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.


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 “entropy” 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


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.


Clinical research into classic psychedelic drugs is expanding rapidly, and resting-state fMRI has emerged as a commonly used brain imaging strategy to identify associated neural mechanisms in both clinical and healthy populations.

Here we review the existing psychedelic resting-state fMRI literature and suggest that the field is at a crossroads where it must consider the critical importance of consistency and replication to effectively converge on stable representations of the neural effects of psychedelics.

Psychedelics have re-emerged in research since the early 2000s. They include psilocybin, LSD, ayahuasca and monoamine oxidase inhibitors.

Small clinical trials with psilocybin and ayahuasca have shown promising results in treating major depressive disorder, obsessive compulsive disorder, smoking addiction, alcohol abuse, demoralisation, and depressive and anxiety symptoms associated with diagnosis of terminal cancer.

Psychedelics bind multiple receptor targets, including alpha-adrenergic receptors, most serotonin receptors, trace amine receptors and sigma receptors, but the psychoactive effects in humans appear to be driven primarily by agonist effects at the serotonin 2A (5-HT2A) receptor.

Psychedelics last 4-6 hours orally and 812 hours intravenously, and are characterised by profound alterations in affect, perceptual alterations and synaesthesia, and mystical-like experiences. These effects have motivated research into how the brain processes psychedelics.

Resting-state functional Magnetic Resonance Imaging (fMRI) measures brain function and connectivity while the participant is lying still in the scanner and not engaged in a specific task. The primary analysis endpoint is functional connectivity.

Resting-state fMRI provides a passive framework for observing brain function during psychedelic sessions, and is therefore a particularly appealing framework for evaluating psychedelic effects on the brain.

In 2012, Carhart-Harris and colleagues published the first study investigating the effects of psychedelics on resting-state functional connectivity measures.

Sixteen datasets investigating resting-state fMRI have been collected from participants given psychedelics. These datasets range in size from 9-24 participants and were collected in clinical cohorts and healthy volunteers.

Three studies investigated the effects of drug administration within 24 hours, two studies investigated effects one week after, one study investigated effects three months after, and eleven studies investigated effects at any time after drug administration.

Eleven datasets have yielded one publication, three have yielded two publications, two studies have yielded nine and thirteen publications, and two publications draw data from both of these datasets. We encourage other groups in this space to make their data and analysis scripts available to encourage collaboration.

The Carhart-Harris 2016 dataset contains three 7.2 minute eyes-closed resting-state acquisitions between 70 and 130 mins after the IV administration of 75 g of LSD. Music played during the second acquisition was analysed in one study.

Across 16 unique datasets, participants were instructed to close their eyes during the resting-state session. Four studies performed multiple resting-state scans during the psychedelic sessions, and most of the data collected so far has opted for eyes-closed imaging.

The 37 published studies differ substantially in how they analyse BOLD data, with most studies using static functional connectivity analyses.

15 articles used seed-based connectivity analysis, 13 used small localised seeds only, one used both small and network seeds and one used network seeds only. The most commonly selected seeds were the medial prefrontal cortex, posterior cingulate cortex and thalamus.

Twelve articles investigated the connectivity of networks using a network-based approach. Of these, seven investigated within- and between-network connectivity, one studied the connectivity of the DMN with all other networks, one study performed gradient-based connectivity mapping and one study investigated the global connectivity of all networks defined.

Four studies investigated ROI to ROI connectivity, one study used complex network theory, one study calculated grand-average static functional connectivity, five studies investigated whole brain effects, and one study presented a voxel wise contract of psilocybin vs placebo of brain activity.

There are several system-based models of psychedelic effects on consciousness, including the DMN disintegration theory, the thalamic gating theory, and the claustro-cortical model. These theories can be reconciled using novel data.

The burgeoning interest in psychedelic effects has facilitated the application of novel neurocomputational models to the two IV datasets. These models include temporal variability, homological scaffolds, gradient-based connectivity, retinotopic coordination, whole-brain functional connectivity dynamics, connectome-harmonic decomposition, and leading eigenvector dynamic analysis.

Six studies have investigated the “entropy” of the RSFC signal, but they have calculated the entropy in different ways. Tagliazucchi, (2014) calculated the Shannon entropy of dynamic functional connectivity states, Lebedev, (2015) calculated the Shannon entropy of functional connectivity between first two, then 200 ROIs with 5 different networks, Viol, (2017) calculated the multi-scale sample entropy.

Although different studies may use different analysis methods, they may be measuring similar phenomena. Therefore, it is important to align in terms of how this phenomenon should be measured.

Spatial parcellation is important for replicable results in seed-, network-, or ROI-based analyses. Atlases and independent component analyses can be used to parcellate data.

24 studies used atlases derived from functional data, 14 studies used atlases derived from anatomical data, and two studies used atlases that took into account both structural and functional data.

Of the studies that used predefined atlases, five used the Harvard-Oxford Probabilistic anatomical atlas, four used the Automated Anatomical Labelling (AAL) atlas, six studies used the Yeo 2011 atlas, and three studies used the 17 network parcellation.

The twelve studies investigating the effects of psychedelics on network connectivity use six different atlases of networks, which vary in number of networks described and labels and descriptions of voxels that constitute each network.

Psychedelic drugs have been shown to have persistent effects on mood and personality in both healthy and patient population groups. More data on the persistent effects of psychedelics on neural function is needed to understand the remarkable therapeutic effects of psychedelics as well as highlighting potential risks.

Neuroimaging studies have been performed on LSD, psilocybin and ayahuasca, but there are many more untested psychedelic drugs that may be 5-HT2A receptor agonists and provide candidates for investigation as therapeutic agents.

Of 37 published studies, no pair of studies used the same core data processing and analysis strategy. This limits the ability to leverage early observations and hypotheses across datasets.

Data and analysis scripts should be shared on online repositories, plasma drug levels should be considered during acute imaging sessions, resting-state scans should be clearly demarcated from “resting-state”, and unique analyses should be replicated with independent data.

This paper has reviewed the heterogeneity within psychedelic resting state neuroimaging and how this leaves the field vulnerable to generating non-reproducible features of psychedelic effects.

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