Therapeutic use of psilocybin: Practical considerations for dosing and administration
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Updated: Mar 20
ORIGINAL RESEARCH ARTICLE
published: 27 May 2014
doi: 10.3389/fnhum.2014.00204
The effects of psilocybin and MDMA on between-network
resting state functional connectivity in healthy volunteers
Leor Roseman 1,2*, Robert Leech 2, Amanda Feilding 3, David J. Nutt 1 and Robin L. Carhart-Harris 1
1 Centre for Neuropsychopharmacology, Division of Brain Sciences, Department of Medicine, Imperial College London, London, UK
2 Computational, Cognitive and Clinical Neuroscience Laboratory, Division of Brain Sciences, Department of Medicine, Imperial College London, London, UK
3 The Beckley Foundation, Oxford, UK
Edited by:
Enrico Facco, University of Padua,
Italy
Reviewed by:
Alexander Schaefer, Max Planck
Institute for Human Cognitive and
Brain Sciences, Germany
Enzo Tagliazucchi, Goethe University
Frankfurt, Germany
*Correspondence:
Leor Roseman, Department of
Medicine, Imperial College London,
Hammersmith Campus, Du Cane
Road, London, W12 0NN, UK
e-mail: leoroseman@gmail.com
Perturbing a system and observing the consequences is a classic scientific strategy for
understanding a phenomenon. Psychedelic drugs perturb consciousness in a marked
and novel way and thus are powerful tools for studying its mechanisms. In the present
analysis, we measured changes in resting-state functional connectivity (RSFC) between
a standard template of different independent components analysis (ICA)-derived resting
state networks (RSNs) under the influence of two different psychoactive drugs, the
stimulant/psychedelic hybrid, MDMA, and the classic psychedelic, psilocybin. Both were
given in placebo-controlled designs and produced marked subjective effects, although
reports of more profound changes in consciousness were given after psilocybin.
Between-network RSFC was generally increased under psilocybin, implying that networks
become less differentiated from each other in the psychedelic state. Decreased RSFC
between visual and sensorimotor RSNs was also observed. MDMA had a notably less
marked effect on between-network RSFC, implying that the extensive changes observed
under psilocybin may be exclusive to classic psychedelic drugs and related to their
especially profound effects on consciousness. The novel analytical approach applied here
may be applied to other altered states of consciousness to improve our characterization
of different conscious states and ultimately advance our understanding of the brain
mechanisms underlying them.
Keywords: psilocybin, MDMA, serotonin, 5HT2A, resting state, functional connectivity, brain networks,
psychedelic
INTRODUCTION
Psychedelic drugs have been used throughout history by different
cultures as a means of altering consciousness. They are power-
ful tools for understanding the neurobiology of consciousness yet
they have been underutilized by modern science, arguably due to
political rather than scientific circumstances (Nutt et al., 2013).
The majority of consciousness research has focused on states of
reduced consciousness such as coma and sleep (Laureys, 2005).
Indeed, consciousness has been defined as that which is lost dur-
ing dreamless sleep (Tononi, 2004) but consciousness can also
be studied in terms of changes in the mode or style of waking
consciousness, such as is seen in the psychedelic state. Another
popular model of consciousness describes it using two parame-
ters: (1) wakefulness or arousal and (2) awareness (Laureys et al.,
2009). It is recognized that these parameters have a mostly lin-
ear relationship; however, REM sleep and the vegetative state are
considered anomalies, since the former involves greater awareness
than would be predicted by wakefulness and the latter displays
less (Laureys et al., 2009). The position of the psychedelic state
in this model has never been considered before and it presents
another interesting anomaly. There is no evidence of reduced
wakefulness in the psychedelic state and although awareness is
altered, it would be misleading to say that it is reduced. Indeed,
the psychedelic state has been referred to as an “expansive” state of
consciousness (Huxley, 1959). Thus, it is important to investigate
what the neurobiological basis of this putative broadening of
consciousness is.
One of the most popular theories of consciousness is the
“information integration” theory of Tononi (2012). This proposes
that consciousness depends on the presence of two key parame-
ters: (1) information and (2) integration. Information is derived
from information theory (Shannon and Weaver, 1949) and in
the context of consciousness, refers to the potential size of the
repertoire of different metastable states (Tognoli and Kelso, 2014)
(or “sub-states”) the mind/brain can enter over time. Integration
refers to the capacity of the mind/brain to integrate processes
into a collective whole. The parameter of awareness is likely to be
related to the property of information, since the greater the reper-
toire of sub-states the mind can enter, and the easier it can move
between these, the broader consciousness will be.
In recent years, there has been an increasing interest in human
fMRI measures of resting state functional connectivity (RSFC)
(Damoiseaux and Greicius, 2009). Resting state networks (RSN)
can be identified using seed-based approaches (Biswal et al.,
1995) and independent component analysis (ICA) (Beckmann
et al., 2005). These RSNs resemble stimulus-evoked networks
(Smith et al., 2009) and may be thought of as metastable sub-
states making-up a particular (macro) state of consciousness
(Carhart-Harris et al., 2014a). Thus, one way to describe the
quality of a macro-state of consciousness may be to investigate
Frontiers in Human Neuroscience www.frontiersin.org May 2014 | Volume 8 | Article 204 | 1Roseman et al. Psilocybin, MDMA, between-network RSFC
the integrity and dynamics of its sub-states and how they inter-
act with each other. One way this can be done is by looking
at the internal stability (integrity) of an RSN, i.e., reflected in
the strength of the coupling between its constituent nodes. For
example, we have found decreased intra-RSN connectivity post-
psilocybin with both fMRI (Carhart-Harris et al., 2012a) and
magnetoencephalography (MEG) (Muthukumaraswamy et al.,
2013), implying a general breakdown of the integrity or internal
stability of RSNs under psilocybin.
Another way to address the behavior of a system’s sub-states
is to look at their relationship with each other, e.g., by mea-
suring between-RSN functional connectivity or coupling. A fre-
quently investigated RSN is the default mode network (DMN)
(Raichle et al., 2001). The DMN is known to be more active
during rest than during goal-directed cognition and its activity
has been found to be “anti-correlated” or at least uncorrelated
or orthogonal with activity in networks that are engaged during
goal-directed cognition - referred to generically as “task positive
networks” or TPNs. This anticorrelation is preserved under task
free conditions (Fox et al., 2005), implying that it is an important
feature of normal consciousness, perhaps accounting for the dis-
tinction between externally focused cognition and introspection
(Carhart-Harris et al., 2012b). We recently found that the classic
psychedelic drug psilocybin reduces the anticorrelation between
DMN and a number of TPNs during resting conditions, and this
was interpreted as a decrease in the natural distinction between
externally-focused attention and introspection (Carhart-Harris
et al., 2012b), which is relevant to the notion of “ego-boundaries,”
i.e., an agent’s sense of being apart from or separate to its envi-
ronment. It would be a natural extension of the above analysis
to address the full gamut of between-RSN FC identified by ICA
rather than just focusing on just the DMN-RSN RSFC. This was
the aim of the present study.
The primary focus of the present paper is the classic
psychedelic state and determining its underlying neurodynam-
ics as measured with fMRI. However, in order to understand the
psychedelic state, it is useful to compare it with other states of
consciousness to see how it relates to these. Thus, the present
analysis focuses on the brain effects of a classic psychedelic drug,
psilocybin (the active component of magic mushrooms) and
compares this with the effects of the pro-serotonergic stimu-
lant, 3–4 methylenedioxymethamphetanine, MDMA. MDMA is
a potent monoamine releaser that produces an acute euphoria in
most individuals but it is not considered a classic psychedelic, as
psilocybin is. Direct 5-HT2AR stimulation is the defining phar-
macological property of classic serotonergic psychedelics, but
relative to classic psychedelics, MDMA has a far weaker affinity
for the 5-HT2A receptor (Green et al., 2003). Instead, MDMA pro-
duces a more generalized, non-selective activation of monoamine
receptors by increasing the concentration of their endogenous
ligands in the synapse via transporter-mediated release (Green
et al., 2003). The primary subjective effects of MDMA include
increased positive mood, heightened sensations and prosocial
sentiments and although it can produce mild visual hallucinatory
phenomena, it does not alter consciousness in the same fun-
damental manner as classic psychedelics (Gouzoulis-Mayfrank
et al., 1996).
Thus, comparing changes in RSFC under psilocybin and
MDMA can enable us to isolate and identify effects that are
unique to the psychedelic-induced altered state of consciousness
produced by classic psychedelics such as psilocybin. Considering
the previous findings of decreased intra-RSN FC and DMN-
TPN anti-correlation under psilocybin (Carhart-Harris et al.,
2012a,b; Muthukumaraswamy et al., 2013), we hypothesized that
the normal differentiation between RSNs would be affected by
psilocybin such that RSNs whose activity is usually highly cor-
related would show reduced RSFC under psilocybin (but not
MDMA) and that networks that are normally anti-correlated
would show reduced anti-correlation under psilocybin (but not
MDMA). If the hypothesized effects are present under psilocybin
but absent under MDMA, this will strengthen the inference that
they are specifically related to psilocybin more profound effects
on consciousness.
MATERIALS AND METHODS
DESIGN
Psilocybin
This is an entirely new analysis on a previously published
data set (Carhart-Harris et al., 2012a,b). This was a within-
subjects placebo-controlled study that was approved by a local
NHS Research Ethics Committee and Research and Development
department, and conducted in accordance with Good Clinical
Practice guidelines. A Home Office License was obtained for stor-
age and handling of a Schedule 1 drug. The University of Bristol
sponsored the research. The research was carried out at CUBRIC,
University of Cardiff.
