Increased Global Functional Connectivity Correlates
with LSD-Induced Ego Dissolution
dHigh-level cortical regions and the thalamus show increased
connectivity under LSD
dThe brain’s modular and rich-club organization is altered
dIncreased global connectivity under LSD correlates with ego
Enzo Tagliazucchi, Leor Roseman,
Mendel Kaelen, ..., Amanda Feilding,
David J. Nutt, Robin Carhart-Harris
Tagliazucchi et al. ﬁnd that increased
global communication mediated by the
brain’s key integration centers underlies
LSD-induced ‘‘ego dissolution.’’ This
globally enhanced integration impairs the
functional identity of brain systems,
leading to feelings of ego dissolution and
disturbed ego boundaries.
Tagliazucchi et al., 2016, Current Biology 26, 1043–1050
April 25, 2016 ª2016 Elsevier Ltd All rights reserved
Increased Global Functional Connectivity
Correlates with LSD-Induced Ego Dissolution
Suresh D. Muthukumaraswamy,
David J. Nutt,
and Robin Carhart-Harris
Department of Sleep and Cognition, Netherlands Institute for Neuroscience (NIN), an institute of the Royal Netherlands Academy of Arts and
Sciences, Amsterdam 1105 BA, the Netherlands
Institute for Medical Psychology, University of Kiel, Kiel 24113, Germany
Centre for Neuropsychopharmacology, Department of Medicine, Imperial College London, London W12 0NN, UK
Computational, Cognitive and Clinical Neuroscience Laboratory (C3NL), Department of Medicine, Imperial College London, London
W12 0NN, UK
Cardiff University Brain Research Imaging Centre (CUBRIC), Department of Psychology, Cardiff CF10 3AT, UK
Schools of Pharmacy and Psychology, University of Auckland, Auckland 1010, New Zealand
Neurology Department, Schleswig-Holstein University Hospital, University of Kiel, Kiel 24113, Germany
Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neurosciences, King’s College London, London WC2R 2LS, UK
Department of Psychiatry, University of Cambridge, Cambridge CB2 2QQ, UK
Cambridgeshire & Peterborough NHS Foundation Trust, Cambridge CB21 5EF, UK
Alternative Discovery & Development, GlaxoSmithKline, Brentford TW8 9GS, UK
AWP Mental Health NHS Trust, Blackberry Centre, Manor Road, Bristol BS16 2EW, UK
The Beckley Foundation, Oxford OX3 9SY, UK
*Correspondence: firstname.lastname@example.org (E.T.), email@example.com (R.C.-H.)
Lysergic acid diethylamide (LSD) is a non-selective
serotonin-receptor agonist that was ﬁrst synthesized
in 1938 and identiﬁed as (potently) psychoactive in
1943. Psychedelics have been used by indigenous
cultures for millennia ; however, because of
LSD’s unique potency and the timing of its discovery
(coinciding with a period of major discovery in psy-
chopharmacology), it is generally regarded as the
quintessential contemporary psychedelic . LSD
has profound modulatory effects on consciousness
and was used extensively in psychological research
and psychiatric practice in the 1950s and 1960s .
In spite of this, however, there have been no modern
human imaging studies of its acute effects on the
brain. Here we studied the effects of LSD on intrinsic
functional connectivity within the human brain using
fMRI. High-level association cortices (partially over-
lapping with the default-mode, salience, and fron-
toparietal attention networks) and the thalamus
showed increased global connectivity under the
drug. The cortical areas showing increased global
connectivity overlapped signiﬁcantly with a map of
serotonin 2A (5-HT
) receptor densities (the key
site of action of psychedelic drugs ). LSD also
increased global integration by inﬂating the level of
communication between normally distinct brain net-
works. The increase in global connectivity observed
under LSD correlated with subjective reports of
‘‘ego dissolution.’’ The present results provide the
ﬁrst evidence that LSD selectively expands global
connectivity in the brain, compromising the brain’s
modular and ‘‘rich-club’’ organization and, simulta-
neously, the perceptual boundaries between the
self and the environment.
We used fMRI to investigate global and local changes in func-
tional connectivity following intravenous injection of lysergic
acid diethylamide (LSD) versus placebo to 15 healthy volunteers.
The experiment followed a randomized and balanced within-
subject design, and both whole-brain exploratory and more
selective hypothesis-driven data-analysis approaches were
employed. Based on the predominantly cortical distribution of
serotonin 2A (5-HT
) receptors [5, 6] (the principal receptor
mediating psychedelic effects ), as well as previous ﬁndings
with other psychedelics [5, 7, 8], we hypothesized that connec-
tivity changes would implicate high-level cortical networks
such as the default-mode network (DMN)  and salience
network . The association between these networks and
self-consciousness [7, 11, 12] led us to expect a parametric cor-
relation with the intensity of subjective reports of ‘‘ego dissolu-
tion’’ under LSD, i.e., a compromised sense of possessing an
integrated and distinct personality or identity.
We ﬁrst studied changes in the overall connectivity of 401
even-sized regions of interest (ROIs) completely covering cortical
and sub-cortical gray matter and obtained using a method intro-
duced by Zalesky and colleagues . We computed the func-
tional connectivity density (FCD)  as the average correlation
Current Biology 26, 1043–1050, April 25, 2016 ª2016 Elsevier Ltd All rights reserved 1043
between the spontaneously ﬂuctuating blood oxygen level-
dependent (BOLD) signal at each region of interest and the time
series from all remaining ROIs. Thus, high FCD values corre-
spond to regions whose activity is strongly correlated to that
of the rest of the brain, whereas activity in regions with low
FCD values is weakly correlated to that of the rest of the
brain. The average FCDs measured under LSD and placebo are
shown in Figure 1A as a 3D rendering on top of a gray-matter
surface. Histograms depicting the distribution of FCD values
across all ROIs were obtained for both conditions. In the LSD
condition there was a tail of highly coupled regions that was
less prominent in the placebo condition (Figure 1B). FCD values
were globally increased under LSD compared with placebo (Fig-
ure 1B inset).
The increases in global connectivity under LSD were observed
in predominantly in frontal, parietal, and inferior temporal
cortices, as well as in the bilateral thalamus. In Figure 1Cwe
present a rendering of these effects together with the outline of
three resting state networks (RSNs) obtained by applying inde-
pendent component analysis  to resting-state data from 35
Figure 1. LSD Selectively Increases Global
Functional Connectivity of Higher-LevelInte-
grative Cortical and Sub-cortical Regions
(A) Average FCD under the placebo and LSD
(B) Normalized histogram (P) of all FCD values for
both conditions (mean ± SEM). The inset shows
the whole-brain FCD averages (*p < 0.05, two-
tailed t test).
