The Neurobiology of Emotion Regulation in
Posttraumatic Stress Disorder: Amygdala
Downregulation via Real-Time fMRI
Neurofeedback
Andrew A. Nicholson,
1,2,3
Daniela Rabellino,
2,3
Maria Densmore,
3
Paul A. Frewen,
1,4
Christian Paret,
5,6
Rosemarie Kluetsch,
6
Christian Schmahl,
6
Jean Th
eberge,
2,3,7,8,9
Richard W.J. Neufeld,
1,2,4
Margaret C. McKinnon,
10,11
Jim Reiss,
2
Rakesh Jetly,
12
and
Ruth A. Lanius
1,2,3
*
1
Department of Neuroscience, Western University, London, Ontario, Canada
2
Department of Psychiatry, Western University, London, Ontario, Canada
3
Department of Imaging, Lawson Health Research Institute, London, Ontario, Canada
4
Department of Psychology, Western University, London, Ontario, Canada
5
Department of Neuroimaging, Central Institute of Mental Health Mannheim, Medical, Facul-
ty Mannheim/Heidelberg University, Heidelberg, Germany
6
Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental
Health Mannheim, Medical Faculty Mannheim/Heidelberg University, Heidelberg, Germany
7
Department of Medical Imaging, Western University, London, Ontario, Canada
8
Department of, Medial Biophysics, Western University, London, Ontario, Canada
9
Department of Diagnostic Imaging, St. Joseph’s Healthcare, London, Ontario, Canada
10
Mood Disorders Program and Clinical Neuropsychology Service, St. Joseph’s Healthcare,
Hamilton, Ontario, Canada
11
Department of Psychiatry and Behavioural Neuroscience, McMaster University, Hamilton,
Ontario, Canada
12
Canadian Forces, Health Services, Ottawa, Ontario, Canada
r r
Abstract: Amygdala dysregulation has been shown to be central to the pathophysiology of posttrau-
matic stress disorder (PTSD) representing a critical treatment target. Here, amygdala downregulation
was targeted using real-time fMRI neurofeedback (rt-fMRI-nf) in patients with PTSD, allowing us to
examine further the regulation of emotional states during symptom provocation. Patients (n 5 10)
Additional Supporting Information may be found in the online
version of this article.
Contract grant sponsor: Canadian Institutes of Health Research
(CIHR), General Dynamics Land Systems, and the Canadian Insti-
tute for Veteran Health Research (CIMVHR)
Corrections added on 23 September 2016 after first online publication.
*Correspondence to: Ruth A. Lanius; University Hospital,
Windermere Road, PO Box 5339, London, ON N6A 5A5, Canada.
E-mail: Ruth.lanius@lhsc.on.ca
Daniela Rabellino is the shared first author.
The authors declare that they have no conflicts of interest.
Furthermore, this study is original research that has not been pre-
viously published or submitted for publication elsewhere.
Received for publication 3 June 2016; Revised 31 August 2016;
Accepted 31 August 2016.
DOI: 10.1002/hbm.23402
Published online 20 September 2016 in Wiley Online Library
(wileyonlinelibrary.com).
r
Human Brain Mapping 38:541–560 (2017)
r
V
C
2016 Wiley Periodicals, Inc.
completed three sessions of rt-fMRI-nf with the instruction to downregulate activation in the amygda-
la, while viewing personalized trauma words. Amygdala downregulation was assessed by contrasting
(a) regulate trials, with (b) viewing trauma words and not attempting to regulate. Training was followed
by one transfer run not involving neurofeedback. Generalized psychophysiological interaction (gPPI)
and dynamic causal modeling (DCM) analyses were also computed to explore task-based functional
connectivity and causal structure, respectively. It was found that PTSD patients were able to success-
fully downregulate both right and left amygdala activation, showing sustained effects within the trans-
fer run. Increased activation in the dorsolateral and ventrolateral prefrontal cortex (PFC), regions
related to emotion regulation, was observed during regulate as compared with view conditions. Impor-
tantly, activation in the PFC, rostral anterior cingulate cortex, and the insula, were negatively correlat-
ed to PTSD dissociative symptoms in the transfer run. Increased functional connectivity between the
amygdala- and both the dorsolateral and dorsomedial PFC was found during regulate, as compared
with view conditions during neurofeedback training. Finally, our DCM analysis exploring directional
structure suggested that amygdala downregulation involves both top-down and bottom-up informa-
tion flow with regard to observed PFC-amygdala connectivity. This is the first demonstration of suc-
cessful downregulation of the amygdala using rt-fMRI-nf in PTSD, which was critically sustained in a
subsequent transfer run without neurofeedback, and corresponded to increased connectivity with pre-
frontal regions involved in emotion regulation during the intervention. Hum Brain Mapp 38:541–560,
2017.