MDMA
This is also an entirely new analysis on a previously published
dataset (Carhart-Harris et al., 2014b). This was a within-subjects,
double-blind, randomized, placebo-controlled study. Participants
were scanned twice, 7 days apart, once after MDMA and once
after placebo. The study was approved by NRES West London
Research Ethics Committee, Imperial College London’s Joint
Compliance and Research Office (JCRO), Imperial College’s
Research Ethics Committee (ICREC), the Head of Imperial
College’s Department of Medicine, Imanova Center for Imaging
Science and Imperial College London’s Faculty of Medicine, and
was conducted in accordance with Good Clinical Practice guide-
lines. A Home Office License was obtained for the storage and
handling of a Schedule 1 drug and Imperial College London
sponsored the research.
PARTICIPANTS
Psilocybin
Fifteen healthy subjects took part: 13 males and 2 females
(mean age= 32, SD= 8.9). Recruitment was via word of
mouth. All subjects were required to give informed consent
and undergo health screens prior to enrolment. Entry crite-
ria were: at least 21 years of age, no personal or immediate
family history of a major psychiatric disorder, substance depen-
dence, cardiovascular disease, and no history of a significant
adverse response to a hallucinogenic drug. All of the subjects
had used psilocybin at least once before (mean number of uses
Frontiers in Human Neuroscience www.frontiersin.org May 2014 | Volume 8 | Article 204 | 2Roseman et al. Psilocybin, MDMA, between-network RSFC
per subject= 16.4, SD= 27.2) but not within 6 weeks of the
study.
MDMA
The original study sample comprised of 25 healthy participants
(mean age= 34, SD= 11, 7 females) with at least 1 previ-
ous experience with MDMA. None of the participants had used
MDMA for at least 7 days and other drugs for at least 48 h, and
this was confirmed by a urine screen. As a conservative step to
control for between-study differences in the global intensity of
the subjective effects produced by the different drugs, 11 subjects
who gave ratings of <50% for the intensity of MDMA’s effects
were excluded from the analysis. This step meant that ratings
of drug effects intensity were comparable across the two stud-
ies (i.e., the mean intensity of psilocybin’s subjective effects was
67 ± 19 at peak and MDMA’s was 69 ± 15). An additional sub-
ject was excluded because of significant head movements (mean
head motion > one voxel width). Thus, a total of 13 subjects
were included in the analysis (i.e., 12 excluded). An alcohol
Breathalyzer test confirmed that none of the participants had
recently consumed alcohol. For the sample of 13, participants
had used MDMA an average of 29 (±35) times before (range=
1–100) and the mean time since last use was 983 (±1998) days
(range= 7–6570 days). Participants were screened for general
health, MR-compatibility and present mental health. Screening
involved routine blood tests, electrocardiogram, heart rate, blood
pressure and a brief neurological exam. All subjects were deemed
physically and mentally healthy at the time of study entry and
none had any history of drug or alcohol dependence.
ANATOMICAL SCANS
Psilocybin
Imaging was performed on a 3T GE HDx system. Anatomical
scans were performed before each functional scan. These were 3D
fast spoiled gradient echo scans in an axial orientation, (1 mm
isotropic voxels).
MDMA
Imaging was performed on a 3T Siemens Tim Trio (Siemens
Healthcare, Erlangen, Germany) using a 32-channel phased array
head coil. Anatomical reference images were acquired using the
ADNI-GO recommended MPRAGE parameters (1 mm isotropic
voxels).
DRUG AND SCANNING PARAMETERS
Psilocybin
All subjects underwent two 12-min eyes-closed resting-state
blood oxygen–level dependent (BOLD) fMRI scans on 2 sepa-
rate occasions at least 7 days apart: placebo (10 ml saline, 60-s
intravenous injection) was given on 1 occasion and psilocybin
(2 mg dissolved in 10 ml saline) on the other. Seven of the subjects
received psilocybin in scan 1, and 8 received it in scan 2. Injections
were given manually by a study doctor situated within the scan-
ning suite. The 60-s infusions began exactly 6 min after the start
of the 12-min scans. Subjective ratings were given post-scan using
visual analog scales (VAS). The subjective effects of psilocybin
were felt almost immediately after injection and were sustained
for the duration of the scan.
MDMA
Two BOLD resting-state scans were performed during each func-
tional scanning session (duration of functioning scanning=
60 min). The first resting-state BOLD scan took place 60 min
after capsule ingestion and the second resting-state BOLD scan
occurred 113 min after capsule ingestion. Peak subjective effects
were reported 100 min post administration of MDMA, gener-
ally consistent with the plasma t-max of MDMA (Kolbrich et al.,
2008). The order of MDMA and placebo administration was
counterbalanced.
fMRI DATA ACQUISITION
Psilocybin
BOLD-weighted fMRI data were acquired using a gradient echo
planar imaging sequence, 3 mm isotropic voxels, TR= 3000 ms,
TE= 35 ms, field-of-view= 192 mm, 90◦ flip angle, 53 axial
slices in each TR, parallel acceleration factor= 2, 64 × 64 acqui-
sition matrix. The psilocybin and placebo scans for this analysis
were of 5 min (1 min post infusion).
