Psychedelic resting-state neuroimaging: a review and perspective on balancing replication and novel analyses

This pre-print review article of fMRI studies with psychedelics finds that there are no studies that use the same analysis techniques. They propose eight steps to standardize measurements and propose future fMRI studies to be done.

Abstract

Clinical research into classical psychedelic drugs including psilocybin, LSD and N,N-DMT (ayahuasca) is expanding rapidly and clinical trials across a range of psychiatric conditions have shown promising efficacy, with larger trials ongoing. Resting-state functional magnetic resonance imaging (fMRI) has emerged as a commonly used brain imaging strategy to identify associated neural mechanisms in both clinical and healthy populations. To date, 37research articles have been published analysing resting-state fMRI data from 16unique datasets involving the administration of a psychedelic drug. This provides a promising foundation for resolving imaging markers of the perceptual and clinical effects of psychedelics. Here we review the existing psychedelic resting-state fMRI literature through a lens that brings attention to emerging variation in core methodological decisions. We find that across the existing literature, no two studies employ the same data processing and analysis strategy. Two datasets are the foundation of more than half of the published literature and individual terms such as β€œentropy” are being used to represent distinct metrics across studies. In light of these characteristics, we offer suggestions for future studies that we hope encourages coherence in the field. As a budding field of interest, psychedelic resting-state imaging requires the development of novel models, hypotheses and quantification methods that will expand our understanding of the neural mechanisms mediating the intriguing acute perceptual and lasting clinical effects of psychedelics. Our review of the existing literature suggests that the psychedelic resting-state brain imaging field is at a crossroads at which it must also consider the critical importance of consistency and replication to effectively converge on stable representations of the neural effects of psychedelics.

Authors: Drummond E-W. McCulloch, Gitte M. Knudsen & Patrick M. Fisher

Notes

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.

These are the variations in the data

  • 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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