Astrocyte reactivity moderates the effect of education on cognitive reserve
Bibliographic record
Abstract
Abstract Background Cognitive reserve and resilience account for differential susceptibility to brain aging and disease across individuals. Previous data suggest that functional connectivity among different brain areas is associated with cognitive reserve. However, its neural basis is yet unclear. In this study, we investigated the moderating effect of astrocyte reactivity in cognitive reserve. Method Cognitively unimpaired individuals who had plasma GFAP analyzed were selected from ADNI at baseline (n = 130). Years of education were used as a proxy of cognitive reserve; the Montreal Cognitive Assessment (MoCA) total score was retrieved from all individuals. A moderation analysis was performed according to the most recent framework for reserve and resilience in aging concepts. The total MoCA score was the outcome, and we investigated the moderation of plasma GFAP in the association of years of education and MoCA. Data are shown as mean+‐SD. Result A total of 130 individuals had complete data for all variables (72.3+‐6.3 years of age; 16.36+‐2.75 years of education; 43.1% females). A significant moderating effect was found in plasma GFAP and years of education (beta = 0.004, p = 0.0006) as a predictor of total MoCA scores (Model R2 = 0.37, p < 0.0001) when correcting for age and gender. Plasma GFAP and sex were also predictors of total MOCA scores independently (p<0.001 for both). Plasma GFAP was a moderator in individuals below 18 years of education (p = 0.003), but not above 18 years of education (p = 0.9). Conclusion Astrocyte reactivity may play a potential role in the neural basis of cognitive reserve. The neuroprotective effect of astrocytes in cognitive reserve may have a ceiling effect. Further studies may investigate the importance of glial interactions with brain regions and its ability to predict cognitive changes in a longitudinal analysis.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".