Contributions of Brain Microstructures and Metabolism to Visual Field Loss Patterns in Glaucoma Using Archetypal and Information Gain Analyses
Bibliographic record
Abstract
Purpose: To investigate the contributions of the microstructural and metabolic brain environment to glaucoma and their association with visual field (VF) loss patterns by using advanced diffusion magnetic resonance imaging (dMRI), proton magnetic resonance spectroscopy (MRS), and clinical ophthalmic measures. Methods: Sixty-nine glaucoma and healthy subjects underwent dMRI and/or MRS at 3 Tesla. Ophthalmic data were collected from VF perimetry and optical coherence tomography. dMRI parameters of microstructural integrity in the optic radiation and MRS-derived neurochemical levels in the visual cortex were compared among early glaucoma, advanced glaucoma, and healthy controls. Multivariate regression was used to correlate neuroimaging metrics with 16 archetypal VF loss patterns. We also ranked neuroimaging, ophthalmic, and demographic attributes in terms of their information gain to determine their importance to glaucoma. Results: In dMRI, decreasing fractional anisotropy, radial kurtosis, and tortuosity and increasing radial diffusivity correlated with greater overall VF loss bilaterally. Regionally, decreasing intra-axonal space and extra-axonal space diffusivities correlated with greater VF loss in the superior-altitudinal area of the right eye and the inferior-altitudinal area of the left eye. In MRS, both early and advanced glaucoma patients had lower gamma-aminobutyric acid (GABA), glutamate, and choline levels than healthy controls. GABA appeared to associate more with superonasal VF loss, and glutamate and choline more with inferior VF loss. Choline ranked third for importance to early glaucoma, whereas radial kurtosis and GABA ranked fourth and fifth for advanced glaucoma. Conclusions: Our findings highlight the importance of non-invasive neuroimaging biomarkers and analytical modeling for unveiling glaucomatous neurodegeneration and how they reflect complementary VF loss patterns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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 teacher head, 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".