Qualitative and quantitative educational disparities and brain signatures in healthy aging and dementia across global settings
Bibliographic record
Abstract
Background While education is crucial for brain health, evidence mainly relies on individual measures of years of education (YoE), neglecting education quality (EQ). The effect of YoE and EQ on aging and dementia has not been compared. Methods We conducted a cross-sectional assessment of the effect of EQ and YoE on brain health in 7533 subjects from 20 countries, including healthy controls (HCs), Alzheimer's disease (AD), and frontotemporal lobar degeneration (FTLD). EQ was based on country-level quality indicators provided by the programme for international student assessment (PISA). After applying neuroimage harmonization, we examined its effect, along with YoE, on gray matter volume and functional connectivity. Regression models were adjusted for age, sex, and cognition, controlling for multiple comparisons. The influence of image quality was assessed through sensitivity analysis. Data collection was conducted between June 1 and October 30, 2024. Findings Less EQ and YoE were associated with brain alterations across groups. However, EQ had a stronger influence, mainly targeting the critical areas of each condition. At the whole-brain level, EQ influenced volume (HCs: Δmean = 2·0 [1·9–2·0] × 10 −2 , p < 10 −5 ; AD: Δmean = 0·1 [−0·0 to 0·3] × 10 −2 , p = 0·18; FTLD: Δmean = 3·5 [3·0–4·0] × 10 −2 , p < 10 −5 ; all with 95% confidence intervals) and networks (HCs: Δmean = 13·5 [13·2–13·7] × 10 −2 , p < 10 −5 ; AD: Δmean = 5·9 [5·2–6·7] × 10 −2 , p < 10 −5 ; FTLD: Δmean = 13·2 [11·2–13·7] × 10 −2 , p < 10 −5 ) 1·3 to 7·0 times more than YoE. These effects remain robust despite variations in income and socioeconomic factors at country and individual levels. Interpretation The results support the need to incorporate education quality into studying and improving brain health, underscoring the importance of country-level measures. Funding Multi-partner consortium to expand dementia research in Latin America (ReDLat).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".