bioRxiv Preprint Server
December 19, 2024
Evan Lewis-Healey, Carla Pallavicini, Federico Cavanna et al.
1 citation
preprint
A dose of the fast-acting psychedelic DMT rapidly reorganizes conscious experience and brain dynamics, but the link between neural complexity and subjective effects is weaker than previously thought. Nineteen participants received 20 mg or 40 mg of DMT in two sessions. The higher dose produced more extreme visual hallucinations and emotionally intense experiences. Contrary to earlier claims, Lempel-Ziv complexity—a measure of neural signal diversity—was the least strongly associated neural marker of the psychedelic state. The findings suggest the relationship between neural complexity and phenomenology during psychedelic experiences is less clear than originally hypothesized.
bioRxiv
May 4, 2026
Tomás Ariel D’amelio, Tomás Gil Garbagnoli, Jerónimo Rodríguez Cuello et al.
Inhalation of DMT, a serotonergic psychedelic, produces a brief surge in sympathetic nervous system activity—heart rate, skin conductance, and respiration—that closely tracks the intensity of the emotional experience. Nineteen participants received 20 or 40 mg of DMT under a semi-naturalistic blinded design. Higher doses caused heart rate and breathing to increase within the first two minutes, while skin conductance rose only later, indicating a prolonged autonomic response. As the drug's effects waned, feelings of pleasantness and bliss emerged. Combining simple physiological measures with moment-by-moment self-reports offers a way to objectively characterize psychedelic-induced emotional states, which may aid future clinical biomarker research.
bioRxiv Preprint Server
April 22, 2026
Nicolás Bruno, Federico Cavanna, Federico Zamberlán et al.
preprint
Spontaneous thoughts make up most of everyday inner experience, but studying them is difficult because traditional methods disrupt the natural flow of thinking or introduce motor artifacts. An alternative approach combined delayed verbal retrospective free reports with automated ratings from large language models. Twenty-two participants performed an eyes-closed free-thinking task, and their reports were evaluated on ten dimensions by four LLMs and human raters. Machine-learning models trained on EEG features achieved above-chance accuracy for predicting emotional valence. LLMs showed higher inter-rater agreement than humans, supporting their use for scalable annotation and suggesting that affective dimensions of spontaneous thoughts can be decoded from brain activity.