Abstract:Understanding the phenomenology and content of the inhaled N, N, dimethyltryptamine (N, N-DMT) experience is critical to facilitate and support ongoing research and therapeutic models targeting mental health conditions and central nervous system pathology. A qualitative analysis was conducted of all N, N-DMT experiences posted to the r/DMT Reddit community over a 10-year period from 2009 to 2018. A total of 3778 experiences from 3305 posts were included in this study. A median dose of N, N-DMT of 40.0 mg [inte… Show more
“…Confirming its reputation as the "spirit molecule", DMT displayed heightened trajectories for the tag "Mystical Experiences" and, even more dramatically, for the tag "Entities and Beings", echoing themes uncovered in manual DMT-specific analyses [27]. As expected, the "Depression" tag trajectory highlights antidepressants, while the "Addiction Habituation" tag is consistently elevated for the stimulants cocaine and methamphetamine, see Supplementary Fig.…”
Section: Bertowidsupporting
confidence: 65%
“…Metadata semantic tags are also learnable, with some of the best performing being "Medical Use", "Mystical Experiences", "Alone" and "Addiction Habituation", with areas under the receiver operating characteristic curves (ROC AUC) ranging from 0.88 to 0.95, areas under the precision-recall and ROC curves for all tags are shown in Figure : Results. Confirming its reputation as the "spirit molecule", DMT displayed heightened predicted trajectories for the tag "Mystical Experiences" and, even more dramatically, for the tag "Entities and Beings", echoing themes uncovered in manual DMT-specific analyses [35]. As expected, the Depression tag trajectory highlights antidepressants, while the "Addiction Habituation" tag is consistently elevated for the stimulants cocaine and methamphetamine, see Figure : Trajectories.…”
Section: Bertowidsupporting
confidence: 56%
“…Repeated inference with BERTowid over contiguous windows of text in held-out testimonials followed by interpolation constructs the trajectories shown Figure: Trajectories. Confirming its reputation as the “spirit molecule”, DMT displayed heightened predicted trajectories for both “Mystical Experiences” and even more dramatically for the tag “Entities and Beings”, echoing themes uncovered in manual DMT-specific analyses (22). As expected, the Depression tag trajectory highlights antidepressants, while the “Addiction Habituation” tag is consistently elevated for the stimulants cocaine and methamphetamine.…”
Drugs like entactogens and psychedelics appear poised to enter clinical psychiatry, however we lack a unified framework for quantifying the changes in conscious awareness optimal for treatment. We address this need using transformers (i.e. BERT) and 11,821 publicly-available, natural language testimonials from Erowid. First, we predicted 28 dimensions of sentiment across each narrative, validated with clinical psychiatrist annotations. Secondly, another model was trained to predict biochemical (pharmacological and chemical class, molecule name, receptor affinity) as well as demographic (sex, age) information from the testimonials. Thirdly, canonical correlation analysis (CCA) linked the 52 drugs' affinities for 61 receptor subtypes with word usage across the testimonials, revealing 11 latent receptor-experience factors each mapped to a 3D cortical atlas of receptor gene-expression. Together, these 3 machine learning methods elucidate a neurobiologically-informed, temporally-sensitive portrait of drug-induced subjective experiences. These models are mutually confirmatory, pointing to an underlying structure of psychoactive experience dominated by the distinction between the lucid and the mundane, but also sensitive to effects unique to individual drugs. For example, MDMA was singularly linked to mid-experience swelling of "Love", potent psychedelics like DMT, and 5-MeO-DMT were associated with "Mystical Experiences", while other tryptamines were associated with an emotional constellation of "Surprise", "Curiosity" and "Realization". Applying these models to real-time biofeedback (e.g. EEG, or MRI) with zero-shot learning that tunes the sentimental trajectory of the experience through changes in audiovisual outputs, practitioners could guide the course of therapeutic sessions, maximizing benefit and minimizing harm for patients.
