The entropic brain - revisited.

Responses to psychedelics are notoriously difficult to predict, yet significant work is currently underway to assess their therapeutic potential and the level of interest in psychedelics among the general public appears to be increasing. We aimed to collect prospective data in order to improve our ability to predict acute- and longer-term responses to psychedelics. Individuals who planned to take a psychedelic through their own initiative participated in an online survey (www.psychedelicsurvey.com). Traits and variables relating to set, setting and the acute psychedelic experience were measured at five different time points before and after the experience. Principle component and regression methods were used to analyse the data. Sample sizes for the five time points included N= 654, N= 535, N= 379, N= 315, and N= 212 respectively. Psychological well-being was increased two weeks after a psychedelic experience and remained at this level after four weeks. This increase was larger for individuals who scored higher for a ‘mystical-type experience’, and smaller for those who scored higher for ‘challenging experience’. Having ‘clear intentions’ for the experience was conducive to mystical-type experiences. Having a positive ‘set’, as well as having the experience with intentions related to ‘recreation’, were both found to decrease the likelihood of having a challenging experience. The trait ‘absorption’ and higher drug doses promoted both mystical-type and challenging experiences. When comparing different types of variables, traits variables seemed to explain most variance in the change in well-being after a psychedelic experience. These results confirm the importance of extra-pharmacological factors in determining responses to a psychedelic. We view this study as an early step towards the development of empirical guidelines that can evolve and improve iteratively with the ultimate purpose of guiding crucial clinical decisions about whether, when, where and how to dose with a psychedelic, thus helping to reduce risks while maximising potential benefits in an evidence-based manner.

/pdf/predicting-responses-to-psychedelics-a-prospective-study-1sa4vfe9zk.pdf

Predicting Responses to Psychedelics: A Prospective Study.

The current state of artificial intelligence in ophthalmology.

Using text mining to extract depressive symptoms and to validate the diagnosis of major depressive disorder from electronic health records.

The current global threat brought on by the Covid-19 pandemic has led to widespread social isolation, posing new challenges in dealing with metal suffering related to social distancing, and in quickly learning new social habits intended to prevent contagion. Neuroscience and psychology agree that dreaming helps people to cope with negative emotions and to learn from experience, but can dreaming effectively reveal mental suffering and changes in social behavior? To address this question, we applied natural language processing tools to study 239 dream reports by 67 individuals, made either before the Covid-19 outbreak or during the months of March and April, 2020, when lockdown was imposed in Brazil following the WHO's declaration of the pandemic. Pandemic dreams showed a higher proportion of anger and sadness words, and higher average semantic similarities to the terms "contamination" and "cleanness". These features seem to be associated with mental suffering linked to social isolation, as they explained 40% of the variance in the PANSS negative subscale related to socialization (p = 0.0088). These results corroborate the hypothesis that pandemic dreams reflect mental suffering, fear of contagion, and important changes in daily habits that directly impact socialization.

/pdf/dreaming-during-the-covid-19-pandemic-computational-5fftrwnyfv.pdf

Dreaming during the Covid-19 pandemic: Computational assessment of dream reports reveals mental suffering related to fear of contagion.

Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression.

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Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression.