Brain and retina in Alzheimer's disease: Pathological intersections and estimates from imaging
Bibliographic record
Abstract
Recent studies have highlighted retinal optical coherence tomography (OCT) imaging as a promising biomarker for the early detection of Alzheimer's disease (AD). This review connects AD brain pathology - particularly amyloid beta (Aβ), tau, and vascular changes - with corresponding retinal changes. Evidence suggests that abnormal Aβ and tau deposits in the retina may reflect brain pathology, though their formation mechanisms remain unclear. Retinal vascular changes may also mirror brain pathology, with recent data emerging on other co-pathologies. Retinal thickness changes, especially in acetylcholine-producing layers, can differentiate AD from controls, although not in early AD; however, emerging high-resolution OCT techniques may enhance early detection. We find that correlations between retinal thickness and brain structures are often weak, and retinal vascular imaging shows promise in estimating cerebrovascular disease markers from retinal vascular changes. Novel imaging modalities (e.g., hyperspectral imaging) for detecting retinal Aβ deposits may improve early AD screening when combined with other biomarkers. HIGHLIGHTS: Retinal Aβ/tau is equivocal; peripheral retinal p-tau shows diagnostic promise. OCT retinal/choroid thickness diagnostic/prognostic AUC is small to medium. Hyperspectral imaging and electroretinography may aid early diagnosis. OCTA may differentiate MCI from controls, but preclinical studies are needed. The added value of retinal biomarkers for risk stratification remains uncertain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".