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The mixed serotonin receptor agonist psilocybin reduces threat-induced
modulation of amygdala connectivity
Rainer Kraehenmann
a,b,
*
, André Schmidt
c,d
, Karl Friston
e
,KatrinH.Preller
b
,
Erich Seifritz
a
, Franz X. Vollenweider
b
a
Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zürich 8032, Switzerland
b
Neuropsychopharmacology and Brain Imaging, Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zürich 8032, Switzerland
c
Department of Psychiatry (UPK), University of Basel, Basel 4012, Switzerland
d
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King3s College London, London SE5 AF, United Kingdom
e
Wellcome Centre for Imaging Neuroscience, University College London, London WC1N 3BG, United Kingdom
abstractarticle info
Article history:
Received 16 June 2015
Received in revised form 27 July 2015
Accepted 17 August 2015
Available online 22 August 2015
Keywords:
Serotonin
Psilocybin
Depression
fMRI
Dynamic causal modeling
Stimulation of serotonergic neurotransmission by psilocybin has beenshown to shift emotional biases awayfrom
negative towards positive stimuli. We have recently shown that reduced amygdala activity during threat pro-
cessing might underlie psilocybin3s effect on emotional processing. However, it is still not known whether psilo-
cybin modulates bottom-up or top-down connectivity within the visual-limbic-prefrontal network underlying
threat processing.We therefore analyzedour previous fMRI data usingdynamic causal modeling and usedBayes-
ian model selection to inferhow psilocybin modulated effective connectivity within thevisual–limbic–prefrontal
network during threat processing. First, both placebo and psilocybin data were best explained by a model in
which threat affect modulated bidirectional connections between the primary visual cortex, amygdala,and later-
al prefrontal cortex.Second, psilocybin decreased the threat-induced modulation of top-down connectivity from
the amygdala to primary visual cortex, speaking to a neural mechanism that might underlie putative shifts to-
wards positive affect states after psilocybin administration. These findings may have important implications
for the treatment of mood and anxiety disorders.
© 2015 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction
Serotonin (5-hydroxytryptamine, 5-HT) is an important neurotrans-
mitter within neural networks related to emotion processing.
We have recently shown that 5-HT2A receptor activation by psilocybin
(4-phosphoryloxy-N,N-dimethyltryptamine) attenuates amygdala acti-
vation in response to threat-related visual stimuli in healthy volunteers
and that the reduction of amygdala blood oxygen level-dependent
(BOLD) signal is related to psilocybin3s mood-enhancing effect
(Kraehenmann et al., 2014). Here, we addressed the hypothesis that
connectivity changes between the amygdala (AMG) and visualand pre-
frontal cortical (PFC) areas contribute to the observed effects of psilocy-
bin on threat processing previously observed (Kraehenmann et al.,
2014). This hypothesis is based on evidence showing that the
processing of threat-related visual stimuli may be modulated via feed-
back connections from the amygdala to the visual cortex (Furl et al.,
2013). Such top-down input from the amygdala to the visual cortex
may be an important mechanism at the interface between emotion pro-
cessingand visual perception —given that the amygdala has been impli-
cated in tuning visual processing to allocate resources towards sensory
processing of –and coordinating responses to –emotionally salient
stimuli (Morris et al., 1998). Furthermore, processing of threat signals
may be modulated via inhibitory feedback connections from the PFC
to the AMG (Hahn et al., 2011;Aznar and Klein, 2013). Using DCM for
fMRI, Sladky et al. (2015) recently analyzed the effects of the selective
serotonin reuptakeinhibitor (SSRI) (S)-citalopram onamygdala–PFC ef-
fective connectivity in healthy volunteers. They found that the PFC ex-
hibited a down-regulatory effect on amygdala activation, and that this
effect was significantly increased by the antidepressant (S)-citalopram.
Importantly, the inhibitory feedback from the PFC to the AMG has been
found to be correlated with 5-HT2A receptor stimulation (Fisher et al.,
2009). Therefore, it is conceivable that the psilocybin-induced
attenuation of amygdala activation (Kraehenmann et al., 2014) might
NeuroImage: Clinical 11 (2016) 53–60
* Corresponding author at: Neuropsychopharmacologyand Brain Imaging,Department
of Psychiatry, Psychotherapy and Psychosoma tics, Psychiatr ic Hospital, University of
Zurich, Lenggstrasse 31, Zürich CH-8032, Switzerland. Tel.: +41 44 384 2827.