(C) Rendering of signiﬁcant FCD increases under
LSD versus placebo (thresholded at p < 0.05, two-
tailed t test, false discovery rate [FDR]-controlled
for multiple comparisons). Outlines of the bilateral
frontoparietal, salience, and default-mode RSN
are overlaid on top of the map of FCD signiﬁcant
(D) Quantitative analysis of the overlap between
signiﬁcant FCD increases and eight RSNs (FP,
frontoparietal; Sal, salience; DMN, default-mode
network; DAN, dorsal attention network; Vis L,
lateral visual; Aud, auditory; Vis M, medial visual;
SM, sensorimotor) obtained from 35 subjects
scanned in the Human Connectome Project, as
well as 5-HT
receptor concentration and FCD
increases under psilocybin. Only FP, Sal, DMN,
and the maps of 5-HT
and FCD increases under psilocybin had an over-
lap signiﬁcantly greater than that observed when
spatially randomizing the networks (mean ± SD,
*p < 0.05, Bonferroni corrected for multiple com-
parisons). For a description of the randomization
procedure, see  and the Supplemental Exper-
imental Procedures. See also Figure S1.
healthy subjects in the Human Connec-
tome Project (HCP) dataset (http://www.
three RSNs (bilateral frontoparietal,
default-mode, and salience networks)
showed a signiﬁcant overlap with FCD
increases under LSD (Figure 1D) and
have been implicated in the action of other psychedelics [5, 8].
Additionally, we found a signiﬁcant overlap between FCD in-
creases under LSD and the distribution of 5-HT
(the key site of action of psychedelic drugs ), obtained using
positron emission tomography (PET) , as well as with FCD
increases observed under psilocybin (same data and prepro-
cessing as reported in ) (Figure 1D, right). We did not observe
signiﬁcant overlap between FCD increases and the distribution
of other serotonin receptors (i.e., the 5-HT
Subsequently, we correlated the magnitude of regional FCD
increases observed under LSD with the intensity of ego disso-
lution reported by the participants (LSD minus placebo) across
all ROIs. Regions surviving correction for multiple comparisons
included the bilateral temporo-parietal junction (angular gyrus)
and the bilateral insular cortex (red rendering in Figure 2A).
The speciﬁcity of this ﬁnding was assessed by also correlating
all other VAS (visual analog scale) scores with FCD increases
under LSD. Importantly, ego dissolution was the only subjective
rating that survived this multiple-comparisons correction (see
1044 Current Biology 26, 1043–1050, April 25, 2016
Figure S2 for more information on VAS and ASC [altered state
of consciousness questionnaire] scores). In the green rendering
in Figure 2A, we identify those regions presenting correlations
with ego dissolution scores (corrected for multiple compari-
sons) and uncorrelated to all other VAS scores (at a level
of p < 0.05, uncorrected). Scatterplots of FCD versus ego
dissolution are shown in Figure 2B for four example regions
located in the left/right angular gyrus and insula. These regions
were selected based on their overlap with the corresponding
Automated Anatomical Labeling (AAL) atlas regions and their
association with self-awareness [11, 12, 17, 18]. Scatterplots
of FCD versus the other ﬁve VAS scores are provided in Fig-
ure S4, and plots for four additional regions are provided in
The FCD increases indicated that the overall global connectiv-
ity of the regions in Figure 1C increased under LSD relative to
placebo. Next, we asked which areas of the brain became
especially more engaged with these highly globally connected
brain areas under LSD. To do this, we divided the FCD differ-
ence map (Figure 1C) into four components: a frontal seed
(comprising parts of inferior, middle, and superior frontal gyri),
Figure 2. FCD Increases Correlate with Sub-
jective Reports of Ego Dissolution
(A) Brain regions where a signiﬁcant (p < 0.05, two-
tailed, FDR-controlled for multiple comparisons)
correlation between FCD and subjective reports of
ego dissolution (LSD minus placebo) was found
are colored in red. Brain regions where none of the
other VAS scores correlated with FCD at p < 0.05,
two-tailed, uncorrected (i.e., regions presenting
the most selective correlations between FCD in-
creases and ego dissolution scores) are colored in
(B) Association between FCD increases and
reports of ego dissolution in four example ROIs
(bilateral angular gyrus and insular cortex). See
also Figures S2 and S3.
a parietal seed (bilateral temporo-parietal
junction/angular gyrus), the precuneus,
and the bilateral thalamus. Seed-based
regression analyses were subsequently
conducted based on each of these four
seeds (as the independent variables)
with all 401 ROIs as dependent variables.
Figure 3 displays difference maps with
the regions becoming more coupled with
four FCD-determined seeds (left panel)
under LSD relative to placebo. In all
four cases, sensory cortices were impli-
cated (right panel). This result was further
conﬁrmed by repeating the permutation
analysis  conducted for the FCD
map (Figure 1D)—yielding a signiﬁcant
overlap between the difference maps
and four HCP-derived RSNs: a sensori-
motor RSN spanning the pre- and post-
central gyri, two visual RSNs (medial and
lateral), and an auditory RSN encompassing the superior tempo-
ral cortex (including the primary auditory cortex in the Heschl’s
gyrus). For comparison, the contour of these RSNs is overlaid
with the maps of statistically signiﬁcant regions in Figure 3 (right
Next, we evaluated whether LSD only scaled the magnitude of
the coupling or also rearranged connectivity patterns in the brain,
independently of the coupling strength. To do this, we studied
the modularity of whole-brain functional connectivity networks
having the ROIs as nodes. In the present context, modularity
measures how well the brain can be parcellated into modules
having dense within-module and sparse between-module con-
nectivity . Based on the observation of increased between-
network connectivity under LSD (Figure 3), as well as previous
ﬁndings with other psychedelics , we predicted that there
would be a decrease in brain modularity under the drug,
indicating a reduction in the separation of intrinsic brain net-
works. As shown in Figure 4A, this prediction was supported
over an extended range of functional network link densities
(ratio of the number of binary connections present in the
network to the maximum possible number of connections; see
Current Biology 26, 1043–1050, April 25, 2016 1045
Supplemental Experimental Procedures). In Figure 4B, we show
the modules identiﬁed by the modularity optimization algo-
rithm. We also computed the participation coefﬁcient of each
node (measuring how much each node communicates
across modules relative to how much they communicate within
their own module ) and observed increased participation
coefﬁcients in frontal and midline regions (Figure 4C) overlap-
ping with those in Figure 1C, suggesting that these areas serve
as conduits for increased between-module communication
Finally, we investigated changes in the level of integration be-
tween highly coupled regions by means of the so-called ‘‘rich-
club’’ coefﬁcient F(k). This metric calculates the ratio of links
between nodes of degree (i.e., the number of attached links)
higher than a certain number (k) over the maximum possible
number of links between them, and it is normalized by the
same metric computed after degree-preserving randomization
of the network . In Figure 4D we show that the rich-club co-
efﬁcient is higher under placebo than LSD, indicating that LSD
decreases the level of (preferential) communication between
the brain’s dominant hub regions. These hub regions are found
within a single module (corresponding to primary sensory
areas; green module in Figure 4B) as revealed by the k-core
of the LSD and placebo networks (with k = 100), deﬁned as
the smallest subset of nodes with degree at least equal to
k (see Figure 4E). Thus, LSD enhances between-module inte-
gration at the expense of impairing within-module communica-
tion of highly coupled nodes.
Figure 3. LSD Increases Between-System
Results of seed correlation analyses based on four
ROIs (leftmost column) deﬁned from the map of
signiﬁcant FCD increases (Figure 1C). In the three
columns at right, regions in red indicate signiﬁ-
cantly higher connectivity (p < 0.05, two-tailed
t test, FDR-controlled for multiple comparisons)
with the seed (leftmost column, in blue) under LSD
relative to the placebo. A permutation test revealed
that only four RSNs present a signiﬁcant (p < 0.05,
Bonferroni-corrected for multiple comparisons)
overlap with the functional connectivity increases
under LSD: the sensorimotor (SM), auditory (Aud),
visual medial (Vis M), and visual lateral (Vis L)
RSNs. The contour of these RSNs is jointly
rendered with the maps of functional connectivity
changes. See also Figures S1 and S3.
