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Record W2078510704 · doi:10.1097/wnr.0000000000000204

The relationship between uncinate fasciculus white matter integrity and verbal memory proficiency in children

2014· article· en· W2078510704 on OpenAlexaff
David J. Schaeffer, Cynthia E. Krafft, Nicolette F. Schwarz, Lingxi Chi, Amanda Rodrigue, Jordan E. Pierce, Jerry D. Allison, N. E. Yanasak, Tianming Liu, Catherine L. Davis, Jennifer E. McDowell

Bibliographic record

VenueNeuroreport · 2014
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsInstitute of Population and Public Health
FundersNational Heart, Lung, and Blood Institute
KeywordsUncinate fasciculusFractional anisotropyPsychologyFasciculusWhite matterVerbal memoryDiffusion MRIAudiologyInferior longitudinal fasciculusDevelopmental psychologyNeuroscienceCognitionMedicineMagnetic resonance imaging

Abstract

fetched live from OpenAlex

During childhood, verbal learning and memory are important for academic performance. Recent functional MRI studies have reported on the functional correlates of verbal memory proficiency, but few have reported the underlying structural correlates. The present study sought to test the relationship between fronto-temporal white matter integrity and verbal memory proficiency in children. Diffusion weighted images were collected from 17 Black children (age 8-11 years) who also completed the California Verbal Learning Test. To index white matter integrity, fractional anisotropy values were calculated for bilateral uncinate fasciculus. The results revealed that low anisotropy values corresponded to poor verbal memory, whereas high anisotropy values corresponded to significantly better verbal memory scores. These findings suggest that a greater degree of myelination and cohesiveness of axonal fibers in uncinate fasciculus underlie better verbal memory proficiency in children.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.010
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

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

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 head0.060
GPT teacher head0.342
Teacher spread0.282 · 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 teacher head, 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

Citations25
Published2014
Admission routes1
Has abstractyes

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