E-mail address: r.kraehenmann@bli.uzh.ch (R. Kraehenmann).
http://dx.doi.org/10.1016/j.nicl.2015.08.009
2213-1582/© 2015 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Contents lists available at ScienceDirect
NeuroImage: Clinical
journal homepage: www.elsevier.com/locate/ynicl
be caused by increased inhibitory connectivity from the PFC to theAMG.
Finally, given the abundance of feed-forward projections from visual
input regions (e.g. primary visual cortex, V1) to the AMG (Pessoa and
Adolphs, 2010) and from the AMG to the PFC (Volman et al., 2013),
bottom-up connectivity changes may also contribute to psilocybin3sef-
fects on threat processing.
To test these hypotheses, we analyzed the functional magnetic reso-
nance imaging (fMRI) data of our previous study (Kraehenmann et al.,
2014) using dynamic causal modeling (DCM) (Friston et al., 2003)and
Bayesian model selection (BMS) (Stephan et al., 2009). DCM is a general
framework for inferring hidden mechanisms at the neuronal level from
measurements of brain activity such as fMRI. Recent studies have dem-
onstrated its sensitivity to detect pharmacological manipulations in
fMRI data (Grefkes et al., 2010;Schmidt et al., 2013b); in particular,
after serotonergic stimulation (Volman et al., 2013). BMS is an essential
aspect of DCM studies, as it can be used to test competing hypotheses
(different DCMs) about the neural mechanisms generating data. We ap-
plied DCM and BMS to address the following questions: First, which is
the most likely mechanism underlying threat processing, (1) threat-
induced modulation of bottom-up connectivity, (2) threat-induced
modulation of top-down connections, or (3) modulation of both
bottom-up and top-down connections by threat stimuli. Secondly,
which of these mechanisms –changes in bottom-up or top-down con-
nectivity –contributed to the psilocybin-induced reduction of AMG
(Kraehenmann et al., 2014) and V1 activation (Schmidt et al., 2013a)
in response to threat-related visual stimuli.
2. Methods
2.1. Subjects
In total, 25 healthy, right-handed subjects (16 males, mean age
24.2 ± 3.42 years) with normal or corrected-to-normal vision were re-
cruited through advertisements placed in local universities. Subjects
were screened for DSM-IV mental and personality disorders using the
Mini-International Neuropsychiatric Interview (Sheehan et al., 1998)
and the Structured Clinical Interview II (First et al., 1997). Exclusion
criteria were as follows: pregnancy, left-handedness, poor knowledge
of the German language, personal or first-degree relatives with history
of psychiatric disorder, history of alcohol or illicit drug dependence, cur-
rent alcohol abuse or illicit drug use, current use of a medication that af-
fects cerebral metabolism or blood flow, cardiovascular disease, history
of head injury or neurological disorder, magnetic resonance imaging ex-
clusion criteria (including claustrophobia), and previous significant ad-
verse reactions to a hallucinogenic drug. Subjects were healthy
according to medical history, physical examination, routine blood anal-
ysis, electrocardiography, and urine tests for drug abuse and pregnancy.
The study was approved by the Cantonal Ethics Committee of Zurich
(KEK). Written informed consent was obtained from all subjects and
the study was performed in accordancewith the Declarationof Helsinki.
2.2. Experimental design
As previously reported (Kraehenmann et al., 2014), the study design
was randomized, double-blind, placebo-controlled, cross-over. Subjects
received either placebo or 0.16 mg/kg oral psilocybin in two separate
imaging sessions at least 14 days apart. The use of psilocybin was autho-
rized by the Federal Office of Public Health, Federal Department of
Home Affairs, Bern, Switzerland. Psilocybin and lactose placebo were
administered in gelatin capsules of identical number and appearance.
A 0.16-mg/kg dose of psilocybin was selected because it reliably induces
changes in mood and consciousness, but minimally disrupts behavioral
task performance and reality testing (Studerus et al., 2011). Mood state
was assessed using the using the Positive and Negative Affect Schedule
(PANAS) (Watson et al., 1988) and the state portion of the State–Trait
Anxiety Inventory (STAI) (Spielberger and Gorsuch, 1983) before and
210 min after each drug treatment. The scanning experiment was con-
ducted between 70 and 90 min after drug administration to coincide
with the plateau in the subjective effects of psilocybin (Hasler et al.,
2004). Subjects were released about 360 min after drug administration,
after all acute drug effects had completely subsided.