Taken together, the present results indi-
cate that LSD enhances global and
between-module communication while
diminishing the integrity of individual
modules, and that this effect is mediated
by the brain’s key integration centers
such as those that are rich in 5-HT
ceptors. These results invite comparisons
with those of our previous functional im-
aging studies with psilocybin, a related
compound and another serotonergic psychedelic. For example,
in  we reported decreases in functional connectivity between
anterior and posterior nodes of the DMN under psilocybin, and
in  and  we suggested that decreased within-network
integrity was a general property of psychedelics. Furthermore,
two subsequent reports detailed increased between-RSN con-
nectivity under psilocybin [20, 24], matching the directionality
of the effects found here with LSD. Indeed, re-analysis of our pre-
viously acquired psilocybin fMRI data revealed FCD increases in
regions similar to those observed here with LSD (Figures 1D and
S1). Importantly, despite overlap with the default-mode, fronto-
parietal, and salience networks, the results of the current FCD
analysis were not constrained a priori to these or any other spe-
Intriguingly, a formal analysis revealed signiﬁcant overlap be-
tween the regions of increased global connectivity under LSD
and those that express the 5-HT
receptors in especially
high concentrations. 5-HT
receptor agonism is known to in-
crease cell excitability (in particular that of layer V pyramidal
neurons) , which may result in higher metabolic demands.
Increased glucose metabolism in frontal, temporal, and subcor-
tical regions has been reported for serotonergic psychedelics,
and these increases correlate with subjective reports of ego
dissolution . Glucose metabolism is also known to covary
with the density of functional connections , thus establish-
ing a possible connection between the FCD increases ob-
served here and 5-HT
receptor-mediated changes in neural
1046 Current Biology 26, 1043–1050, April 25, 2016
Electroencephalography (EEG) studies performed during the
1950s and 1960s reported broadband decreases in oscillatory
power under LSD , and magnetoencephalography recently
revealed diminished power in a broad range of frequency bands
after psilocybin infusion . 5-HT
tory desynchronization can be traced to an uncoupling of layer 5
pyramidal cell ﬁring from local ﬁeld potential oscillations ,
suggesting that dysregulating the ﬁring of these neurons is
critical. In the human cortex, decreases in alpha power after
psilocybin infusion are particularly marked, and decreased alpha
in the posterior DMN (precuneus/posterior cingulate cortex)
correlates with the intensity of ego dissolution . A number
of multimodal EEG-fMRI studies have now revealed an inverse
correlation between global functional connectivity and power
in the alpha band [30–32], which reconciles these electrophysio-
logical observations with our ﬁndings of increased global con-
nectivity in high-level association areas under both LSD and
psilocybin. Alpha oscillations have been hypothesized to inhibit
or regulate task-irrelevant (i.e., ‘‘spontaneous’’ or ‘‘ongoing’’)
neural processes ; thus, ﬁndings of reduced alpha under psy-
chedelics suggest that these drugs could reduce this inhibition
(i.e., be disinhibitory). It must be noted, however, that alpha
oscillations are linked to a number of cognitive processes (e.g.,
attention, memory, executive control, and conscious access)
, and the hypothesized disinhibition cannot be directly in-
ferred from the present results.
The areas of the brain that displayed increased global connec-
tivity under LSD have different functional roles. The frontoparietal
cortex is implicated in conscious information access , and its
activity is suppressed in some states of diminished conscious
awareness (such as seizures or deep sleep) , even though
an unequivocal link between frontoparietal activity and the
conscious state is lacking. Different DMN components perform
functions related to self-consciousness: activity in the precuneus
correlates with self-reﬂection processes and autobiographical
memory retrieval , while the activation of temporo-parietal
junctions is typical of out-of-body experiences . The bilateral
insular cortex is related to self-awareness , as well as to the
processing of emotional information , that could also play an
important role in the psychedelic experience. One intriguing pos-
sibility is that increased cross-talk between these networks and
other brain systems underlies the experience of ego dissolution
under LSD. This scenario is supported by our observation of pos-
itive correlations between increased FCD in the bilateral tem-
poro-parietal junction and insular cortex and subjective reports
of ego dissolution. Furthermore, we observed that the increases
in global connectivity in these high-level regions particularly
involved sensory areas. This increased communication between
Figure 4. LSD Increases Global Integration
(A) Modularity versus link density for the LSD and placebo conditions (mean ± SEM, *p < 0.05, two-tailed t test, FDR-controlled for multiple compa risons). The
inset shows the same for networks after degree-preserving randomization (no signiﬁcant differences were found).
(B) Rendering of the modules identiﬁed at a reference link density of 0.3, for the placebo (top) and LSD (bottom) conditions.
(C) Regions presenting increased participation coefﬁcient in LSD versus placebo (link density = 0.3, p < 0.05 two-tailed t test, FDR-controlled for multiple
(D) Normalized rich-club coefﬁcient f(k) for LSD and placebo. The inset shows the difference between both conditions (mean ± SEM, link density = 0.3, *p < 0.05,
two-tailed t test, FDR-controlled for multiple comparisons).
(E) k-cores (k = 100) for placebo (top) and LSD (bottom) conditions. See also Figure S4.
Current Biology 26, 1043–1050, April 25, 2016 1047
high-level (association) and lower-level (sensory) cortices might
represent a collapse in the normal hierarchical organization of
the brain  such that the boundaries between lower-level
systems anchored to the external world and higher-level
systems operating more autonomously from sensory information
become blurred. It is intriguing to speculate whether this blurring
of boundaries and putative expansion of the ‘‘global work-
space’’  are related to the blurring of ego boundaries and
the experiences of ego dissolution and ‘‘expanded awareness’’
reported in relation to psychedelics.
It is deserving of mention that our exploratory imaging analysis
revealed signiﬁcant (corrected) correlations with only one (out of
six) VAS items, i.e., the one that enquired about feelings of ego
dissolution. That the results of these exploratory whole-brain an-
alyses correlated selectively with ego dissolution may be signif-
icant, as it suggests that this phenomenon is important  and
dependent on changes that implicate the whole of the brain
rather than just speciﬁc functional modules. It remains possible,
however, that other aspects of the psychedelic experience (e.g.,
visual hallucinations) may depend on changes in the functioning
of a particular module (e.g., the visual cortex), and this is some-
thing that we intend to investigate in the future.
