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Behavioral/Cognitive
Different Brain Circuitries Mediating Controllable and
Uncontrollable Pain
Anne-Kathrin Bra¨scher,
1,2
*XSusanne Becker,
1,3
* Marie-Eve Hoeppli,
1,5
and Petra Schweinhardt
1,4,5
1
Alan Edwards Centre for Research on Pain, Faculty of Dentistry, McGill University, Montreal, Quebec H3A 1G1, Canada,
2
Department of Clinical
Psychology, Psychotherapy and Experimental Psychopathology, Johannes Gutenberg University Mainz, Mainz 55122, Germany,
3
Department of Cognitive
and Clinical Neuroscience, Central Institute of Mental Health, Mannheim 68159, Germany,
4
Department of Neurology and Neurosurgery, Faculty of
Medicine, McGill University, Montreal, Quebec H3A 2B4, Canada, and
5
Faculty of Dentistry, McGill University, Montreal QC H3A 0C7, Canada
Uncontrollable, compared with controllable, painful stimulation can lead to increased pain perception and activation in pain-processing
brain regions, but it is currently unknown which brain areas mediate this effect. When pain is controllable, the lateral prefrontal cortex
(PFC) seems to inhibit pain processing, although it is unclear how this is achieved. Using fMRI in healthy volunteers, we examined brain
activation during controllable and uncontrollable stimulation to answer these questions. In the controllable task, participants self-
adjusted temperatures applied to their hand of pain or warm intensities to provoke a constant sensation. In the uncontrollable task, the
temperature time courses of the controllable task were replayed (yoked control) and participants rated their sensation continuously.
During controllable pain trials, participants significantly downregulated the temperature to keep their sensation constant. Despite
receiving the identical nociceptive input, intensity ratings increased during the uncontrollable pain trials. This additional sensitization
was mirrored in increased activation of pain-processing regions such as insula, anterior cingulate cortex, and thalamus. Further, in-
creased connectivity between the anterior insula and medial PFC (mPFC) in the uncontrollable and increased negative connectivity
between dorsolateral PFC (dlPFC) and insula in the controllable task were observed. This suggests a pain-facilitating role of the mPFC
during uncontrollable pain and a pain-inhibiting role of the dlPFC during controllable pain, both exerting their respective effects via the
anterior insula. These results elucidate neural mechanisms of context-dependent pain modulation and their relation to subjective
perception.
Key words: controllability; dorsolateral prefrontal cortex (dlPFC); insula; medial prefrontal cortex (mPFC); pain; pain modulation
Introduction
Would you rather remove a splinter from your finger yourself or
let somebody else do it? Many people would choose the former,
illustrating the empowerment of having control over pain (Bow-
ers, 1968). The clinical relevance of pain controllability has been
shown for acute (Tinti et al., 2011) as well as chronic pain
(Ha¨rka¨pa¨a¨ et al., 1991;Jensen and Karoly, 1991) and experimen-
tal work confirms that controllable pain stimuli are perceived as
less intense than uncontrollable stimuli (Arntz and Schmidt,
Received May 20, 2015; revised March 14, 2016; accepted March 17, 2016.
Author contributions: A.-K.B., S.B., and P.S. designed research; A.-K.B. and S.B. performed research; M.-E.H.
contributed unpublished reagents/analytic tools; A.-K.B. and S.B. analyzed data; A.-K.B., S.B., and P.S. wrote the
paper.
This work was supported by the Canadian Institutes of Health Research (Operating Grant to P.S.). A.-K.B. was
supported by a scholarship from the German National Academic Foundation. S.B. was supported by an International
Association for the Study of Pain International Trainee Fellowship funded by the Scan兩Design Foundation BY INGER
& JENS BRUUN. We thank Tor Wager for help with the NPS analysis.
The authors declare no competing financial interests.
*A.-K.B. and S.B. contributed equally to this work.
Correspondence should be addressed to Anne-Kathrin Bra¨scher, Department of Clinical Psychology, Psychother-
apy and Experimental Psychopathology, Johannes Gutenberg University Mainz, Wallstra

e 3, Mainz 55122, Ger-
many. E-mail: abraesch@uni-mainz.de.
DOI:10.1523/JNEUROSCI.1954-15.2016
Copyright © 2016 the authors 0270-6474/16/365013-13$15.00/0
Significance Statement
Pain control is of uttermost importance and stimulus controllability is an important way to achieve endogenous pain modulation.
Here, we show differential effects of controllability and uncontrollability on pain perception and cerebral pain processing. When
pain was controllable, the dorsolateral prefrontal cortex downregulated pain-evoked activation in important pain-processing
regions. In contrast, sensitization during uncontrollable pain was mediated by increased connectivity of the medial prefrontal
cortex with the anterior insula and other pain-processing regions. These novel insights into cerebral pain modulation by stimulus
controllability have the potential to improve treatment approaches in pain patients.
