Psychedelics in developmental stuttering to modulate brain functioning: a new therapeutic perspective?
Bibliographic record
Abstract
Developmental stuttering (DS) is a neurodevelopmental speech-motor disorder characterized by symptoms such as blocks, repetitions, and prolongations. Persistent DS often has a significant negative impact on quality of life, and interventions for it have limited efficacy. Herein, we briefly review existing research on the neurophysiological underpinnings of DS -specifically, brain metabolic and default mode/social-cognitive networks (DMN/SCN) anomalies- arguing that psychedelic compounds might be considered and investigated (e.g., in randomized clinical trials) for treatment of DS. The neural background of DS is likely to be heterogeneous, and some contribution from genetically determinants of metabolic deficiencies in the basal ganglia and speech-motor cortical regions are thought to play a role in appearance of DS symptoms, which possibly results in a cascade of events contributing to impairments in speech-motor execution. In persistent DS, the difficulties of speech are often linked to a series of associated aspects such as social anxiety and social avoidance. In this context, the SCN and DMN (also influencing a series of fronto-parietal, somato-motor, and attentional networks) may have a role in worsening dysfluencies. Interestingly, brain metabolism and SCN/DMN connectivity can be modified by psychedelics, which have been shown to improve clinical evidence of some psychiatric conditions (e.g., depression, post-traumatic stress disorder, etc.) associated with psychological constructs such as rumination and social anxiety, which also tend to be present in persistent DS. To date, while there have been no controlled trials on the effects of psychedelics in DS, anecdotal evidence suggests that these agents may have beneficial effects on stuttering and its associated characteristics. We suggest that psychedelics warrant investigation in DS.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".