As mentioned above, the quality of consciousness under
psychedelics is frequently referred to as ‘‘expanded’’ . It is
reasonable to infer, therefore, that the neurophysiology of the
psychedelic state will contrast with that of states of ‘‘diminished
consciousness,’’ such as deep sleep or general anesthesia. Our
results support this inference on many levels. As discussed
above, increased frontoparietal FCD under LSD suggests higher
metabolism in these regions, whereas unconscious states are
generally characterized by diminished frontoparietal meta-
bolism and connectivity . Deep sleep, for instance, presents
decreased density and efﬁciency of frontoparietal functional
connections . Both sleep and anesthesia are characterized
by a breakdown of global functional integration, resulting in
increased modularity values [40, 41], whereas we observed
decreased modularity values under LSD, reﬂecting enhanced
between-module cross-talk. Broadly speaking, this study’s re-
sults are consistent with the previous hypothesis that the psy-
chedelic and unconscious states occupy polar-opposite ends
of a spectrum of conscious states, deﬁned by their level of en-
tropy or randomness . This hypothesis can now be updated
to state that the brain’s level of modularity (low modularity being
characteristic of random and disordered networks ), during a
particular period of time (e.g., the duration of resting-state scan),
is predictive of the subjective quality of consciousness that is
experienced during that period. Further work is required to
develop our characterization and subsequent quantiﬁcation of
the subjective nature of conscious states ; however, the pre-
sent measure of ego dissolution can be viewed as a start in this
Some limitations of our study must be acknowledged. First,
while we attempted by all available means to reduce the impact
of head motion in our results and to show that our results cannot
be attributed to motion confounds (see section ‘‘Motion’’ in
the Supplemental Experimental Procedures), signiﬁcant differ-
ences in head motion persisted between conditions. Second,
the particularly strict criteria used to combat motion artifacts
reduced our original sample of 20 subjects to a smaller sample
of 15 ‘‘clean’’ datasets. Third, our analysis of ego dissolution
was based on a single numerical report by experienced psyche-
delic drug users; future studies should attempt a more thorough
characterization of the subjective dimension of this experience.
Finally, since the participants were experienced psychedelic
drug users, it is more likely that they could differentiate the
LSD from the placebo, potentially leading to demand character-
istics. It would be interesting to repeat these analyses in psyche-
delic-naive participants to test whether past use of psychedelics
can be predictive of the reported effects, although we failed to
observe any correlations between past use and the above-
reported effects of LSD in the present study.
In conclusion, the present study aimed to explore one of the
most remarkable and least understood domains of the psyche-
delic experience, known both colloquially and academically as
‘‘ego dissolution.’’ It revealed an increase in global integration
within the brain (but a decrease in within-module integrity),
seemingly mediated by high-level cortical association regions
that are rich in 5-HT
receptors, as well as the thalamus.
Importantly, the increases in global integration in cortical asso-
ciation regions selectively correlated with subjective ratings of
ego dissolution. These results help to inform not only on the
neurobiology of the psychedelic experience but on a funda-
mental aspect of human consciousness, namely the sense of
possessing a coherent ‘‘self’’ or ‘‘ego’’ that is distinct from
others and separate from the external environment. Further
work is required to develop these insights and explore other
interesting aspects of the phenomenology of the psychedelic
experience. Finally, the present study reinforces the view that,
conducted with appropriate care, human research with psyche-
delic drugs is safe and can provide valuable insights in human
This study was approved by the National Research Ethics Service Committee
London – West London and was conducted in accordance with the revised
Declaration of Helsinki (2000), the International Committee on Harmonisation
Good Clinical Practices guidelines, and the National Health Service Research
Governance Framework. Imperial College London sponsored the research,
which was conducted under a Home Ofﬁce license for research with Schedule
1 drugs. Detailed experimental procedures (including information on subject
recruitment, experimental design, data acquisition, and data analysis) can
be found in the Supplemental Information.
Supplemental Information includes four ﬁgures and Supplemental Experi-
mental Procedures and can be found with this article online at http://dx.doi.
E.T. analyzed the data, produced all ﬁgures, and wrote the manuscript. L.R.
contributed to study design, analyzed the data, and wrote the manuscript.
M.K. contributed to study design, recruitment of volunteers, and data analysis.
C.O. contributed to data analysis. S.D.M. contributed to study design and co-
ordination. K.M. contributed to data analysis. H.L. contributed to data analysis
and edited the manuscript. R.L. contributed to study design and edited the
manuscript. J.M. contributed to data analysis. N.C. and E.B. contributed to
data analysis and edited the manuscript. T.W. and M.B. helped perform the
research, cared for participants, administered the LSD, and served as
1048 Current Biology 26, 1043–1050, April 25, 2016
medical/psychiatric cover for the study. A.F. was instrumental in initiating the
research and edited the manuscript. D.J.N. advised on the study design and
implementation and edited the manuscript. R.C.-H. designed and led the
study, oversaw recruitment, contributed to data analysis, and edited the
This research received ﬁnancial support from the Safra Foundation (who fund
D.J.N. as the Edmond J. Safra Professor of Neuropsychopharmacology) and
the Beckley Foundation (it was conducted as part of the Beckley/Imperial
Research Programme). E.T. is supported by a postdoctoral fellowship of the
AXA Research Fund. R.C.-H. is supported by an MRC clinical development
scheme grant. S.D.M. is supported by a Royal Society of New Zealand Ruth-
erford Discovery Fellowship. K.M. is supported by a Wellcome Trust fellowship
(WT090199). The researchers would like to thank supporters of the Walacea
crowdfunding campaign (https://walacea.com/) for helping to secure the
funds required to complete the study. This report presents independent
research carried out at the NIHR/Wellcome Trust Imperial Clinical Research
E.B. is employed half-time by University of Cambridge and half-time by
GlaxoSmithKline; he holds stock in GlaxoSmithKline.
Received: September 18, 2015
Revised: January 6, 2016
Accepted: February 2, 2016
Published: April 13, 2016
1. Metzner, R. (1998). Hallucinogenic drugs and plants in psychotherapy and
shamanism. J. Psychoactive Drugs 30, 333–341.
2. Hofmann, A. (1980). LSD: My Problem Child (McGraw-Hill).
3. Grob, C. (1996). Psychiatric Research with Hallucinogens: What Have We
Learned? (VWB - Verlag fu
¨r Wissenschaft und Bildung).
4. Glennon, R.A., Titeler, M., and McKenney, J.D. (1984). Evidence for 5-HT2
involvement in the mechanism of action of hallucinogenic agents. Life Sci.
5. Carhart-Harris, R.L., Erritzoe, D., Williams, T., Stone, J.M., Reed, L.J.,
Colasanti, A., Tyacke, R.J., Leech, R., Malizia, A.L., Murphy, K., et al.
(2012). Neural correlates of the psychedelic state as determined by fMRI
studies with psilocybin. Proc. Natl. Acad. Sci. USA 109, 2138–2143.
6. Saulin, A., Savli, M., and Lanzenberger, R. (2012). Serotonin and molecular
neuroimaging in humans using PET. Amino Acids 42, 2039–2057.
7. Lebedev, A.V., Lo
´n, M., Rosenthal, G., Feilding, A., Nutt, D.J., and
Carhart-Harris, R.L. (2015). Finding the self by losing the self: Neural cor-
relates of ego-dissolution under psilocybin. Hum. Brain Mapp. 36, 3137–
8. Palhano-Fontes, F., Andrade, K.C., Tofoli, L.F., Santos, A.C., Crippa,
J.A.S., Hallak, J.E., Ribeiro, S., and de Araujo, D.B. (2015). The psyche-
delic state induced by ayahuasca modulates the activity and connectivity
of the default mode network. PLoS ONE 10, e0118143.
