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SCIentIfIC RePoRTs | 7: 13187 | DOI:10.1038/s41598-017-13282-7
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Psilocybin for treatment-resistant
depression: fMRI-measured brain
mechanisms
Robin L Carhart-Harris1, Leor Roseman1,2, Mark Bolstridge1, Lysia Demetriou5,6,
J Nienke Pannekoek1,7, Matthew B Wall1,4,5, Mark Tanner5, Mendel Kaelen1, John McGonigle5,
Kevin Murphy3, Robert Leech2, H Valerie Curran4 & David J Nutt1
Psilocybin with psychological support is showing promise as a treatment model in psychiatry but its
therapeutic mechanisms are poorly understood. Here, cerebral blood ow (CBF) and blood oxygen-level
dependent (BOLD) resting-state functional connectivity (RSFC) were measured with functional magnetic
resonance imaging (fMRI) before and after treatment with psilocybin (serotonin agonist) for treatment-
resistant depression (TRD). Quality pre and post treatment fMRI data were collected from 16 of 19
patients. Decreased depressive symptoms were observed in all 19 patients at 1-week post-treatment and
47% met criteria for response at 5 weeks. Whole-brain analyses revealed post-treatment decreases in
CBF in the temporal cortex, including the amygdala. Decreased amygdala CBF correlated with reduced
depressive symptoms. Focusing on a priori selected circuitry for RSFC analyses, increased RSFC was
observed within the default-mode network (DMN) post-treatment. Increased ventromedial prefrontal
cortex-bilateral inferior lateral parietal cortex RSFC was predictive of treatment response at 5-weeks,
as was decreased parahippocampal-prefrontal cortex RSFC. These data ll an important knowledge
gap regarding the post-treatment brain eects of psilocybin, and are the rst in depressed patients. The
post-treatment brain changes are dierent to previously observed acute eects of psilocybin and other
‘psychedelics’ yet were related to clinical outcomes. A ‘reset’ therapeutic mechanism is proposed.
Psilocybin is the prodrug of psilocin (4-OH-dimethyltryptamine), a non-selective serotonin 2A receptor
(5-HT2AR) agonist and classic ‘psychedelic’ drug1. Both compounds occur naturally in the ‘psilocybe’ genus
of mushrooms, and are structurally related to the endogenous neurotransmitter serotonin (5-OH-tryptamine,
5-HT). Psilocybin has an ancient and more recent history of medicinal-use. Administered in a supportive envi-
ronment, with preparatory and integrative psychological care, it is used to facilitate emotional breakthrough
and renewed perspective2. Accumulating evidence suggests that psilocybin with accompanying psychological
support can be used safely to treat a range of psychiatric conditions1, including: end-of-life anxiety and depres-
sion3–5, alcohol and tobacco addiction6,7, obsessive compulsive disorder8, and most recently from our group,
treatment-resistant major depression9. Findings from healthy volunteer studies10 and trials with other psyche-
delics11–13 supplement those from clinical studies showing that these drugs can have a rapid and lasting positive
impact on mental health, oen aer just one or two doses. Such outcomes raise a number of important questions,
including: what brain mechanisms mediate these eects?
