Corpus callosum morphology and relationships to illness phenotypes in individuals with anorexia nervosa
Bibliographic record
Abstract
Anorexia nervosa is an often-severe psychiatric illness characterized by significantly low body weight, fear of gaining weight, and distorted body image. Multiple neuroimaging studies have shown abnormalities in cortical morphology, mostly associated with the starvation state. Investigations of white matter, while more limited in number, have suggested global and regional volume reductions, as well as abnormal diffusivity in multiple regions including the corpus callosum. Yet, no study has specifically examined thickness of the corpus callosum, a large white matter tract instrumental in the inter-hemispheric integration of sensory, motor, and cognitive information. We analyzed MRI data from 48 adolescents and adults with anorexia nervosa and 50 healthy controls, all girls/women, to compare corpus callosum thickness and examined relationships with body mass index (BMI), illness duration, and eating disorder symptoms (controlling for BMI). There were no significant group differences in corpus callosum thickness. In the anorexia nervosa group, severity of body shape concerns was significantly, positively correlated with callosal thickness in the rostrum, genu, rostral body, isthmus, and splenium. In addition, there were significant positive correlations between eating disorder-related obsessions and compulsions and thickness of the anterior midbody, rostral body, and splenium. There were no significant associations between callosal thickness and BMI or illness duration. In sum, those with AN with worse concerns about bodily appearance and worse eating disorder-related obsessive thought patterns and compulsive behaviours have regionally thicker corpus callosum, independent of current weight status. These findings provide important neurobiological links to key, specific eating disorder behavioural phenotypes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".