Baseline estimation of cognition with retinal thickness in aging and Alzheimer disease: an ONDRI study
Bibliographic record
Abstract
Abstract Background The contribution of retinal total thickness in optical coherence tomography (OCT) imaging towards estimating a comprehensive cognitive profile in aging and dementia is not well studied. To investigate the contribution of retinal total thickness towards comprehensively estimating cognitive functions. Methods This was an observational cross‐sectional study. Participants were from the Ontario Neurodegenerative Research Initiative (ONDRI) study from Ontario, Canada. One hundred thirty‐three adults across aging‐mild cognitive impairment (MCI)‐Alzheimer disease (AD) were included. Outcomes were scores across attention & working memory, language, executive, memory, and visuospatial domains and Montreal Cognitive Assessment total score. Measures included macular and peripapillary retinal nerve fibre layer total thickness measured by retinal OCT. Analysis considered age, sex, years of education, and diagnosis. Cognitive measures were estimated using ordinary least squares (OLS) and partial least squares (PLS) regression. Results Mean age was 70±8 and 56% (75/133) were women. Diagnostic groups included normal aging ( n =44), MCI ( n =58), and AD ( n =31). Macular volume/thickness, particularly non‐foveal thickness, estimated language composite scores in all participants via OLS (R 2 = 0.24–0.29, β = 0.17–0.25, SE = 0.08, p < 0.05). The contribution of retinal thickness towards estimating cognitive measures was markedly improved in the AD subgroup using PLS to estimate MoCA total score and composite scores of executive functions, and attention and working memory (f 2 = 0.04–0.13, SE = 0.01–0.02). Retinal thickness also contributed towards estimating attention and working memory, and language in normal aging (f 2 = 0.02–0.04, SE = 0.00–0.01). There seemed to be greater estimation of ventral visual stream functions (e.g., object recognition) or switching tasks. However, the overall explanatory ability of both OLS and PLS models was low (R 2 < 60%) with many non‐significant associations in OLS. Adding retinal thickness actually slightly lowered the explanatory ability of 73–91% of OLS and PLS models respectively in all participants (f 2 <0). Conclusions Retinal thickness may estimate functions related to attention and executive function in later disease stages, but language appears to be better estimated in normal controls. Additional information : Canadian Institutes of Health Research fund reference number: 177881.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".