AB058. A longitudinal study on the effects of the optic nerve crush on behavioural visual acuity measures in mice
Bibliographic record
Abstract
Background: Visual deficits, caused by ocular disease or trauma to the visual system, can cause lasting damage with insufficient treatment options available. However, recent research has focused on neural plasticity as a means to regain visual abilities. In order to better understand the involvement of neural plasticity and reorganization in partial vision restoration, we aim to evaluate the partial recovery of a visual deficit over time using three behavioural tests. In our study, a partial optic nerve crush (ONC) serves as an induced visual deficit, allowing for residual vision from surviving cells. Methods: Three behavioural tests—optokinetic reflex, object recognition, and visual cliff—were conducted in 9 mice prior to a bilateral, partial ONC, then 1, 3, 7, 14, 21, and 28 days after the ONC. The optokinetic reflex test measured the tracking reflex in response to moving sinusoidal gratings. These gratings increase in spatial frequency until a reflex is no longer observed, i.e., a visual acuity threshold is reached. The object recognition test examines the animal’s exploratory behaviour in its capacity to distinguish high versus low contrast objects. The visual cliff test also evaluates exploratory behaviour, by simulating a cliff to observe the animal’s sense of depth perception. All three tests provide an estimate of the rodent’s visual abilities at different levels of the visual pathway. Results: The partial optic nerve crush resulted in a total loss of visual acuity as measured by the optokinetic reflex. The deficit did not show improvement during the 4 following weeks. Despite the visual cliff test showing a non-significant decrease in deep end preference 1-day post ONC, though this was not the case for subsequent test occasions. The object recognition test showed no significant trends. Conclusions: In conclusion, the optokinetic reflex test showed a significant loss of function following the visual deficit, but no recovery. However, a complimentary pilot study shows visual recovery using lighter crush intensities. The spatial visual function does not seem to be affected by the ONC, suggesting that the object recognition and visual cliff tests, in their current design, may rely on somatosensory means of exploration.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".