9. Raichle, M.E., MacLeod, A.M., Snyder, A.Z., Powers, W.J., Gusnard, D.A.,
and Shulman, G.L. (2001). A default mode of brain function. Proc. Natl.
Acad. Sci. USA 98, 676–682.
10. Seeley, W.W., Menon, V., Schatzberg, A.F., Keller, J., Glover, G.H., Kenna,
H., Reiss, A.L., and Greicius, M.D. (2007). Dissociable intrinsic connectiv-
ity networks for salience processing and executive control. J. Neurosci.
11. Vogeley, K., May, M., Ritzl, A., Falkai, P., Zilles, K., and Fink, G.R. (2004).
Neural correlates of ﬁrst-person perspective as one constituent of human
self-consciousness. J. Cogn. Neurosci. 16, 817–827.
12. Carhart-Harris, R.L., and Friston, K.J. (2010). The default-mode, ego-func-
tions and free-energy: a neurobiological account of Freudian ideas. Brain
13. Zalesky, A., Fornito, A., Harding, I.H., Cocchi, L., Yu
¨cel, M., Pantelis, C.,
and Bullmore, E.T. (2010). Whole-brain anatomical networks: does the
choice of nodes matter? Neuroimage 50, 970–983.
14. Tomasi, D., and Volkow, N.D. (2010). Functional connectivity density map-
ping. Proc. Natl. Acad. Sci. USA 107, 9885–9890.
15. Tagliazucchi, E., Carhart-Harris, R., Leech, R., Nutt, D., and Chialvo, D.R.
(2014). Enhanced repertoire of brain dynamical states during the psyche-
delic experience. Hum. Brain Mapp. 35, 5442–5456.
16. Beckmann, C.F., DeLuca, M., Devlin, J.T., and Smith, S.M. (2005).
Investigations into resting-state connectivity using independent compo-
nent analysis. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360, 1001–1013.
17. Blanke, O., Ortigue, S., Landis, T., and Seeck, M. (2002). Stimulating
illusory own-body perceptions. Nature 419, 269–270.
18. Craig, A.D. (2011). Signiﬁcance of the insula for the evolution of human
awareness of feelings from the body. Ann. N Y Acad. Sci. 1225, 72–82.
19. Bullmore, E., and Sporns, O. (2009). Complex brain networks: graph theo-
retical analysis of structural and functional systems. Nat. Rev. Neurosci.
20. Roseman, L., Leech, R., Feilding, A., Nutt, D.J., and Carhart-Harris, R.L.
(2014). The effects of psilocybin and MDMA on between-network resting
state functional connectivity in healthy volunteers. Front. Hum. Neurosci.
21. van den Heuvel, M.P., and Sporns, O. (2011). Rich-club organization of the
human connectome. J. Neurosci. 31, 15775–15786.
22. Carhart-Harris, R.L., Leech, R., Hellyer, P.J., Shanahan, M., Feilding, A.,
Tagliazucchi, E., Chialvo, D.R., and Nutt, D. (2014). The entropic brain: a
theory of conscious states informed by neuroimaging research with psy-
chedelic drugs. Front. Hum. Neurosci. 8,20.
23. Muthukumaraswamy, S.D., Carhart-Harris, R.L., Moran, R.J., Brookes,
M.J., Williams, T.M., Errtizoe, D., Sessa, B., Papadopoulos, A.,
Bolstridge, M., Singh,K.D., et al. (2013). Broadband cortical desynchroniza-
tion underlies the human psychedelic state. J. Neurosci. 33, 15171–15183.
24. Carhart-Harris, R.L., Leech, R., Erritzoe, D., Williams, T.M., Stone, J.M.,
Evans, J., Sharp, D.J., Feilding, A., Wise, R.G., and Nutt, D.J. (2013).
Functional connectivity measures after psilocybin inform a nov el hypothe-
sis of early psychosis. Schizophr. Bull. 39, 1343–1351.
25. Andrade, R. (2011). Serotonergic regulation of neuronal excitability in the
prefrontal cortex. Neuropharmacology 61, 382–386.
26. Vollenweider, F.X., Leenders, K.L., Scharfetter, C., Maguire, P.,
Stadelmann, O., and Angst, J. (1997). Positron emission tomography and
ﬂuorodeoxyglucosestudies of metabolic hyperfrontality and psychopathol-
ogy in the psilocybin model of psychosis. Neuropsychopharmacology 16,
27. Tomasi, D., Wang, G.J., and Volkow, N.D. (2013). Energetic cost of brain
functional connectivity. Proc. Natl. Acad. Sci. USA 110, 13642–13647.
28. Fink, M. (1969). EEG and human psychopharmacology. Annu. Rev.
Pharmacol. 9, 241–258.
29. Celada, P., Puig, M.V., Dı
´az-Mataix, L., and Artigas, F. (2008). The halluci-
nogen DOI reduces low-frequency oscillations in rat prefrontal cortex:
reversal by antipsychotic drugs. Biol. Psychiatry 64, 392–400.
30. Scheeringa, R., Petersson, K.M., Kleinschmidt, A., Jensen, O., and
Bastiaansen, M.C. (2012). EEG apower modulation of fMRI resting-state
connectivity. Brain Connect. 2, 254–264.
31. Tagliazucchi, E., von Wegner, F., Morzelewski, A., Brodbeck, V., and
Laufs, H. (2012). Dynamic BOLD functional connectivity in humans and
its electrophysiological correlates. Front. Hum. Neurosci. 6, 339.
32. Chang, C., Liu, Z., Chen, M.C., Liu, X., and Duyn, J.H. (2013). EEG corre-
lates of time-varying BOLD functional connectivity. Neuroimage 72,
33. Klimesch, W., Sauseng, P., and Hanslmayr, S. (2007). EEG alpha oscilla-
tions: the inhibition-timing hypothesis. Brain Res. Brain Res. Rev. 53,
Current Biology 26, 1043–1050, April 25, 2016 1049
34. Bazanova, O.M., and Vernon, D. (2014). Interpreting EEG alpha activity.
Neurosci. Biobehav. Rev. 44, 94–110.
35. Dehaene, S., and Naccache, L. (2001). Towards a cognitive neuroscience
of consciousness: basic evidence and a workspace framework. Cognition
36. Boly, M., Phillips, C., Tshibanda, L., Vanhaudenhuyse, A., Schabus, M.,
Dang-Vu, T.T., Moonen, G., Hustinx, R., Maquet, P., and Laureys, S.
(2008). Intrinsic brain activity in altered states of consciousness: how
conscious is the default mode of brain function? Ann. N Y Acad. Sci.
37. Johnson, S.C., Baxter, L.C., Wilder, L.S., Pipe, J.G., Heiserman, J.E., and
Prigatano, G.P. (2002). Neural correlates of self-reﬂection. Brain 125,
38. Phan, K.L., Wager, T., Taylor, S.F., and Liberzon, I. (2002). Functional
neuroanatomy of emotion: a meta-analysis of emotion activation studies
in PET and fMRI. Neuroimage 16, 331–348.
39. Uehara, T., Yamasaki, T., Okamoto, T., Koike, T., Kan, S., Miyauchi, S.,
Kira, J., and Tobimatsu, S. (2014). Efﬁciency of a ‘‘small-world’’ brain
network depends on consciousness level: a resting-state FMRI study.
Cereb. Cortex 24, 1529–1539.
