Visual stimulus-evoked transient blood flow and blood vessel diameter changes in the healthy human retina measured with a combined OCT+ERG system
Bibliographic record
Abstract
Neurodegenerative retinal diseases, such as glaucoma, age-related macular degeneration and diabetic retinopathy, cause gradual damage to the retinal morphology, blood vasculature, and neuronal function, and ultimately lead to blindness. In this study, a retinal OCT system was combined with a clinical electroretinography (ERG) system to investigate visually-evoked transient changes in the retinal blood flow (RBF) and blood vessel diameter (BVD) in the healthy human retina. The OCT system offered 2.7 µ m axial resolution in retinal tissue and 98 dB sensitivity for 1.1 mW imaging power and 250 kHz image acquisition rate. Doppler OCT (double circular scans around the optic nerve head) and ERG traces were acquired from healthy subjects in response to 10 Hz, white light flicker stimuli and different stimulus intensities. The ERG system was used to generate visual stimuli of precise timing, duration, luminance, and flicker frequency, as well as to confirm the retinal neuronal response to the visual stimulation. MATLAB-based custom algorithms were developed to track time-dependent changes in the RBF and BVD from the OCT images. Results from this study revealed a rapid transient increase in the RBF accompanied by transient vasoconstriction and vasodilation of the retinal blood vessels in response to the flicker stimulation. The amplitude and latency of the RBF and BVD responses were dependent on the stimulus intensity as well as the blood vessel type (arteries or veins).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".