V
C
2016 Wiley Periodicals, Inc.
Key words: fMRI neurofeedback; posttraumatic stress disorder; amygdala; emotion; brain connectivity
r r
INTRODUCTION
It has been well documented that dysregulation of amyg-
dala neural circuitry—a brain region associated with the
generation and processing of emotions [Duvarci and Pare,
2014; Frank et al., 2014; LeDoux, 2007]—is central to the
development and maintenance of symptoms experienced by
patients with posttraumatic stress disorder (PTSD) [Aghajani
et al., 2016; Birn et al., 2014; Etkin and Wager, 2007; Lanius
et al. 2010, 2015; Mickleborough et al., 2011; Patel et al., 2012;
Pitman et al., 2012; Shin and Liberzon, 2010; Stevens et al.,
2013; Weston, 2014; Yehuda et al., 2015]. The amygdala,
along with the prefrontal cortex (PFC), a region central to
emotion regulation [Etkin et al., 2011, 2015], displays unique
activation patterns among PTSD patients across a number of
modalities, including symptom provocation [Frewen et al.,
2011; Hayes et al., 2012; Hopper et al., 2007], fear processing
[Bruce et al., 2013; Bryant et al., 2008; Williams et al., 2006;
Wolf and Herringa, 2016; Zhu et al., 2016], and resting state
[Brown et al., 2014; Huang et al., 2014; Koch et al., 2016;
Nicholson et al., 2015]. Critically, during rest, the amygdala
also displays altered connectivity to the cingulate cortex
[Brown et al., 2014; Nicholson et al., 2015; Sripada et al.,
2012], insula [Fonzo et al., 2010; Nicholson et al., 2016a; Rabi-
nak et al., 2011; Sripada et al., 2012] and PFC [Birn et al.,
2014; Brown et al., 2014; Nicholson et al., 2015; Stevens et al.,
2013] among patients with PTSD.
Notably, heightened symptoms of hyperarousal in PTSD
are correlated with negative medial PFC-amygdala coupling
[Sadeh et al., 2014], and hyper/hypo-activation of the amyg-
dala and medial PFC, respectively, during emotional proc-
essing [Bruce et al., 2013]. This pattern of findings points
towards attenuated top-down inhibition from the PFC and
rostral anterior cingulate (ACC) on the amygdala in PTSD
patients, leading to hyperactivation of the limbic system,
contributing to the emotion dysregulation observed in the
disorder [Admon et al., 2013; Aupperle et al., 2012; Lanius
et al., 2010; Pitman et al., 2012; Ronzoni et al., 2016; Shin and
Liberzon, 2010]. Accordingly, it has been suggested that
downregulation of the amygdala through recruitment of
emotion regulatory resources from the PFC may represent a
potential treatment for patients with PTSD [Doll et al., 2016;
Koch et al., 2016]. Indeed, the efficacy of electroencephalog-
raphy neurofeedback (EEG-nf) targeting these regions has
already been illustrated [Kluetsch et al., 2014; Reiter et al.,
2016]. Here, EEG-nf has been shown to plastically modify
the aforementioned neural circuitry mediating PTSD, lead-
ing to acute symptom alleviation [Kluetsch et al., 2014]. Spe-
cifically, one 30-minute session of alpha desynchronizing
EEG-nf was shown to shift amygdala complex connectivity
away from fear/defense processing and memory regions
towards prefrontal emotion regulation areas after interven-
tion [Nicholson et al., 2016b]. In contrast to EEG-nf, real-
time fMRI neurofeedback (rt-fMRI-nf) offers enhanced spa-
tial resolution thereby increasing potential for targeted
treatment. To date however, rt-fMRI-nf has not been uti-
lized with PTSD patients to investigate and normalize aber-
rant amygdala activity/connectivity.
Rt-fMRI-nf utilizes a brain-computer interface to process
and feedback real-time BOLD signal activation in a region-of-
interest (ROI) to individuals inside the scanner. Ergo, partici-
pants are presented with online information that corresponds
to their success in regulating the ROI. This neuroimaging
method allows for the exploration of neural mechanisms that
r
Nicholson et al.
