The Relationship Between Macula Retinal Ganglion Cell Density and Visual Function in the Nonhuman Primate

Antwi-Boasiako, Kwame; Carter-Dawson, Louvenia; Harwerth, Ronald S.; Gondo, Margaret; Patel, Nimesh B.

doi:10.1167/iovs.62.1.5

Cited by 16 publications

(26 citation statements)

References 64 publications

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“…These results highlight the linkage between the thickness of RGC related layers and human contrast sensitivity. Importantly, the fact that our results are well aligned with various previous findings 4 , 43 , 45 – 53 further helped us assure the quality of our OCT image acquisition and preprocessing.…”

Section: Resultssupporting

confidence: 86%

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Identifying the Retinal Layers Linked to Human Contrast Sensitivity Via Deep Learning

Shamsi

Liu

Owsley

et al. 2022

Invest. Ophthalmol. Vis. Sci.

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Purpose Luminance contrast is the fundamental building block of human spatial vision. Therefore contrast sensitivity, the reciprocal of contrast threshold required for target detection, has been a barometer of human visual function. Although retinal ganglion cells (RGCs) are known to be involved in contrast coding, it still remains unknown whether the retinal layers containing RGCs are linked to a person's contrast sensitivity (e.g., Pelli-Robson contrast sensitivity) and, if so, to what extent the retinal layers are related to behavioral contrast sensitivity. Thus the current study aims to identify the retinal layers and features critical for predicting a person's contrast sensitivity via deep learning. Methods Data were collected from 225 subjects including individuals with either glaucoma, age-related macular degeneration, or normal vision. A deep convolutional neural network trained to predict a person's Pelli-Robson contrast sensitivity from structural retinal images measured with optical coherence tomography was used. Then, activation maps that represent the critical features learned by the network for the output prediction were computed. Results The thickness of both ganglion cell and inner plexiform layers, reflecting RGC counts, were found to be significantly correlated with contrast sensitivity ( r = 0.26 ∼ 0.58 , Ps < 0.001 for different eccentricities). Importantly, the results showed that retinal layers containing RGCs were the critical features the network uses to predict a person's contrast sensitivity (an average R 2 = 0.36 ± 0.10). Conclusions The findings confirmed the structure and function relationship for contrast sensitivity while highlighting the role of RGC density for human contrast sensitivity.

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Section: Resultssupporting

confidence: 86%

“…This result was well aligned with previous findings showing that the thickness of the ganglion cell and inner plexiform layers (i.e., the RGC+ layer) were closely related to RGC counts/density. 43 – 45 …”

Section: Resultsmentioning

confidence: 99%

Identifying the Retinal Layers Linked to Human Contrast Sensitivity Via Deep Learning

Shamsi

Liu

Owsley

et al. 2022

Invest. Ophthalmol. Vis. Sci.

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“…The increase of receptive fields was proportional to the degree of glaucomatous damage, highlighting the close linkage between the size of signal integration zones and ganglion cell damage. It is also important to note that the macular RGC+ layer thickness is closely correlated with RGC counts 89 – 91 : the thinner the layer gets, the more the RGCs are being lost.…”

Section: Discussionmentioning

confidence: 99%

Binocularly Asymmetric Crowding in Glaucoma and a Lack of Binocular Summation in Crowding

Shamsi

Liu

Kwon

2022

Invest. Ophthalmol. Vis. Sci.

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Purpose Glaucoma is associated with progressive loss of retinal ganglion cells. Here we investigated the impact of glaucomatous damage on monocular and binocular crowding in parafoveal vision. We also examined the binocular summation of crowding to see if crowding is alleviated under binocular viewing. Methods The study design included 40 individuals with glaucoma and 24 age-similar normal cohorts. For each subject, the magnitude of crowding was determined by the extent of crowding zone. Crowding zone measurements were made binocularly in parafoveal vision (i.e., at 2° and 4° retinal eccentricities) visual field. For a subgroup of glaucoma subjects ( n = 17), crowding zone was also measured monocularly for each eye. Results Our results showed that, compared with normal cohorts, individuals with glaucoma exhibited significantly larger crowding—enlargement of crowding zone (an increase by 21%; P < 0.01). Moreover, we also observed a lack of binocular summation (i.e., a binocular ratio of 1): binocular crowding was determined by the better eye. Hence, our results did not provide evidence supporting binocular summation of crowding in glaucomatous vision. Conclusions Our findings show that crowding is exacerbated in parafoveal vision in glaucoma and binocularly asymmetric glaucoma seems to induce binocularly asymmetric crowding. Furthermore, the lack of binocular summation for crowding observed in glaucomatous vision combined with the lack of binocular summation reported in a previous study on normal healthy vision support the view that crowding may start in the early stages of visual processing, at least before the process of binocular integration takes place.