MDMA
BOLD-weighted fMRI data were acquired using a gradient echo
planar imaging sequence, 3 mm isotropic voxels, TR= 2000 ms,
TE= 31 ms, field-of-view= 192 mm, 80◦ flip angle, 36 axial
slices in each TR, GRAPPA acceleration= 2, bandwidth=
2298 Hz/pixel. For each condition, MDMA and placebo, two
scans were used for the analysis, each one of 6 min (performed
60 min and 113 min post-capsule ingestion)
RESTING STATE NETWORKS (RSN)
We used RSNs that were identified in Smith et al. (2009) using
ICA (Figure 1). Ten of these components were given functional
labels based on their correspondence to the BrainMap database
of functional imaging studies, involving task-evoked FMRI
data from nearly 30,000 human subjects. These networks were:
Visual-Medial Network (VisM), Visual-Lateral Network (VisL),
Visual-Occipital pole Network (VisO), Auditory Network
(AUD), Sensorimotor Network (SM), Default Mode Network
(DMN), Executive Control Network (ECN), Left frontoparietal
Network(lFP), Right frontoparietal Network (rFP) and Cerebellar
network. In addition, we used three more components from
Smith et al, that we named DMN2 (an anterior DMN and ECN
hybrid), Dorsal Attention Network 1 and 2 (DAN1 and DAN2).
Another 6 components were identified as non-neural noise
(likely generated by head motion and non-neural physiological
fluctuations).
PREPROCESSING
All analyses were performed using the Functional Magnetic
Resonance Imaging of the Brain (FMRIB) Software Library (FSL,
www.fmrib.ox.ac.uk/fsl) (Smith et al., 2004). We used the stan-
dard imaging preprocessing FSL pipeline that involved brain
extraction (Smith, 2002), motion correction using MCFLIRT
(Jenkinson et al., 2002), spatial smoothing (FWHM) of 5 mm
(Smith and Brady, 1997) and a high-pass filter of 100 s. The
scans were registered to the subjects’ T1-weighted high-resolution
(2 × 2 × 2 mm) anatomical scans and were then registered to the
Frontiers in Human Neuroscience www.frontiersin.org May 2014 | Volume 8 | Article 204 | 3Roseman et al. Psilocybin, MDMA, between-network RSFC
FIGURE 1 | Non-noise resting State Networks (RSN) from Smith
et al., 2009: (1) Visual–Medial (VisM), (2) Visual–Lateral (VisL), (3)
Visual–Occipital pole (VisO), (4) Auditory (AUD), (5) Sensorimotor (SM), (6)
Default Mode Network (DMN), (7) DMN2–A hybrid of anterior DMN and
Executive Control Network, (8) Executive Control Network (ECN), (9) left
Frontoparietal Network (lFP), (10) right Frontoparietal Network (rFP), (11)
Dorsal Attention Network (DAN), (12) DAN2, (13) Cerebellum. Ten of
these components were given functional labels based on their
correspondence to the BrainMap database of functional imaging studies.
(RSNs 1, 2, 3, 4, 5, 6, 8, 9, 10, 13), additional networks (7, 11, 12) were
labeled by the experimenters in the current study based on the regional
distribution of activity.
Montreal Neurological Institute standard brain (2 × 2 × 2 mm)
(Jenkinson et al., 2002). The data was resampled into 4 mm space
as part of the default processing pipeline for Melodic and was
done to make the analysis more computational efficient.
BETWEEN NETWORKS FUNCTIONAL CONNECTIVITY (FC)
Psilocybin
To extract time courses for each subject for each RSN and for
each condition, we back-projected the components from Smith
et al. (2009) into each 4D fMRI dataset using a general linear
model. Specifically, we took the 20 components ICA map from
Smith et al. as the set of template ICAs for the dual regression
pipeline. The first step of the “dual regression” pipeline was then
applied to each 4D dataset, resulting in a specific timecourse
for each component for each dataset (Beckmann et al., 2009).
Between-RSN coupling was presented graphically using a 13 × 13
correlation (or more strictly, regression) matrix in which the color
in each square represents a beta weight or coupling strength for
the corresponding RSN-RSN pair. Specifically, these weights were
calculated by entering the time course for a specific RSN as a
dependent variable in a general linear model, with the time course
of another RSN entered as an independent variable—with this
procedure repeated for each RSN pair. The mean head motion
under psilocybin and its placebo condition were 0.1 ± 0.05 mm
Frontiers in Human Neuroscience www.frontiersin.org May 2014 | Volume 8 | Article 204 | 4Roseman et al. Psilocybin, MDMA, between-network RSFC
and 0.06 ± 0.015 mm, respectively (p < 0.01). Therefore, to fur-
ther partial out non-neural noise confounds, six motion time
courses (estimated from the motion correction) and motion out-
liers (estimated using the “fsl_motionoutlier” command imple-
mented in FSL), as well as the time courses for 6 non-neural
noise components were entered as confounds (some of this noise
is driven by head motion). The model resulted in a parame-
ter estimate or unstandardized beta weight (β) representing the
strength of functional coupling between each RSN pair. The gen-
eral linear model was estimated twice for each RSN pair: with each
RSN as dependent variable in one model and as an independent
variable in the second model. Since we were not looking at effec-
tive or directed connectivity (Friston et al., 2003), we created a
symmetrical connectivity matrix by averaging together each sub-
ject’s two β values for each RSN pair. For each RSN pair, three
results were calculated: (a) group mean β value for the placebo
condition; (b) group mean β value for the psilocybin condition;
(c) Paired t-test (2-tail) for the difference between the mean β
values of each condition (Figure 2). To correct for multiple com-
parisons, a false discovery rate (FDR) threshold was calculated
using q= 0.05 and q= 0.1 (N= 78).