The Journal of Neuroscience, May 4, 2016 •36(18):5013–5025 • 5013
1989;Mu¨ller, 2011). Imaging studies have extended these find-
ings by demonstrating that activity in pain-processing brain re-
gions, including the anterior cingulate cortex (ACC), insula,
thalamus, primary and secondary somatosensory cortex (SI, SII),
as well as in pain-modulatory regions such as the amygdala, peri-
aqueductal gray (PAG), and the prefrontal cortex (PFC) varies
with pain stimulation being controllable or uncontrollable (Sa-
lomons et al., 2004;Mohr et al., 2005;Helmchen et al., 2006;
Wiech et al., 2006;Salomons et al., 2007). Human and animal
studies point to an important role of the lateral PFC, particularly
the anterior and dorsolateral PFC (dlPFC), in mediating the
pain-inhibitory effects of (perceived) control over pain (Amat et
al., 2005;Wiech et al., 2006;Borckardt et al., 2011), predomi-
nantly in individuals who do not believe that the outcomes of
their behavior is determined by their decisions and efforts (i.e.,
who have low internal control beliefs; Wiech et al., 2006). How-
ever, it is currently unclear how the lateral PFC affects pain-
processing regions to achieve pain inhibition when pain is
controllable. In addition, although implicitly assumed in previ-
ous studies, it is has not been shown whether uncontrollability
facilitates pain processing and, if so, which brain areas might
drive pain facilitation by uncontrollability. Candidate regions for
such a facilitation are the pain-modulatory regions medial PFC
(mPFC), amygdala, and PAG, which show increased activation
when pain is uncontrollable (Salomons et al., 2004;Mohr et al.,
2005;Wiech et al., 2006). The mPFC, including the cingulofron-
tal cortex, is a pain-modulatory area that has been shown to
facilitate pain in different contexts (Ploghaus et al., 2001;Mayer
et al., 2005;Schweinhardt et al., 2008;Berna et al., 2010). The
PAG plays an important role in descending pain-modulatory
pathways, including facilitation (Porreca et al., 2002;Gwilym et
al., 2009;Yoshida et al., 2013). Finally, the amygdala plays a key
role in fear and threat processing, also in the context of pain
(Neugebauer et al., 2004;Phelps and LeDoux, 2005), and can be
considered a candidate region because it has been hypothesized
that a lack of control increases the threatening value of pain
(Bowers, 1968;Arntz and Schmidt, 1989).
Here, we tested in healthy volunteers in an fMRI) experiment
whether uncontrollability of pain stimuli facilitates pain percep-
tion and if such facilitation is reflected by increased activation in
pain-processing regions such as insula, thalamus, ACC, SI, and
SII. Univariate analysis and comparison with a multivariate cere-
bral pattern of pain described previously (Wager et al., 2013)
were used to test whether uncontrollable pain is associated with
more activation in pain-processing regions than controllable
pain. We also investigated whether the functional connectivity of
an important pain-processing region, the insula, with mPFC,
amygdala, or PAG is increased during uncontrollability and
whether the insula is inversely connected to the lateral PFC dur-
ing controllability, indicating potentially downregulation.
Materials and Methods
Subjects
Inclusion criteria included age between 18 and 40 years and good health.
Exclusion criteria were the presence or history of significant neurological
or psychiatric disease, chronic pain, any significant medical condition or
sleep disorders; regular consumption of alcohol or recreational drugs;
recent use of any pain medication; or regular or frequent night shift work.
The study was approved by the local ethics committee and written in-
formed consent was obtained from all subjects. Subjects were compen-
sated with $50 US for their participation.
Three subjects were excluded because they did not rate the painful
stimulus as painful during ⬎50% of the pain trials. The final sample
consisted of 23 volunteers (13 males; 1 left-handed) age 19 –30 years
(mean 24.2 ⫾3.57 years).
Experimental design
The study followed a within-subject yoked-control design with two
within-subjects factors with two levels each (task “controllable” vs “un-
controllable” and stimulation intensity “pain” vs “warm”). Warm inten-
sities were used to identify pain-specific effects in the behavioral
responses and brain activation. The sequence of stimulation intensities
was pseudorandomized with the same order across participants. In each
functional scan, the first 12 trials were controllable, followed by 12 un-
controllable trials to avoid frequent switching between the two tasks so
that subjects would not be confused which task was to be performed in a
specific trial (Fig. 1). Because of the yoked design, uncontrollable trials
had to be preceded by controllable trials (see below). In the controllable
task, subjects performed a temperature regulation task (Kleinbo¨hl et al.,
1999;Ho¨lzl et al., 2005;Becker et al., 2011). After the thermode had
reached the target temperature, the subject had to keep his/her sensation
constant by antagonizing any perceived temperature change using a re-
sponse unit. Therefore, subjects had full instrumental control (Miller,
1979) over the physical stimulus intensity. The response unit allowed
regulating the temperature down or up with a left or right button, respec-
tively. If the sensation had not changed, subjects had to press a middle
button to control for motor responses. Because the temperature only
changed when the subject regulated it, any change perceived by the sub-
ject was due to sensitization or habituation. A flashing arrow displayed
on a screen visible to the subject reflected the subject’s response and
served as visual feedback to minimize unspecific differences to the un-
controllable task. After 20 s, the temperature returned to baseline. In the
uncontrollable task, the temperature profiles of the previous controllable
trials were replayed unbeknownst to the subject. Now, the subject rated
his or her sensation on a visual analog scale (VAS; see below) projected
onto the screen. The right mouse button increased the rating; the left
mouse button decreased the rating. If the sensation had not changed, the
subject had to press the middle mouse button. Due to the yoked design,
the nociceptive input was identical in both tasks.