Most human functional neuroimaging studies of psychedelics have focused on their acute eects with the aim
of elucidating the neural correlates of the ‘psychedelic state’14,15. Consistent with ndings from animal research16,
psychedelics appear to dysregulate cortical activity14,17, producing an ‘entropic’ brain state18, characterised by
compromised modular but enhanced global connectivity - referred to previously as network ‘disintegration’ and
1Psychedelic Research Group, Psychopharmacology Unit, Centre for Psychiatry, Department of Medicine, Imperial
College London, W12 0NN, London, UK. 2Computational, Cognitive and Clinical Neuroscience Laboratory (C3NL),
Department of Medicine, Imperial College London, W12 0NN, London, UK. 3Cardi University Brain Research Imaging
Centre (CUBRIC), School of Physics and Astronomy, CF24 4HQ, Cardiff, UK. 4Clinical Psychopharmacology Unit,
University College London, WC1E 6BT, London, United Kingdom. 5Imanova Centre for Imaging Sciences, Burlington
Danes Building, Hammersmith Hospital, Du Cane Road, London, W12 0NN, UK. 6Investigative Medicine, Department
of Medicine, Imperial College London, London, United Kingdom. 7SU/UCT MRC Unit on Risk and Resilience in Mental
Disorders, Department of Psychiatry and Mental Health, University of Cape Town, South Africa. Correspondence and
requests for materials should be addressed to R.L.C.-H. (email: r.carhart-harris@imperial.ac.uk)
Received: 1 June 2017
Accepted: 19 September 2017
Published: xx xx xxxx
OPEN
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SCIentIfIC RePoRTs | 7: 13187 | DOI:10.1038/s41598-017-13282-7
‘desegregation’14. ese eects have been found to correlate with important aspects of the ‘psychedelic expe-
rience’, including ‘ego-dissolution’14,17,19, and were predictive of post-acute changes in the personality domain
‘openness’20. To our knowledge, only one other very recent study has investigated >12 hour post-acute eects of
psychedelics on human brain function (although see12 and now21), and few have looked at anatomical changes
possibly related to psychedelic use22,23.
The present study focused on changes in brain function before versus after psilocybin in patients with
treatment-resistant depression who received two doses of the drug (10 mg followed by 25 mg, one-week apart)
as part of an open-label clinical trial. Arterial spin labelling (ASL) and blood oxygen level dependent (BOLD)
resting state functional connectivity (RSFC), were used to measure changes in cerebral blood ow (CBF) and
functional connectivity before (baseline) and one-day aer treatment with psilocybin (i.e. one day aer the 25 mg
dose). It has been suggested that the days subsequent to a psychedelic experience constitute a distinct phase,
referred to as the ‘aer-glow’, that is characterised by mood improvements and stress relief24. e rationale for
scanning one-day post-treatment was to capture brain changes related to this so-called aer-glow that might
correlate with current mood improvements and/or longer-term prognoses. We predicted that resting-state CBF
and FC would be altered post treatment and correlate with immediate and longer-term clinical improvements.
With regards to ‘longer-term’ clinical outcomes, we chose to focus on a 5-week post-treatment endpoint due
to a virtual 50:50 split between responders and non-responders at this time-point (QIDS-SR16) and that none of
the patients went on to additional (and thus, confounding) treatments within this time frame. A select number
of regions of interest were chosen a priori for CBF and RSFC analyses due to previous work implicating their
involvement in depression and its treatment, e.g25–27.
Results
Nineteen patients with diagnoses of treatment resistant major depression completed pre-treatment and one-day
post-treatment fMRI scanning. Excessive movement or other artefact meant that three patients were removed
from the ASL analyses and four from the RSFC (SI Appendix), leaving sample sizes of 16 (mean age = 42.8 ±
10.1 y, 4 females) and 15 (mean age = 42.8 ± 10.5 y, 4 females) for the ASL and BOLD analyses, respectively.
Treatment with psilocybin produced rapid and sustained antidepressant eects. For the patients included
in the ASL analysis (minus one patient whose scan 1 rating was not collected), the mean depression score
(QIDS-SR16) for the week prior to the pre-treatment scan was 16.9 ± 5.1, and for the day of the post-treatment
scan, it was 8.8 ± 6.2 (change = −8.1 ± 6, t = −5.2, p < 0.001). e mean QIDS-SR16 score at baseline (screen-
ing) was 18.9 ± 3, and for 5-weeks post-treatment, it was 10.9 ± 4.8 (change = −8 ± 5.1, t = −6.3, p < 0.001).
Mean change values for those included in the BOLD analyses were −7.3 ± 5.3 (change from scan 1 to scan 2) and
−8.2 ± 5.2 (change from baseline to 5 weeks post-treatment). Both contrasts were highly signicant (t = −5.2 and
−6.2, p < 0.001). Six of the 15 (BOLD) and 16 (ASL) patients met criteria for treatment response (≤50% reduc-
tions in QIDS-SR16 score) at 5 weeks. Of the full 19 patients, all showed some decrease in depressive symptoms
at 1 week, with 12 meeting criteria for response (change = −10.2 ± 5.3, t = −6.4, p < 0.001). All but one patient
showed some decrease in QIDS-SR16 score at week 5 (with one showing no change) and 47% met criteria for
response (change = −9.2 ± 5.6, t = −6.7, p < 0.001).
