Neuroimaging insights into brain mechanisms of early-onset restrictive eating disorders
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it