During both tasks, a green square flashed every 2 s, prompting the
subject to give a response. The intertrial interval was 20 s.
General procedure
Each subject underwent two sessions: a familiarization session and an
fMRI session, 1–3 d apart.
Familiarization session
The study was explained to the subject and, after obtaining informed
consent, participants were familiarized with the stimuli, the tasks, and
the rating scales. Cutaneous heat stimuli were delivered using a 30 ⫻30
mm
2
contact thermode (Pathway; Medoc Advanced Medical Systems).
Stimulus intensities were individually determined (see below), but tem-
peratures ⬎50°C were not allowed for safety reasons. A vertically ori-
ented VAS anchored with 0 (“no sensation”), 40 (“just painful,” defined
as the pain threshold), and 100 (“most intense pain tolerable”) was used
to rate nonpainful and painful sensations. This VAS has been shown to
possess linear properties (Lautenbacher et al., 1992). Pain tolerance test-
ing was performed so that the subjects experienced a sensation that cor-
responded to the upper end of the VAS, thereby providing a perceptual
anchor. For this, the thermode was applied to the subject’s forearm and
the temperature slowly increased (rise rate 0.5°C/s) from 32°C until the
subject stopped it by pressing a button on the response unit when the
sensation became intolerable. This procedure was repeated twice with a
rise rate of 1.5°C/s.
To assess subjects’ pain threshold, a series of stimuli was applied to the
testing site to be used during the fMRI: the subject’s nondominant thenar
eminence. After each stimulus, the subject indicated their most intense
sensation during the stimulation on the VAS. The baseline temperature
was 36°C and the first target temperature was 39°C (stimulus duration
5 s). The subsequent target temperatures each increased by 1°C until the
participant rated a stimulus as “just painful.” Based on this temperature,
5 more temperatures around this initial painful temperature (⫺1°C,
⫹0.5°C, ⫹/⫺0°C, ⫹1°C, ⫺0.5°C) were applied and the one closest to the
5014 •J. Neurosci., May 4, 2016 •36(18):5013–5025 Bra¨scher, Becker et al. •Neural Correlates of Un/Controllable Pain
“just painful” anchor was used as an estimate of the pain threshold. The
resulting average pain threshold was 44.2°C (SD ⫽1.42°C), consistent
with the literature (Rolke et al., 2006).
The temperature to be used as initial temperature in the controllable
pain trials was the temperature of the individual’s pain threshold plus
1.5°C. Adjustments were made if this temperature was not rated as mod-
erately painful or did not fall between 45.5°C and 48°C (mean tempera-
ture ⫽47°C, SD ⫽0.78°C). For the controllable warm trials, the initial
temperature was 39°C or 40°C, depending on the participant’s rating
(mean temperature ⫽39.5°C, SD ⫽0.5°C).
FMRI session
At the beginning of the fMRI session, subjects were reminded of the tasks
and stimulus intensities were adjusted if necessary to achieve moderately
painful and warm sensations. FMRI data acquisition was performed in
two functional scans separated by an anatomical scan (Fig. 1).
Stimulus presentation was controlled by a laptop computer using
Eprime software (Psychology Software Tools). The display was back-
projected onto a screen and was visible via a mirror that was mounted on
the head coil of the MRI scanner. A computer mouse with three buttons,
modified in-house to ensure MR compatibility, served as response unit
so that the subjects could perform the experimental tasks.
Questionnaires
The internal– external control (IPC) scale (Levenson, 1981) was used to
identify the locus of control beliefs (subscales: “internal,” “powerful oth-
ers,” and “chance”). Twenty subjects completed this questionnaire. All
subjects completed the State-Trait Anxiety Inventory (Spielberger et al.,
1970) indicating their level of state anxiety before the scan and their level
of trait anxiety after the scan.
FMRI data acquisition
Imaging data were acquired ona3TSiemens TRIO MRI scanner at the
McConnell Brain Imaging Center, Montreal Neurological Institute
(MNI), using a 32-channel head coil. A gradient echo planar imaging
(EPI) sequence covering the whole brain was used for functional scans
[TR ⫽2.62 s, TE ⫽30 ms, flip angle ⫽90 degree, 44 interleaved, 3.5 mm
thick axial slices (parallel to the AC–PC line), field of view (FoV) 224
mm ⫻224 mm, matrix 64 ⫻64, resulting in an in-plane resolution of
3.5 ⫻3.5 mm
2
, 441 image volumes]. The first two images were discarded
to allow steady-state magnetization. Field maps were obtained using a
gradient echo sequence (TE ⫽20 ms, 0.47 ms dwell time, FoV and matrix
identical to EPI). High-resolution, anatomical T1-weighted images (RF
spoiled, prescan normalized MPRAGE sequence, TR ⫽2300 ms, TE ⫽
2.98 ms, TI ⫽900 ms, flip angle ⫽9 degree, FoV 192 mm ⫻256 mm ⫻
256 mm, matrix 192 ⫻256 ⫻256, resulting in a voxel size of 1 mm
3
)
were acquired for all subjects for coregistration purposes.
