Cognitive Reserve Is Not Associated With Hippocampal Microstructure in Older Adults Without Dementia
Bibliographic record
Abstract
OBJECTIVE: Mean Diffusivity (MD) as measured by diffusion tensor imaging (DTI) can be used to detect microstructural alterations of the brain's gray matter (GM). A previous study found that higher education, which is a proxy for cognitive reserve (CR), was related to decreased hippocampal MD in middle-aged healthy adults, indicating decreased microstructural damage in more educated participants. Based on this study, we aimed at determining the role of hippocampal GM MD in the interaction of AD pathology and CR in older people without dementia. METHOD: We used a sample of 52 cognitively normal people and 38 participants with late mild cognitive impairment (LMCI) from the ADNI database. MCI and cognitively normal participants were analyzed separately. Using linear models, we regressed hippocampal GM MD on CR (quantified by a composite score), amyloid status and the interaction of both, adjusting for age, gender and memory score. RESULTS: CR was not associated with hippocampal GM MD and hippocampal GM volume. Also, no interaction of amyloid status and CR was found. CONCLUSION: Our results do not confirm an association of CR and hippocampal GM MD in older adults. In contrast to previous studies, we did not find an association between CR and microstructural, nor macrostructural alterations of the hippocampus in older adults. More research is needed to determine the influence of CR on hippocampal microstructural integrity in relation to age and AD pathology.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".