Diagnosing neurodegenerative disorders using retina as an external window: A systematic review of OCT‐MRI correlations
Bibliographic record
Abstract
BACKGROUND: Due to their shared embryological origin, retinal and brain tissues are affected by neurodegenerative diseases in similar ways. Optical coherence tomography (OCT) and OCT-angiography, two non-invasive retinal imaging modalities, have been increasingly studied as potential biomarkers for Alzheimer's disease (AD) in recent years. However, correlations between OCT/OCT-A and neuroimaging remain understudied. We thus performed a systematic review of OCT/OCT-A - MRI correlations in different neurodegenerative disorders associated with cognitive decline. METHOD: Medline, Embase, and other databases were searched from January to June 2023, using keywords related to neurodegenerative conditions and OCT/OCT-A parameters. RESULT: We screened 2962 citations and 93 full-text articles. We included 28 studies in the final review. For non-vascular neurodegenerative diseases, layer-specific retinal metrics, especially retinal nerve fiber layer (RNFL) thinning, and region-specific retinal parameters (e.g. decreased foveal thickness) best correlated with changes on brain MRI. Vascular retinal biomarkers, especially reduced vessel and perfusion densities, have the unique capacity to reflect cerebrovascular lesions in vascular cognitive conditions. Both layer- or region-specific retinal biomarkers and vascular retinal metrics can reflect global brain atrophy patterns. Microstructural alterations of the brain parenchyma best correlated with layer-specific thinning of retina. CONCLUSION: Layer- or region-specific retinal markers are better suited for non-vascular dementias, while vascular markers more closely reflect vascular neurodegeneration. Future research must overcome several challenges including methodological heterogeneity and the complex interactions between different degenerative mechanisms. A better understanding of the associations between retinal and brain lesions could ultimately lead to the clinical use of retinal biomarkers for the early diagnosis of neurodegenerative diseases.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".