The association between visual discrimination and cognitive decline prior to clinical diagnosis
Bibliographic record
Abstract
Abstract Background The development of dependable screening methods would allow for the early detection of incipient Alzheimer's disease (AD). Alzheimer’s pathology accumulates in the perirhinal cortex (PRC) well before diagnosis. Converging lines of research show the PRC critically contributes to object visual processing, especially when disambiguating between objects that have highly overlapping features (Barense et al., 2010; Bussey et al., 2002). Previous research has shown that healthy older adults with a genetic risk (i.e., family history) for AD, performed worse at discriminating novel objects than those without a genetic risk (Mason et al., 2017). A dependable screening method, such as a visual discrimination task, could help identify cognitive decline earlier than a typical screening tool. Method Twenty‐nine healthy older adults were tested on a visual discrimination task and were also administered the Montreal Cognitive Assessment (MoCA). Based on their MoCA scores, the participants were divided into an “at‐risk” group (n=13) and a “healthy” group (n=16). Each trial consisted of the simultaneous presentation of three visually similar images. Participants were instructed to identify the image that was inconsistent with the other two as quickly and accurately as possible. Images were divided into three categories: faces, scenes, and objects. Result The at‐risk group scored significantly lower on the visual discrimination task when compared to the healthy group (p = 0.004). Further, there was a significant interaction between visual image category and MoCA performance on accuracy. This interaction was due to the large difference in accuracy between the two groups in the object category (p < 0.001). Finally, the category had a significant effect on accuracy, where both groups scored significantly lower in the object category (p < 0.001). Conclusion This study shows participants who are at‐risk for mild cognitive impairment performed significantly worse on the visual discrimination task especially in the object condition, which is consistent with previous results in healthy older adults with a genetic risk of AD. Impaired visual discrimination could reflect very early AD pathology affecting the PRC. Visual discrimination tests could be developed into dependable screening tools allowing clinicians to make earlier predictions about who is most at risk for developing AD.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".