MDMA
The MDMA RSFC was analyzed using the same procedure
described above (Figure 2). The only difference was that there
were two resting state scans in the MDMA study, so β values
FIGURE 2 | Scheme of the analysis by steps. Calculating t-values for each RSN pair that represent the change in coupling strength between placebo and drug.
Frontiers in Human Neuroscience www.frontiersin.org May 2014 | Volume 8 | Article 204 | 5Roseman et al. Psilocybin, MDMA, between-network RSFC
from the two scans (performed 60 min and 113 min post-capsule
ingestion) were averaged together before comparing between the
placebo and drug conditions. The mean head motion under
MDMA and its placebo condition were 0.083 ± 0.036 mm and
0.061 ± 0.019 mm, respectively (p= 0.047). The same proce-
dure to control for motion in the psilocybin analysis was used for
MDMA.
RESULTS
SUBJECTIVE EFFECTS
Psilocybin
The subjective effects of psilocybin have been documented else-
where (Carhart-Harris et al., 2011, 2012a). Briefly, the subjec-
tive effects of 2 mg psilocybin given as an intravenous injection
over 60 s begin at the end of the injection period, reach a sus-
tained peak after approximately 5 min, and subside completely
after 45–60 min. Primary subjective effects include altered visual
perception (e.g., hallucinated motion and geometric patterns),
an altered sense of space and time, and vivified imagination.
The intensity of psilocybin’s global subjective effects was rated
using a VAS format. The mean intensity at peak effects (5 min
post-infusion) was 67% ±19.
MDMA
The subjective effects of MDMA are reported in a separate paper
(Carhart-Harris et al., 2014b). At their peak, the average inten-
sity of MDMA’s global subjective effects was 69% ±15 (n = 13).
There was no significant difference between intensity ratings
under the two different drugs.
BETWEEN NETWORKS FC
Psilocybin
The coupling strengths (β) for each condition can be seen
graphically in the correlation matrixes in Figure 3 and numer-
ically in the Supplementary material. For the placebo condi-
tion, see Figure 3A and Supplementary Table 1A and for the
psilocybin condition see Figure 3B and Supplementary Table
1B. A paired t-test (2-tail) was done across subjects to com-
pare the β values for each RSN pair in the drug and placebo
(Figure 3C and Supplementary Table 1C). The results were
corrected for multiple comparisons using FDR with q= 0.05
(resulting in a threshold of p < 0.0167) and q= 0.1 (result-
ing a threshold of p < 0.042). The RSN pairs that showed a
significant decrease in coupling under psilocybin were: SM-
VisM (p= 0.0265), SM-VisL (p= 0.0051) and SM-VisO (p=
0.0151). The RSN pairs that showed a significant increase
in coupling were: VisM-lFP (p= 0.0001), VisM-DAN (p=
0.0156), VisM-rFP (p= 0.0023), VisM-DAN2 (p= 0.0002),
VisM-Cerebellum (p= 0.0108), VisL-DMN (p= 0.0046), VisL-
lFP (p= 0.0056), VisL-rFP (p= 0.0031), VisL-DAN2 (p=
0.0142), VisO-DAN2 (p= 0.0256), AUD-DMN (p= 0.028),
AUD-ECN (p= 0.0323), AUD-lFP (p= 0.0029), AUD-rFP (p=
0.0001), AUD-DAN2 (p= 0.0005), SM-ECN (p= 0.0105), SM-
lFP (p= 0.022), SM-rFP (p= 0.0026), SM-DAN2 (p= 0.034),
DMN-lFP (p= 0.0029), DMN-DAN (p= 0.0058), DMN2-
ECN (p= 0.0071) DMN2-lFP (p= 0.0101), DMN2-DAN (p=
0.0005), DMN2-DAN2 (p= 0.0091), ECN-lFP (p= 0.0077),
ECN-rFP (p= 0.0098), lFP-DAN (p= 0.0026), rFP-DAN (p=
0.0187), and DAN-DAN2 (p= 0.0161).
