Tau and amyloid biomarkers modify the degree to which cognitive reserve and brain reserve predict cognitive decline
Bibliographic record
Abstract
Abstract Objective: Brain reserve, cognitive reserve, and education are thought to protect against late-life cognitive decline, but these variables have not been directly compared to one another in the same model, using future cognitive and functional decline as outcomes. We sought to determine whether the influence of these protective factors on executive function (EF) and daily function decline was dependent upon Alzheimer’s disease (AD) pathology severity, as measured by the total tau to beta-amyloid (T-τ/Aβ1-42) ratio in cerebrospinal fluid (CSF). Method: Participants were 1201 older adult volunteers in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. Brain reserve was defined using a composite index of structural brain volumes (total brain matter, hippocampus, and white matter hyperintensity). Cognitive reserve was defined as the variance in episodic memory performance not explained by brain integrity and demographics. Results: At higher levels of T-τ/Aβ1-42, brain and cognitive reserve predicted slower decline in EF. Only brain reserve attenuated decline at lower levels of T-τ/Aβ1-42. Education had no independent association with cognitive decline. Conclusions: These results point to a hierarchy of protection against aging- and disease-associated cognitive decline. When pathology is low, only structural brain integrity predicts rate of future EF decline. The ability of cognitive reserve to predict future EF decline becomes stronger as CSF biomarker evidence of AD increases. Although education is typically thought of as a proxy for cognitive reserve, it did not show any protective effects on cognition after accounting for brain integrity and the residual cognitive reserve index.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".