Statistical analysis of behavioral data
Mean values and SDs were calculated for the self-adjusted temperature
changes and intensity ratings. Two-sided ttests were used to test whether
the temperatures or VAS ratings at the end of the trial differed from those
at the beginning of the trial. A linear mixed model was used to test
whether VAS ratings changed over the course of the experiment. IPC
scores and anxiety scores were correlated with the psychophysical data
using Pearson’s correlations (uncorrected for multiple comparisons).
Alpha ⫽0.05 was used as the significance level. Statistical tests were
performed with PASW Statistics 17.0.3.
Statistical analysis of fMRI data
All image processing and statistical analysis was performed using the
software package FSL 5.0.8 (FMRIB’s Software Library; Smith et al.,
2004). Of 1104 trials in total, 19 trials were excluded due to missing data
caused by technical problems such as thermode malfunction. Eleven
uncontrollable trials were shortened due to technical failure.
Subject-level analysis. To eliminate scanner artifacts, denoising was
performed on the raw images, before preprocessing, using MELODIC
(Multivariate Exploratory Linear Optimized Decomposition into Inde-
pendent Components; Beckmann and Smith, 2004) within FEAT (FMRI
Expert Analysis Tool). The number of independent components was
estimated from the data. Components were individually inspected and
Figure 1. Experimental procedure. Twelve controllable trials (CON) were followed by 12 uncontrollable trials (UNCON) during the first functional scan. Participants saw instructions for the
respective trials and visual feedback of their responses on a screen, indicated here by the insets. After an anatomical scan (10 min), a second functional scan was performed during which 12
controllable trials were again followed by 12 uncontrollable trials. The sequence of stimulation intensities (painful, solid line; warm, dashed line) was pseudorandomized.
Bra¨scher, Becker et al. •Neural Correlates of Un/Controllable Pain J. Neurosci., May 4, 2016 •36(18):5013–5025 • 5015
removed using regression if the spatial map or the associated time series
was clearly abnormal, such as the spatial map being striped or the time
series showing spikes ⬎5 SDs. Then, the following preprocessing steps
were applied to each functional dataset: spatial smoothing (Gaussian
kernel, full width at half-maximum: 5 mm), motion correction, and
temporal high-pass filtering (Gaussian-weighted least-squares straight
line fitting with
⫽100 s). Susceptibility-related distortions were cor-
rected using FSL field map correction routines.
The time courses of CSF and white matter masks were extracted to be
used as nuisance variables. To generate CSF and white matter masks, the
individual structural images were segmented into CSF, white matter, and
gray matter using FAST (Zhang et al., 2001), thresholded at 0.8 (Biswal et
al., 2010). CSF and white matter masks were transformed into functional
space by applying the inverse transformation matrix of the individual
motion-corrected EPI to the structural image using FLIRT. The masks
were rebinarized and partial volumes edges excluded after interpolation.
Finally, the time series averaged across the voxels of the respective mask
was extracted.
A general linear model (GLM) was applied to each functional dataset
with the following regressors of interest: four condition regressors (con-
trollable pain, controllable warm, uncontrollable pain, uncontrollable
warm), the time–rating curves (i.e., the time courses of the intensity
ratings) of uncontrollable trials, and time–temperature curves of con-
trollable trials (excluding the stimulus rise time; time bins of 2000 ms).
Temperature rise and fall times, motion outliers, time series for CSF and
white matter, and time–temperature curves in the uncontrollable condi-
tions were included in the model as nuisance variables. In addition, the
first4softhe20sstimulation period were modeled as nuisance regres-
sors to account for the time subjects needed to “catch up” with their VAS
ratings with the initial temperature increase of the thermode in the un-
controllable task (Fig. 2). Regressors were convolved with a gamma he-
modynamic response function and the first temporal derivatives were
included. This model allowed identifying the main effects and interaction
of task (controllable or uncontrollable) and stimulation intensity (pain
or warm), as well as brain activation correlating with sensitization or
habituation in the respective tasks (i.e., with self-adjusted temperature
changes in the controllable tasks and with subjective ratings in the un-
controllable tasks).
Voxelwise parameter estimates were derived using the appropriate
contrasts. Individual’s functional images were registered to their own
structural scan using linear transformation (FLIRT; Jenkinson et al.,
2002), followed by registration to the International Consortium for Brain
Mapping (ICBM) 152 nonlinear sixth-generation symmetric template in
MNI standard space using nonlinear transformations (FNIRT, warp res-
olution ⫽10 mm). The parameter estimates and the corresponding es-
timates of the variance from the two functional scans of each subject were
merged in a second-level fixed-effects analysis.
Group level analysis. Third-level analyses were performed using a
mixed-effects model implemented in FLAME (Beckmann et al., 2003).
Statistical inference was based on a voxel-based threshold of Z⫽2.3
cluster corrected across the whole brain at p⬍0.05.
Neurological Pain Signature. We tested the correspondence of the dif-
ferent stimulation intensities and tasks with the so-called “Neurological
Pain Signature” (NPS; Wager et al., 2013), which was provided by its
authors. The NPS is a distributed pattern of fMRI activations defined by
multivariate pattern analysis, which sensitively and specifically tracks
changes in perceived pain intensity achieved by altering nociceptive in-
put (Wager et al., 2013). The rationale for testing any modulatory influ-
ences on the NPS was its high sensitivity and the fact that it does not
correspond to nonpainful emotional events that otherwise activate sim-
ilar brain areas. Modulation of the NPS by un/controllability would be
consistent with the interpretation that pain facilitation by uncontrolla-
bility occurs in pain intensity coding regions. Lack of effects on the NPS,
in conjunction with no change in the magnitude of pain-related activa-
tion in the univariate GLM analysis, would point to un/controllability-
induced pain modulation by other cerebral systems.
