Brain serotonin, oxytocin, and their interaction: Relevance for eating disorders
Bibliographic record
Abstract
INTRODUCTION: Eating disorders are characterized by maladaptive eating behaviors and preoccupations around body shape, weight, and eating. The serotonin system has been among the most widely studied neurobiological factors in relation to eating disorders. Recent research also highlighted the role of oxytocin. AIMS AND METHODS: This article aims to review animal and human studies on the involvement of central serotonin and oxytocin, and their interplay in eating disorders in particular. We synthesize results from studies using animal models of eating disorders and from research conducted in healthy individuals and clinical populations. RESULTS/OUTCOMES: Altered serotonin neurotransmission and oxytocin levels in the brain-particularly in the hypothalamus, brainstem, and limbic regions-were associated with disturbances in eating behaviors and related maladaptive cognitions and emotions. These brain regions were found to constitute a typical neural network through which both central serotonin and oxytocin might operate in a bidirectional manner. CONCLUSIONS/INTERPRETATION: Based on the preceding findings, we describe a developmental biopsychosocial model relevant to eating disorders, including the role of serotonin-oxytocin interactions in the brain. While it is clear that eating disorders are multifactorial in which many biopsychosocial pathways are involved, the current review highlights the importance of well-designed translational research when studying mechanisms of serotonin-oxytocin interactions in the brain. Such research would help to better understand the effects of joint central oxytocin and serotonin administration as a possible preventive or therapeutic intervention for eating disorders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".