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Record W4399918218 · doi:10.1002/hbm.26758

Can gray matter loss in early adolescence be explained by white matter growth?

2024· article· en· W4399918218 on OpenAlexafffund
Jordan A. Chad, Catherine Lebel

Bibliographic record

VenueHuman Brain Mapping · 2024
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsOntario Brain InstituteAlberta Children's HospitalBaycrest HospitalUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsGray (unit)White matterPsychologyDevelopmental psychologyMedicineMagnetic resonance imagingNuclear medicineRadiology

Abstract

fetched live from OpenAlex

Abstract A fundamental puzzle about brain development is why the volume of gray matter (GM) apparently declines as white matter (WM) grows when children enter adolescence. Since pruned synapses are too small to affect GM volume, a prevailing theory posits that an expanded distribution of myelin causes the inner edge of the GM to “whiten” while total brain volume remains steady, shifting the MRI‐measured WM:GM boundary closer to the brain's outer surface. This theory inherently predicts that GM volume loss is concurrent with WM volume growth across regions, within sexes and over time, although these predictions have yet to be explicitly tested. In this study, we test these predictions by mapping regional GM and WM volumetric changes in 2333 participants of the Adolescent Brain Cognitive Development study aged 9–14 years who each received three MRI scans 2 years apart. We show that average GM and WM volume changes follow distinct spatial, temporal, and sex‐specific patterns, indicating that GM volume loss is not balanced by WM volume growth, although cortical GM thinning is weakly correlated with WM growth in some regions. We conclude that myelin is not the main source of measured GM volume loss, and we propose alternative candidates.

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 score0.403
Threshold uncertainty score0.740

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 integrity0.0000.000
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 head0.040
GPT teacher head0.313
Teacher spread0.273 · 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

Citations7
Published2024
Admission routes2
Has abstractyes

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