The Neurobiology of Anorexia Nervosa and Abnormal Reward Learning Processes: A Systematic Review
Bibliographic record
Abstract
Abstract Objective: Given the array of neuroimaging studies examining the neurobiology of anorexia nervosa, the purpose of this paper was to further highlight localized neurobiological abnormalities when examining reward learning pathways in patients suffering from AN. Methods: Literature searches using PsycINFO, EMBASE, and MEDLINE were conducted in March of 2022 using search terms relating to AN, reward learning, and neuroimaging. Relevant studies that were included must be written in English, have at least one clinical sample of either acute and/or recovered adult and/or adolescent female patients with anorexia nervosa, be controlled studies with a healthy female control group, be published in a peer-reviewed journal, and containing a neuroimaging component. Results: For this systematic review, 20 relevant studies were included to elucidate the brain regional and functional differences with respect to reward learning pathways in individuals with AN when compared to healthy controls. These differences include dopamine neuron and cortisol release abnormalities in relation to dopaminergic pathways, as well as regional differences involved in reward learning subconstructs that include reward expectancies, punishment sensitivity, social reward, and body dissatisfaction and body image distortion. Conclusion: In examining the brain regional and functional differences across individuals with AN with respect to the reward learning construct, further research efforts that focus on these important reward learning abnormalities might help inform the development of new clinical interventions aimed at these particular pathways.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".