2.3. fMRI paradigm: amygdala reactivity task
Inside the scanner, subjects performed an amygdala reactivity task
comprising alternating blocks of emotional (threat and neutral) picture
discrimination tasks. The picture discrimination task was interspersed
with shape discrimination tasks, which served as baseline tasks and
allowed amygdala responses to return to baseline.
Stimulus material for the amygdala reactivity task was obtained
from the International Affective Picture System (IAPS), a standardized
and broadly validated collection of emotionally evocative pictures
(Lang et al., 2005). Stimulus sets of 48 different pictures were arranged
in picture-triplets on a gray background. The stimulus triplets com-
prised the target picture in the upper center position, and two pictures
as potential matching targets on the left and right sides at the bottom of
the slide. Twenty-four pictures were categorized as threat and 24 as
neutral. The threat pictures were aversive, threat-related pictures such
as attacking animals, aimed weapons, car accidents, and mutilations,
and the neutral pictures depicted activities of daily living, portraits of
humans and animals, and everyday objects.
During the emotional picture discrimination task, subjects were re-
quired to select one of the two IAPS pictures at the bottom of the stim-
ulus triplet that matched the target picture at the top of the triplet.
Selection was indicated by pressing one of two buttons on a magnetic
resonance (MR)-compatible response device with the dominant hand.
A shape discrimination task was performed as a sensorimotor control
and baseline task. This required matching of geometric shapes (circles,
ovals, and rectangles) analogous to the picture discrimination task and
was implemented to control for activation due to non-emotional cogni-
tive and visual processing. Both tasks were shown as alternating 24-s
blocks without intermittent pauses. Each block was preceded by a 2-s
instruction (“Match Pictures”or “Match Forms”) and consisted of six
target images that were presented sequentially for a period of 4 s in a
randomized order. The experimental design comprised four repetitions
of the sequence threat →shapes →neutral →shapes, cumulating to a
total duration of 420 s for the complete run. Individual trial durations
were not determined by the subjects3responses, and no feedback was
provided regarding correct or incorrect responses.
2.4. fMRI image acquisition and data analysis
Scanning was performed on a 3 T scanner (Philips Achieva, Best, The
Netherlands) using an echo planar sequence with 2.5 s repetition time,
30 ms echo time, a matrix size of 80 × 80 and 40 slices without inter-
slice gap, providing a resolution of 3 × 3 × 3 mm
3
and a field of view
of 240 × 240 mm
3
.
Data analysis was performed with SPM12b (http://www.fil.ion.ucl.
ac.uk). All volumes were realigned to the mean volume, co-registered
to the structural image, normalized to the Montreal Neurological Insti-
tute space using unified segmentation (Ashburner and Friston, 2005)
including re-sampling to 3 × 3 × 3 mm voxels, and spatially smoothed
with an 8-mm full-width at half-maximum Gaussian kernel. First-level
analysis was conducted using a general linear model applied to the
time series, convolved with a canonical hemodynamic response func-
tion (Friston et al., 1994). Serial correlations and low-frequency signal
drift were removed using an autoregressive model and a 128-s high-
pass filter, respectively.Single-subjectGLM analysis for the two sessions
(placebo and psilocybin) comprised regressors for threat, neutral
pictures, and shapes. These conditions were modeled as box-car
54 R. Kraehenmann et al. / NeuroImage: Clinical 11 (2016) 53–60
regressors representing the onset of each block type. Subject-specific
condition effects for threat minus shapes were computed using
t-contrasts, producing a contrast image for each subject that was used
as a summary statistic for second-level (between subject) analyses.
2.5. Dynamic causal modeling (DCM)
The current DCM analyses (version 12 with SPM12b) are based on the
GLM analyses of the fMRI data described above (Kraehenmann et al.,
2014). In DCM for fMRI, the dynamics of the neural states underlying re-
gional BOLD responses are modeled by a bilinear differential equation
that describes how the neural states change as a function of endogenous
interregional connections, modulatory effects on these connections, and
driving inputs (Friston et al., 2003). The endogenous connections repre-
sent constant coupling strengths, whereas the modulatory effects repre-
sent context-specific and additive changes in coupling (task-induced
alterations in connectivity). The modeled neuronal dynamic is then
mapped to the measured BOLD signal using a hemodynamic forward
model (Stephan et al., 2007). We explicitly examined how the coupling
strengths between V1, AMG, and PFC are changed by threat during the
AMG reactivity task (modulatory effect).
