Serotonergic psychedelics for depression: What do we know about neurobiological mechanisms of action?
Bibliographic record
Abstract
Introduction: Current treatment options for major depressive disorder (MDD) have limited efficacy and are associated with adverse effects. Recent studies investigating the antidepressant effect of serotonergic psychedelics-also known as classic psychedelics-have promising preliminary results with large effect sizes. In this context, we conducted a review of the putative neurobiological underpinnings of the mechanism of antidepressant action of these drugs. Methods: A narrative review was conducted using PubMed to identify published articles evaluating the antidepressant mechanism of action of serotonergic psychedelics. Results: Serotonergic psychedelics have serotonin (5HT)2A agonist or partial agonist effects. Their rapid antidepressant effects may be mediated-in part-by their potent 5HT2A agonism, leading to rapid receptor downregulation. In addition, these psychedelics impact brain derived neurotrophic factor and immunomodulatory responses, both of which may play a role in their antidepressant effect. Several neuroimaging and neurophysiology studies evaluating mechanistic change from a network perspective can help us to further understand their mechanism of action. Some, but not all, data suggest that psychedelics may exert their effects, in part, by disrupting the activity of the default mode network, which is involved in both introspection and self-referential thinking and is over-active in MDD. Conclusion: The mechanisms of action underlying the antidepressant effect of serotonergic psychedelics remains an active area of research. Several competing theories are being evaluated and more research is needed to determine which ones are supported by the most robust evidence.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".