Neuroimaging insights into brain mechanisms of early-onset restrictive eating disorders
Bibliographic record
Abstract
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). However, the impact of rEO-ED on brain morphometry remains largely unknown. Here we performed the largest magnetic resonance imaging-derived brain features comparison of children and early adolescents (<13 years) with EO-AN (n = 124) or ARFID (n = 50) versus typically developing individuals (TD, n = 116). EO-AN was associated with widespread cortex thinning, while underweight patients with ARFID exhibited reduced surface area and volumes compared with TD. Despite similar body mass index distributions, EO-AN and ARFID showed distinct structural patterns, suggesting independent brain mechanisms. Finally, we identified overlapping patterns of brain thickness differences between EO-AN and obsessive–compulsive disorder and between ARFID and autism spectrum disorder. Future studies are required to partition the contribution of body mass index versus rEO-ED mechanisms, as well as to identify shared mechanisms with other neurodevelopmental conditions toward a multidimensional approach of eating disorders. In this cross-sectional study, the authors used structural MRI to compare subcortical volumes, cortical thickness and surface area between early-onset anorexia nervosa, avoidant/restrictive food intake disorder and typically developing young individuals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".