MDMA
The same analysis as above was repeated for the MDMA condi-
tion using a q of 0.05, resulting in a threshold of p < 0.0006 and
q= 0.1, resulting in a threshold of p < 0.0012. Only one RSN
pair showed a significant change in coupling under MDMA, i.e.,
increased coupling between the DMN2-ECN (p= 0.0001).
DIFFERENCES IN MOVEMENT
Both drugs showed significant, yet relatively modest, increased
head motion between conditions. The mean head motion
under psilocybin and its placebo condition were 0.1 ± 0.05 mm
and 0.06 ± 0.015 mm, respectively (p < 0.01). The mean head
motion under MDMA and its placebo condition were 0.083 ±
0.036 mm and 0.061 ± 0.019 mm, respectively (p= 0.047).
Power et al. (2012) suggest that head motion can change the
results of RSFC, therefore, in the regression analysis, we added
several motion confounds: six motion time courses, motion
outliers [similar to the procedure of scrubbing within regres-
sion (spike regression) mentioned by Yan et al. (2013) and
Satterthwaite et al. (2013)] and time courses of RSNs that
were driven by motion. However, it still remains possible that
the increased movement under the drugs may have caused the
changes in RSFC. Hence, we investigated if there was a relation-
ship between the change in estimated motion (mean framewise
displacement) between placebo and drug and the change in
coupling strength (for pairs of RSNs that showed significant
differences in coupling). For most of the RSN pairs no rela-
tionship was found (p < 0.05). However, under psilocybin, there
were significant correlations with motion in the following RSN
pairs: VisM-SM (p= 0.002), VisL-SM (p= 0.001), VisO-SM
(p= 0.02), VisL-DMN (p= 0.03), VisM-rFN (p= 0.048), VisL-
rFN (p= 0.01), VisO-DAN2, DMN-lFN (p= 0.001), DMN-
DAN (p= 0.01). For that reason, the significant results of these
RSN pairs should be approached with caution.
DISCUSSION
To our knowledge, this is the first analysis to test the effects of
different pharmacological agents using a standard ICA-derived
template of RSNs to construct between-network functional con-
nectivity matrixes for different drug states. This approach may
have wider application, enabling researchers to determine con-
nectivity “fingerprints” for characterizing different states of con-
sciousness, i.e., not only those induced by pharmacological agents
but sleep states and even pathological states. This will enable
informed comparisons to be made between different states,
potentially allowing us to categorize different states based on
their connectivity profiles. Functional connectivity matrixes have
been used before to differentiate between pathology states such as
schizophrenia and bipolar disorder (Mamah et al., 2013) and here
we suggest that they could be used more broadly to characterize
states of consciousness, including those induced by psychoactive
drugs.
Probably the most striking result of the present study was the
marked increases in between-network RSFC under psilocybin.
Frontiers in Human Neuroscience www.frontiersin.org May 2014 | Volume 8 | Article 204 | 6Roseman et al. Psilocybin, MDMA, between-network RSFC
FIGURE 3 | Between networks resting state functional connectivity
results. Within each matrix, each colored square represents coupling
between corresponding RSN pairs with the color of the square denoting the
coupling strength (A,B,D,E) or change in coupling strength (C,F) between the
RSN pairs (blue, negative coupling or a decrease in coupling; red, positive
coupling or an increase in coupling). The six images are: (A) Group mean of β
values for the placebo of psilocybin condition. (B) Group mean of β values for
(Continued)
Frontiers in Human Neuroscience www.frontiersin.org May 2014 | Volume 8 | Article 204 | 7Roseman et al. Psilocybin, MDMA, between-network RSFC
FIGURE 3 | Continued
the psilocybin condition. (C) Paired t-test (2-tail) for the difference between
the mean β values of psilocybin and placebo. (D) Group mean of β values
for the placebo of MDMA condition. (E) Group mean of β values for the
MDMA condition. (F) Paired t-test (2-tail) for the difference between the
mean β values of MDMA and placebo. The networks from Smith et al.
(2009) are: (1) Visual–Medial (VisM), (2) Visual–Lateral (VisL), (3)
Visual–Occipital pole (VisO), (4) Auditory (AUD), (5) Sensorimotor (SM), (6)
Default Mode Network (DMN), (7) DMN2–A hybrid of anterior DMN and
Executive Control Network, (8) Executive Control Network (ECN), (9) left
Frontoparietal Network (lFP), (10) right Frontoparietal Network (rFP), (11)
Dorsal Attention Network (DAN), (12) DAN2, (13) Cerebellum. FDR
correction for multiple comparison (N= 78) was applied on the t-tests:
∗0.05 < q < 0.1. ∗∗q < 0.05.