For calculating the strength of expression of the NPS response in the
different conditions, the a priori defined pattern of regression weights for
each voxel (Wager et al., 2013) was used to calculate the scalar product
with the brain activation maps (maps of parameter estimates obtained
from the subject-level fMRI analysis) of each contrast of interest (con-
trollable pain, uncontrollable pain, controllable warm, uncontrollable
warm, the time–rating curves of uncontrollable trials, and the time–
temperature curves of controllable trials), resulting in one scalar value for
each subject for each contrast. These values were analyzed for differences
with a repeated-measuremes ANOVA design using mixed-model proce-
dures with the factors “stimulation intensity” (pain or warm), “task”
(controllable or uncontrollable), and “regressor type” (i.e., constant re-
gressors coding the four conditions and the time–rating/time–tempera-
ture regressors).
Connectivity analyses. To investigate the connectivity between pain
processing and potential pain-modulatory brain regions, depending on
un/controllability, psychophysiological interaction analyses (PPIs; Fris-
ton et al., 1997) were performed. PPI analyses provide a model of how a
psychological context (i.e., uncontrollability) changes the influence of
one area on another area and is regarded as a measure of effective con-
nectivity (Friston et al., 1997). The anterior insula served as seed region
because it is a core region implicated in pain processing (Apkarian et al.,
2005) and is consistently modulated by psychological interventions in-
cluding controllability (Salomons et al., 2004;Wiech et al., 2006). For the
seed, an ellipsoid was created that comprised the peak coordinates for the
contrasts associated with significant insula activation (controllable pain,
uncontrollable pain, controllable warm, uncontrollable warm, time–rat-
ing curve of uncontrollable pain). Four PPI regressors were computed,
Figure 2. Psychophysical data of painful (A) and warm (B) trials. Illustrated are the temperature courses in blue, averaged across trials and participants, and the VAS ratings, equally averaged
across trials and participants, in red. For better illustration, we downsampled the displayed data (interval length ⫽250 ms) and do not display the temperature rise in the beginning of the trial; that
is, illustrated are the 20 s after reaching the target temperature. Gray ribbons represent the SEM. Because increasing ratings at the beginning of the uncontrollable trials do not reflect sensitization
(thesubjectsneeded time to adjust theirratingafter the thermode hadreachedthe target temperature), thefirst4softheuncontrollabletrialswere excluded. The red barsindicatethe time segment
that was included in the comparison of intensity ratings between end and beginning of the trial, as well as as pain-rating regressors in the fMRI analysis of the uncontrollable trials (see Materials and
Methods). The red dotted line depicts the “just painful” anchor of the VAS. The blue dotted line marks habituation (values ⬎0) and sensitization behavior (values ⬍0) for the controllable trials. The
initial variations in temperature were due to fluctuations of the thermode.
5016 •J. Neurosci., May 4, 2016 •36(18):5013–5025 Bra¨scher, Becker et al. •Neural Correlates of Un/Controllable Pain
each as the scalar product of the time series of the activity averaged across
the voxels in the anterior insula seed and a vector coding for the control-
lable pain, uncontrollable pain, controllable warm, and uncontrollable
warm conditions, respectively. All PPI regressors were included in the
same GLM to model the full space of the conditions of interest for this
analysis. Commonly, two conditions are subtracted from each other and
modeled as one PPI regressor. However, such an approach makes as-
sumptions about the interrelationship between the conditions and does
not allow comparisons of similarities between the PPI contrasts. Evi-
dence demonstrates also a better single-subject model fit and higher
sensitivity for true positive findings when modeling the full space of
conditions (McLaren et al., 2012). In addition to the PPI regressors,
regressors coding all four conditions (controllable painful, controllable
warm, uncontrollable painful, uncontrollable warm), the average time
course extracted from the seed, and the same nuisance variables as in the
GLM were included to ensure that the variance explained by the PPI
regressors was not confounded by main effects of the conditions or other
factors (O’Reilly et al., 2012). Therefore, variance explained by the PPI
regressors represents specifically variance explained over and above the
variance explained by the conditions, the time series, and error variance
explained by the nuisance regressors, indicating which brain regions are
functionally connected to the seed dependent on the experimental con-
dition. The regions of interest (ROIs) were the lateral PFC (controllable
task) and the mPFC, amygdala, and PAG (uncontrollable task). To test
the specificity of the effects, the PPI analyses were repeated with the
lateral PFC serving as ROIs for the uncontrollable tasks and the mPFC,
amygdala, and PAG serving as ROIs for the controllable tasks. Lateral
PFC was defined as Brodmann areas 9, 10, and 46. The amygdalae were
defined with the Harvard–Oxford Subcortical Structural Atlas imple-
mented in FSL (signal intensity minimum at 20%). The ROI for the PAG
was manually drawn on the ICBM 152 template and comprised 112
voxels. The ROI for mPFC was generated by combining the cingulate
cortex, paracingulate cortex, frontal medial cortex, frontal pole, and sub-
callosal cortex as defined in the Harvard–Oxford Cortical Structural At-
las implemented in FSL. The resulting area was restricted in the x
dimension by planes at x⫽⫺16 and x⫽16 and in the ydimension by a
plane extending from the vertical ramus of the Sylvian fissure ( y⫽20;
Kates et al., 2002) to obtain the final mPFC ROI. Statistical inference was
based on a voxel-based threshold of Z⫽2.3 cluster corrected across the
ROIs at p⬍0.05.
