Dataset Information

Longitudinal In Vivo Changes in Radial Peripapillary Capillaries and Optic Nerve Head Structure in Non-Human Primates With Early Experimental Glaucoma.

ABSTRACT:

Purpose

There is conflicting evidence regarding whether a loss of radial peripapillary capillaries (RPCs) precedes neuronal loss in glaucoma. We examined the time course of in vivo changes in RPCs, optic nerve head (ONH) structure, and retinal nerve fiber layer thickness (RNFLT) in experimental glaucoma (EG).

Methods

Spectral domain optical coherence tomography images were acquired before and approximately every two weeks after inducing unilateral EG in nine rhesus monkeys to quantify mean anterior lamina cribrosa surface depth (ALCSD), minimum rim width (MRW), and RNFLT. Perfused RPC density was measured from adaptive optics scanning laser ophthalmoscope images acquired on the temporal half of the ONH. The time of first significant change was quantified as when values fell and remained outside of the 95% confidence interval established from control eyes.

Results

Mean ALCSD and/or MRW were the first parameters to change in eight EG eyes. RPC density changed first in the ninth. At their first points of change, mean ALCSD posteriorly deformed by 100.2 ± 101.2 µm, MRW thinned by 82.3 ± 65.9 µm, RNFLT decreased by 25 ± 14 µm, and RPC density decreased by 4.5 ± 2.1%. RPC density decreased before RNFL thinning in 5 EG eyes. RNFLT decreased before RPC density decreased in two EG eyes, whereas two EG eyes had simultaneous changes.

Conclusions

In most EG eyes, RPC density decreased before (or simultaneous with) a change in RNFLT, suggesting that vascular factors may play a role in axonal loss in some eyes in early glaucoma.

SUBMITTER: Musial G

PROVIDER: S-EPMC8742514 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposeTo assess 3-dimensional surface shape patterns of the optic nerve head (ONH) and peripapillary retinal nerve fiber layer (RNFL) in glaucoma with unsupervised artificial intelligence (AI).DesignRetrospective study.ParticipantsPatients with OCT scans obtained between 2016 and 2020 from Massachusetts Eye and Ear.MethodsThe first reliable Cirrus (Carl Zeiss Meditec, Inc) ONH OCT scans from each eye were selected. The ONH and RNFL surface shape was represented by the vertical positions of the inner limiting membrane (ILM) relative to the lowest ILM vertical position in each eye. Nonnegative matrix factorization was applied to determine the ONH and RNFL surface shape patterns, which then were correlated with OCT and visual field (VF) loss parameters and subsequent VF loss rate. We tested whether using ONH and RNFL surface shape patterns improved the prediction accuracy for associated VF loss and subsequent VF loss rates measured by adjusted r 2 and Bayesian information criterion (BIC) difference compared with using established OCT parameters alone.Main outcome measuresOptic nerve head and RNFL surface shape patterns and prediction of the associated VF loss and subsequent VF loss rates.ResultsWe determined 14 ONH and RNFL surface shape patterns using 9854 OCT scans from 5912 participants. Worse mean deviation (MD) was most correlated (r = 0.29 and r = 0.24, Pearson correlation; each P < 0.001) with lower coefficients of patterns 10 and 12 representing inferior and superior para-ONH nerve thinning, respectively. Worse MD was associated most with higher coefficients of patterns 5, 4, and 9 (r = -0.16, r = -0.13, and r = -0.13, respectively), representing higher peripheral ONH and RNFL surfaces. In addition to established ONH summary parameters and 12-clock-hour RNFL thickness, using ONH and RNFL surface patterns improved (BIC decrease: 182, 144, and 101, respectively; BIC decrease ≥ 6; strong model improvement) the prediction of accompanied MD (r 2 from 0.32 to 0.37), superior (r 2 from 0.27 to 0.31), and inferior (r 2 from 0.17 to 0.21) paracentral loss and improved (BIC decrease: 8 and 8, respectively) the prediction of subsequent VF MD loss rates (r 2 from 0 to 0.13) and inferior paracentral loss rates (r 2 from 0 to 0.16).ConclusionsThe ONH and RNFL surface shape patterns quantified by unsupervised AI techniques improved the structure-function relationship and subsequent VF loss rate prediction.