The fidelity of the retinotopic cortical map in amblyopia measured with BOLD-fMRI
Bibliographic record
Abstract
Purposes: To study the locations of early visual areas in amblyopia and the fidelity of their retinotopic maps using fMRI. Methods: fMR images were acquired with a Siemens Sonata 1.5T. The stimuli consisted of abruptly randomly changing (8Hz) sharp-edged checkerboard stimuli of 80% contrast presented to either the normal or amblyopic eye of 11 amblyopic subjects and 6 normal controls. A phase-encoded design was used in which the attention of the subjects was controlled using a target detection task. All the known retinotopic visual areas were delineated by the normal and fellow amblyopic eye in V1 to V4. Correlation between the fixing eye boundaries and amblyopic eye boundaries as well as the boundaries between dominant and nondominant eyes in normal controls were compared. Distortion and variability in amblyopic eyes measured using a novel psychophysical mapping task were correlated to the phase variance in different cortical regions of our subject group. Results: Boundaries in amblyopic subjects defined in term of fMRI retinotopic mapping using fixing eye and amblyopic eye show differences compared with normal controls. This could not be explained simply by the reduced signal-to-noise ratio of the amblyopic cortex. We did not find a strong correlation between the larger phase variances typical of amblyopic visual areas and distortion or variability measured psychophysically. Conclusion: The fidelity of the retinotopic map is not as good in amblyopic eyes. Supported by CIHR grant MOP53346 to RFH.
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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) | 0.002 | 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".