r
r
542
r
underlie concomitant shifts in performance due to feedback
training [Sitaram et al., 2007]. Several studies have examined
the capacity to regulate emotions by targeting neurofeedback
of the amygdala using rt-fMRI-nf, in healthy individuals
[Br
€
uhl et al., 2014; Keynan et al., 2016; Paret et al., 2014,
2016b; Zotev et al., 2011] as well as in psychiatric popula-
tions, including borderline personality disorder (BPD) [Paret
et al., 2016a], and major depressive disorder [Young et al.,
2014; Zotev et al., 2016]. In support of this concept, self-
regulation of the amygdala as compared with sham regions
via rt-fMRI-nf has been shown to concomitantly affect activa-
tion in PFC areas involved in emotion regulation, as well as
enhance amygdala-PFC connectivity [Koush et al., 2013;
Paret et al., 2014; 2016b; Zotev et al., 2011] and amygdala-
rostral ACC coupling [Zotev et al., 2011]. Similarly, using rt-
fMRI-nf to target the regulation of the lateral PFC during cog-
nitive reappraisal resulted in decreased amygdala BOLD
response [Sarkheil et al., 2015]. Moreover, active pain coping
through rt-fMRI-nf was associated with increased activity in
the PFC and ACC [Emmert et al., 2016]. Critically, in a feasi-
bility rt-fMRI amygdala downregulation study, involving
three patients with PTSD [Gerin et al, 2016], patients reported
an acute decrease in symptoms along with a concatenate nor-
malization of brain connectivity, albeit, explicit amygdala
downregulation was not reported.
The aim of the present study was therefore to investi-
gate the ability of PTSD patients to self-regulate PTSD-
related emotional states by utilizing rt-fMRI-nf to downre-
gulate the amygdala. An additional aim was to better
understand the neural connectivity underlying the psycho-
pathology of this disorder by use of online emotion regu-
lation. We predicted that exposure to personalized trauma
words while downregulating the amygdala would recruit
prefrontal emotion regulation regions (dorsolateral and
ventrolateral) [Etkin et al., 2015] as compared to simply
viewing personalized trauma words. Moreover, we pre-
dicted that during neurofeedback training, amygdala con-
nectivity to the same PFC regions would be strengthened.
Finally, we predicted that activation of the PFC, rostral
ACC and insula would be correlated to state PTSD symp-
toms during neurofeedback training.
METHODS
Participants
The sample consisted of n 5 10 PTSD patients (see Table
I for demographic and clinical information). Participants
were recruited in 2015 through flyers and clinician refer-
rals. Exclusion criteria for participants with PTSD includ-
ed: noncompliance with 3T fMRI safety standards, a
history of head injury with loss of consciousness, signifi-
cant untreated medical illness, neurological disorders, per-
vasive developmental disorders, and pregnancy. Further
clinical exclusion criteria for PTSD patients included a his-
tory of bipolar disorder or schizophrenia, and alcohol or
substance dependence/abuse not in sustained full remis-
sion within 6 months prior to participation in the study.
Participants were assessed using the DSM-IV Structured
Clinical Interview (SCID) [First et al., 1997], the Clinical
Administered PTSD Scale (CAPS-5) [Blake et al., 1995],
Beck’s Depression Inventory (BDI) [Beck et al., 1997], the
Childhood Trauma Questionnaire (CTQ) [Bernstein et al.,
2003], and the Multiscale Dissociation Inventory (MDI)
[Briere et al., 2005]. In addition, to assess state changes in
PTSD and dissociative symptoms, participants completed
the Response to Script Driven Imagery (RSDI) Scale [Hop-
per et al., 2007] after each of the four fMRI runs, which
consisted of the following subscales: dissociation, hyper-
arousal, avoidance, and reliving. All scanning took place
at the Lawson Health Research Institute in London, Ontar-
io, Canada. The research ethics board at the University of
Western Ontario approved the current study, and all par-
ticipants provided written informed consent.
Experimental Conditions, Visual Feedback, and
Instructions
Participants were instructed to “regulate the feeling cen-
ter of their brain,” referencing the role of this region (refer-
ring to the amygdala) to the perception and processing of
emotions. In order to elicit unbiased regulatory strategies,
specific instructions on how to regulate the brain region-
of-interest (ROI) was not provided. During training trials,
neurofeedback of the amygdala was displayed in the form
of two identical thermometers on the left and right side of
the screen inside the scanner (to ensure high visibility),
where the bars on the thermometer increased or decreased
as BOLD signal increased versus decreased in the amygda-
la respectively. Patients were told that the orange line
TABLE I. Demographic and clinical information
Measure PTSD (n 5 10)
Age M 5 49.6 6 6.5
Sex Females 5 6
CAPS Severity 32.2 6 9.6
CTQ 56.7 6 25.8
BDI 27.1 6 14.4
MDI-DENG 15.1 6 5.0
MDI-DEPR 11.1 6 7.0
MDI-DERL 11.6 6 6.6
MDI-ECON 13.3 6 5.0
MDI-MEMD 11.8 6 5.8
MDI-DDIS 9 6 5.8
Current medication n 5 9
Abbreviations: CAPS, Clinician Administered PTSD Scale; CTQ,
Childhood Trauma Questionnaire; BDI, Becks Depression Invento-
ry; MDI, Multiscale Dissociation Inventory; DENG, Disengage-
ment; DEPR, Depersonalization; DERL, Derealization; ECON,
Emotional Constriction; MEMD, Memory Disturbance; IDDID,
Identity Dissociation.