2.5.1. Regions of interest and time series extraction
We selected three regions of interest (ROIs) within a right-
hemispheric network implicated in visual threat processing, based on:
(1) previously published conventional SPM analyses of these data
(Fig. 1)(Kraehenmann et al., 2014), (2) previous anatomical and structur-
al connectivity studies (Freese and Amaral, 2005), and (3) previous DCM
studies of threat processing using visual stimuli (Volman et al., 2013). In
DCM for fMRI, a neural network is analyzed in terms of directed connec-
tivity changes among selected regions of interest. Regions of interest are
selected based on both a priori knowledge and hypotheses, and on signif-
icant task-induced activations. We chose a right-hemispheric (subgraph)
analysis based on our previous GLM analysis of psilocybin effects on
threat processing, (Kraehenmann et al., 2014). The rationale for this
choice was to ask whether the observed psilocybin-induced decrease of
right amygdala activation in response to threat was mediated by top-
down connectivity changes from the right prefrontal cortex or by
bottom-up connectivity changes from the right visual cortex. In addition,
we limited our DCM analyses to a right-hemispheric network or subgraph
in view of statistical efficiency: it is common practice to test only a small
number of regions of interest with DCM. Future DCM studies of psilocybin
effects on threat processing could include the contralateral homologues of
the regions investigated here, although our previous GLM analysis did not
motivate a DCM analysis of the left-hemispheric network.
The ROIs included: rV1 (x = 12, y = −82, z = −7), rAMG (x = 24,
y=−1, z = −13), and the right inferior frontal gyrus within the lateral
PFC (rLPFC) (x = 54, y = 32, z = 20). The coordinates for the rV1, rAMG
and rLPFC were based on the contrast of threat pictures minus shapes.
Regional time series from each subject and session were extracted
from (10 mm) spherical volumes of interest centered on the
suprathreshold voxel nearest the group maxima. Time series were sum-
marized with the first eigenvariate of voxels above a subject-specificF
threshold of p b0.01 (uncorrected) within the anatomical areas, as de-
fined by thePick Atlas toolbox. During time series extraction it may hap-
pen that a subject does not show activation at the group maximum and
that the nearest suprathreshold voxel lies outside the anatomical regions.
By additionally using an anatomical mask, we ensured that time series
were extracted from within a certain distance of the group maxima
(10 mm), but were not extracted from a region outside the anatomical
structure (Dima et al., 2011). We could not extract an rLPFC time series
in two subjects due to lack of individual activations fulfilling both the
above functional and anatomical criteria. Although it is not necessary to
preclude subjects who did not show significant activations from the
DCM analysis, the purpose of DCM is to explain observed activations in
terms of functional coupling. We therefore restricted our analyses to sub-
jects who showed significant responses under the assumption that their
data would provide more efficient estimators of connectivity.
2.5.2. DCM model space
First, we specified a three-area base model with bidirectional endog-
enous connections between V1 and AMG and between AMG and LPFC
(Fig. 2A). V1 was selected as the visual input region in our models. All
visual stimuli were used as inputs. These restrictions allowed us to de-
fine a small model space. The basic model was then systematically var-
ied to provide alternative models of the modulatory effect (induced by
threat stimuli). The three model variants corresponded to the three al-
ternative hypotheses about modulatory effects (bottom-up, top-down,
or a combination of bottom-up and top-down) and allowed us to distin-
guish between the three hypothesized mechanisms under the two
treatments (psilocybin, placebo) (Fig. 2B–D).
