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Record W4404318488 · doi:10.1101/2024.11.12.24317128

Neuroimaging Insights into Brain Mechanisms of Early-onset Restrictive Eating Disorders

2024· preprint· en· W4404318488 on OpenAlexaff
Clara Moreau, Anaël Ayrolles, Christopher R. K. Ching, Robin Bonicel, Alexandre Mathieu, Coline Stordeur, Pierre Bergeret, Nicolas Traut, Lydie Tran, David Germanaud, Marianne Alison, Monique Elmaleh, Stefan Ehrlich, Paul M. Thompson, Thomas Bourgeron, Richard Delorme

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersUniversité de ParisCentre National de la Recherche ScientifiqueFondation de FranceNational Institute of Mental HealthDeutsche Forschungsgemeinschaft
KeywordsNeuroimagingEating disordersNeurosciencePsychologyClinical psychology

Abstract

fetched live from OpenAlex

Background: Early-onset restrictive eating disorders (rEO-ED) encompass a heterogeneous group of conditions, including early-onset anorexia nervosa (EO-AN) and avoidant restrictive food intake disorders (ARFID). Almost nothing is known about the consequences of rEO-ED on brain development. Methods: We performed the largest comparison of MRI-derived brain features in children and early adolescents (<13 years) with EO-AN (n=124), ARFID (n=50), and typically developing individuals (TD, n=112). Results: Despite similar body mass index (BMI) distributions, EO-AN and ARFID showed divergent structural patterns, suggesting independent brain mechanisms. Half the regional brain measures were correlated with BMI in EO-AN and none in ARFID, indicating a partial mediation of EO-AN signal by BMI. EO-AN was associated with a widespread pattern of thinner cortex, while underweight ARFID patients exhibited smaller surface area and subcortical volumes than TD. Conclusion: . ED mechanisms in neurodevelopmental disorders.

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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.021
GPT teacher head0.316
Teacher spread0.295 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same venuemedRxiv→Same topicEating Disorders and Behaviors→French-language works237,207→