Regions that were identified in the PPI as pain-modulatory areas were
used as seeds in a “reversed” PPI analysis. This was done to test whether
pain-processing brain regions other than anterior insula showed in-
creased connectivity to the regions identified in the first PPI (i.e., dlPFC
for the controllable pain and controllable warm conditions, two clusters
in mPFC for the uncontrollable pain condition and one cluster in
mPFC for the uncontrollable warm condition). For the “reversed”
PPI analyses, a mask of pain-processing brain regions served as ROIs
(composed of bilateral insula, SI, SII, thalamus, and ACC derived
from the Harvard–Oxford Cortical Structural Atlas and Harvard–
Oxford Subcortical Structural Atlas implemented in FSL) and statis-
tical inference was based on a voxel-based threshold of Z⫽2.3 cluster
corrected across the mask at p⬍0.05.
Localization of activation was achieved by inspection of group activa-
tion maps overlaid on the nonlinear ICBM-152 template. Images are
displayed in radiological convention; that is, the right side of the brain is
on the left. Coordinates are given in MNI space.
Additional analyses. Several additional analyses were performed,
mainly for control purposes. A GLM containing only the four condition
regressors (controllable pain, uncontrollable pain, controllable warm,
uncontrollable warm) and the nuisance variables was performed to con-
firm the main effects of condition.
To test the specificity of brain activation associated with additional
sensitization in the uncontrollable pain condition, a “control GLM” was
performed using the pain ratings recorded during the uncontrollable trials as
additional regressor for the controllable pain condition. The other regressors
and nuisance variables were the same as in the original GLM.
In the uncontrollable pain condition, subjects pressed the button more
frequently than at every cue, reflecting the increased sensitization in this
condition. We therefore repeated the GLM analysis as well as the PPI
analyses with an additional regressor of no interest coding the button
presses. This analysis is not ideal because the variance cannot be unam-
biguously explained due to multicollinearity of the button press regressor
with the time–intensity and time–temperature curves. It was nevertheless
performed to gain insight into the potential confound caused by the
button presses.
Last, the modeled time courses of signal change were extracted for all
contrasts with significant anterior insula activation; 5 mm spheres were
created around the anterior insula peaks of the significant contrasts and
the time courses of the percentage signal change extracted.
Results
Behavioral data
In the controllable task during painful trials, subjects regulated
the temperature down to keep their sensation constant (Fig. 2A),
which indicates that sensitization (as a specific form of pain
facilitation) occurred over the course of the 20-s-long trials (tem-
perature difference between end and beginning of the trial: M ⫽
⫺0.7°C, SD ⫽0.398°C; T ⫽8.41, p⬍0.001). During warm trials,
subjects on average increased the temperature; that is, they habit-
uated to the stimulation (M ⫽0.19°C, SD ⫽0.305°C; T ⫽⫺3.00,
p⫽0.007; Fig. 2B).
In the uncontrollable task, subjects received the identical no-
ciceptive input as in the controllable task by being administered
the self-adjusted temperature time courses of the controllable
trials. Consistent with the notion that uncontrollability facilitates
pain, they rated the sensation as continuously getting more pain-
ful during pain trials (rating difference between end and begin-
ning of trial: M ⫽23.2, SD ⫽9.22, T ⫽10.95, p⬍0.001; Fig. 2A),
although they had regulated the temperature time courses to pro-
duce constant sensations in the controllable task. During warm
trials, the ratings remained stable throughout the trial (M ⫽0.38,
SD ⫽1.06, T ⫽0.51, p⫽0.62; Fig. 2B) and thus matched the
perceived sensation in the controllable task, indicating that sub-
jects were able to perform the temperature regulation task. These
results indicate that the effects of uncontrollability on the percep-
tion of thermal stimuli are specific for painful sensations.
Although the yoked design required that the uncontrolla-
ble trials were presented after the controllable trials, sensitiza-
tion over the course of the experiment can be ruled out as an
alternative explanation for the increasing pain ratings in the
uncontrollable trials because pain ratings did not increase
across trials (mixed-model analysis, interaction effect trial ⫻
intensity: F
(5,514)
⫽0.7, p⫽0.626).
Locus of control and anxiety scores
The magnitude of self-regulated temperature changes in the
controllable painful condition correlated positively with the
“powerful others” subscale of the IPC locus of control sc-
ale (r⫽0.59, p⫽0.012). The more a subject believed that his/her
life was controlled by others, the more he/she sensitized in re-
sponse to the painful stimulation. Neither trait (M ⫽36.4, SD ⫽
9.74) nor state anxiety scores (M ⫽29.1, SD ⫽8.07) correlated
with the self-regulated temperature in the controllable task or the
subjective ratings in the uncontrollable task (all p-values ⬎0.4).