Whole-brain CBF was calculated pre and post treatment and contrasted (Fig.1). Only decreases in CBF were
observed post treatment (vs pre), and these reached statistical signicance in the le Heschl’s gyrus, le precentral
gyrus, le planum temporale, le superior temporal gyrus, le amygdala, right supramarginal gyrus and right
parietal operculum (TableS1). Based on previous ndings of increased amygdala blood ow and metabolism in
depression25, reductions in amygdala CBF were compared with the reductions in depressive symptoms between
scan 1 and 2 (i.e. decreased depressed mood at the time of scanning), and a signicant relationship was found
(r = 0.59; p = 0.01). Aer splitting the sample into responders and non-responders at 5-weeks post-treatment, and
then comparing CBF changes in a t-test, no signicant dierence was found (t = 0.11; p = 0.46).
Next, seed-based RSFC analyses were performed using the BOLD data. Based on previous data implicating
their involvement in the pathophysiology of depression and response to treatments25–27, four regions of interest
(ROIs) were chosen: 1) the subgenual anterior cingulate cortex (sgACC), 2) the ventromedial prefrontal cortex
(vmPFC), 3) the bilateral amygdala, and 4) the bilateral parahippocampus (PH) (Figs2–4 and SI Appendix,
TableS1).
Increased sgACC RSFC was observed with the posterior cingulate cortex/precuneous (PCC) post-treatment
(Fig.2) but this eect did not correlate with reductions in depressive symptoms between scan 1 and 2 (r = −0.2;
p = 0.24) and nor did it predict treatment response at 5 weeks (t = −1.3; p = 0.11).
Increased vmPFC RSFC was observed with the bilateral inferior-lateral parietal cortex (ilPC) post-treatment.
is eect did not correlate with reductions in depressive symptoms between scan 1 and 2 (r = −0.26; p = 0.17)
but did predict treatment response at 5 weeks, with responders showing signicantly greater vmPFC-ilPC RSFC
increases than non-responders (t = 2.1; p = 0.03).
Decreased PH RSFC was observed with a PFC cluster incorporating the lateral and medial prefrontal cortex.
is eect did not correlate with reductions in depressive symptoms between scan 1 and 2 (r = 0.08; p = 0.38)
but did relate to treatment response at 5 weeks, with responders showing signicantly greater PH-PFC RSFC
decreases than non-responders (t = −1.9, p = 0.04). Amygdala RSFC was not signicantly altered post treatment.
Analyses of within network RSFC using 12 previously identified canonical RSNs14 revealed increased
default-mode network (DMN) (t = 2.7, p = 0.018), dorsal attention network (DAN) (t = 2.2, p = 0.042), and
posterior opercular network (POP) (t = 2.7, p = 0.016) RSFC post-treatment; however, these changes failed to
survive Bonferonni correction for multiple comparisons (revised α = 0.05/11 = 0.0042) and did not correlate
with depression outcomes, e.g. the relationship between change in DMN RSFC and reduced QIDS-SR16 scores
between scan 1 and 2 were non-signicant (r = 0.25; p = 0.18) and neither were changes in DMN RSFC predictive
of outcomes at 5 weeks (t = 0.58; p = 0.28). Analyses of between network RSFC using the same 12 RSNs, revealed
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SCIentIfIC RePoRTs | 7: 13187 | DOI:10.1038/s41598-017-13282-7
decreased RSFC between the DMN and right frontoparietal network (rFP) (t = −3.6, p = 0.0031) and increased
RSFC between the sensorimotor network (SM) and rFP (t = 2.2, p = 0.045) (Fig.5); however, these eects did not
survive FDR correction for multiple comparisons and did not relate to reduced QID-16 scores between scan 1
and 2, nor response at 5 weeks.
