Antisaccadic eye movements in middle-aged individuals with a family history of Alzheimer's disease
Bibliographic record
Abstract
Background Antisaccade is closely associated with cognitive ability in Alzheimer's disease (AD). However, studies regarding antisaccade in the early stages of AD are scarce. Considering that first-degree family history is a well-established risk factor for AD, we explored the influence of family history on the performance of antisaccade tasks in individuals with normal cognition. Methods In total, 44 participants (aged 50–66 years) with a family history of AD (FH+) and 44 age-, gender-, and educational level-matched controls (FH-) were enrolled in our study. After cognitive assessment using the Montreal Cognitive Assessment and Mini-mental State Examination, participants underwent antisaccade trials, and all parameters were recorded using an eye tracker. Results While the average velocity was relatively lower in FH+ individuals than in FH− individuals (107.9 ± 14.3°/s vs. 132.9 ± 23.7°/s, p < 0.001), FH+ individuals surprisingly showed relatively fewer uninhibited reflexive saccades (44.7 ± 26.0% vs. 56.2 ± 24.7%, p = 0.037) than the control group. They also required a relatively shorter time to detect and correct false saccades (121.6 ± 40.7 ms vs. 143.9 ± 37.0 ms, p = 0.023). Conclusions This study showed that family history is associated with alterations in antisaccadic parameters, suggesting that eye tracking can be used to assess oculomotor control and executive function in individuals at risk of developing dementia.
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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 | 0.001 |
| 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".