40. Tagliazucchi, E., von Wegner, F., Morzelewski, A., Brodbeck, V., Borisov,
S., Jahnke, K., and Laufs, H. (2013). Large-scale brain functional modu-
larity is reﬂected in slow electroencephalographic rhythms across the
human non-rapid eye movement sleep cycle. Neuroimage 70, 327–339.
41. Schrouff, J., Perlbarg, V., Boly, M., Marrelec, G., Boveroux, P.,
Vanhaudenhuyse, A., Bruno, M.A., Laureys, S., Phillips, C., Pe
Issac, M., et al. (2011). Brain functional integration decreases during pro-
pofol-induced loss of consciousness. Neuroimage 57, 198–205.
42. Studerus, E., Gamma, A., and Vollenweider, F.X. (2010). Psychometric
evaluation of the altered states of consciousness rating scale (OAV).
PLoS ONE 5, e12412.
1050 Current Biology 26, 1043–1050, April 25, 2016
Current Biology, Volume 26
Increased Global Functional Connectivity
Correlates with LSD-Induced Ego Dissolution
Enzo Tagliazucchi, Leor Roseman, Mendel Kaelen, Csaba Orban, Suresh D.
Muthukumaraswamy, Kevin Murphy, Helmut Laufs, Robert Leech, John
McGonigle, Nicolas Crossley, Edward Bullmore, Tim Williams, Mark
Bolstridge, Amanda Feilding, David J. Nutt, and Robin Carhart-Harris
Supplemental Figures and Legends
Figure S1: Psilocybin selectively increases global functional connectivity of higher-level integrative
cortical and sub-cortical regions, and increases between-system functional connectivity. (A) Average
FCD under the placebo and psilocybin conditions. (B) Rendering of significant FCD increases under
psilocybin vs. placebo (thresholded at p<0.05, FDR-controlled for multiple comparisons). Subjects and
data preprocessing is as reported in Tagliazucchi et al., 2014. (C) Regions for seed correlation analysis
(defined from the map of FCD increases in Panel B). (D) Results of seed correlation. Regions in red
present significantly higher connectivity (p<0.05, FDR-controlled for multiple comparisons) with the
seeds (panel C) under psilocybin compared with placebo. Subjects and data preprocessing is as
reported in [S1]. Related to Figures 1 and 3.
Figure S2: (A) Average VAS scores for LSD (red) and placebo (blue) (mean ± SEM). (B) Average ASC
scores for LSD (red) and placebo (blue) (mean ± SEM). Significance tested with the nonparametric
Wilcoxon’s signed-rank test, Bonferroni correction for multiple comparisons applied. (C) Correlation
between VAS scores across participants. (D) Correlation between ASC scores across participants. (E)
Correlation between VAS scores and ASC scores across participants. For the full description of the VAS
and ASC items see the corresponding subsections in the Supplemental Experimental Procedures.
Related to Figure 2.
Figure S3: (A) Regions where a significant correlation between FCD increases and ego-dissolution
scores was observed under LSD (p<0.05, FDR-corrected for multiple comparisons). For a description of
the colour scheme, see the analogous Figure 2A in the manuscript text. (B) Scatter plots of ego-
dissolution changes under LSD (minus placebo) vs. FCD increases for four additional regions: left/right
middle frontal gyrus (MFG) and left/right middle occipital gyrus (MOG). In the inset, Pearson’s R and the
associated (uncorrected) p-values are provided. Related to Figures 2 and 3.
Figure S4: Scatter plots of FCD increases (LSD minus placebo) vs. VAS items: “Intensity”, “Simple
hallucinations”, “Complex hallucinations”, “Emotional arousal” and “Positive mood”, for the regions
presented in Figure 2B (Angular gyrus R/L and insula R/L). In the insets, the following statistics are
provided. R: Pearson’s linear correlation coefficient between variables, pcorr: p-value for the correlation
(FDR-controlled for multiple comparisons across all 401 regions in the template), p: uncorrected p-value
for the correlation, z: z-values for the test of higher correlation between FCD increases and ego-
dissolution vs. FCD increases and other VAS items, p’: the p-value corresponding to the z-value. These
z and p-values were obtained using the following web utility: http://quantpsy.org/corrtest/corrtest2.htm.
For all except four of the comparisons we obtained p’<0.05, supporting the specificity of the correlations
with ego-dissolution in this set of regions. Of those comparisons with p’>0.05, half of them corresponded
to correlations with the “Intensity of the experience” item. This seems to indicate that feelings of ego-
dissolution carry the most weight when participants rate the intensity of their experience, also supported
by the fact that, among all VAS items, “Intensity” presented the strongest correlations with “Ego-
dissolution” (Figure S2, panel C). Related to Figure 4.
Supplemental Experimental Procedures
Subject recruitment and experimental design
This study was approved by the National Research Ethics Service (NRES) committee London
- West London, and was conducted in accordance with the revised declaration of Helsinki
(2000), the International Committee on Harmonization Good Clinical Practice guidelines and
the National Health Service (NHS) Research Governance Framework. Imperial College
London sponsored the research, which was conducted under a Home Office license for
research with schedule 1 drugs.
Participants were recruited via word of mouth and provided written informed consent to
participate after being screened for physical and mental health. The screening for physical
health included electrocardiogram, routine blood tests, and urine test for recent drug use and
pregnancy. A psychiatric interview was conducted with participants providing full disclosure of
their drug use history. Key exclusion criteria included: < 21 years of age, personal history of
diagnosed psychiatric illness, immediate family history of a psychotic disorder, an absence of
previous experience with a classic psychedelic drug (e.g. LSD, mescaline, psilocybin/magic
mushrooms or DMT/ayahuasca), psychedelic drug use within 6 weeks of the first scanning
session, pregnancy, problematic alcohol use (i.e. > 40 units consumed per week), or a
medically significant condition rendering the volunteer unsuitable for the experimental
environment. Before intravenous LSD infusion participants gave a urine test for recent drug
use and pregnancy, and carried out a breathalyser test for recent alcohol use.
Twenty healthy participants (4 females, mean age = 30.9±7.8 years) attended two scanning
sessions in different days at 8:00 (LSD and placebo) at least two weeks apart in a balanced
order, within-subjects design. Scanning days included arterial spin labeling (ASL) and fMRI
followed by a MEG session. LSD (75 !g in 10 ml saline) or placebo (10 ml saline) was
delivered as bolus injections over 2 minutes while participants were instructed to close their
eyes and relax. Following an acclimatization period of 60 minutes inside a mock MRI scanner,
three fMRI scans were conducted in the following order: eyes-closed resting state, rest while
listening to music and another eyes-closed resting state session. Only results for the first
resting state session (prior to music) are reported here
The intensity of subjective effects was stable for BOLD scans. Participants carried out VAS-
style ratings after each scan by pressing buttons on a button-box (see next sub-section). The
11 factor altered states of consciousness (ASC) questionnaire was completed at the end of
each dosing day. Here we focused on the VAS ratings, since they were specific to the time of
fMRI acquisition and do not rely on retrospective evaluation of the experience, like the ASC
questionnaire (moreover, it would have been impractical to have completed the ASC during
scanning, as it contains 94 items, and none of them directly refers to the experience of “ego-
dissolution”). In particular, we assessed correlations with the intensity of ego-dissolution
experienced by the participants. All participants reported closed-eye visual hallucinations and
marked changes in consciousness under LSD.
