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Record W2154241112 · doi:10.1093/cercor/bhm211

Identification of Genetically Mediated Cortical Networks: A Multivariate Study of Pediatric Twins and Siblings

2008· article· en· W2154241112 on OpenAlexaff
J. Eric Schmitt, Rhoshel Lenroot, Gregory L. Wallace, Sarah J. Ordaz, Keith Taylor, Noor Jehan Kabani, Deanna Greenstein, Jason P. Lerch, Kenneth S. Kendler, Michael C. Neale, Jay N. Giedd

Bibliographic record

VenueCerebral Cortex · 2008
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsHospital for Sick ChildrenMontreal Neurological Institute and Hospital
FundersNational Institute on Drug AbuseNational Institute of Mental HealthNational Institutes of Health
KeywordsMultivariate statisticsMultivariate analysisPsychologyFunctional magnetic resonance imagingGenetic heterogeneityBiologyNeuroscienceGeneticsEvolutionary biologyGenePhenotypeComputer scienceMachine learning

Abstract

fetched live from OpenAlex

Structural magnetic resonance imaging data from 308 twins, 64 singleton siblings of twins, and 228 singletons were analyzed using structural equation modeling and selected multivariate methods to identify genetically mediated intracortical associations. Principal components analyses (PCA) of the genetic correlation matrix indicated a single factor accounting for over 60% of the genetic variability in cortical thickness. When covaried for mean global cortical thickness, PCA, cluster analyses, and graph models identified genetically mediated fronto-parietal and occipital networks. Graph theoretical models suggest that the observed genetically mediated relationships follow small world architectural rules. These findings are largely concordant with other multivariate studies of brain structure and function, the twin literature, and current understanding on the role of genes in cortical neurodevelopment.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.042
GPT teacher head0.274
Teacher spread0.232 · 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

Citations197
Published2008
Admission routes1
Has abstractyes

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