End-to-end evaluation of white matter microstructure of the visual pathway in asymmetric glaucoma
Bibliographic record
Abstract
Abstract Diffusion magnetic resonance imaging is a non-invasive neuroimaging technique that enables in vivo evaluation of white matter microstructure, providing sensitivity to tissue abnormalities caused by disease. Glaucoma, the second leading cause of blindness worldwide, is characterized by progressive loss of retinal ganglion cells and axonal damage in the optic nerve, leading to degeneration along the entire visual pathway. This degeneration includes secondary effects on fiber crossings within the optic chiasm, which are challenging to characterize with conventional diffusion models. In this study, we evaluated 31 patients with asymmetric glaucoma and 31 healthy controls using advanced diffusion magnetic resonance imaging methods, including Diffusion Tensor Imaging, Constrained Spherical Deconvolution, multi-tensor fit via Multi-Resolution Discrete Search method, and Fixel-Based Analysis. We found significant differences of diffusion metrics in white matter tracts of the visual system, including the optic nerve, optic chiasm, optic tracts, and optic radiations. Moreover, diffusion metrics correlated with clinical ophthalmological parameters such as cup-to-disc ratio, visual field mean deviation, and retinal nerve fiber layer thickness. These findings support the use of advanced diffusion magnetic resonance imaging models as sensitive tools for detecting Wallerian degeneration and resolving complex white matter architecture in the human visual pathway, and demonstrate their utility to study other fiber-crossing regions throughout the brain.
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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 | 0.001 | 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".