A Novel Retinal Nerve Fiber Layer Biomarker of Amyotrophic Lateral Sclerosis (ALS) Identified Using Longitudinal in vivo Ocular Imaging
Bibliographic record
Abstract
Purpose: Like motor neurons, retinal ganglion cells (RGCs) have long axons and high metabolic demands, making them vulnerable to disruption of axonal transport. Unlike motor neurons, the RGC axons are accessible to high-resolution non-invasive optical imaging in their intraocular portion. A non-invasive in vivo retinal imaging biomarker can be valuable for amyotrophic lateral sclerosis (ALS) diagnosis and monitoring. We aim to assess the presence of inner retinal pathology in a mouse model of ALS and its possible progression with age. Methods: Transgenic SOD1G93A mice (n=8, 4M/4F) and age-matched controls (n=8, 4M/4F) underwent in vivo retinal imaging with confocal scanning laser ophthalmoscopy (cSLO) coupled with optical coherence tomography (OCT) at 20 weeks of age. Another group of SOD1G93A mice (n=20, 6M/14F) and age-matched controls (n=20, 6M/14F) underwent longitudinal in vivo retinal imaging with the same device. Each retinal imaging session included infrared reflectance (IR) and blue reflectance (BR) cSLO coupled with OCT. Hyperreflective puncta located in the retinal nerve fiber layer (RNFL) were counted in a blinded fashion in ALS and control mice. The number of puncta at 20 weeks of age in ALS mice was compared with controls using Wilcoxon test. The rates of increase of puncta number were analyzed using a Generalized Linear Mixed-Effect Model (GLMM) for genotype, time, and sex. Results: IR-cSLO coupled with OCT revealed hyperreflective puncta located in the RNFL of ALS mice. IR-cSLO fundus imaging at the age of 20 weeks showed ALS mice had significantly higher number of puncta compared to controls (2.1±2.3 vs 0.5±0.8; (mean±SD), respectively, p=0.036). GLMM analysis showed both ALS mutation and age were significantly associated with the rate of increase of puncta number (p=0.000232 and p=0.000366, respectively). In addition, female ALS mice had a steeper increase of puncta compared to male ALS mice (0.21±0.04 log number puncta/week vs 0.16±0.04, respectively; p=0.037). Conclusion: Our findings demonstrate distinct inner retinal nerve fiber layer pathology, detected using cSLO coupled with OCT, which worsens over time. These findings support the potential of retinal imaging as a translationally relevant, non-invasive biomarker for ALS diagnosis or disease monitoring in humans.
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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 | 0.001 | 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) | 0.001 | 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".