Structural neuroimaging signatures of anorexia nervosa features in a mixed sample enriched for disease vulnerability
Bibliographic record
Abstract
ABSTRACT Brain-behavior relationships that could provide insight into risk-associated pathophysiology have not been thoroughly assessed in anorexia nervosa (AN). Therefore, we sought to identify grey and white matter signatures of AN symptoms and risk factors (trait anxiety, set-shifting impairment) in a sample enriched for AN vulnerability, including acute and remitted AN patients and their unaffected sisters ( n = 72, aged 18 – 48 years). MRI/DTI data were acquired on a 3T scanner and processed with Freesurfer and FSL TBSS. Relationships between clinical variables of interest and regional subcortical volume, vertex-wise cortical surface architecture (thickness, surface area, local gyrification), and voxel-wise white matter microstructure (FA, MD) were tested with separate linear regressions, including age, BMI, lifetime AN diagnosis, and intracranial volume as covariates, where appropriate. Significance was determined using a Bonferroni-corrected threshold, p ( t ) ≤ 0.001. We detected distinct associations linking AN symptoms to lateral occipital cortical thickness and insular/cingulate gyrification and trait anxiety to lingual cortical thickness and superior parietal gyrification, and we detected overlapping associations linking AN symptoms and set-shifting impairment to frontoparietal gyrification. No other brain-behavior relationships emerged. Our findings suggest that variations in site-specific cortical morphology could give rise to core features of AN and shared temperamental and cognitive-behavioral risk factors for AN.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".