Sci Adv
March 16, 2022
Galen Ballentine, Samuel Freesun Friedman, Danilo Bzdok
66 citations
Psychedelics likely alter consciousness by disrupting the usual hierarchy between higher-level association cortex and incoming sensory signals. Analyzing 6,850 free-form testimonials about 27 drugs alongside 40 neurotransmitter receptor subtypes and their gene expression maps in the brain, the authors found that drug-induced changes in awareness correspond to cortex-wide distributions of receptor density. Each receptor-experience factor spanned a continuum from higher-level association to sensory input, possibly reflecting a collapse of hierarchical order among large-scale brain networks. The framework reveals the semantic structure underlying diverse drug experiences and links it directly to brain anatomy.
bioRxiv (Cold Spring Harbor Laboratory)
July 14, 2021
Galen Ballentine, Sam Friedman, Danilo Bzdok
6 citations
preprint
Psychedelic drugs alter consciousness by disrupting how the brain's higher association cortex processes incoming sensory signals. Analyzing 6,850 free-form testimonials about 27 drugs and linking them to 40 neurotransmitter receptor subtypes via gene transcription maps, a pattern-learning approach revealed that specific changes in awareness—such as dissolving self-world boundaries or fractal visual distortions—correspond to distinct distributions of receptor densities across the cortex. Ego-dissolution-like experiences were tied to 5-HT2A, D2, KOR, and NMDA receptors in both deep hierarchical (associative higher-order cortex) and shallow hierarchical (visual cortex) brain regions. Emotional effects involved 5-HT2A and Imidazoline1 receptors, while auditory and visual sensations involved SERT, 5-HT1A, and 5-HT2A receptors. Each receptor-experience factor spanned between higher-level association and sensory input poles, potentially relating to a collapse of hierarchical order among large-scale brain networks.
Human Psychopharmacology Clinical and Experimental
December 20, 2023
Sam Friedman, Galen Ballentine
5 citations
Machine learning models trained on 11,816 publicly available testimonials about 52 different drugs can predict and quantify subjective experiences. The models identified 11 statistically significant latent factors linking drug receptor affinities to word usage, which mapped to brain regions. A pervasive distinction emerged between universal psychedelic experiences of heightened feeling and the grim, mundane experiences of addiction and mental illness. MDMA was linked to “Love”, DMT and 5-MeO-DMT to “Mystical Experiences” and “Entities and Beings”, and other tryptamines to “Surprise”, “Curiosity”, and “Realization”. The methods show potential for characterizing psychoactivity through data-driven sentiment analysis.
Research Square
August 17, 2022
Sam Friedman, Galen Ballentine
1 citation
Machine learning applied to 11,816 publicly available drug testimonials reveals distinct subjective experiences linked to specific substances. Using BERT, a transformer model, the study predicted 28 dimensions of sentiment across narratives, validated by psychiatrist annotations. Canonical correlation analysis connected 52 drugs' receptor affinities with word usage, uncovering 11 latent receptor-experience factors mapped to a 3D cortical atlas. Results distinguished lucid from mundane phenomena: MDMA was linked to "Love," DMT and 5-MeO-DMT to "Mystical Experiences," and other tryptamines to "Surprise," "Curiosity," and "Realization." These models could potentially guide real-time biofeedback in therapeutic sessions.