Bilingualism and cognitive reserve: Preserving cortical thickness in aging individuals
Bibliographic record
Abstract
BACKGROUND: This study investigates the relationship between bilingualism, cognitive reserve, and cortical thickness in aging bilingual individuals. It explores the potential role of bilingualism in preserving brain structure against age-related decline. Cognitive reserve was evaluated using established measures of bilingualism (Dash et al., 2022) and the cognitive reserve index (CRIq; Nucci et al., 2012), accounting for gender effects to minimize bias. METHOD: A total of 79 bilingual individuals, aged 30 to 79 years, underwent neuroimaging analysis to assess cortical thickness using the FreeSurfer atlas. Partial least squares regression (PLSR) was employed to examine the relationship between age, bilingualism, and cognitive reserve indicators (such as CRIq) and cortical thickness across several brain regions. RESULTS: The analysis revealed three latent variables (LVs) that elucidate key brain-behavior relationships: The first latent variable, primarily driven by age, was associated with cortical thinning in regions typically impacted by aging, including the bilateral middle temporal cortex, hippocampus, lateral orbitofrontal cortex, and inferior temporal cortex. The second latent variable, incorporating both age and CRIq as predictors, highlighted regions linked to cognitive control and sensory processing, such as the insula, thalamus, and superior frontal cortex, indicating a protective effect of cognitive reserve against age-related cortical thinning. The third latent variable, with bilingualism-related measures as the predictor, revealed preserved cortical thickness in regions associated with language and executive control, including the pars opercularis, orbitofrontal cortex, and inferior parietal cortex, suggesting that bilingualism may provide a neural shield against aging-related brain changes. CONCLUSION: These findings support the hypothesis that bilingualism and cognitive reserve contribute to the preservation of cortical thickness in key regions, particularly those involved in cognitive control, language, and executive functions. This study provides further evidence for the protective role of bilingualism in maintaining brain structure and function in aging, contributing to the broader understanding of cognitive reserve mechanisms.
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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) | 0.002 | 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".