Lastly, based on indications from previous work4,5,10 we explored the possibility that the quality of the acute
‘psychedelic’ experience may have mediated the post-acute brain changes. We focused on a rating scale factor
related to ‘peak’ or ‘mystical’ experience and used scores for the high-dose psilocybin session as a covariate in a
Figure 1. Whole-brain cerebral blood ow maps for baseline versus one-day post-treatment, plus the dierence
map (cluster-corrected, p < 0.05, n = 16). Correlation chart shows post-Treatment changes in bilateral amygdala
CBF versus changes in depressive symptoms (r = 0.59, p = 0.01). One patient failed to completed the scan 2
QIDS-SR16 rating, reducing the sample size to n = 15 for the correlation analysis. In all of the images, the le of
the brain is shown on the le.
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PH RSFC analysis. e PH was specically chosen due to previous work implicating its involvement in related
states14. Results revealed that patients scoring highest on ‘peak’ or ‘mystical’ experience had the greatest decreases
in PH RSFC in limbic (e.g. bilateral amygdala) and DMN-related cortical regions (e.g. the PCC). See the supple-
mentary le for the relevant maps and discussion.
Discussion
e present study goes some way to addressing an important knowledge gap concerning the post-acute brain
eects of serotonergic psychedelics. Its ndings suggest that changes in brain activity observed just one-day aer a
high dose psychedelic experience are very dierent to those found during the acute psychedelic state. Specically,
whereas the acute psychedelic state in healthy volunteers is characterised by modular disintegration14,15,28 and
global integration14,19,29, there are trends towards modular (re)integration and minimal eects on global integra-
tion/segregation post psilocybin for depression. Relating the blood ow ndings to what has been seen previously
in the acute psychedelic state is somewhat more complicated due to inconsistencies in this literature – likely due
to analysis approaches and interpretation14,15,30: Here we saw decreased CBF bilaterally in the temporal lobes,
including the le amygdala one-day post treatment. Decreased absolute CBF in subcortical and high-level asso-
ciation cortices have been previously reported with intravenous (I.V.)15 and now oral psilocybin30 but increased
CBF and metabolism have also been reported with I.V. LSD14, oral psilocybin31, and oral ayahuasca32.
Much recent research has focused on the involvement of the default-mode network in psychiatric disor-
ders33, and particularly depression34,35. We previously observed decreased DMN functional integrity under psil-
ocybin15 and LSD14, and others have with ayahuasca28. Here however, increased DMN integrity was observed
one-day post treatment with psilocybin, both via seed (i.e. vmPFC and sgACC) and network-based approaches.
Previous work has suggested that increased DMN integrity may be a marker of depressed mood and speci-
cally, depressive rumination34,36. On this basis, increased DMN integrity post psilocybin may be surprising. e
post-treatment increases in within-DMN RSFC and sgACC-PCC RSFC did not relate to symptom improvements
but vmPFC-ilPC RSFC did (see Fig.3). is apparent divergence from previous ndings36,37 is intriguing, and
deserves further discussion (below).
It should be noted that ndings of elevated within-DMN RSFC in depression are not entirely consistent in
the literature38–41. For example, using a DMN-focused analysis, precuneus-DMN RSFC39 was found to be lower
in patients than in healthy controls, and normalised aer treatment with electroconvulsive therapy (ECT) - and
only in responders39 – consistent with the present ndings. Lower precuneus-DMN RSFC in depression was also
seen in a separate study and the degree of this abnormality correlated with autobiographical memory decits40.
In another study, lower PCC-dmPFC and PCC-ilPC RSFC were seen in rst-episode depressed patients relative
Figure 2. Top two rows = sgACC (purple) RSFC before and aer psilocybin treatment (hot colours = regions
of signicantly positive coupling). Bottom row reveals regions where there was a signicant increase in sgACC
RSFC post-treatment (hot colours). All maps are cluster-corrected, p < 0.05, Z > 2.3.
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to healthy controls41. In the present study, we saw increased within-DMN RSFC post treatment with psilocybin,
and increased vmPFC-bilateral ilPC RSFC was predictive of treatment response at 5 weeks (Fig.3). ese ndings
suggest a commonality in the antidepressant action of ECT and psilocybin39 in which DMN integrity is decreased
acutely (at least by the latter14,15,28) and increased (or normalised) post-acutely, accompanied by improvements
in mood. is process might be likened to a ‘reset’ mechanism in which acute modular disintegration (e.g. in the
DMN) enables a subsequent re-integration and resumption of normal functioning.
