Contralateral delay activity is not a robust marker of cognitive function in older adults at risk of mild cognitive impairment
Bibliographic record
Abstract
Abstract Contralateral delay activity (CDA) has been proposed as a pre‐clinical neural marker for mild cognitive impairment (MCI). However, existing evidence is limited to one study with a small sample size ( n = 24). Our aim was to extend previous work by investigating the relationship between the CDA and MCI risk in a large sample of older adults ( n = 76). We used a regression approach to determine whether (and when) CDA amplitude predicted MCI risk, as indexed by the Montreal Cognitive Assessment (MoCA). CDA amplitude from ~300‐500 and ~800‐900 ms predicted MoCA performance. However, significant effects were only observed for specific electrodes (P5/P6 and CP3/CP4, but not PO7/PO8) and the nature of the relationship between the CDA and MoCA scores differed across time and according to set size. Bayesian regression analysis indicated partial evidence in favour of the null hypothesis (BF 10 values = 4–1.18). Contrary to previous results, our findings suggest that the CDA may not a robust marker of MCI risk. More broadly, our results highlight the difficulty in identifying at‐risk individuals, particularly as MCI is a heterogeneous, unstable condition. Future research should prioritise longitudinal approaches in order to track the progression of the CDA and its association with cognitive decline in later life.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".