Modeling retinotopic maps in amblyopia reveals cortical reorganization across the visual hierarchy
Bibliographic record
Abstract
Amblyopia is a common cortical developmental disorder affecting 3.7% to 5% of the adult population. It is defined as a monocular visual acuity impairment despite healthy or corrected-to-normal optical components. Deficits associated with amblyopia include reduced contrast sensitivity, impaired foveal localization, and stereoblindness. While traditionally viewed as a developmental cortical pathology given the preserved ocular function, the neural bases of amblyopia remain debated. This study aims at understanding the neural mechanisms underlying amblyopia by modeling fMRI data collected during retinotopic mapping in strabismic, anisometropic, and mixed amblyopia patients. Population receptive field (pRF) parameters were extracted across early and intermediate visual areas and compared between amblyopic patients and controls. We found that amblyopic patients systematically exhibited larger pRF sizes across all visual areas examined, with greater effect sizes from V1 to V3 and in LO/VO. Effect sizes were stronger in foveal stimulation than in the periphery, a result compatible with disordered cortical projection theories of amblyopia. Anisometropic and mixed amblyopia patients showed larger pRF sizes than strabismic patients, unexpectedly given their clinical symptoms. These results advance our understanding of amblyopia’s neural underpinnings. Foveal-peripheral differences point to abnormal cortical projections. Moreover, the increased pRF sizes, mismatching clinically-predicted patterns, suggests a more widespread plastic dysfunction. Overall, our findings point to a substantial receptive field reorganization in amblyopia, mostly in the early visual system.
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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 | 0.000 | 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) | 0.000 | 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".