Natural language signatures of psilocybin microdosing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F22%3A43920881" target="_blank" >RIV/00023752:_____/22:43920881 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60461373:22330/22:43925422 RIV/60461373:22810/22:43925422

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s00213-022-06170-0" target="_blank" >https://link.springer.com/article/10.1007/s00213-022-06170-0</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00213-022-06170-0" target="_blank" >10.1007/s00213-022-06170-0</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Natural language signatures of psilocybin microdosing

Popis výsledku v původním jazyce

Rationale Serotonergic psychedelics are being studied as novel treatments for mental health disorders and as facilitators of improved well-being, mental function, and creativity. Recent studies have found mixed results concerning the effects of low doses of psychedelics (&quot;microdosing&quot;) on these domains. However, microdosing is generally investigated using instruments designed to assess larger doses of psychedelics, which might lack sensitivity and specificity for this purpose. Objectives Determine whether unconstrained speech contains signatures capable of identifying the acute effects of psilocybin microdoses. Methods Natural speech under psilocybin microdoses (0.5 g of psilocybin mushrooms) was acquired from thirty-four healthy adult volunteers (11 females: 32.09 +/- 3.53 years; 23 males: 30.87 +/- 4.64 years) following a double-blind and placebo-controlled experimental design with two measurement weeks per participant. On Wednesdays and Fridays of each week, participants consumed either the active dose (psilocybin) or the placebo (edible mushrooms). Features of interest were defined based on variables known to be affected by higher doses: verbosity, semantic variability, and sentiment scores. Machine learning models were used to discriminate between conditions. Classifiers were trained and tested using stratified cross-validation to compute the AUC and p-values. Results Except for semantic variability, these metrics presented significant differences between a typical active microdose and the inactive placebo condition. Machine learning classifiers were capable of distinguishing between conditions with high accuracy (AUC approximate to 0.8). Conclusions These results constitute first evidence that low doses of serotonergic psychedelics can be identified from unconstrained natural speech, with potential for widely applicable, affordable, and ecologically valid monitoring of microdosing schedules.

Název v anglickém jazyce

Natural language signatures of psilocybin microdosing

Popis výsledku anglicky

Rationale Serotonergic psychedelics are being studied as novel treatments for mental health disorders and as facilitators of improved well-being, mental function, and creativity. Recent studies have found mixed results concerning the effects of low doses of psychedelics (&quot;microdosing&quot;) on these domains. However, microdosing is generally investigated using instruments designed to assess larger doses of psychedelics, which might lack sensitivity and specificity for this purpose. Objectives Determine whether unconstrained speech contains signatures capable of identifying the acute effects of psilocybin microdoses. Methods Natural speech under psilocybin microdoses (0.5 g of psilocybin mushrooms) was acquired from thirty-four healthy adult volunteers (11 females: 32.09 +/- 3.53 years; 23 males: 30.87 +/- 4.64 years) following a double-blind and placebo-controlled experimental design with two measurement weeks per participant. On Wednesdays and Fridays of each week, participants consumed either the active dose (psilocybin) or the placebo (edible mushrooms). Features of interest were defined based on variables known to be affected by higher doses: verbosity, semantic variability, and sentiment scores. Machine learning models were used to discriminate between conditions. Classifiers were trained and tested using stratified cross-validation to compute the AUC and p-values. Results Except for semantic variability, these metrics presented significant differences between a typical active microdose and the inactive placebo condition. Machine learning classifiers were capable of distinguishing between conditions with high accuracy (AUC approximate to 0.8). Conclusions These results constitute first evidence that low doses of serotonergic psychedelics can be identified from unconstrained natural speech, with potential for widely applicable, affordable, and ecologically valid monitoring of microdosing schedules.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30104 - Pharmacology and pharmacy

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Psychopharmacology

ISSN

0033-3158

e-ISSN

—

Svazek periodika

239

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

2841-2852

Kód UT WoS článku

000807942300001

EID výsledku v databázi Scopus

2-s2.0-85131581931