Inner Plexiform Layer Substrata Are Discernible with Commercial OCT and Affected by Aging
Bibliographic record
Abstract
Purpose: This study aims to evaluate the inner plexiform layer (IPL) microstructure and its changes with aging using commercial spectral-domain OCT macular scans of healthy individuals with a semiautomated segmentation program. Design: Cross-sectional study conducted at the Athens Vision Eye Institute from January to July 2024. Participants: The study included 92 healthy participants. Methods: tests combined with bootstrap analyses. Main Outcome Measures: The primary outcomes measured were signal intensity of the IPL, contrast between its hyperreflective and hyporeflective bands, and the percentage of IPL with identifiable sublayers. The secondary outcomes included inner retinal thickness measurements, including the IPL, nerve fiber layer (NFL), and ganglion cell complex (GCC). Results: The IPL exhibited a multilayered structure with 5 sublayers, 3 hyperreflective and 2 hyporeflective, arranged in an alternating pattern. Aging was associated with higher signal intensity from hyporeflective bands and minimal changes in hyperreflective bands, resulting in an overall reduced contrast between the 5 sublayers. Older participants showed a lower percentage of IPL with identifiable sublayers, along with a lower contrast variance within the IPL. Aging also correlated with reduced inner retinal thickness, including the IPL, NFL, and GCC, with a stronger association for the IPL. Inner plexiform layer analysis exhibited high intraeye and intereye repeatability, with significant correlations and nonsignificant mean differences observed in most key parameters. Conclusions: Analysis of the IPL and its sublayers is both feasible and reproducible using commercially available OCT along with a semiautomated segmentation program. Our findings indicate that the IPL microstructure changes with aging. A comprehensive evaluation of the IPL could serve as a valuable biomarker for early diagnosis and monitoring of diseases affecting synaptic health in this layer. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".