Ever since the modern rediscovery of psychedelic substances by Western society, several authors have independently proposed that their effects bear a high resemblance to the dreams and dreamlike experiences occurring naturally during the sleep-wake cycle. Recent studies in humans have provided neurophysiological evidence supporting this hypothesis. However, a rigorous comparative analysis of the phenomenology (“what it feels like” to experience these states) is currently lacking. We investigated the semantic similarity between a large number of subjective reports of psychoactive substances and reports of high/low lucidity dreams, and found that the highest-ranking substance in terms of the similarity to high lucidity dreams was the serotonergic psychedelic lysergic acid diethylamide (LSD), whereas the highest-ranking in terms of the similarity to dreams of low lucidity were plants of the Datura genus, rich in deliriant tropane alkaloids. Conversely, sedatives, stimulants, antipsychotics, and antidepressants comprised most of the lowest-ranking substances. An analysis of the most frequent words in the subjective reports of dreams and hallucinogens revealed that terms associated with perception (“see,” “visual,” “face,” “reality,” “color”), emotion (“fear”), setting (“outside,” “inside,” “street,” “front,” “behind”) and relatives (“mom,” “dad,” “brother,” “parent,” “family”) were the most prevalent across both experiences. In summary, we applied novel quantitative analyses to a large volume of empirical data to confirm the hypothesis that, among all psychoactive substances, hallucinogen drugs elicit experiences with the highest semantic similarity to those of dreams. Our results and the associated methodological developments open the way to study the comparative phenomenology of different altered states of consciousness and its relationship with non-invasive measurements of brain physiology.

The real or perceived proximity to death often results in a non-ordinary state of consciousness characterized by phenomenological features such as the perception of leaving the body boundaries, feelings of peace, bliss and timelessness, life review, the sensation of traveling through a tunnel and an irreversible threshold. Near-death experiences (NDEs) are comparable among individuals of different cultures, suggesting an underlying neurobiological mechanism. Anecdotal accounts of the similarity between NDEs and certain drug-induced altered states of consciousness prompted us to perform a large-scale comparative analysis of these experiences. After assessing the semantic similarity between ≈15,000 reports linked to the use of 165 psychoactive substances and 625 NDE narratives, we determined that the N-methyl-D-aspartate (NMDA) receptor antagonist ketamine consistently resulted in reports most similar to those associated with NDEs. Ketamine was followed by Salvia divinorum (a plant containing a potent and selective κ receptor agonist) and a series of serotonergic psychedelics, including the endogenous serotonin 2A receptor agonist N,N-Dimethyltryptamine (DMT). This similarity was driven by semantic concepts related to consciousness of the self and the environment, but also by those associated with the therapeutic, ceremonial and religious aspects of drug use. Our analysis sheds light on the longstanding link between certain drugs and the experience of "dying", suggests that ketamine could be used as a safe and reversible experimental model for NDE phenomenology, and supports the speculation that endogenous NMDA antagonists with neuroprotective properties may be released in the proximity of death.

Classic psychedelics are substances of paramount cultural and neuroscientific importance. A distinctive feature of psychedelic drugs is the wide range of potential subjective effects they can elicit, known to be deeply influenced by the internal state of the user (“set”) and the surroundings (“setting”). The observation of cross-tolerance and a series of empirical studies in humans and animal models support agonism at the serotonin (5-HT)2A receptor as a common mechanism for the action of psychedelics. The diversity of subjective effects elicited by different compounds has been attributed to the variables of “set” and “setting,” to the binding affinities for other 5-HT receptor subtypes, and to the heterogeneity of transduction pathways initiated by conformational receptor states as they interact with different ligands (“functional selectivity”). Here we investigate the complementary (i.e., not mutually exclusive) possibility that such variety is also related to the binding affinity for a range of neurotransmitters and monoamine transporters including (but not limited to) 5-HT receptors. Building on two independent binding affinity datasets (compared to “in silico” estimates) in combination with natural language processing tools applied to a large repository of reports of psychedelic experiences (Erowid’s Experience Vaults), we obtained preliminary evidence supporting that the similarity between the binding affinity profiles of psychoactive substituted phenethylamines and tryptamines is correlated with the semantic similarity of the associated reports. We also showed that the highest correlation was achieved by considering the combined binding affinity for the 5-HT, dopamine (DA), glutamate, muscarinic and opioid receptors and for the Ca+ channel. Applying dimensionality reduction techniques to the reports, we linked the compounds, receptors, transporters and the Ca+ channel to distinct fingerprints of the reported subjective effects. To the extent that the existing binding affinity data is based on a low number of displacement curves that requires further replication, our analysis produced preliminary evidence consistent with the involvement of different binding sites in the reported subjective effects elicited by psychedelics. Beyond the study of this particular class of drugs, we provide a methodological framework to explore the relationship between the binding affinity profiles and the reported subjective effects of other psychoactive compounds.

Introduction Automated speech analysis has emerged as a scalable, cost‐effective tool to identify persons with Alzheimer's disease dementia (ADD). Yet, most research is undermined by low interpretability and specificity. Methods Combining statistical and machine learning analyses of natural speech data, we aimed to discriminate ADD patients from healthy controls (HCs) based on automated measures of domains typically affected in ADD: semantic granularity (coarseness of concepts) and ongoing semantic variability (conceptual closeness of successive words). To test for specificity, we replicated the analyses on Parkinson's disease (PD) patients. Results Relative to controls, ADD (but not PD) patients exhibited significant differences in both measures. Also, these features robustly discriminated between ADD patients and HC, while yielding near‐chance classification between PD patients and HCs. Discussion Automated discourse‐level semantic analyses can reveal objective, interpretable, and specific markers of ADD, bridging well‐established neuropsychological targets with digital assessment tools.