r
Amygdala Down-Regulation in PTSD
r
r
543
r
within the thermometer indicated the activation level in the
ROI at rest (see Fig. 1). Participants were provided with
written instructions, followed by a sham example within
the scanner to ensure that they understood the task.
Our experiment consisted of three conditions (i) regulate,
(ii) view, and (iii) neutral (see Fig. 1). During the regulate
condition, patients were asked to decrease activity in the
ROI (decrease bars on the thermometer corresponding to
the amygdala), while viewing a personalized trauma word
according to standard methods [Rabellino et al., 2015a, b].
During the view condition, patients were asked to refrain
from regulating the thermometer bars and to simply view
their personalized trauma word. During the neutral condi-
tion, patients were simply presented with a personalized
neutral word, and also asked to refrain from regulating
the bars. Trials were separated by an inter-trial fixation
cross interval. Our experimental design consisted of three
consecutive neurofeedback training runs, and one transfer
run in which patients received the same three conditions
albeit without neurofeedback from the thermometer (to
assess learning effects immediately after training). An
experimental run lasted about 9 minutes, consisting of 15
trials (5 of each condition, counterbalanced). Personalized
trauma and neutral words were matched on subjective
units of distress to control for between subject variability.
Stimuli were presented with Presentation software (Neuro-
behavioral Systems, Berkeley, CA).
One bar on the thermometer display corresponded to
0.2% signal change in the amygdala. Here, the orange line
(baseline), divided the thermometer into an upper activa-
tion range (maximum 2.8% signal changes) and a lower
activation range (maximum 1.2% signal change) [Paret
et al., 2014; 2016b; Zotev et al., 2011]. In order to circumvent
regulation by avoiding the trauma word and directing
attention to the thermometers, participants were asked to
visually focus on the word during its entire presentation,
and to view the two thermometers in their peripheral
vision. Participants were also informed of the temporal
delay that would occur during neurofeedback, correspond-
ing to the BOLD signal delay. Finally, when a neurofeed-
back run was completed, patients were asked to rate their
perceived ability to regulate their emotion center.
Delineation and BOLD Processing of the
Amygdala for Real-Time Neurofeedback
In order to present amygdala neural activity to patients in
real-time through the thermometer display, anatomical scans
were first imported into BrainVoyager (version QX2.4, Brain
Innovations, Maastrict, Netherlands), then skull-stripped and
transformed into Talairach space. Subsequently, normaliza-
tion parameters were loaded into TurboBrainVoyager (TBV)
(version 3.0, Brain Innovations, Maastricht, Netherlands).
Motion correction features and spatial smoothing using a 4-
mm full-width-half-maximum (FWHM) Gaussian kernel
were implemented in TBV, and the initial 2 volumes of the
functional scans were discarded before real-time processing.
An anatomical mask of the bilateral amygdala was then load-
ed, and the “best voxel selection” tool was used in TBV to cal-
culate the BOLD signal amplitude of the ROI. This method
identified the 33% of voxels with the highest beta-values for
the view > neutral contrast. As previously outlined by Paret
et al., [2014; 2016b], the voxels were dynamically determined
based on (a) the voxel with the largest beta value, and (b) on
Figure 1.
Real-time fMRI amygdala neurofeedback experimental design. Participants were only instructed
to downregulate neurofeedback thermometer bars, corresponding to amygdala activation, on
regulate trials. Condition duration was 24 s, with 2 s of instructions prior. Personalized trauma
words were presented in the scanner for regulate and view conditions, while neutral words
were presented for the neutral conditions only. [Color figure can be viewed at wileyonlineli-
brary.com.]
r
Nicholson et al.
r
r
544
r
the magnitude of deviation from the mean of all condition
betas [Goebel, 2014]. This feature ensured that there was no
difference in the number of voxels used for signal extraction
between subjects and was used to counterbalance moderate
shifts in the anatomical delineation due to alignment errors
across runs/movement-related slice shifts. The first two trials
of each neurofeedback run consisted of view and neutral con-
ditions in order to permit an initial selection of voxels based
on the view > neutral contrast, which was updated as voxels
were dynamically refined along the course of training.