The scale for the VAS items ranged from 0 to 20. Items were phrased as follows:
1) “Please rate the intensity of the drug effects during the last scan”, with a bottom anchor of
“no effects”, a mid-point anchor of “moderately intense effects” and a top anchor of “extremely
2) “With eyes closed, I saw patterns and colours”, with a bottom anchor of “no more than
usual” and a top anchor of “much more than usual”;
3) “With eyes closed, I saw complex visual imagery”, with the same anchors as item 2;
4) “How positive was your mood for the last scan?”, with the same anchors as item 2, plus a
mid-point anchor of “somewhat more than usual”;
5) “I experienced a dissolving of my self or ego”, with the same anchors as item 2;
6) “Please rate your general level of emotional arousal for the last scan”, with a bottom
anchor of “not at all emotionally aroused”, a mid-point anchor of “moderately emotionally
aroused” and a top anchor of “extremely emotionally aroused”.
As part of the ASC questionnaire the participants were asked to rate the intensity of the
1) Experience of unity
2) Spiritual experience
3) Blissful state
6) Impaired control and cognition
8) Complex imagery
9) Elementary imagery
10) Audio-visual synesthesia
11) Changed meaning of percepts
Imaging was performed on a 3T GE HDx system. These were 3D fast spoiled gradient echo
scans in an axial orientation, with field of view = 256 × 256 × 192 and matrix = 256 × 256 ×
192 to yield 1mm isotropic voxel resolution. TR/TE = 7.9/3.0ms; inversion time = 450ms; flip
angle = 20°.
fMRI Data Acquisition
BOLD fMRI data was acquired in two sessions using a gradient echo planar imaging
sequence, TR/TE = 2000/35ms, field-of-view = 220mm, 64 × 64 acquisition matrix, parallel
acceleration factor = 2, 90° flip angle. Thirty-five oblique axial slices were acquired in an
interleaved fashion, each 3.4mm thick with zero slice gap (3.4mm isotropic voxels). The
length of the BOLD scan was 7:20 minutes.
fMRI data preprocessing
Resting state fMRI data was analyzed using FSL [S2], AFNI [S3], Freesurfer [S4] and ANTs
[S5]. Of the total 20 subjects scanned, one subject did not complete the BOLD scans and four
subjects were discarded due to high levels of head movement – as measured with framewise
displacement (FD) [S6] - between LSD and placebo (difference in mean FD = 0.351±0.397).
The following preprocessing stages were performed to the remaining 15 subjects: removing
the first three volumes; despiking (3dDespike, AFNI); slice time correction (3dTshift, AFNI);
motion correction (3dvolreg, AFNI) by registering each volume to the volume most similar, in
the least squares sense, to all others (in-house code); brain extraction (BET, FSL); rigid body
registration to anatomical scans (twelve subjects with FSL’s BBR, one subject with
Freesurfer’s bbregister and two subjects manually); non-linear registration to 2mm MNI brain
(Symmetric Normalization (SyN), ANTs); scrubbing [S7] using an FD threshold of 0.4 (the
mean percentage of volumes scrubbed for placebo and LSD was 0.3±0.8% and 1.8±2.4%,
respectively. The maximum number of scrubbed volumes per scanning session was 7.8%).
Scrubbed volumes were replaced with the mean of the surrounding volumes. Further
preprocessing included spatial smoothing (FWHM) of 6mm (3dBlurInMask, AFNI); bandpass
filtering between 0.01 to 0.08 Hz (3dFourier, AFNI); linear and quadratic detrending
(3dDetrend, AFNI); regressing out 9 nuisance regressors: out of these 6 were motion related
(3 translations, 3 rotations) and 3 were anatomical (not smoothed). The anatomical nuisance
regressors were: ventricles (Freesurfer, eroded in 2mm space); draining veins (DV) (FSL’s
CSF minus Freesurfer’s Ventricles, eroded in 1mm space); and, local white matter (WM)
(FSL’s WM minus Freesurfer’s subcortical grey matter (GM) structures, eroded in 2mm
space. AFNI’s 3dLocalstat was used to calculate the mean local WM time series for each
voxel (using a 25mm radius sphere centered on each voxel) [S8]
After discarding four subjects due to head motion, fifteen subjects were left for the BOLD
analysis. The difference in motion for these subjects was still significant (mean FD of placebo
= 0.076±0.036, mean FD of LSD = 0.12±0.05, p=0.0005). RSFC analysis is very sensitive to
head motion [S7] and therefore special consideration was put to control for motion. More
detail about the dedicated preprocessing steps we performed to reduce motion artifacts and
other sources of noise is provided below. Despiking has been shown to improve motion
correction and create more accurate FD values . The lowpass filter of 0.08 Hz has been
shown to perform well in removing high frequency motion [S9]. We covaried out six motion
regressors; this is because using more than six regressors (e.g., Friston’s 24) is likely to be
redundant and even remove real neural signal [S10]. Using anatomical regressors is also a
common step to clean noise. In our pipeline instead of using global WM regressors, we used
local WM which has been suggested to preform better than using the global WM [S11]. It is
suggested that head movement changes RSFC results in a distance dependant matter [S6].
Therefore, as a quality control step, at the end of the preprocessing, we plotted cloud plots to
look for correlation between inter-node euclidian distance and FD-RSFC correlation across
subjects. In some cases in which motion is affecting the results, proximal nodes will have high
FD-RSFC correlations and distal nodes will have low FD-RSFC correlations. This would result
in a negative correlation between distance and FD-RSFC correlation. In our data this
correlation was very close to zero for both placebo and LSD suggesting that preprocessing
successfully controlled for distance-related motion artifacts.
As a final control for motion artifacts we correlated the difference in FCD values (LSD minus
placebo) against the difference in FC (LSD minus placebo) across all 15 participants and the
401 regions of interest. Only 5 regions revealed a significant (uncorrected) p-value (p values
= 0.0329, 0.0250, 0.0287, 0.0347, 0.0439), with none surviving correction for multiple
comparisons. Furthermore, we did not observe significant FCD differences in these 5 ROI
between the LSD and placebo conditions (corrected p values for the FCD comparison in the 5
ROIs: 0.1845, 0.2099, 0.1379, 0.1827, 0.0906). The same computation was performed for the
time series of individual realignment parameters (three translations and three rotations), with
null findings in all cases except translations along the x-axis. In this case only 3 ROIs
presented significant differences between groups, but none were located among those where
we observed differences in FCD.
Functional connectivity density and seed correlation
To compute the functional connectivity density (FCD) we first extracted the average BOLD
signal from each of the 401 ROIs. Then, we computed the linear correlation (functional
connectivity) between each one of the (401!−401)2!pairs of ROIs, resulting in a correlation
matrix C!" having in its i,j the functional connectivity between the i-th and j-th ROIs. The FCD
of the k-th ROI is defined as the average of C!" with respect to index j, i.e. !
!!!! (where n
equals the total number of ROIs, in this case n=401).
Seed correlation was computed by extracting the average BOLD signal from each of four
masks based on significant FCD increases under LSD (frontal, parietal, precuneal and
thalamic masks). Then, for all 401 ROIs we computed the linear correlation between its
averaged BOLD signal and the signal from the mask, and assigned the resulting correlation
value to the ROI. This resulted in a spatial map of correlations with the seed.