Pain-related brain activation
The uncontrollable pain and controllable pain conditions (Fig.
3A) revealed largely overlapping activations of typical pain-
processing brain regions (i.e., regions commonly activated dur-
ing experimental heat in fMRI studies in healthy volunteers;
Apkarian et al., 2005;Farrell et al., 2005), including bilateral in-
sula, thalamus, SII, ACC, PFC, premotor cortex, cerebellum, and
Bra¨scher, Becker et al. •Neural Correlates of Un/Controllable Pain J. Neurosci., May 4, 2016 •36(18):5013–5025 • 5017
brainstem. The main effects of the uncontrollable warm and con-
trollable warm conditions showed similar, yet less expanded pat-
terns (Fig. 3B). For both tasks, painful stimulation was associated
with significantly more activation in pain-processing brain
regions than warm stimulation (Table 1). This finding was
corroborated by testing the correspondence of the experimen-
tal conditions with the NPS, a brain system that sensitively
scales with perceived pain (Fig. 4A): the NPS response in-
creased significantly for painful compared with warm stimu-
lation intensities (main effect “stimulus intensity”: F
(1,61)
⫽
5.03, p⫽0.029; Fig. 4B).
Brain activation during uncontrollable stimulation
The increases in pain perception over the course of a trial in the
uncontrollable task were reflected by increasing activation in typ-
ical pain-processing regions, including bilateral insula, ACC, bi-
lateral medial thalamus, and pallidum. In addition, activation
scaling with intensity ratings was detected in the lateral orbito-
frontal cortex (OFC) and occipital lobe (Fig. 5,Table 2). Note
that this activation is controlled for activation unrelated to sub-
jective perception by the constant condition regressor.
The GLM was used to contrast brain responses in the uncon-
trollable and controllable tasks for the painful stimulation, con-
trolling for warm stimulation [contrast (uncontrollable pain ⬎
uncontrollable warm) ⬎(controllable pain ⬎controllable
warm)], masked with a ROI consisting of pain-processing brain
regions. This contrast only revealed activation in SI/primary mo-
tor cortex (Table 3). Consistent with this result, the comparison
with the NPS did not reveal an interaction between “stimulation
intensity,” “task” (uncontrollable or controllable), and “regres-
sor type” (F
(1,62)
⫽0.52, p⫽0.473) nor between “stimulation
intensity” and “task” (F
(1,62)
⫽0.08, p⫽0.783). The results from
the NPS and the GLM indicate that the uncontrollable pain con-
dition was not associated with more pain-related activation per
se. Importantly, this was also the case when activation related to a
constant regressor as well as to a regressor reflecting the increas-
ing pain ratings was taken into account, as evidenced by the non-
significant interaction for the NPS analysis between “stimulation
intensity” and “task.”
The PPI demonstrated that, during uncontrollable painful
stimulation, connectivity was increased between the anterior
insula seed (Fig. 6A) and two clusters in mPFC, one in peri-
genual ACC (pgACC) (⫺10, 38, ⫺8, Z⫽3.22) and one in the
anteromedial PFC (amPFC) (8, 58, 10, Z⫽3.14) (Fig. 6B,
Table 4). Neither the amygdala nor the PAG showed signifi-
cantly increased connectivity with the anterior insula, but 36
contiguous voxels above a voxel-based threshold of Z⫽1.6,
uncorrected for spatial extent, were detected in the amygdala
(peak: 12, ⫺2, ⫺20, Z⫽2.72). In the uncontrollable warm
condition, increased connectivity of the insula seed with a
cluster in dorsal ACC (8, 28, 30, Z⫽3.40) was observed (Table
4). No increased connectivity was observed with the lateral
PFC (hypothesized ROI for controllable task) in the uncon-
trollable task (painful or warm stimulation intensity). In the
“reversed” PPIs of the uncontrollable pain condition with
pgACC (Fig. 6C) and amPFC as seeds (Fig. 6D), increased
connectivity with the anterior insula, ACC, and inferior pari-
etal lobe was detected (Table 4). In the “reversed” PPI of the
Figure 3. Pain-related brain responses. Shown are uncontrollable pain (A, blue) and controllable pain (A, red) and uncontrollable warm (B, blue) and controllable warm (B, red) conditions of the
GLM analysis (main effects). INS, Insula; BSG, basal ganglia; THA, thalamus; NCA, nucleus caudatus; PFC, prefrontal cortex; PMC, premotor cortex.