Amygdala BOLD signal amplitude was passed to Pre-
sentation when a new volume had been processed. Laten-
cy of the feedback was equal to the TR (2 s) plus the time
needed for real-time calculation/visual display by the pre-
sentation software (about half a second). For each trial, the
mean of the last four data points before stimuli onset were
taken as a baseline. The signal was smoothed by calculat-
ing the mean of the current and the preceding three data
points [Paret et al., 2014, 2016b].
fMRI Image Acquisition
We utilized a 3 Tesla MRI Scanner (Trio, Siemens Medi-
cal Solutions, Erlangen, Germany) with a 32 channel head
coil for brain imaging. Functional whole brain images of
the BOLD contrast were acquired with a gradient echo T2*
weighted echo-planar-imaging sequence (TE 5 30 ms,
TR 5 2 s, FOV 5 192 3 192 mm, flip angle 5 808, inplane
resolution 5 3 3 3 mm). One volume comprised 36 ascend-
ing interleaved slices tilted 2208 from AC-PC orientation
with a thickness of 3 mm and slice gap of 1 mm. Partici-
pants’ heads were stabilized. The experimental runs com-
prised 284 volumes each, where T1-weighted anatomical
images were acquired with a Magnetization Prepared Rap-
id Acquisition Gradient Echo sequence (TE 5 3.03 ms,
TR 5 2.3 s, 192 slices and FOV 5 256 3 256 mm).
fMRI Preprocessing
Preprocessing of the functional images was conducted
with SPM12 (Wellcome Department of Cognitive Neurolo-
gy, London, United Kingdom). After discarding the four
initial volumes, the standard preprocessing routine includ-
ed slice time correction to the middle slice, followed by
spatial alignment to the mean image using a rigid body
transformation, reslicing, and coregistration of the func-
tional mean image to the anatomical. We then performed
segmentation of all tissue types, and normalization to the
Montreal Neurological Institute (MNI) standard template.
Images were then smoothed using a 6 mm kernel FWHM.
Additional correction for motion was implemented using
the ART software package (www.nitrc.org/projects/arti-
fact_detect), which computes regressors that account for
outlier volumes, in addition to the six movement regres-
sors computed during standard realignment in general lin-
ear modeling.
Statistical Analyses
First level analysis
The three neurofeedback runs and the transfer run were
defined as separate sessions, and all events were modeled
as blocks of brain activation and convolved with the
hemodynamic response function. Here, ART software
computations were included as nuisance variables to
account for movement artifacts. Scans in the experiment
corresponding to the instruction phase and initial baseline
were also modeled. All experimental conditions were
modeled separately; we also generated the t-contrast regu-
late > view on the first level.
Online region of interest amygdala downregulation
analysis
In order to determine if participants were successfully
able to downregulate amygdala activation using real-time
fMRI neurofeedback, we investigated parameter estimates
of the left and right amygdala during the regulate and view
condition. Parameter estimates were extracted and
graphed using rfx-plot software [Gl
€
ascher, 2009] via ana-
tomical definition from the PickAtlas toolbox [Maldjian
et al., 2003]. Extracted values were passed to SPSS version
20 for statistical analyses, where we computed a 3 (neuro-
feedback run) 3 2 (condition) 3 12 (2 s time bins across
the 24 s condition) randomized block analysis of variance
(ANOVA) for each amygdala hemisphere. We included
time as a factor in the ANOVA, as we a-priori hypothe-
sized that participants would be able to better regulate
during the middle-end of the regulate condition as opposed
to the beginning where patients are only beginning to
learn how to regulate their amygdala activity.
We specified a-priori directional hypotheses, such that
we expected amygdala activation to be lower across train-
ing runs and the transfer run during the regulate as com-
pared with view condition. Therefore, we computed
paired-sample t-tests for amygdala parameter estimates
during the regulate as compared with the view condition,
during the training and transfer runs separately for each
amygdala hemisphere. We conducted the same paired
sample t-tests on the middle-end (i.e., 8–24 s) of the condi-
tion, as again, we predicted that patients would be more
successful in amygdala downregulation toward the end of
the condition. In order to be statistically conservative, we
implemented a Bonferroni correction for multiple compari-
sons for all paired-sample t-tests.
Offline analysis of brain activation during
neurofeedback and transfer run
In addition to investigating amygdala downregulation
during neurofeedback, we had previously defined 4 a-
prior ROIs, including the dlPFC, vlPFC, rostral ACC/
mPFC and the insula, in which we wanted to observe
r
Amygdala Down-Regulation in PTSD
r
r
545
r