Emotion brain alterations in anorexia nervosa: a candidate biological marker and implications for treatment
Bibliographic record
Abstract
BACKGROUND: Identification of the biological markers of anorexia nervosa (AN) is crucial for the development of new treatments. We aimed to determine whether AN is associated with disturbances in the nonconscious neural processing of innate signals of emotion and whether these disturbances persist after weight gain. METHODS: In a retest design, 28 adolescent females with AN were tested at first ad not mission to hospital and again after they had gained weight. Matched healthy control participants were tested at the same times. We assessed emotion-elicited event-related potentials (ERPs) during overt and covert presentation of emotion expressions, scores on an emotion-identification behavioural task, and symptom measures. We performed between and within group analyses. RESULTS: Individuals with AN had a marked alteration in ERPs relative to healthy controls. Irrespective of the form of stimulus, early and late ERP componotnents were significantly reduced in AN patients at baseline (when underweight) and on retest (after weight gain), especially in the temporo-occipital regions, suggesting a persistent disruption of the early automatic appraisal of salient emotional signals. LIMITATIONS: This study could have been improved with a longer standardized retest interval. CONCLUSION: There is likely a core, generic disturbance in AN in the early "automatic" neural processing of emotion irrespective of weight or nutritional status. New innovative emotion-based psychologic or pharmacologic treatments targeting these nonconscious processes may prove beneficial.
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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 | 0.000 | 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".