Methods:

Fifty eyes from 25 glaucoma suspects were analyzed using ssPERG and spectral-domain optical coherence tomography (OCT).
Estimated RGC counts (eRGCC) were calculated using ssPERG-Magnitude (eRGCC_Mag), ssPERG-MagnitudeD (eRGCC_MagD), and CSFI (eRGCC_CSFI).
Linear regression and multivariable models assessed the prediction of average RNFLT (AvRNFLT), average GCL-IPLT (AvGCL-IPLT), and rim area.

Main Results:

ssPERG-derived models (eRGCC_Mag, eRGCC_MagD) significantly outperformed eRGCC_CSFI in predicting AvRNFLT and AvGCL-IPLT.
eRGCC_MagD demonstrated the strongest association with AvGCL-IPLT, indicating superior prediction of inner retinal structural changes.
eRGCC_Mag and eRGCC_CSFI showed better prediction for rim area, while PERG-based models suggest a linear relationship with structural changes.

Conclusions:

ssPERG-derived estimates, particularly eRGCC_MagD, are superior to eRGCC_CSFI in predicting early structural changes in glaucoma suspects.
eRGCC_MagD shows significant potential as an early biomarker for glaucomatous damage, especially for GCL-IPLT.
ssPERG-based models offer a simpler, more sensitive method for glaucoma risk stratification and monitoring treatment response.