Bilingual experience and intrinsic functional connectivity in adults, aging, and Alzheimer's disease
Bibliographic record
Abstract
The past decade marked the beginning of the use of resting-state functional connectivity (RSFC) imaging in bilingualism studies. This paper intends to review the latest evidence of changes in RSFC in language and cognitive control networks in bilinguals during adulthood, aging, and early Alzheimer's disease, which can add to our understanding of brain functional reshaping in the context of second language (L2) acquisition. Because of high variability in bilingual experience, recent studies mostly focus on the role of the main aspects of bilingual experience (age of acquisition (AoA), language proficiency, and language usage) on intrinsic functional connectivity (FC). Existing evidence accounts for stronger FC in simultaneous rather than sequential bilinguals in language and control networks, and the modulation of the AoA impact by language proficiency and usage. Studies on older bilingual adults show stronger FC in language and frontoparietal networks and preserved FC in posterior brain regions, which can protect the brain against cognitive decline and neurodegenerative processes. Altered RSFC in language and control networks subsequent to L2 training programs also is associated with improved global cognition in older adults. This review ends with a brief discussion of potential confounding factors in bilingualism research and conclusions and suggestions for future research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".