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“… 17 , 18 The trained networks were specific for the optic nerve head region (RNFL), macula region (RNFL and GCIPL), and widefield imaging (RNFL and GCIPL). Scans previously segmented for other studies, which included controls and eyes with experimental glaucoma, were used for training the optic nerve head and macular region networks, 2 , 19 – 22 whereas a separate series of 6117 b-scans was used to train the network for widefield scans. Segmentation criteria used for the training dataset have been previously reported, 2 , 21 , 22 and none of the b-scans used for the training sets were included in the analysis for this study.…”

Section: Methodsmentioning

confidence: 99%

“…For glaucoma management, retinal ganglion cell content is assessed using circumpapillary retinal nerve fiber layer (RNFL) thickness and macula ganglion cell inner plexiform layer (GCIPL) thickness. 1 , 2 In combination or independently, these measures demonstrate good repeatability and diagnostic value. 3 – 5 …”

Section: Introductionmentioning

confidence: 99%

Widefield OCT Imaging for Quantifying Inner Retinal Thickness in the Nonhuman Primate

Srinivasan

Das

Patel

2022

Trans. Vis. Sci. Tech.

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Purpose To determine the agreement and repeatability of inner retinal thickness measures from widefield imaging compared to standard scans in healthy nonhuman primates. Methods Optical coherence tomography (OCT) scans were acquired from 30 healthy rhesus monkeys, with 11 animals scanned at multiple visits. The scan protocol included 20° × 20° raster scans centered on the macula and optic nerve head (ONH), a 12° diameter circular scan centered on the ONH, and a 55 × 45° widefield raster scan. Each scan was segmented using custom neural network–based algorithms. Bland–Altman analysis were used for comparing average circumpapillary retinal nerve fiber layer (RNFL) thickness and ganglion cell inner plexiform layer (GCIPL) thickness for a 16° diameter region. Comparisons were also made for similar 1° × 1° superpixels from the raster scans. Results Average circumpapillary RNFL thickness from the circular scan was 114.2 ± 5.8 µm, and 113.2 ± 7.3 µm for an interpolated scan path from widefield imaging (bias = −1.03 µm, 95% limits of agreement [LOA] −8.6 to 6.5 µm). GCIPL thickness from standard raster scans was 72.7 ± 4.3 µm, and 73.7 ± 3.7 µm from widefield images (bias = 1.0 µm, 95% LOA −2.4 to 4.4 µm). Repeatability for both RNFL and GCIPL standard analysis was less than 5.2 µm. For 1° × 1° superpixels, the 95% limits of agreement were between −13.9 µm and 13.7 µm for RNFL thickness and −2.5 µm and 2.5 µm for GCIPL thickness. Conclusions Inner retinal thickness measures from widefield imaging have good repeatability and are comparable to those measured using standard scans. Translational Relevance Monitoring retinal ganglion cell loss in the non-human primate experimental glaucoma model could be enhanced using widefield imaging.

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The Relationship Between Macula Retinal Ganglion Cell Density and Visual Function in the Nonhuman Primate

Cited by 16 publications

References 64 publications

Identifying the Retinal Layers Linked to Human Contrast Sensitivity Via Deep Learning

Identifying the Retinal Layers Linked to Human Contrast Sensitivity Via Deep Learning

Binocularly Asymmetric Crowding in Glaucoma and a Lack of Binocular Summation in Crowding

Widefield OCT Imaging for Quantifying Inner Retinal Thickness in the Nonhuman Primate

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