Visually driven functional MRI techniques for characterization of optic neuropathy
Bibliographic record
Abstract
Optic neuropathies are conditions that cause disease to the optic nerve, and can result in loss of visual acuity and/or visual field defects. An improved understanding of how these conditions affect the entire visual system is warranted, to better predict and/or restore the visual loss. In this article, we review visually-driven functional magnetic resonance imaging (fMRI) studies of optic neuropathies, including glaucoma and optic neuritis (ON); we also discuss traumatic optic neuropathy (TON). Optic neuropathy-related vision loss results in fMRI deficit within the visual cortex, and is often strongly correlated with clinical severity measures. Using predominantly flickering checkerboard stimuli, glaucoma studies indicated retinotopic-specific cortical alteration with more prominent deficits in advanced than in early glaucoma. Some glaucoma studies indicate a reorganized visual cortex. ON studies have indicated that the impacted cortical areas are briefly hyperactive. For ON, brain deficits are greater in the acute stages of the disease, followed by (near) normalization of responses of the LGN, visual cortex, and the dorsal visual stream, but not the ventral extrastriate cortex. Visually-driven fMRI is sensitive, at least in ON, in discriminating patients from controls, as well as the affected eye from the fellow eye within patients. The use of a greater variety of stimuli beyond checkerboards (e.g., visual motion and object recognition) in recent ON studies is encouraging, and needs to continue to disentangle the results in terms of change over time. Finally, visually-driven fMRI has not yet been applied in TON, although preliminary efforts suggest it may be feasible. Future fMRI studies of optic neuropathies should consider using more complex visual stimuli, and inter-regional analysis methods including functional connectivity. We suggest that a more systematic longitudinal comparison of optic neuropathies with advanced fMRI would provide improved diagnostic and prognostic information.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".