Recent meta-analyses of studies of resting-state CBF in depression have yielded relatively mixed results34,42,
although ndings of increased thalamic34,42 and sgACC metabolism are relatively consistent34. Here, we did not
nd any post-treatment changes in thalamic or sgACC CBF with psilocybin, either in whole-brain or ROI-based
Figure 3. Top two rows = vmPFC (purple) RSFC before and aer psilocybin treatment (hot colours = regions
of signicantly positive coupling). Bottom row reveals regions where there was a signicant increase in vmPFC
RSFC post-treatment (hot colours). All maps are cluster-corrected, p < 0.05, Z > 2.3. Increased coupling
between the vmPFC and the displayed regions (bottom row) was predictive of clinical response at 5-weeks post-
treatment. Chart shows mean values and positive standard errors.
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analyses. We did observe decreased CBF bilaterally in the temporal cortex however, including the le medial tem-
poral lobe and specically, the le amygdala. Given previous ndings of elevated resting-state amygdala CBF and
metabolism in mood disorders25,43,44, the reduction in amygdala CBF observed here, and its relation to symptom
severity, could be viewed as a possible remediation eect. Moreover, generalised decreases in CBF are (again) con-
sistent with what has been previously reported with ECT45, i.e. most studies have documented an increase in CBF
in the acute ‘ictal’ state, including in the amygdala45; however, the post-ictal period is characterised by decreased
CBF, and oen in those regions that were most perfused during seizure45. Acutely increased CBF has previously
been reported with ayahuasca32 and LSD15 and increased glucose metabolism has been observed in the acute
Figure 4. Top two rows = Bilateral PH (purple) RSFC before and aer psilocybin treatment (hot
colours = regions of signicantly positive coupling). Bottom row reveals regions where there was a signicant
decrease in PH RSFC post-treatment (cold colours). All maps are cluster-corrected, p < 0.05, Z > 2.3. Decreased
coupling between the PH and the displayed regions (bottom row) was predictive of clinical response at 5-weeks
post-treatment (t = −1.9, p = 0.04). Chart shows mean values and negative standard errors.
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state with oral31 but not I.V. psilocybin15. us, a post-acute reversal of acute increases in CBF could be seen as
consistent with the post-treatment ‘reset’ mechanism proposed above – although recent work has laid into ques-
tion whether oral psilocybin does indeed cause increases in brain absolute CBF30. It would be challenging (but
not impossible) to carry out acute and post-acute imaging in future trials of psilocybin for depression, and this
may be necessary if the ‘reset’ model is to be properly tested. In such a study, we would advise focusing on BOLD
RSFC (and perhaps simultaneous EEG-related measures) rather than CBF, due to RSFC and EEG oering more
direct and reliable indices of brain activity and function than more dicult to interpret measures such as CBF.
e inclusion of a healthy control group, exposed to a consistent treatment procedure, would further strengthen
the design of such a study, as would the inclusion of a placebo and/or active comparator arm.
e present study’s other major positive nding was a decrease in RSFC between the bilateral parahippocam-
pus and the PFC, an eect that (like increased vmPFC-ilPC RSFC) was predictive of treatment response at 5
weeks. Curiously, a post-hoc exploratory analysis suggested that acute ‘peak’ or ‘mystical-type’ experiences under
psilocybin may mediate the post-acute changes in parahippocampal RSFC (including decreased PH-PCC RSFC).
Focusing on parahippocampal-PFC RSFC, this has generally been found to be elevated in depression46, and con-
sistently so across the duration of a resting-state scan47. Prefrontal-limbic circuitry has been linked with top-down
suppression of aective responsiveness48 and lower resting-state amygdala-vmPFC RSFC in combination with
amygdala hyperfusion was found to relate to state-anxiety in healthy individuals43, corroborating separate nd-
ings49. Seven days of citalopram has been found to reduce amygdala-vmPFC50 and dorso-medial PFC-le hip-
pocampal RSFC51 in healthy volunteers, somewhat consistent with the present ndings.