Modularity of functional networks
To compute the modularity of functional networks we first derived the binary adjacency matrix
A!" from the weighted correlation matrix C!". This was done by thresholding the correlation
matrix so that A!" =1 if C!" >!ρ and A!" =0 otherwise. The threshold ρ was selected to
achieve a fixed value of link density, defined as the number of connections present in the
network over the total number of possible connections (i.e. !
!"!!). It is important to
compare binary networks with the same link density, thus guaranteeing changes are driven
by the topological re-organization of connections between conditions and not by the total
number of connections. The link densities under study ranged from 0.01 to 0.5; following
previous work (e.g. [S12]) the upper limit prevents the construction of excessively connected
The intuition behind modules is that of a group of nodes having denser within-group
connections in comparison to between-group connections (i.e. connections with other
modules). The modular structure of a network can be obtained by maximizing the modularity
Q, which estimates the difference between the number of intra-module links and the expected
number (for the same partition) in a random network of the same size [S13]. Given a certain
partition of the nodes into non-overlapping subsets, the modularity associated with this
partition with this partition is defined as,
The sum runs over all network nodes, L is the total number of links A!"
!!! is the
degree of the i-th node (i.e. number of links attached to the node) and δi,j=1 if the i-th and
j-th nodes belong to the same subset in the partition and 0 otherwise. We computed the
modularity of networks by keeping the maximal value obtained after 500 iterations of the
Louvain algorithm as implemented in the Brain Connectivity Toolbox
Rich club coefficient
The rich club coefficient Φ(k) is computed by dividing the number of links present between all
nodes with degree higher than k by the maximum possible number of links between them.
The rich club coefficient is normalized by the same metric computed after degree-preserving
randomization of the network. We computed Φ(k) using the algorithms provided in the Brain
Connectivity Toolbox (https://sites.google.com/site/bctnet/) [S14].
Resting State Networks (RSN)
RSNs were obtained using Independent Component Analysis (ICA) on scans from the Human
Connectome Project (HCP) [S15]. All of the scans were preprocessed as part of the HCP
[S16]. Two BOLD resting state scans (with opposite direction of phase encoding gradient) for
35 subjects were used in this analysis. Each scan was 14:33 minute long
(TR/TE=720/33.2ms, 2mm isotropic voxels). All scans were bandpassed using the same filter
as our BOLD scans (0.01 to 0.08 Hz). FSL’s MELODIC was used to extract the ICA
components which were then visually identified [S17].
Testing the significance of the spatial overlap between networks
The overlap was determined by comparing the percentage of voxels in the networks included
in the FCD/seed correlation difference maps to the same numbers obtained from 500
instances of spatial randomisation of the networks (preserving the first-order statistics of the
images). Spatial randomization was performed by phase randomization on the frequency
space; i.e. applying a Fast Fourier Transform to the 3D volumes, randomizing phases and
then back-transforming to the spatial domain. Empirical p-values were derived from the
number of instances in which the overlap with the spatially randomised networks was higher
than the overlap computed using the real RSN.
In all cases we compared the LSD and placebo conditions using paired t-tests.
We applied FDR to control for the rate of false positives when performing one test per ROI
(n=401). This comprised the results presented in Fig. 1C, Fig. 2A, Fig. 3, Fig. 4C and Fig. S1.
When the number of comparisons was smaller (e.g. one test per RSN, n=8) we used the
Bonferroni correction instead. This comprised the results presented in Fig. 1D and Fig. S2.
S1 Tagliazucchi, E., Carhart‐Harris, R., Leech, R., Nutt, D., Chialvo, D. R. (2014).
Enhanced repertoire of brain dynamical states during the psychedelic experience. Hum. Brain
Mapp. 35, 5442-5456.
S2 Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E.,
Johansen-Berg, H., Bannister, P.R., De Luca, M., Drobnjak, I., Flitney, D.R., et al. (2004).
Advances in functional and structural MR image analysis and implementation as
FSL. Neuroimage 23, S208-S219.
S3 Cox, R. W. (1996). AFNI: software for analysis and visualization of functional
magnetic resonance neuroimages. Comp. Biomed. Res. 29, 162-173.
S4 Dale, A. M., Fischl, B., Sereno, M. I. (1999). Cortical surface-based analysis: I.
Segmentation and surface reconstruction. Neuroimage 9, 179-194.
S5 Avants, B. B., Tustison, N., Song, G. (2009). Advanced normalization tools
(ANTS). Insight J. 2, 1-35.
S6 Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., Petersen, S.
E. (2014). Methods to detect, characterize, and remove motion artifact in resting state fMRI.
Neuroimage 84, 320-341.
S7 Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., Petersen, S. E. (2012).
Spurious but systematic correlations in functional connectivity MRI networks arise from
subject motion. Neuroimage 59, 2142-2154.
S8 Jo, H. J., Saad, Z. S., Simmons, W. K., Milbury, L. A., Cox, R. W. (2010). Mapping
sources of correlation in resting state FMRI, with artifact detection and removal. Neuroimage
S9 Satterthwaite, T. D., Elliott, M. A., Gerraty, R. T., Ruparel, K., Loughead, J., Calkins,
M. E., Eickhoff, S., Hakonarson, H., Gur, R.C., Gur, R.E., et al. (2013). An improved
framework for confound regression and filtering for control of motion artifact in the
preprocessing of resting-state functional connectivity data. Neuroimage 64, 240-256.
S10 Bright, M. G., Murphy, K. (2015). Is fMRI “noise” really noise? Resting state nuisance
regressors remove variance with network structure. NeuroImage 114, 158-169.
S11 Jo, H. J., Gotts, S. J., Reynolds, R. C., Bandettini, P. A., Martin, A., Cox, R. W., Saad,
Z. S. (2013). Effective preprocessing procedures virtually eliminate distance-dependent
motion artifacts in resting state FMRI. J. Appl. Math. 935154.
S12 Schröter, M. S., Spoormaker, V. I., Schorer, A., Wohlschläger, A., Czisch, M., Kochs,
E. F, Zimmer, C., Hammer, B., Schneider, G., Jordan, D., et al. (2012). Spatiotemporal
reconfiguration of large-scale brain functional networks during propofol-induced loss of
consciousness. J. Neurosci. 32, 12832-12840.
S13 Newman, M.E., and Girvan, M. (2004). Finding and evaluating community structure in
networks. Phys. Rev. E 69, 026113.
S14 Rubinov, M., and Sporns, O. (2010). Complex network measures of brain
connectivity: uses and interpretations. Neuroimage 52, 1059-1069.
S15 Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E., Yacoub, E., Ugurbil,
K., WU-Minn HCP Consortium. (2013). The WU-Minn human connectome project: an
overview. Neuroimage 80, 62-79.
S16 Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B.,
Andersson, J. L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J.R., et al. (2013). The minimal
preprocessing pipelines for the Human Connectome Project. Neuroimage 80, 105-124.
S17 Beckmann, C. F., Mackay, C. E., Filippini, N., Smith, S. M. (2009). Group
comparison of resting-state FMRI data using multi-subject ICA and dual regression.
Neuroimage 47, S148.