These increases were evident for heteromodal networks, both in
terms of increased unimodal-heteromodal (e.g., AUD-rFP) and
heteromodal-heteromodal network RSFC (e.g., lFP-ECN). Based
on previous analyses (Carhart-Harris et al., 2012b), we had pre-
dicted that RSN pairs with weak or negative RSFC at baseline
would show increased coupling post-psilocybin, and this was
found (e.g., DMN-VisL). However, the increases in between-
network RSFC were more fundamental than this, being evident
for RSN pairs that were already positively coupled at baseline (e.g.,
DMN2-ECN). The increase in correlated brain activity across
normally distinct brain networks was particularly true for het-
eromodal RSNs, where the distribution of 5-HT2A receptors is
known to be highest (Erritzoe et al., 2010) and 5-HT2A recep-
tor stimulation is linked to desynchronous cortical activity (Riba
et al., 2002; Wood et al., 2012; Muthukumaraswamy et al., 2013)
and network disintegration (Muthukumaraswamy et al., 2013;
Carhart-Harris et al., 2014a).
The pattern of increased between-network RSFC under psilo-
cybin did not apply universally for the whole of the brain.
Decreased RSFC was observed between the three visual RSNs and
the sensorimotor network [these networks are known to be highly
connected (Wise et al., 1997; Van Den Heuvel et al., 2008)], and
there was a general trend toward decreased unimodal-unimodal
network RSFC (e.g., VisM-AUD and SM-AUD showed decreased
RSFC under psilocybin but this failed to survive FDR correction,
see Supplementary Table 1). However these decreases can also be
explained by the changes in head motion between conditions and
further work is required to test whether these decreases in sen-
sory RSN RSFC under psilocybin relate to the drug’s characteristic
perceptual/hallucinatory effects.
Previous neuroimaging studies with psychedelics have so far
failed to reveal a simple and compelling explanation for their
characteristic hallucinogenic effects (Vollenweider et al., 1997;
Carhart-Harris et al., 2012a; Muthukumaraswamy et al., 2013)
(but see De Araujo et al., 2012) and so drug-induced visual
hallucinations remain poorly understood. Under normal condi-
tions, activity in the visual cortex is driven by and thus anchored
to visual input. Moreover, activity in other networks (e.g., the
DMN), concerned with other distinct functions (e.g., introspec-
tion), is often weakly or inversely coupled to visual activity (e.g.,
see the pale and blue colored squares for the visual-RSN pairs
in Figures 3A,D). Thus, increased communication between the
visual system and systems that are usually reserved for distinct
functions may lead to erroneous perceptual associations. For
example, increased DMN-visual network RSFC, may relate to
an increased influence of imagination (mediated by the DMN)
on visual perception (mediated by the visual networks). A simi-
lar process may occur in situations of sensory deprivation where
sensory processing becomes decoupled from sensory stimula-
tion, allowing the system to “free-wheel” with the potential
for the spontaneous emergence of internally-generated percepts.
Decreased cross-modality RSFC and increased unimodal to het-
eromodal network RSFC may be a common characteristic of such
states but future studies are required to test this. For example,
comparisons between the present results and changes in RSFC in
the meditative state could inform these speculations.
Given reports of synesthesia-like experiences under
psychedelics (e.g., participants reported that the noise of
the MR scanner influenced the rate and content of their closed
eye visual hallucinations Carhart-Harris et al., 2012a and see
also Luke and Terhune, 2013) one may have predicted increased
cross-modality communication under psilocybin rather than
the decreased coupling that was observed here. However, it has
yet to be determined whether synesthesia-like experiences in
drug-induced altered states of consciousness are qualitatively
and mechanistically related to synesthesia experienced outside
of this context and it is also worth noting that increased visual
to heteromodal cortical functional connectivity has been found
in color-grapheme synesthesia (Dovern et al., 2012; Sinke et al.,
2012) as well as in the present study.
Taking a dynamical systems theory approach to the present
results, RSNs can be conceived of as “attractors,” i.e., patterns of
activity into which the brain tends to gravitate for short periods of
time (Deco et al., 2009; Hellyer et al., 2014). A macro-state of con-
sciousness (such as normal waking, deep sleep or the psychedelic
state) may, therefore, be graphically represented as an “attractor
landscape” in which the depth of “basins of attraction” (valleys
in an otherwise flat 2D-plane) reflect the stability of particular
RSNs or metastable “sub-states,” i.e., more long lasting sub-states
will have deep basins of attraction and unstable sub-states will
have shallow ones. A recent paper (Kanamaru et al., 2013) has
described brain function in these terms, suggesting that the shape
of attractors depends on selective attention. In this particular
model, high levels of acetylcholine activating muscarinic recep-
tors were found to produce an attractor landscape with more
stable sub-states. Relating this to the present results, the increased
RSFC observed between different RSNs could be interpreted as a
flattening of the attractor landscape, in which the basins of attrac-
tion are shallower, implying that the global system will move more
easily between different metastable sub-states. A flattened (but
not flat) attractor landscape would be consistent with increased
“information” in the sense of the “information-integration” the-
ory of consciousness (Tononi, 2012) since greater movement
between metastable sub-states would imply that a larger num-
ber of these sub-states (or a broader “repertoire”) can be entered
over a given time. At a critical flatness, the size of the repertoire
Frontiers in Human Neuroscience www.frontiersin.org May 2014 | Volume 8 | Article 204 | 8Roseman et al. Psilocybin, MDMA, between-network RSFC
of metastable states will be maximal but if the landscape is too
flat, information will be reduced because attractors will become
too unstable. This scenario is referred to as “super-criticality”
(Chialvo, 2010), and if taken to the extreme, an entirely flat land-
scape would imply that the system has no metastable states, or
just one entirely disordered one. Future studies are required to
determine whether the psychedelic state is “critical” or “super-
critical” in this sense (Tagliazucchi et al., 2012; Carhart-Harris
et al., 2014a). Another way these results could be perceived how-
ever, is that increased between-RSN RSFC under psilocybin is
representative of a “sub-critical” system, i.e., one that is more
globally synchronous and therefore ordered; however, that there
were also decreases in between-RSN RSFC under psilocybin, does
not support this view. We intend to follow-up this matter in order
to test our hypothesis that it is specifically the ease of transition (or
transition probability) between RSNs/metastable sub-states that
is facilitated under the drug.
