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The Human Cerebral Cortex Flattens during Adolescence

2013· article· en· W2019085999 on OpenAlexaff
Yasser Alemán‐Gómez, Joost Janssen, Hugo G. Schnack, Evan Balaban, Laura Pina‐Camacho, Fidel Alfaro‐Almagro, Josefina Castro‐Fornieles, Soraya Otero, Immaculada Baeza, Dolores Moreno, Núria Bargalló, Mara Parellada, Celso Arango, Manuel Desco

Bibliographic record

VenueJournal of Neuroscience · 2013
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsMcGill University
FundersComunidad de Madrid
KeywordsGyrificationWhite matterCerebral cortexAnatomyCortex (anatomy)Human brainFlatteningNeurosciencePsychologyMagnetic resonance imagingBiologyMedicinePhysics

Abstract

fetched live from OpenAlex

The human cerebral cortex appears to shrink during adolescence. To delineate the dynamic morphological changes involved in this process, 52 healthy male and female adolescents (11-17 years old) were neuroimaged twice using magnetic resonance imaging, approximately 2 years apart. Using a novel morphometric analysis procedure combining the FreeSurfer and BrainVisa image software suites, we quantified global and lobar change in cortical thickness, outer surface area, the gyrification index, the average Euclidean distance between opposing sides of the white matter surface (gyral white matter thickness), the convex ("exposed") part of the outer cortical surface (hull surface area), sulcal length, depth, and width. We found that the cortical surface flattens during adolescence. Flattening was strongest in the frontal and occipital cortices, in which significant sulcal widening and decreased sulcal depth co-occurred. Globally, sulcal widening was associated with cortical thinning and, for the frontal cortex, with loss of surface area. For the other cortical lobes, thinning was related to gyral white matter expansion. The overall flattening of the macrostructural three-dimensional architecture of the human cortex during adolescence thus involves changes in gray matter and effects of the maturation of white matter.

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)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.907
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.038
GPT teacher head0.269
Teacher spread0.231 · 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.

Study designBench or experimental
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

Citations139
Published2013
Admission routes1
Has abstractyes

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