2.5.3. Model inference
Using random-effects BMS in DCM12, we computed expected proba-
bilities and exceedance probabilities at the group-level to determine the
most plausible of the three model variants for each drug (psilocybin, pla-
cebo) separately (Penny et al., 2004). The expected probability of each
model is the probability that a specific model generated the data of a ran-
domly chosen subject, and the exceedance probability of each model is
the probability that this model is more likely than any other of the models
tested (Stephan et al., 2009). Bayesian model comparison rests solely on
the relative evidence for different models (as scored by the variational
free energy). This evidence comprises the accuracy (i.e., percent variance
explained) minus the complexity (i.e., degrees of freedom used to explain
the data). The evidence therefore reflects the quality of a model in provid-
ing an accurate but parsimonious account of the data (and is preferred
over conventional accuracy measures that may reflect overfitting). Final-
ly, we used random-effects Bayesian model averaging (BMA) to compute
Fig. 1. Regional effects from the contrast of threat picturesminus shapeswithin right lateralprefrontal cortex (rLPFC;z = 20) and right amygdala (rAMG;y = −1) and from the contrastof
all pictures (threatof non-threat) minusshapes within the right primary visual cortex(rV1; x = 12) across bothdrug conditions (placebo, psilocybin).SPM{t} overlaid on canonical brain
slices (thresholded at p b0.001 uncorrected for visualization).
55R. Kraehenmann et al. / NeuroImage: Clinical 11 (2016) 53–60
(subject specific) connectivity estimates (weighted by their posterior
model probability) across all three models separately for psilocybin and
placebo. This conservative analysis allowed the drug effect to be
expressed in all connections and their threat related modulations, where-
by we were able to establish significant effects in relation to intersubject
variability using classical statistics at the between subject level.
2.5.4. Parameter inference
To evaluate the effect of psilocybin on endogenous connections and
their modulation by threat stimuli, BMA values were entered into two
separate 2-way repeated measures ANOVA with factors drug (psilocybin,
placebo) and connection type (endogenous parameters: V1, V1 →AMG,
AMG →V1,AMG,AMG→LPFC, LPFC →AMG, LPFC; modulatory parame-
ters: V1 →AMG, AMG →V1, AMG →LPFC, LPFC →AMG). Where the
ANOVA null hypothesis of equal means was rejected, we used the post-
hoc test (Duncan3s multiple range tests). A paired t test was further
applied to compare direct inputs into V1 across both treatments. A p
value of less than 0.05 was assumed as statistically significant.
2.5.5. Correlation with behavioral and mood measures
To investigate correlations between psilocybin-induced changes of
effective connectivity and behavior or mood, the psilocybin-induced
connectivity changes were correlated using Pearson correlations with
psilocybin-induced changes in behavioral measures (reaction time, ac-
curacy) and mood scores (PANAS positive affect, PANAS negative affect,
STAI state anxiety).
3. Results
3.1. Model inference with Bayesian model selection
Under both psilocybin and placebo, the full model outperformed all
other models with an exceedance probability of 97% (placebo) and 62%
(psilocybin), respectively (Fig. 3). This optimal model comprised bidi-
rectional endogenous connections between V1 and AMG, and between
AMG and LPFC, with threat modulating both forward and backward
connections.
3.2. Parameter inference
To compare connectivity across drug treatments, the subject-specific
parameter estimates were averaged over the three models for each
treatmentusing BMA. The endogenous parameters, their threat induced
modulations, and direct inputs from the BMA are shown in Table 1.Cou-
pling or connectivity in dynamic models is measured in terms of Hz,
where a strong baseline or endogenous connection would typically be
between 0.1 and 0.5 Hz. This means that one can regard the effective
connectivity as a rate-constant. In other words, a strong connection
causes a large rate of increase in the target region, with respect to activ-
ity in the source region. The inverse of the connection strength can
therefore be interpreted in terms of a time constant (i.e., how long it
would take for a source to increase activity in a target).
There was no main effect of drug (F
1,22
= 3.10, p = 0.09, η2
p=0.12),
but a significant main effect of connection type (F
3,66
= 3.94, p = 0.01,
η2
p= 0.15), and a significant drug by connection type interaction
(F
3,66
= 2.84, p = 0.04,η2
p= 0.11) on modulatory coupling parameters.
Post-hoc tests on the drug by connection type interaction showed that
the threat-induced modulation of AMY →V1 connectivity was signifi-
cantly reduced after psilocybin compared to placebo administration
(p = 0.01; Duncan3s multiple range test corrected) (Table 1). There
was no significant effect of psilocybin on endogenous or input parame-
ters (all p N0.05).