In contrast to the marked changes in between-network RSFC
observed with psilocybin, only one RSN-pair showed a signifi-
cant change in RSFC under MDMA, i.e., increased ECN-DMN2
RSFC (Figure 3F). This result is difficult to interpret in isola-
tion; however, it is worth noting that ECN-DMN2 RSFC was also
significantly increased under psilocybin (Figure 3C). MDMA is
not considered a classic psychedelic, although like psilocybin, its
subjective effects are known to be significantly mediated by sero-
tonergic mechanisms (Liechti and Vollenweider, 2001; Van Wel
et al., 2011). Thus, increased ECN-DMN2 RSFC may relate to
a shared aspect of these drugs’ subjective effects, such as their
propensity to alter mood and cognition (Carhart-Harris et al.,
2014b). Pre-treatment studies with selective receptor antagonists
would help to inform these matters.
There is an important caveat to be addressed about the present
analysis. It should be noted that the two studies from which the
data was derived employed quite different methodologies (e.g.,
intravenous administration of psilocybin vs. oral administration
of MDMA, different MR scanners and different study samples).
Thus, it would be problematic to attempt to make inferences
based entirely on a comparison of their relative RSFC profiles.
This analysis was not intended to be a formal comparison of
the brain effects of MDMA and psilocybin and if this was the
intention, then a standardized methodology would need to be
employed. Rather, the present analysis has focused on under-
standing the neural correlates of the psychedelic state as produced
by the classic psychedelic, psilocybin, and the finding that MDMA
had a less marked effects on between-network RSFC has merely
served to emphasize that the psychedelic state rests on a par-
ticularly profound disturbance of brain function. This does not
imply that MDMA’s own subjective effects are unimportant or
that they do not involve some (albeit more subtle) changes in
between-network RSFC.
The significant change in head movement under psilocybin
implies that some of the results should be interpreted with cau-
tion, in particular the decreases in coupling strength. We have
used multiple ways to model motion as a possible confound but
for a subset of the RSN pairs, the changes with drug correlate
with the differences in mean motion. These significant corre-
lations do not necessarily mean that motion is responsible for
these changes, since intensity of drug is likely to be associated
with increased movement, meaning that disambiguating the two
effects is problematic for some RSN pairs. In support of this,
we found a marginally significant correlation between changes in
motion and changes in the subjective intensity rating (r = 0.382,
p= 0.08). Future work restricting head motion in the scanner
and with larger samples is necessary to be able to demonstrate
that changes in these RSN pairs that correlate with motion reflect
genuine brain activity or not.
In conclusion, this new analysis has used between-network
functional connectivity to investigate the effects of two distinct
serotonergic compounds on spontaneous brain function. It was
found that psilocybin produced marked changes in between-
network RSFC, generally in the direction of increased coupling
between RSNs, with an additional decrease in coupling between
visual and sensorimotor networks. MDMA had a notably less
marked effect on between-network RSFC implying that psilo-
cybin’s more profound effects on global brain function (at least
as determined by this measure) may explain its more profound
effects on consciousness. The analytic methods used in this study,
i.e., using ICA templates to determine functional connectivity
matrixes for different drug states, may have wider application,
enabling researchers to more objectively describe and potentially
categorize different states of consciousness.
ACKNOWLEDGMENTS
These studies received financial and intellectual support from the
Beckley Foundation. We would like to thank the reviewers for
their useful comments on previous versions of this manuscript.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: http://www.frontiersin.org/journal/10.3389/fnhum.
2014.00204/abstract
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Conflict of Interest Statement: The authors declare that the research was con-
ducted in the absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Received: 20 December 2013; accepted: 23 March 2014; published online: 27 May 2014.
Citation: Roseman L, Leech R, Feilding A, Nutt DJ and Carhart-Harris RL (2014)
The effects of psilocybin and MDMA on between-network resting state functional con-
nectivity in healthy volunteers. Front. Hum. Neurosci. 8:204. doi: 10.3389/fnhum.
2014.00204
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