Quantifying the ON and OFF Contributions to the Flash ERG with the Discrete Wavelet Transform
Bibliographic record
Abstract
Purpose: Discrete wavelet transform (DWT) analyses suggest that the 20- and 40-Hz components of the short-flash photopic electroretinogram (ERG) are closely related to the ON and OFF pathways, respectively. With the DWT, we examined how the ERG ON and OFF components are modulated by the stimulus intensity and/or duration. Methods: Discrete wavelet transform descriptors (20, 40 Hz and 40:20-Hz ratio) were extracted from ERGs evoked to 25 combinations of flash durations (150–5 ms) and strengths (0.8–2.8 log cd.m−2). Results: In ERGs evoked to the 150-ms stimulus (to separate the ON and OFF ERGs), the 40:20-Hz ratio of ON ERGs (mean ± SD: 0.49 ± 0.04) was significantly smaller (P < 0.05) than that of OFF ERGs (1.71 ± 0.18) owing to a significantly (P < 0.05) higher contribution of the 20 and 40 Hz components to the ON and OFF ERGs, respectively. With brighter stimuli, the ON and OFF components increased similarly (P < 0.05). While progressively shorter flashes had no impact (P > 0.05) on the ON component, it exponentially enhanced (P < 0.05) the OFF component. Conclusions: Discrete wavelet transform allows for an accurate determination of ON and OFF retinal pathways even in ERGs evoked to a short flash. To our knowledge, the significant OFF facilitatory effect evidenced with shorter stimuli has not previously been reported. Translational Relevance: The DWT approach should offer a rapid, easy, and reproducible approach to retrospectively and prospectively evaluate the function of the retinal ON and OFF pathways using the standard (short-flash duration) clinical ERG stimulus.
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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 | 0.001 |
| 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".