In conclusion, here we document for the rst time, changes in resting-state brain blood ow and functional
connectivity post-treatment with psilocybin for treatment-resistant depression. Decreased blood ow was found
to correlate (in the amygdala) with reductions in depressive mood. Increased within-DMN RSFC was observed
post-treatment, using both seed and network-based analyses, and specic increases in RSFC between the vmPFC
and bilateral ilPC nodes of the DMN were greatest in individuals who maintained treatment-response at 5 weeks.
Finally, decreased PH-PFC RSFC was observed post-treatment and this was also predictive of treatment-response
at 5 weeks. An exploratory post-hoc analysis revealed that acute ‘peak’ or ‘mystical’ experience during the
high-dose psilocybin session was predictive of these changes in PH RSFC.
is study is limited by its small sample size and absence of a control condition. Moreover, correction for
multiple testing was applied to the full RSN but not the specic (hypothesis-based) ROI analyses. Future research
with more rigorous controls should serve to challenge and develop the present study’s ndings and inferences.
Assessing the relative contributions of, and potential interactions between, the dierent treatment factors (e.g. the
drug and the accompanying psychological support) may be a particularly informative next step.
Method
is 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 Harmonisation Good Clinical Practice (GCP) guidelines and National Health Service (NHS) Research
Governance Framework. Imperial College London sponsored the research which was conducted under a Home
Oce license for research with schedule 1 drugs. e Medicines and Healthcare products Regulatory Agency
(MHRA) approved the study. All patients gave written informed consent, consistent with GCP.
Figure 5. Dierences in between-RSN RSFC or RSN ‘segregation’ before and aer therapy. Each square
in the matrix represents the strength of functional connectivity (positive = red, negative = blue) between a
pair of dierent RSNs (beta values). e matrix on the far right displays the between-condition dierences
in covariance (t values). e RSNs are: 1) medial visual network, 2) lateral visual network, 3) occipital pole
network, 4) auditory network, 5) sensorimotor network, 6) DMN, 7) parietal cortex network, 8) the dorsal
attention network, 9) the salience network, 10) posterior opercular network, 11) le frontoparietal network, 12)
right frontoparietal network. White asterisks represent signicant dierences (P < 0.05, non-corrected). Both of
the signicant dierences did not survive FDR correction for multiple comparisons.
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Imaging vs clinical outcomes. To explore relationships between signicant imaging outcomes and the
main clinical outcomes, we chose to focus on changes in depressive symptoms from: 1) pre-Treatment to scan
2 (i.e. one-day post-treatment), and 2) pre-Treatment to 5 weeks post-Treatment. e primary clinical outcome
measure, the 16-item Quick Inventory of Depressive Symptoms (QIDS-SR16) was chosen for this purpose.
Relationships between imaging outcomes and contemporaneous decreases in depressive symptoms were calcu-
lated using a standard Pearson’s r, and relationships with the longer-term (i.e. at 5 weeks post-treatment) changes
in depressive symptoms were calculated by splitting the sample into responders (>50% reduction in QIDS-SR16
scores) and non-responders at this time-point, and then performing a one-tailed t-test on the relevant imaging
outcomes (one-tailed as directionality was unequivocally implied by the direction of the signicant imaging out-
come). We used a revised version of the QIDS-SR16 for 24-hour measurement for the post-treatment scan in
order to get a contemporaneous, state-related index of depressive symptoms at this time-point.
Anatomical Scans. Imaging was performed on a 3 T Siemens Tim Trio using a 12-channel head coil
at Imanova, London, UK. Anatomical images were acquired using the ADNI-GO (Alzheimer’s Disease
Neuroimaging Initiative, Grand Opportunity52) recommended MPRAGE parameters (1 mm isotropic vox-
els, TR = 2300 ms, TE = 2.98 ms, 160 sagittal slices, 256 × 256 in-plane FOV, flip angle = 9 degrees, band-
width = 240 Hz/pixel, GRAPPA acceleration = 2).