Parameter estimates were obtained from Bayesian Model Averaging
for placebo (Pla) and psilocybin (Psi), mean ± standard deviation. Sta-
tistically significant differences between placebo and psilocybin treat-
ments (p b0.05 Duncan corrected for multiple comparison) are
printed in bold and marked by an asterisk; V1 = primary visual cortex;
AMG = amygdala; LPFC = lateral prefrontal cortex.
3.3. Correlation with behavioral and mood measures
We assessed correlations between (psilocybin–placebo) modulatory
coupling changes for the AMG →V1 connection from BMA and (psilocy-
bin-placebo) changes of behavioral measures (reaction time, accuracy)
Fig. 2. Model specification. A, Basic structure of the three-area model:visual stimulus presentation drives V1 activity, which is bidirectionally connected to AMG, which inturn is bidirec-
tionally connected to the LPFC. B, Bottom-upmodel: the modulatory effect of threat is only mediated via bottom-up connections from V1 to AMG to LPFC. C, Top-down model: the mod-
ulatory effect of threat is only mediated via top-down connections from LPFC to AMGto V1. D, Full model: the modulatory effect of threatis mediated via both bottom-up and top-down
connections between V1 and AMG, and between AMG and LPFC.
56 R. Kraehenmann et al. / NeuroImage: Clinical 11 (2016) 53–60
and of mood scores (PANAS positive affect, PANAS negative affect, STAI
state anxiety). We found no significant correlations (all p N0.05).
4. Discussion
In this study, we analyzed the fMRI data of our previous psilocybin
study (Kraehenmann et al., 2014) using DCM, an established framework
enabling tests of directed (effective) connectivity. We were interested
whether psilocybin modulated effective connectivity within a network
implicated in threat processing during an amygdala reactivity task. In
particular, our aim was to differentiate between psilocybin-effects on
bottom-up, top-down, and bidirectional connectivity during threat-
processing within a visual–limbic–prefrontal network. There were two
main findings from our study: Firstly, both placebo and psilocybin data
were best explained by a model in which threat affect modulated
bidirectional connections between V1, AMG, and LPFC. Secondly, psilocy-
bin –compared to placebo –substantially reduced the modulatory effect
of threat on the top-down connection from the AMG to V1. This implies
that psilocybin attenuates amygdala-dependent top-down tuning of visu-
al regions during threat processing.
Our BMS finding that the full model, which is characterized by
bidirectional modulatory effects of threat on visual–limbic–
Fig. 3. Resultsof Bayesian model selection. Bar charts showthe expected model probabilities (A, B) andexceedance probabilities (C,D) of the bottom-up model (1), the top-down model
(2), andthe full model (3) for the placebo(left) and psilocybin(right) treatment. Notably, the full modelwith threat-induced modulationof bidirectionalconnectionsis the winning model
for both the placebo and psilocybin treatment.
Table 1
Dynamic causal modeling parameter estimates.
Connection Endogenous Modulation Direct input
Pla Psi Pla Psi Pla Psi
V1 +0.023 ± 0.05 −0.002 ± 0.01 –– +0.011 ± 0.12 −0.003 ± 0.01
V1 →AMG +0.036 ± 0.08 +0.018 ± 0.05 +0.027 ± 0.37 +0.024 ± 0.09 ––
AMG →V1 −0.028 ± 0.09 +0.031 ± 0.11 +0.526 ± 1.05 +0.030 ± 0.14* ––
AMG −0.007 ± 0.02 −0.002 ± 0.01 −− ––
AMG →LPFC +0.005 ± 0.08 −0.005 ± 0.06 +0.103 ± 0.22 +0.023 ± 0.11 ––
LPFC →AMG −0.002 ± 0.05 +0.008 ± 0.00 −0.394 ± 1.12 −0.157 ± 0.76 ––
LPFC −0.014 ± 0.04 −0.001 ± 0.00 –– ––
57R. Kraehenmann et al. / NeuroImage: Clinical 11 (2016) 53–60
prefrontal connectivity, outperformed both the bottom-up and the
top-down model, is in line with previous DCM studies (Herrington
et al., 2011;Goulden et al., 2012). In these studies, BMS consistently
favored models, which implement modulatory effects on both
bottom-up and top-down connections during negative emotion
processing. The winning model in our study contained reciprocal
connections between V1 and AMG (V1 ↔AMG) and between AMG
andLPFC(AMG↔LPFC). Both V1 ↔AMG and AMG ↔LPFC reciprocal
connections are critically involved in negative-emotion processing
(Herrington et al., 2011;Goulden et al., 2012). In fact, it has been
shown that visual threat perception may be enhanced through a
re-entry mechanism of feed-forward connections from V1 to AMG
and feedback connections from the AMG to V1 (Herrington et al.,
2011). Furthermore, visual threat perception may be increased
through feed-forward connections from the AMG to LPFC (Lu et al.,
2012) and attenuated through inhibitory feedback connections
from the LPFC to AMG (Volman et al., 2013). Although BMS did not
directly compare model fits from different datasets (e.g. placebo,
psilocybin), our model selection results indicate a consistent mode
of visual threat processing during placebo and psilocybin
treatments; namely, via modulation of both bottom-up and top-
down connectivity across the visual–limbic–prefrontal hierarchy.
