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Predicting who will quit drugs based on their trip report
Imagine a computer researcher who’s presented with a page of text and the task of dissecting if the text represents a positive or negative mood. The researcher could read the paper and ‘process’ it to come to a conclusion one way or another. What if we change the scenario and now ask the researcher to do the same for 1000 pages of text? Then we can expect the researcher to pop open a can of Red Bull and start typing away making a ‘Natural Language Processing’ (NLP).
NLPs can analyze text and draw conclusions or insights from a text. This has been used to gauge the mood of voters based on their social media posts, the interest in stocks, and now also the content of someone’s psychedelic trip. NLPs are a part of machine learning or even more broadly artificial intelligence. Using this technique, psychedelic researchers have been able to analyze more than 1100 trip reports to understand what makes people quit drugs (alcohol, cannabis, opioids).
How did they do it?
- The NLP algorithm analyzed the trip reports that participants wrote. This trip was the one that prompted them to quit taking a drug.
- Based on the text of the report, through trial and error with a subset of the data, the algorithm was able to predict if someone had actually quit a drug with about 65% accuracy.
- This type of approach may be useful for future studies where the written trip report could help identify the people who will quit a drug and those who might need more guidance.
Still, this type of analysis is in its infancy. Clinicians may be able to use this type of data as another data point if they are treating a patient for substance addiction, but at this time it does nothing more than giving a likelihood of success. Finding out who’s less likely to achieve a reduction or quitting a drug could be beneficial in putting more resources towards integration and support.
Reduced anxiety, depression, and improved self-compassion after an ayahuasca retreat
Many studies have been pointing out that the mental health of people can be improved after going to an ayahuasca retreat. There is an ongoing debate surrounding the different factors leading to this positive change. As people go in with many expectations, could it be that most of the positive outcomes are placebo effects? Or could it be that people who participate in a retreat are at their worst in terms of mental health? And thus a ‘regression to the mean’ takes place, where people become better but that this would have happened regardless of the retreat.
The current paper isn’t able to address these issues head-on. The participants in the study were self-selected and there was no placebo being used. That all being said, the study did confirm some findings that other studies have previously found. Significant improvements in mental health, and these effects being sustained six months later.
The outcomes of the study
- Of those who were depressed before the retreat, 77% were no longer qualified as such after the retreat and at the 6-month follow-up.
- Scores on all the measures (depression, anxiety, lack of self-compassion) were significantly lower after the retreat and this effect held up at the follow-up (i.e. stayed low).
- Genetic analysis of the participants didn’t show any noteworthy results, partly because the analysis of BDNF was not done due to an error.
If we put aside all the reasons why mental health improves after an ayahuasca retreat, we can still wonder at the impressive effect size. A single retreat led to 77% of participants who were depressed no longer qualifying for depression.
The researchers propose that a reduction in the valence, or strength, of negative emotions is a possible explanation for the reduction in depression scores. If negative memories are recalled less, and less intensively, one might break free of the thought loops that we spoke about last week.
Measuring the brain with different interpretations of the data
Functional magnetic resonance imaging (fMRI) measures brain activity by noticing changes in blood flow. If there is more blood flow within, or between, a brain area, we can say that this has become more active. Or when there is a decrease, it can be argued that this part of the brain takes a step back.
Alas, interpreting this information isn’t as straight forward as the above example paints it to be. The cut-off point between active and not can vary from study to study. And defining what configurations of blood flow constitute ‘normal’ or ‘resting state’ measurements is still in flux.
A pre-print article argues that these observations are very much true for the psychedelics field too. Of the 37 research articles with fMRI data (on 16 unique datasets), no two studies use the same data processing and analysis strategy.
Variations in the date include:
- Studies use a variety of patients groups: healthy volunteers, experienced meditators, and people suffering from depression.
- The amounts of drugs varied widely and three different drugs were used: psilocybin, LSD, and ayahuasca.
- The type of analysis was categorized in six different ways, two of which were: network connectivity, and entropy-based.
The authors encourage their colleagues to take eight steps to make data more comparable between studies. These include 1) marking the blood plasma levels (of a drug) at the moment of measurement, 2) differentiating between listening to music and rest, and 3) clearly define resting-state as eyes closed and letting participants’ minds wander freely.
As this research (field) is still in its early stages, standardizing some of this could greatly help prevent a replication crisis. It will also help make comparisons between different drugs easier, and allow for other (novel) drugs to be compared to the ones there is data on already.
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