The Acute Effects of the Atypical Dissociative Hallucinogen Salvinorin A on Functional Connectivity in the Human Brain

Although the psychedelic Salvinorin A works via very different pathways in the brain, it elicited similar effects (lower activity DMN) as other psychedelics.


“Salvinorin A (SA) is a κ-opioid receptor agonist and atypical dissociative hallucinogen found in Salvia divinorum. Despite the resurgence of hallucinogen studies, the effects of κ-opioid agonists on human brain function are not well-understood. This placebo-controlled, within-subject study used functional magnetic resonance imaging for the first time to explore the effects of inhaled SA on strength, variability, and entropy of functional connectivity (static, dynamic, and entropic functional connectivity, respectively, or sFC, dFC, and eFC). SA tended to decrease within-network sFC but increase between-network sFC, with the most prominent effect being attenuation of the default mode network (DMN) during the first half of a 20-min scan (i.e., during peak effects). SA reduced brainwide dFC but increased brainwide eFC, though only the former effect survived multiple comparison corrections. Finally, using connectome-based classification, most models trained on dFC network interactions could accurately classify the first half of SA scans. In contrast, few models trained on within- or between-network sFC and eFC performed above chance. Notably, models trained on within-DMN sFC and eFC performed better than models trained on other network interactions. This pattern of SA effects on human brain function is strikingly similar to that of other hallucinogens, necessitating studies of direct comparisons.”

Authors: Manoj K. Doss, Darrick G. May, Matthew W. Johnson, John M. Clifton, Sidnee L. Hedrick, Thomas E. Prisinzano, Roland R. Griffiths & Frederick S. Barrett


This paper is included in our ‘Top 10 Articles on Psychedelics in the Year 2020

See this excellent explainer thread on twitter by the first author Manoj Doss.

And This Is My Brain on Salvia (Wired, October 2020).


Salvinorin A (SA), a potent, selective -opioid receptor agonist and atypical dissociative hallucinogen found in Salvia divinorum, is administered via vaporization or combustion and produces intense feelings of depersonalization and derealization accompanied by drastic perceptual changes. SA has shown efficacy in the treatment of preclinical models of cocaine abuse.

Several studies have investigated the acute effects of classic psychedelics and dissociative anesthetics on the brain, but only one study investigated the effects of SA. SA decreased oscillatory power in low frequency bands at rest, suggesting that pharmacologically distinct hallucinogens have partially overlapping neural mechanisms.

SA strongly modulated measures of DMN connectivity, as predicted, and entropic connectivity was unpredictably distributed.

Twelve healthy male participants were recruited through advertisements and word-of-mouth referrals. They were screened for hallucinogen use and inhalation drug use, and were excluded if they had a first or second degree relative with schizophrenia, psychotic disorder, bipolar I or II disorder, or current major depression.

This study used a single-blind, placebo-controlled, within-subjects design, and participants were asked to refrain from using psychoactive drugs 24 h before each session.


Participants spent two hours with research personnel to build rapport and discuss the procedure, and were then given a moderately high dose of SA. They were prompted to exhale for five seconds while covering the end of the tube with their finger, and then inhale for 45 seconds.

Once the inhalation procedure was sufficiently understood, a flask containing 15 g/kg of SA was affixed to the delivery device. Participants were then prompted to verbally rate the strength of subjective drug effects on a scale of 0 (no effect) to 10 (extreme, strongest imaginable) after 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, and 45 min.

Nine participants completed two 20-min functional scans, one week after the practice session, in which they inhaled placebo (hot air) and 15 g/kg of SA. They wore eyeshades and MR-safe headphones in the scanner, and no ratings were provided during scans.

The subjective drug strength rating dropped by approximately half from the peak rating by 10 min, so the timeseries were split into first and second halves.

Static functional connectivity was calculated for each participant and experimental condition and 35,778 functional connections were created. Changes in all edge-wise static connections were also mapped and contrasted between drug conditions using paired t-tests.

Dynamic and entropic functional connectivity were computed for each edge using dynamic conditional correlations (DCC). The split-half reliability of all functional connectivity measures was good, and a histogram approximation to differential entropy was used to calculate whole-brain dFC and eFC matrices.

The time course of subjective drug strength of salvinorin A was shown in Figure 1, and the relationship between duration of subjective drug strength and change in default mode network static functional connectivity was moderately associated with longer duration of drug strength.

