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Brain Plasticity and Intellectual Ability Are Influenced by Shared Genes

2010· article· en· W2130530844 on OpenAlexaff
Rachel G.H. Brans, René S. Kahn, Hugo G. Schnack, G. Caroline M. van Baal, Daniëlle Posthuma, Neeltje E.M. van Haren, Claude Lepage, Jason P. Lerch, D. Louis Collins, Alan C. Evans, Dorret I. Boomsma, Hilleke E. Hulshoff Pol

Bibliographic record

VenueJournal of Neuroscience · 2010
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersNederlandse Organisatie voor Wetenschappelijk OnderzoekZonMw
KeywordsNeuroplasticityHuman brainPsychologyNeuroscienceCortex (anatomy)Brain morphometryCognitionCerebral cortexIntelligence quotientMagnetic resonance imagingMedicine

Abstract

fetched live from OpenAlex

Although the adult brain is considered to be fully developed and stable until senescence when its size steadily decreases, such stability seems at odds with continued human (intellectual) development throughout life. Moreover, although variation in human brain size is highly heritable, we do not know the extent to which genes contribute to individual differences in brain plasticity. In this longitudinal magnetic resonance imaging study in twins, we report considerable thinning of the frontal cortex and thickening of the medial temporal cortex with increasing age and find this change to be heritable and partly related to cognitive ability. Specifically, adults with higher intelligence show attenuated cortical thinning and more pronounced cortical thickening over time than do subjects with average or below average IQ. Genes influencing variability in both intelligence and brain plasticity partly drive these associations. Thus, not only does the brain continue to change well into adulthood, these changes are functionally relevant because they are related to intelligence.

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)0.002
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.277
Teacher spread0.240 · 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

Citations109
Published2010
Admission routes1
Has abstractyes

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