MétaCan
Menu
← Back to cohort
Record W2925116753 · doi:10.1101/586768

Neuroanatomical Correlates in Bilinguals: The Case of Children and Elderly

2019· preprint· en· W2925116753 on OpenAlexaff
Lorna García‐Pentón, Yuriem Fernández García, Jon Andoni Duñabeitia, Alejandro Pérez, Manuel Carreiras

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersEusko JaurlaritzaEuropean Commission
KeywordsNeuroscience of multilingualismWhite matterPsychologyCognitive reserveSet (abstract data type)Grey matterGraphCognitionNeuroscienceDevelopmental psychologyComputer scienceMedicineMagnetic resonance imagingCognitive impairment

Abstract

fetched live from OpenAlex

ABSTRACT How bilingualism modulates brain areas beyond the language regions is still controversial. Through a comprehensive set of analyses on brain structure, we investigated brain differences between Basque-Spanish bilinguals and monolinguals in children and the elderly, the most sensitive target groups to detect potential brain differences. In particular, we employed Diffusion MRI in combination with T1-MRI, network-based statistics and a graph-theoretical approach to investigate differences between bilinguals and monolinguals in structural connectivity and topological properties of brain networks. Additionally, regional grey and white matter structural differences between groups were examined. The findings suggest that the effects of bilingualism on brain structure are not solid but unstable. However, lifetime experience of active bilingualism may lead to increased neural reserve in ageing, since better global network graph-efficiency has been observed in the elderly lifelong bilinguals compared to monolinguals.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.017
GPT teacher head0.234
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2019
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicFunctional Brain Connectivity Studies→French-language works237,207→