Network-based analyses were performed on 8 Shen atlas networks for each participant and experimental condition, and within- and between-network changes were visualized in matrices.

A partial least squares (discriminant analysis) model was trained using a “leave-two-participant-out” cross-validation procedure to identify the connectivity measures and network interactions most predictive of drug effects. The discrimination scores were computed from the proportion of data correctly identified as SA first half.

Subjective drug effects were similar to previous reports, peaking 1 – 2 min post-inhalation, and decreasing by approximately 50% at 10 min.

Static functional connectivity was decreased in the first half of the scan by SA, but increased in the second half. A drug by time interaction was only trending, but every participant exhibited reductions in DMN sFC in the first half of the scan by SA.


Due to the variability of SA effects on DMN sFC and subjective drug strength, a moderate but nonsignificant relationship was observed between DMN sFC and AUC of drug strength ratings for the first 20 min.

Because the strongest sFC edges were predominantly within-network connections, the thresholding procedure disproportionately produced within-network connections, and more of these edges were significantly attenuated under SA compared to placebo.

Dynamic and entropic functional connectivity matrices revealed widespread reductions in dFC but widespread increases in eFC under SA. Drug by time interactions were also significant for within- and between-network changes in dFC, but only decreases in dFC during the first half of the scan survived corrections for multiple comparisons.

Connectome-based classification was mostly successful across conditions, with confusion occurring in a predictable fashion. However, classification performance was not equal across connectomes, with models trained on whole-brain sFC being more accurate than models trained on whole-brain dFC.

Classification based only on within- or between-network connectomes was mostly above chance, but dFC was more accurate with most network interactions performing well. Interestingly, the most predictive connectomes involved the DMN, specifically within-network DMN sFC and dFC.


This study used fMRI to measure how inhaled SA alters brain functional connectivity in humans. It found that SA decreases sFC within resting-state networks and increases sFC between these networks, similar to classic psychedelics and dissociative anesthetics.

This study had several limitations, including the lack of a multi-session, crossover design, and the use of experienced hallucinogen users as participants. Additionally, the impact of chronic or extensive hallucinogen use on brain function is not well-understood.

The present study had several limitations, but the scans were longer than previous work and the conclusions were drawn using several statistically conservative approaches.

Salvinorin A induced differences in static functional connectivity within- and between-networks, as well as a Pearson correlation between edges, in the first and second half of scans, averaged across participants. The connections that tended to survive were within-network (specifically, bilateral connections between homologous regions), and the salvinorin A-induced changes were limited to the medial frontal network, frontoparietal network, default mode network, subcortical-cerebellum network, somatosensory-motor network, and medial visual network.

This study focused on reliable, large-scale changes in functional connectivity and avoided drawing strong conclusions about cherry-picked functional connections or networks. It also used a novel approach that included all within- or between-network edges and tested the internal validity of the data using connectome-based classification.

Salvinorin A decreases sFC within the DMN, and using connectome-based classification, we found that sFC and eFC within the DMN are especially predictive of the effects of SA on brain function compared to other network interactions.

Figure 3 shows the average of dynamic and entropic functional connectivity matrices for all pairwise functional connections between nodes in the first and second half of placebo and salvinorin A scans, as well as differences in dynamic and entropic functional connectivity within- and between-networks.

SA decreased within- but increased between-network sFC, especially in the first 10 min of the scan, and several static connections were attenuated under SA.

Interestingly, SA decreased brainwide dFC but tended to increase eFC, but motion may have attenuated some of these effects. Further evidence that motion may not fully explain these effects comes from the fact that dFC of most between-network interactions was a consistently good predictor of SA effect on brain function.

Figure 4 shows the confusion matrices and discrimination scores for the leave-two-participant-out cross-validation procedure, and the leave-one-participant-out cross-validation procedure, for training partial least squares models on whole-brain static, dynamic, and entropic connectivity.

The DMN stood out as the best predictor of brain function for sFC and eFC, which is at odds with the specificity of the entropic brain hypothesis. If such effects are important and related to therapeutic outcomes, then -opioid agonists may be promising treatments for depression and addiction.


This work was supported by the National Institute on Drug Abuse, the Steven and Alexandra Cohen Foundation, and the Heffter Research Institute.

Author contributions

M.K.D. collected data, analyzed data, produced figures, and wrote the manuscript. D.G.M., M.W.J., J.M.C., S.L.H., T.E.P., R.R.G., and F.S.B. contributed to study design and edited the manuscript.

Additional information

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