Ó³»­´«Ã½

Danilo Bzdok
Dept. of Biomedical Engineering, McGill University; Mila Quebec AI Institute

Galen Ballentine
SUNY Downstate Medical Center

Sam Freesun
Friedman Data Sciences Platform, Ó³»­´«Ã½
Meeting: Trips and neurotransmitters; Discovering principled patterns across 6850 hallucinogenic experiences

Recent research indicates that certain hallucinogens can be used to treat a wide variety of mental illnesses and require only a few doses to produce durable effects. Different compounds have distinct but overlapping biological mechanisms, for example the serotonin 2A receptor agonism shown by classical psychedelics. Subjective reports of these drug experiences describe overlapping phenomenological profiles, often including vivid imagery, mystical experiences, and imaginary creatures. Elucidating the mechanisms that underpin the extreme variability of these experiences is vital for developing and refining these drugs for use in the clinic. While most studies on psychedelics include dozens of participants and one molecule, our study integrates 6,850 real-world testimonials and receptor affinity fingerprints of 27 hallucinogenic drugs. With Canonical Correlation Analysis (CCA) we derive the underlying experiential factors from user-generated data in a manner that is intrinsically linked to drug receptor affinity. These distilled receptor-semantic factors are mapped to 3D voxels of the human cortex via RNA expression patterns of different brain regions given by the Allen Brain Atlas. The underlying components show semantic landscapes that span the sensorial (eg audio vs visual hallucinogens), emotional (eg terror vs bliss) and mystical. Each component is simultaneously described in neurochemical terms across the serotonin, opioid, and dopamine systems. Given testimonial data and molecular receptor affinities, this framework extends to new drugs to help identify relationships across domains of experience, molecules, and neuroanatomy.

Danilo Bzdok
Dept. of Biomedical Engineering, McGill University; Mila Quebec AI Institute

Galen Ballentine
SUNY Downstate Medical Center
Primer: Canonical correlation analysis and the structure of psychedelic experience; Towards a neurophenomenological cartography of the cortex

Canonical Correlation Analysis (CCA) describes a family of methods useful in identifying the links between data from different modalities. CCA simultaneously evaluates two different sets of variables, identifying the sources of common variation across the paired high-dimensional datasets. We apply CCA to jointly model natural language reports of psychedelic experiences paired with receptor affinity for 27 different hallucinogenic molecules. Psychedelic drugs are being embraced by researchers as treatments for mental health conditions, but the mechanism of action of these drugs remains the subject of intense inquiry. The quality of the acute drug experience appears to predict the long-term efficacy of these treatments. This suggests better characterizations of the psychedelic experience may inform their therapeutic use. Towards that end, we use CCA to reveal the common structure underlying each drug's unique receptor affinity fingerprint with its phenomenological flavor as captured in subjective testimonials.