Biology subjects

Pallavicini, C.

Publications and source records attributed to Pallavicini, C..

3 recordsLinked to original sources

bioRxiv2020

Neural and subjective effects of inhaled DMT in natural settings

BackgroundN,N-Dimethyltryptamine (DMT) is a short acting psychedelic tryptamine found naturally in many plants and animals. Few studies to date addressed the neural and psychological effects of DMT alone, either administered intravenously or inhaled in freebase form, and none conducted in natural settings. AimsOur primary aim was to study the acute effects of inhaled DMT in natural settings, focusing on questions tuned to the advantages of conducting field research, including the effects of contextual factors (i.e. "set" and "setting"), the possibility of studying a comparatively large number of subjects, and the relaxed mental state of participants consuming DMT in familiar and comfortable settings. MethodsWe combined state-of-the-art wireless electroencephalography (EEG) with psychometric questionnaires to study the neural and subjective effects of naturalistic DMT use in 35 healthy and experienced participants. ResultsWe observed that DMT significantly decreased the power of alpha (8-12 Hz) oscillations throughout all scalp locations, while simultaneously increasing power of delta (1-4 Hz) and gamma (30-40 Hz) oscillations. Gamma power increases correlated with subjective reports indicative of mystical-type experiences. DMT also increased/decreased global synchrony and metastability in the gamma/alpha band, and resulted in widespread increases in signal complexity. ConclusionsOur results are consistent with previous studies of psychedelic action in the human brain, while at the same time suggesting potential EEG markers of mystical-type experiences in natural settings, thus highlighting the importance of investigating these compounds in the contexts where they are naturally consumed.

neuroscience↗

bioRxiv2020

Perturbations in dynamical models of whole-brain activity dissociate between the level and stability of consciousness

Consciousness transiently fades away during deep sleep, more stably under anesthesia, and sometimes permanently due to brain injury. The development of an index to quantify the level of consciousness across these different states is regarded as a key problem both in basic and clinical neuroscience. We argue that this problem is ill-defined since such an index would not exhaust all the relevant information about a given state of consciousness. While the level of consciousness can be taken to describe the actual brain state, a complete characterization should also include its potential behavior against external perturbations. We developed and analyzed whole-brain computational models to show that the stability of conscious states provides information complementary to their similarity to conscious wakefulness. Our work leads to a novel methodological framework to sort out different brain states by their stability and reversibility, and illustrates its usefulness to dissociate between physiological (sleep), pathological (brain-injured patients), and pharmacologically-induced (anesthesia) loss of consciousness.

neuroscience↗

bioRxiv2019

Over eight hundred cannabis strains characterized by the relationship between their psychoactive effects, perceptual profiles, and chemical compositions

BackgroundCommercially available cannabis strains have multiplied in recent years as a consequence of regional changes in legislation for medicinal and recreational use. Lack of a standardized system to label plants and seeds hinders the consistent identification of particular strains with their elicited psychoactive effects. The objective of this work was to leverage information extracted from large databases to improve the identification and characterization of cannabis strains.\n\nMethodsWe analyzed a large publicly available dataset where users freely reported their experiences with cannabis strains, including different subjective effects and flavour associations. This analysis was complemented with information on the chemical composition of a subset of the strains. Both supervised and unsupervised machine learning algorithms were applied to classify strains based on self-reported and objective features.\n\nResultsMetrics of strain similarity based on self-reported effect and flavour tags allowed machine learning classification into three major clusters corresponding to Cannabis sativa, Cannabis indica, and hybrids. Synergy between terpene and cannabinoid content was suggested by significative correlations between psychoactive effect and flavour tags. The use of predefined tags was validated by applying semantic analysis tools to unstructured written reviews, also providing breed-specific topics consistent with their purported medicinal and subjective effects. While cannabinoid content was variable even within individual strains, terpene profiles matched the perceptual characterizations made by the users and could be used to predict associations between different psychoactive effects.\n\nConclusionsOur work represents the first data-driven synthesis of self-reported and chemical information in a large number of cannabis strains. Since terpene content is robustly inherited and less influenced by environmental factors, flavour perception could represent a reliable marker to predict the psychoactive effects of cannabis. Our novel methodology contributes to meet the demands for reliable strain classification and characterization in the context of an ever-growing market for medicinal and recreational cannabis.

neuroscience↗