Steady-State Contrast Response Functions Provide a Sensitive and Objective Index of Amblyopic Deficits
Bibliographic record
Abstract
PURPOSE: Visual deficits in amblyopia are neural in origin, yet are difficult to characterize with functional magnetic resonance imagery (fMRI). Our aim was to develop an objective electroencephalography (EEG) paradigm that can be used to provide a clinically useful index of amblyopic deficits. METHODS: We used steady-state visual evoked potentials (SSVEPs) to measure full contrast response functions in both amblyopic (n = 10, strabismic or mixed amblyopia, mean age: 44 years) and control (n = 5, mean age: 31 years) observers, both with and without a dichoptic mask. RESULTS: At the highest target contrast, the ratio of amplitudes across the weaker and stronger eyes was highly correlated (r = 0.76) with the acuity ratio between the eyes. We also found that the contrast response function in the amblyopic eye had both a greatly reduced amplitude and a shallower slope, but that surprisingly dichoptic masking was weaker than in controls. The results were compared with the predictions of a computational model of amblyopia and suggest a modification to the model whereby excitatory (but not suppressive) signals are attenuated in the amblyopic eye. CONCLUSIONS: We suggest that SSVEPs offer a sensitive and objective measure of the ocular imbalance in amblyopia and could be used to assess the efficacy of amblyopia therapies currently under development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".