J Affect Disord
April 1, 2018
Facundo Carrillo, Mariano Sigman, Diego Fernández Slezak et al.
57 citations
An algorithm analyzing natural speech from baseline interviews can predict which patients with treatment-resistant depression will respond to psilocybin therapy. The approach uses vocal patterns and linguistic features to forecast treatment outcomes, suggesting that speech biomarkers may enable personalized medicine in mental health. This predictive capability could help identify likely responders before treatment begins, advancing precision psychiatry for depression.
Conscious Cogn
December 8, 2020
Camila Sanz, Carla Pallavicini, Facundo Carrillo et al.
34 citations
Language produced under the influence of LSD shows increased entropy and reduced semantic coherence, consistent with a state of heightened neural entropy. Non-semantic analysis of speech organization revealed increased verbosity and a reduced lexicon, changes more similar to manic psychoses than to schizophrenia, as confirmed by direct comparison with reference samples. Features related to language organization allowed machine learning classifiers to identify speech under LSD with accuracy comparable to that obtained by examining semantic content. These results provide a quantitative characterization of disorganized natural speech as a landmark feature of the psychedelic state.
Journal of Cannabis Research
July 17, 2020
Laura Alethia de la Fuente, Federico Zamberlán, Andrés Sánchez Ferrán et al.
30 citations
Machine learning classifiers distinguished between Cannabis sativa and Cannabis indica cultivars based on user-reported flavours and subjective effects with high accuracy. Analysis of a large dataset from Leafly.com and chemical composition data from Psilabs.org revealed significant correlations between terpene and cannabinoid content and subjective effect and flavour tags. Reported effects clustered into three groups: unpleasant, stimulant, and soothing. Terpene profiles matched user perceptual characterizations, particularly for terpene-flavours associations. The findings suggest that flavour perception could serve as a reliable marker to indirectly characterize cannabis psychoactive effects, as terpene content is robustly inherited and less influenced by environmental factors.
bioRxiv (Cold Spring Harbor Laboratory)
February 22, 2022
Camila Sanz, Federico Cavanna, Stephanie Müller et al.
1 citation
preprint
Low doses of psilocybin (microdoses) can be detected in natural speech. In a double-blind, placebo-controlled experiment, participants given 0.5 g of psilocybin mushrooms showed significant differences in verbosity and sentiment scores compared to placebo, though semantic variability did not differ. Machine learning classifiers using these speech metrics distinguished between the psilocybin and placebo conditions with high accuracy (AUC≈0.8). These findings suggest that unconstrained natural language may serve as a practical, low-cost tool for monitoring microdosing effects, addressing limitations of existing questionnaires designed for larger psychedelic doses.
bioRxiv (Cold Spring Harbor Laboratory)
September 8, 2019
Laura Alethia de la Fuente, Federico Zamberlán, Andrés Sánchez Ferrán et al.
1 citation
preprint
Machine learning analysis of a large public dataset where users freely reported their experiences with cannabis strains, combined with chemical composition data, reveals that cannabis strains can be reliably classified into three major clusters corresponding to Cannabis sativa, Cannabis indica, and hybrids based on self-reported effect and flavor tags. Terpene profiles matched users' perceptual characterizations and could predict associations between different psychoactive effects, while cannabinoid content was variable even within individual strains. The findings suggest that flavor perception, reflecting robustly inherited terpene content, could serve as a reliable marker to predict psychoactive effects, offering a data-driven approach to strain classification for the growing medicinal and recreational cannabis market.
Psychopharmacology
September 1, 2022
Camila Sanz, Federico Cavanna, Stephanie Müller et al.
Natural speech can reveal whether someone has taken a microdose of psilocybin. In a double-blind, placebo-controlled experiment, 34 healthy adults provided speech samples after consuming either 0.5 grams of psilocybin mushrooms or a placebo. Machine learning classifiers distinguished between the two conditions with high accuracy (AUC ~0.8), based on features such as verbosity and sentiment scores, though semantic variability did not differ significantly. This suggests that low doses of serotonergic psychedelics leave detectable signatures in unconstrained speech, offering a potential low-cost, non-invasive method for monitoring microdosing regimens.