“…Confirming its reputation as the "spirit molecule", DMT displayed heightened trajectories for the tag "Mystical Experiences" and, even more dramatically, for the tag "Entities and Beings", echoing themes uncovered in manual DMT-specific analyses [27]. As expected, the "Depression" tag trajectory highlights antidepressants, while the "Addiction Habituation" tag is consistently elevated for the stimulants cocaine and methamphetamine, see Supplementary Fig.…”
Section: Bertowidsupporting
confidence: 65%
“…Metadata semantic tags are also learnable, with some of the best performing being "Medical Use", "Mystical Experiences", "Alone" and "Addiction Habituation", with areas under the receiver operating characteristic curves (ROC AUC) ranging from 0.88 to 0.95, areas under the precision-recall and ROC curves for all tags are shown in Figure : Results. Confirming its reputation as the "spirit molecule", DMT displayed heightened predicted trajectories for the tag "Mystical Experiences" and, even more dramatically, for the tag "Entities and Beings", echoing themes uncovered in manual DMT-specific analyses [35]. As expected, the Depression tag trajectory highlights antidepressants, while the "Addiction Habituation" tag is consistently elevated for the stimulants cocaine and methamphetamine, see Figure : Trajectories.…”
Section: Bertowidsupporting
confidence: 56%
“…Repeated inference with BERTowid over contiguous windows of text in held-out testimonials followed by interpolation constructs the trajectories shown Figure: Trajectories. Confirming its reputation as the “spirit molecule”, DMT displayed heightened predicted trajectories for both “Mystical Experiences” and even more dramatically for the tag “Entities and Beings”, echoing themes uncovered in manual DMT-specific analyses (22). As expected, the Depression tag trajectory highlights antidepressants, while the “Addiction Habituation” tag is consistently elevated for the stimulants cocaine and methamphetamine.…”
Drugs like entactogens and psychedelics appear poised to enter clinical psychiatry, however we lack a unified framework for quantifying the changes in conscious awareness optimal for treatment. We address this need using transformers (i.e. BERT) and 11,821 publicly-available, natural language testimonials from Erowid. First, we predicted 28 dimensions of sentiment across each narrative, validated with clinical psychiatrist annotations. Secondly, another model was trained to predict biochemical (pharmacological and chemical class, molecule name, receptor affinity) as well as demographic (sex, age) information from the testimonials. Thirdly, canonical correlation analysis (CCA) linked the 52 drugs' affinities for 61 receptor subtypes with word usage across the testimonials, revealing 11 latent receptor-experience factors each mapped to a 3D cortical atlas of receptor gene-expression. Together, these 3 machine learning methods elucidate a neurobiologically-informed, temporally-sensitive portrait of drug-induced subjective experiences. These models are mutually confirmatory, pointing to an underlying structure of psychoactive experience dominated by the distinction between the lucid and the mundane, but also sensitive to effects unique to individual drugs. For example, MDMA was singularly linked to mid-experience swelling of "Love", potent psychedelics like DMT, and 5-MeO-DMT were associated with "Mystical Experiences", while other tryptamines were associated with an emotional constellation of "Surprise", "Curiosity" and "Realization". Applying these models to real-time biofeedback (e.g. EEG, or MRI) with zero-shot learning that tunes the sentimental trajectory of the experience through changes in audiovisual outputs, practitioners could guide the course of therapeutic sessions, maximizing benefit and minimizing harm for patients.
“…Con rming its reputation as the "spirit molecule", DMT displayed heightened trajectories for the tag "Mystical Experiences" and, even more dramatically, for the tag "Entities and Beings", echoing themes uncovered in manual DMT-speci c analyses [27]. As expected, the "Depression" tag trajectory highlights antidepressants, while the "Addiction Habituation" tag is consistently elevated for the stimulants cocaine and methamphetamine, see Supplementary Fig.…”
With novel hallucinogens poised to enter psychiatry, we lack a unified framework for quantifying which changes in consciousness are optimal for treatment. Using transformers (i.e. BERT) and 11,816 publicly-available drug testimonials, we first predicted 28-dimensions of sentiment across each narrative, validated with psychiatrist annotations. Secondly, BERT was trained to predict biochemical and demographic information from testimonials. Thirdly, canonical correlation analysis (CCA) linked 52 drugs' receptor affinities with testimonial word usage, revealing 11 latent receptor-experience factors, mapped to a 3D cortical atlas. Together, these 3 machine learning methods elucidate a neurobiologically-informed, temporally-sensitive portrait of drug-induced subjective experiences. Different models’ results converged, revealing a pervasive distinction between lucid and mundane phenomena. MDMA was linked to "Love", DMT and 5-MeO-DMT to "Mystical Experiences", and other tryptamines to "Surprise", "Curiosity" and "Realization". Applying these models to real-time biofeedback, practitioners could harness them to guide the course of therapeutic sessions.
“…Although the main strength of our study is our extensive coverage of both neurotransmitters and pharmacological data, it is important to acknowledge that neither is complete: in particular, our sample did by no means exhaustively include all mind-altering drugs that have been studied: prominent additions for future work may include psilocybin, DMT (as separate from the other components of the ayahuasca infusion) 156,157 , the kappa opioid receptor agonist salvinorin-A 158 , the alpha-2 receptor agonist dexmedetomidine [159][160][161] , and anaesthetic doses of ketamine 24,148,162 , just to name a few that have been recently studied -but also alcohol or caffeine, arguably the two most widely used psychoactive substances.…”
Section: Limitations and Future Directionsmentioning
To understand how pharmacological interventions can exert their powerful effects on brain function, we need to understand how they engage the brain's rich neurotransmitter landscape. Here, we bridge microscale molecular chemoarchitecture and pharmacologically-induced macroscale functional reorganisation, by relating the regional distribution of 18 neurotransmitter receptors and transporters obtained from Positron Emission Tomography, and the regional changes in functional MRI connectivity induced by 7 different mind-altering drugs including anaesthetics, psychedelics, and cognitive enhancers. Our results reveal that psychoactive drugs exert their effects on brain function by engaging multiple neurotransmitter systems. Intriguingly, the effects of both anaesthetics and psychedelics on brain function, though opposite, are organised along hierarchical gradients of brain structure and function. Finally, we show that regional co-susceptibility to pharmacological interventions recapitulates co-susceptibility to disorder-induced structural alterations. Collectively, these results highlight rich statistical patterns relating molecular chemoarchitecture and drug-induced reorganisation of the brain's functional architecture.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.