Our main finding was that psilocybin (compared to placebo) reduced
the modulatory effect of visual threat on the top-down connection from
the AMG to V1. In both humans and animals, visual threat poses a strong
salience signal, which needs to be processed efficiently and therefore
binds attentional resources (Pessoa and Adolphs, 2010). The “tuning”
of visual regions via feedback projections from the AMG during threat
processing is an important mechanism underlying visual threat process-
ing and may enhance perception of visual threat signals (Morris et al.,
1998). In addition, the AMG has been closely linked to salience process-
ing and may, via top-down predictive signals, guide bottom-up informa-
tion processing (Vuilleumier, 2015). Therefore, the amygdala may
actually determine the affective meaning of visual percepts by its effects
on sensory pathways —an effect which mainly occurs subconsciously
and which may be greatly amplified in psychopathological conditions,
such as anxiety disorders or depression. In this context, increased AMG
reactivity may lead to an increased attentional focus on negatively
valenced environmental or social stimuli and thus effectively blocks
out the processing of positive information (Disner et al., 2011). This is es-
pecially relevant for hallucinogenic drugs such as psilocybin, because
there has been a close and psychotherapeutically interesting relationship
between visual perception and affective processes during hallucinogen-
induced states (Leuner, 1981). The psilocybin-induced attenuation of
top-down threat signaling from the amygdala to visual cortex may
therefore lead to decreased threat sensitivity in the visual cortex. This
mechanism may crucially underlie the previously observed decrease of
behavioral and electrophysiological responses in the visual cortex to
threat stimuli during psilocybin administration (Vollenweider and
Kometer, 2010;Schmidt et al., 2013a) and may explain the psilocybin-
induced shifts away from negative towards positive valence during
emotion processing (Kometer et al., 2012). In line with the notion that
attenuation of the top-down connection from the AMG to visual cortex
may reduce threat processing, a recent study showed that habituation
to visual threat stimuli may parallel attenuation of top-down connectiv-
ity from the AMG to visual cortex (Herrington et al., 2011). In addition, it
has been found that hyper-connectivity between the AMG and visual
cortex may underlie increased threat processing and anxiety (Frick
et al., 2013).
Given the relevance of LPFC in regulating AMG activity during threat
processing, and given previous studies showing that serotonergic stim-
ulation may increase inhibitory top-down connectivity from LPFC to
AMG (Pessoa and Adolphs, 2010;Volman et al., 2013), we hypothesized
that psilocybin-induced reduction in AMY activity might be due to an
increased LPFC →AMG top-down connectivity during threat processing.
