Familiarity deficits in cognitively normal aging individuals with <i>APOE</i> ε4: A follow‐up investigation of medial temporal lobe structural correlates
Bibliographic record
Abstract
Abstract Introduction The apolipoprotein E ε4 (APOE ε4) allele is a well‐documented risk factor for Alzheimer's disease (AD). Accordingly, aging individuals carrying one or more ε4 alleles are at considerably greater risk of developing AD over time. In an effort to characterize early cognitive manifestations of AD, we previously outlined selective deficits in familiarity‐based recognition in otherwise asymptomatic carriers of the APOE ε4 allele (Schoemaker et al., 2016). In this follow‐up report, we aimed to explore the neural correlates of this selective cognitive impairment. Methods For this purpose, within the same population and using high‐resolution structural neuroimaging, we explored relationships between volumes of the hippocampus, entorhinal, and perirhinal cortices and performance in recollection and familiarity. Results Overall, our results revealed significant positive relationships between familiarity performance and volumes of the perirhinal and entorhinal cortices in aging individuals with APOE ε4. In APOE ε4 carriers, a positive correlation between recollection performance and hippocampal volume was also found. In contrast, no correlation reached statistical significance in the group of noncarriers. Conclusion These findings suggest that familiarity performance might be a useful marker of the integrity of the rhinal cortex, especially in populations at risk of 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 | 0.000 | 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".