Natural language signatures of psilocybin microdosing

Result code in 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>

Alternative codes found

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

Result on the web

<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>

Result language

angličtina

Original language name

Natural language signatures of psilocybin microdosing

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30104 - Pharmacology and pharmacy

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

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

Publication year

2022

Confidentiality

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

Name of the periodical

Psychopharmacology

ISSN

0033-3158

e-ISSN

—

Volume of the periodical

239

Issue of the periodical within the volume

9

Country of publishing house

US - UNITED STATES

Number of pages

12

Pages from-to

2841-2852

UT code for WoS article

000807942300001

EID of the result in the Scopus database

2-s2.0-85131581931