However, psilocybin did not appear to increase top-down connectivity
from LPFC to AMG in the current analysis. Two reasons might account
for this. First, the source of the psilocybin-induced reduction of AMG ac-
tivity, as observed in our previous GLM analysis (Kraehenmann et al.,
2014), might not reflect an increased top-down effect from LPFC, but
rather a suppression of recurrent interactions with visual areas mediated
by a reduced top-down connectivity with the visual cortex. The synaptic
basis of this reduced top-down modulation might reflect a direct effect of
psilocybin in the amygdala: amygdala neurons abundantly express 5-
HT2A receptors, and DOI and other 5-HT2A agonists produce direct ef-
fects in the amygdala (Rainnie, 1999). In addition, a directly decreased
AMG reactivity would result in a reduced load on the LPFC to regulate
AMG activation. This view is supported by a recent DCM study showing
that increased AMG-related load on the PFC yields subsequent responses
in the PFC to regulate the AMG (Volman et al., 2013). Second, the AMG
might be regulated by prefrontal cortical regions other than the LPFC,
such as the medial PFC (MPFC), the anterior cingulate cortex (ACC), or
the orbitofrontal cortex (OFC), which have also been related to the ‘aver-
sive amplification’circuit (Robinson et al., 2013). For example, Sladky
et al. (2015) recently analyzed the effects of the selective serotonin reup-
take inhibitor (SSRI) (S)-citalopram on amygdala–OFC effective connec-
tivity in healthy volunteers. They found that the OFC exhibited a down-
regulatory effect on amygdala activation, and that this effect was signif-
icantly increased by the antidepressant (S)-citalopram. Although Sladky
et al. used a similar threat-inducing amygdala reactivity task (Hariri et al.,
2002) and likewise tested the effects in healthy volunteers, their study
procedures differ substantially from our study, both in terms of task de-
sign (e.g. face stimuli instead of pictures, scrambled control stimuli, lon-
ger baseline conditions) and in terms of drug administration (e.g. chronic
and repeated instead of acute and single treatment). Therefore, it is not
easy to disambiguate task- from drug-specific effects in terms of PFC in-
volvement and our DCM might have missed top-down effects from PFC
on the AMG. However, given the cognitive task requirements in our
task –where subjects were not explicitly required to evaluate or regu-
late their emotional responses to the threat stimuli –and given that
the GLM analyses (Kraehenmann et al., 2014) did not show significant
BOLD responses in the MPFC, ACC, or OFC, one might argue that top-
down effects from other prefrontal regions are unlikely. Overall, both
the hallucinogen psilocybin and the non-hallucinogen (S)-citalopram
may normalize amygdala hyper-reactivity to threat-related stimuli;
leading to their antidepressant and anxiolytic efficacy, but psilocybin
appears to regulate emotion processing and mood by acting on network
interactions which are different from classical antidepressants such as
(S)-citalopram, such as the affective regulation of visual information
processing shown here.
4.1. Limitations and future directions
There are some limitations to be considered in the present study. We
used a fairly simplistic neuronal network underlying threat related effec-
tive connectivity. There are also other brain regions involved in threat
processing, such as the ACC, the OFC, or the fusiform gyrus (Robinson
et al., 2013), but that we did not include in our present model for reasons
of parsimony and based on our a priori hypotheses. Furthermore, to max-
imize statistical efficiency, we only considered right-hemispheric net-
works in our DCM analyses. Therefore, top-down connectivity from the
left LPFC to the right AMG might have been missed. Given the importance
of the left LPFC in regulating the right AMG during emotion processing
and in serotonergic modulation (Outhred et al., 2013), we cannot exclude
this possibility. Therefore, further effective connectivity studies using
tasks that differentially recruit left and right prefrontal cortical regions
during threat processing, are needed.
4.2. Conclusion
This effective connectivity study shows that a decrease of top-down
connectivity from the AMG to the visual cortex underliesthe psilocybin
58 R. Kraehenmann et al. / NeuroImage: Clinical 11 (2016) 53–60
effect on visual threat processing. This result suggests that decreased
threat sensitivity in the visual cortex during emotion processing may
explain the potential of psilocybin to acutely shift emotional biases
away from negative towards positive valence: the capacity of the visual
cortex to process multiple stimuli is limited and hence top-down sup-
pression of negative stimuli enhances the processing of positive stimuli
(Kastneret al., 1998). This may have important therapeutic implications
for mood and anxiety disorders, where over-loading with negative
stimuli and persistence of negative cognitive biases is a central feature
(Disner et al., 2011). In post-traumatic stress disorder, for example, psi-
locybin might help inhibit fear-responses during exposure-based psy-
chotherapy, which might facilitate therapeutic progress.
Disclosure and conflict of interest
This work was supported by grants from the Swiss Neuromatrix
Foundation, Switzerland (R.K., F.X.V., No. ER2-2014) and the Heffter
Research Institute, USA (R.K., F.X.V., No. 2-190414); and by the Swiss
National Science Foundation (A.S., No. 155184); K.F. was funded by a
Wellcome Trust Principal research fellowship (Ref: 088130/Z/09/Z).
The authors report no biomedical financial interests or potential
conflicts of interest.
Acknowledgments
We thank the staff at the Department of Psychiatry Psychotherapy
and Psychosomatics for the medical and administrative support.
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