BOLD fMRI Resting State Acquisition. T2*-weighted echo-planar images (EPI) were acquired for the
functional scan using 3 mm isotropic voxels, TR = 2000 ms, TE = 31 ms, 36 axial slices, 192 mm in-plane FOV,
ip angle = 80 degree, bandwidth = 2298 Hz/pixel, GRAPPA acceleration = 2, number of volumes = 240, 8 min.
BOLD Pre-processing. Four dierent but complementary imaging soware packages were used to analyse
the fMRI data. Specically, FMRIB Soware Library (FSL)53, AFNI54, Freesurfer55 and Advanced Normalization
Tools (ANTS)56 were used. Fieen subjects were used for this analysis: one subject was discarded from the
analysis due to an injury in parietal cortex and three subjects were discarded due to high levels of head move-
ment. Principally, motion was measured using frame-wise displacement (FD)57. e criterion for exclusion was
subjects with >20% scrubbed volumes with a scrubbing threshold of FD = 0.5. For the 15 subjects that were
used in the analysis, there was no signicant dierence in the mean FD (meanFDbefore = 0.179 ± 0.088, mean-
FDaer = 0.158 ± 0.084, p = 0.23). e mean percentage of scrubbed volumes for before and aer treatment was
4.6 ± 5% and 3.5 ± 5.2%, respectively (p = 0.56). e maximum of scrubbed volumes for before and aer treat-
ment was 17.3% and 17.7%, respectively. e following pre-processing stages were performed: 1) removal of the
rst three volumes; 2) de-spiking (3dDespike, AFNI); 3) slice time correction (3dTshi, AFNI); 4) motion cor-
rection (3dvolreg, AFNI) by registering each volume to the volume most similar, in the least squares sense, to all
others (in-house code); 5) brain extraction (BET, FSL); 6) rigid body registration to anatomical scans (BBR, FSL);
7) non-linear registration to 2 mm MNI brain (Symmetric Normalization (SyN), ANTS); 8) scrubbing58 - using
an FD threshold of 0.5, scrubbed volumes were replaced with the mean of the surrounding volumes. 9) spatial
smoothing (FWHM) of 6 mm (3dBlurInMask, AFNI); 10) band-pass ltering between 0.01 to 0.08 Hz (3dFou-
rier, AFNI); 11) linear and quadratic de-trending (3dDetrend, AFNI); 12) regressing out 9 nuisance regressors
(all nuisance regressors were band-pass ltered with the same band-pass lter as above): out of these, 6 were
motion-related (3 translations, 3 rotations) and 3 were anatomically-related (not smoothed). Specically, the ana-
tomical nuisance regressors were: 1) ventricles (Freesurfer, eroded in 2 mm space), 2) draining veins (DV) (FSL’s
CSF minus Freesurfer’s Ventricles, eroded in 1 mm space) and 3) local white matter (WM) (FSL’s WM minus
Freesurfer’s subcortical grey matter (GM) structures, eroded in 2 mm space). Regarding local WM regression,
AFNI’s 3dLocalstat was used to calculate the mean local WM time-series for each voxel, using a 25 mm radius
sphere centred on each voxel59.
Seed-based RSFC. Based on prior hypotheses, 4 seeds were chosen for these analyses: 1) the bilateral PH,
vmPFC, sgACC and bilateral amygdala. e PH seed was constructed by combining the anterior and poste-
rior parahippocampal gyrus from the Harvard-Oxford probabilistic atlas, which was then thresholded at 50%.
e vmPFC seed was the same as one previously used by our team in analyses of the acute eects of LSD60,
psilocybin61 and MDMA62. e sgACC seed was a 5 mm sphere centred at ±2 28 -5 (MNI_152 coordinates)
based on63. Bilateral amygdala seed was based on Harvard-Oxford probabilistic atlas, threshold at 50%. Mean
time-series were derived for these seeds for each RS scan. RSFC analyses were performed using FSL’s FEAT for
each subject. Pre-whitening (FILM) was applied. A higher level analysis was performed to compare pre-treatment
and post-treatment conditions using a mixed-eects GLM (FLAME 1 + 2), cluster corrected (z > 2.3, p < 0.05).
