A neural mechanism of cognitive reserve: The case of bilingualism
Bibliographic record
Abstract
Abstract Cognitive Reserve (CR) refers to the preservation of cognitive function in the face of age-or disease-related neuroanatomical decline. While bilingualism is known to contribute to CR, the extent to which, and what particular aspect of, second language experience contributes to CR are debated, and the underlying neural mechanism(s) unknown. Intrinsic functional connectivity reflects experience-dependent neuroplasticity that occurs across timescales ranging from minutes to decades, and may be a neural mechanism underlying CR. To test this hypothesis, we used voxel-based morphometry and resting-state functional connectivity analyses of MRI data to compare structural and functional brain integrity between bilingual and monolingual older adults, matched on cognitive performance using a rigorous propensity score matching technique, and across levels of second language proficiency measured as a continuous variable. Bilingualism, and degree of second language proficiency in particular, were associated with lower grey matter integrity in a hub of the default mode network – a region that is particularly vulnerable to decline in aging and dementia – but preserved functional network organization that resembled the young adult brain. Our findings confirm that lifelong bilingualism contributes to CR through experience-dependent maintenance of optimal functional network structure of the domain-general attentional control network across the lifespan.
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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 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".