Evaluating the Potential Use of Serotonergic Psychedelics in Autism Spectrum Disorder
Bibliographic record
Abstract
Recent clinical and preclinical evidence points towards empathogenic and prosocial effects elicited by psychedelic compounds, notably the serotonin 5-HT 2A agonists lysergic acid diethylamide (LSD), psilocybin, N,N-Dimethyltryptamine (DMT), and their derivatives. These findings suggest a therapeutic potential of psychedelic compounds for some of the behavioural traits associated with autism spectrum disorder (ASD), a neurodevelopmental condition characterized by atypical social behaviour. In this review, we highlight evidence suggesting that psychedelics may potentially ameliorate some of the behavioural atypicalities of ASD, including reduced social behaviour and highly co-occurring anxiety and depression. Next, we discuss dysregulated neurobiological systems in ASD and how they may underlie or potentially limit the therapeutic effects of psychedelics. These phenomena include: 1) synaptic function, 2) serotonergic signaling, 3) prefrontal cortex activity, and 4) thalamocortical signaling. Lastly, we discuss clinical studies from the 1960s and 70s that assessed the use of psychedelics in the treatment of children with ASD. We highlight the positive behavioural outcomes of these studies, including enhanced mood and social behaviour, as well as the adverse effects of these trials, including increases in aggressive behaviour and dissociative and psychotic states. Despite preliminary evidence, further studies are needed to determine whether the benefits of psychedelic treatment in ASD outweigh the risks associated with the use of these compounds in this population, and if the 5-HT 2A receptor may represent a target for social-behavioural 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 | 0.001 | 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) | 0.002 | 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".