MRIcron was used to display the results.
Resting State Networks (RSN). RSNs were derived using Independent Component Analysis (ICA) per-
formed on data acquired separately as part of the Human Connectome Project (HCP)64. is procedure is identi-
cal to one used previously with LSD60. Briey, 20 independent components (ICs) were derived, of which the same
12 functionally meaningful RSNs were identied, namely: medial visual network (VisM), lateral visual network
(VisL), occipital pole network (VisO), auditory network (AUD), sensorimotor network (SM), default-mode net-
work (DMN), parietal cortex network (PAR), dorsal attention network (DAN), salience network (SAL), posterior
opercular network (POP), le fronto-parietal network (lFP) and right fronto-parietal network (rFP).
Integrity (within-RSN RSFC). Network integrity was calculated for each RSN for both pre-treatment and
post-treatment. All 20 HCP ICA components were entered into FSL’s dual regression analysis65. e rst step of
the dual regression used the components as regressors applied to the 4D BOLD datasets for each subject, resulting
in a matrix of time-series for each ICA. e second step involved regressing these time-series into the same 4D
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9
SCIentIfIC RePoRTs | 7: 13187 | DOI:10.1038/s41598-017-13282-7
scan data to get a subject-specic set of spatial maps (parameter estimate (PE) images). For each subject and for
each condition, within each of the 12 RSNs of interest (threshold = 3), the mean PE across voxels was calculated.
is mean PE represents the integrity value. Subsequently, paired t-tests were used to calculate the dierence in
integrity between conditions for each RSN (Bonferroni corrected for 11 RSNs, with no correction for DMN as we
had a prior hypothesis).
Segregation (between-RSN RSFC). Between-RSN RSFC was calculated in a similar manner to previ-
ous analyses involving acute LSD60 and psilocybin66. Specically, a 12 × 12 matrix was constructed representing
RSFC between dierent RSN pairs. For each subject and for each condition, the time-series for the relevant pair
of RSNs, was entered into a GLM, resulting in a PE value representing the strength of functional connectivity
between them. GLM was used rather than correlation coecients because dierences between Pearson’s corre-
lations could be a result of either signal or noise dierences; therefore, it is preferable to perform regression and
look for pre-treatment and post-treatment dierences on the PE67. e GLM was estimated twice: 1) each RSN as
a dependant variable in one model, and 2) each RSN as an independent variable in the second model. ese two
PE values were then averaged together, to generate a symmetric 12 × 12 matrix (Fig.4b). ree 12 × 12 matrices
were created as follows: 1) the group mean PE values for pre-Treatment treatment, 2) the group mean PE values
for post-Treatment treatment, and 3) paired t-test to compare the PE values for the two conditions, pre-Treatment
and post-Treatment treatment (two-tailed, 5000 permutations).
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Acknowledgements
is research was supported by a Medical Research Council UK Clinical Development Pathway Funding Scheme
(DPFS). RCH is supported by the Alex Mosley Charitable Trust. DJN is supported by the Safra Foundation (DJN
is the Edmond J. Safra Professor of Neuropsychopharmacology). is report presents independent research, part
of which was carried out at the Imperial Clinical Research Facility.
Author Contributions
R.L.C.-H. designed the study, acquired the data and wrote the paper, R.L.C.-H. and L.R. conceived of the reported
analyses and L.R. performed these, M.B. was the principal study psychiatrist, L.D. helped acquire the data, J.N.P.
supervised patients and helped acquire the data, M.B.W. oversaw the scanning protocol and constructed the
scanner ratings, M.T. was the main radiographer for the study, M.K. supervised patients, J.Mc.G. advised on the
scanning protocol and analysis, K.M. advised on the A.S.L. parameters and carried out the A.S.L. analyses, R.L.
oversaw the R.S.F.C. analyses, H.V.C. was a senior collaborator on the project, D.J.N. sanctioned the study and
edited the paper. All authors viewed and approved the nal manuscript and had the opportunity to comment on
earlier dras.
Additional Information
Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-017-13282-7.
Competing Interests: e authors declare that they have no competing interests.
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