The effect of dextromethorphan on reward-related behaviors: A systematic review of preclinical and clinical evidence
Bibliographic record
Abstract
INTRODUCTION: Extant literature suggests that anhedonia, defined as a loss of the ability to feel pleasure or interest, is subserved by dysregulation of reward processing in the central nervous system. Dextromethorphan (DXM), an uncompetitive N-Methyl-d-Aspartate (NMDA) receptor antagonist and sigma-1 (σ1) receptor agonist, is a glutamatergic modulator with antidepressant properties. The effect of DXM on reward-related outcomes remains inadequately characterized. Herein, we conducted a systematic review of extant literature reporting on the effects of DXM on reward-related behaviors in both preclinical and clinical studies. METHODS: A systematic search of the literature was conducted on online databases (PubMed, OVID, Scopus, Web of Science) of published articles from inception to January 2025. Preclinical and clinical studies that reported on the effect of DXM on reward outcomes were assessed. RESULTS: Preclinical studies (n = 13) indicate that administration of DXM attenuates reward-seeking behavior in rats as measured primarily by performance in the conditioned place preference test and behavioral sensitization. In a single human study (n = 1) evaluating DXM in healthy participants (n = 20), self-reported drug-liking for DXM (400 mg/70 kg) was significantly lower in comparison to psilocybin (20 mg and 30 mg) at 7 h after the dosing session. DISCUSSION: Extant literature suggests that DXM administration attenuates reward-related behaviors in rats. There is a paucity of human studies investigating the effect of DXM on reward outcomes. Future research should prioritize the investigation of the effect of DXM on reward function using validated reward paradigms in persons with anhedonia.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".