Table 1. Brain responses to pain versus warm stimulation for the uncontrollable
and controllable tasks
Brain region
Cluster size
(no. of voxels)
Z-score
peak
MNI peak coordinates (mm)
xyz
Uncontrollable condition
Cluster spanning 27081 6.49 ⫺28 ⫺66 ⫺28
Cerebellum 6.49 ⫺28 ⫺66 ⫺28
4.60 24 ⫺58 ⫺28
Central opercular
cortex
6.16 38 8 8
Thalamus 6.12 14 ⫺20 10
5.95 ⫺18 ⫺20 14
ACC 3.59 6 20 30
Insula 5.91 36 14 6
5.04 ⫺32 16 4
SII 4.78 56 ⫺20 22
Cluster spanning 3241 5.19 ⫺36 ⫺10 64
SMA/premotor
cortex
5.19 ⫺36 ⫺10 64
5.52 22 ⫺254
SI 4.07 ⫺42 ⫺36 54
MI 4.06 ⫺36 ⫺26 56
Cluster spanning 693 4.31 16 ⫺64 42
Precuneus 4.31 16 ⫺64 42
Superior parietal
cortex
3.97 10 ⫺62 60
3.62 ⫺16 ⫺62 58
Controllable condition
Cluster spanning 29104 6.59 42 ⫺12 14
SII 6.59 42 ⫺12 14
Insula 6.32 38 ⫺14 16
5.41 ⫺38 12 ⫺4
Central opercular
cortex
6.25 56 0 4
ACC 4.54 4 12 44
Thalamus 5.50 12 ⫺66
5.41 ⫺14 ⫺18 16
SMA 4.12 6 18 60
Cerebellum 5.26 ⫺26 ⫺62 ⫺22
Three big clusters spanning several areas were significantly more activated by painful compared with nonpainful
stimulationin the uncontrollabletask and onebig cluster forthe controllable task(significant on awhole brain-level,
voxel-based threshold Z⬎2.3 and cluster-based threshold p⬍0.05). Local maxima within the clusters are given
for individual anatomical areas. SMA, Supplementary motor cortex; MI, primary motor cortex; ACC, anterior cingu-
late cortex; SII, secondary somatosensory cortex; SI, primary somatosensory cortex.
5018 •J. Neurosci., May 4, 2016 •36(18):5013–5025 Bra¨scher, Becker et al. •Neural Correlates of Un/Controllable Pain
uncontrollable warm condition with ACC as seed, increased
connectivity was detected only with left SI (Table 4).
Brain activation during controllable stimulation
In the controllable pain condition, activation in pain-processing
regions was not correlated with the downregulation of the tem-
perature, consistent with the interpretation that subjects success-
fully kept their sensation constant. The PPI analysis revealed
increased inverse connectivity between the anterior insula seed
(Fig. 7A) and two clusters in the dlPFC (34, 58, 22; Z⫽3.51; 42,
16, 34, Z⫽3.16; Fig. 7B,Table 4) in the painful condition and,
partly overlapping, one cluster in the dlPFC in the warm condi-
Figure4. Neurologicalpainsignature (NPS) response. A, Apriori-definedpatternof the NPS. The insets showexamplesof the pattern distribution of voxelweightswithin certain brain areas; ACC,
anterior cingulate cortex; SII, secondary somatosensory cortex; pINS, posterior insula. (Figure provided by Dr. Wager, University of Colorado, Boulder, Colorado). B, Estimated values of mixed-model
ANOVA analysis of the NPS responses; depicted are post hoc comparisons of the main effect “stimulation intensity” (levels: pain, warm), the interaction “stimulation intensity” ⫻“task” (levels:
controllable, uncontrollable), and the interaction “stimulation intensity” ⫻“regressor type” (levels: constant, time–rating/temperature). The main effect of “pain” had a significantly higher NPS
response than “warm.” There was no main effect of “task” (F
(1,53)
⫽0.13, p⫽0.720), indicating that correspondence to the NPS was comparable for the uncontrollable and controllable tasks. The
significant interaction “stimulation intensity” ⫻“regressor type” with the respective post hoc tests indicates that a higher NPS response was associated with the condition regressors (i.e., constant
regressors) compared with the time course regressors of ratings and temperature for painful stimulation. This was not the case for warm stimulation. The nonsignificant interaction “stimulation
intensity” ⫻“task” indicates that the NPS correspondence for pain or warmth was not differentially influenced by un/controllability ( post hoc comparison uncontrollable pain versus controllable
pain: p⫽0.592). Bars represent the mean scalar values expressing the NPS across subjects, error bars the SEM. Scaling of the NPS values depends on many factors such as voxel size,
contrast weight, field strength, etc. Because only a within-study comparison was of interest here, we did not attempt to equate scaling of the NPS values with previous studies. *p⬍0.05;
**p⬍0.01.
Bra¨scher, Becker et al. •Neural Correlates of Un/Controllable Pain J. Neurosci., May 4, 2016 •36(18):5013–5025 • 5019
tion (34, 58, 22; Z⫽3.51). No significant connectivity was ob-
served with the mPFC, amygdala, and PAG (hypothesized ROIs
for uncontrollable task) in the controllable pain or controllable
warm conditions. Using dlPFC as seed, the results of the “re-
versed” PPI analysis for the controllable pain condition revealed
increased inverse connectivity with bilateral thalamus (8, ⫺22,
16; Z⫽3.87; Table 4). The “reversed” PPI for the controllable
warm condition did not reveal increased connectivity with any
region.
Additional analyses
Repeating the GLM with only the four condition regressors as
regressors of interest yielded virtually identical results.
No activation was significantly associated with the time course
of the pain ratings in the controllable task (control GLM). There-
fore, the additional sensitization appears to be specific for pain in
the uncontrollable task.
The GLM and the PPI analyses were repeated including a
regressor coding for the button presses. The GLM results were
similar to the original analysis, albeit partly at a lower statistical
threshold. The PPI results were only slightly different with the
main difference that the anterior insula seed did no longer show
significant inverse connectivity with the dlPFC in the controllable
warm condition when the button presses were included.
The time courses of signal change in the anterior insula fur-
ther support the picture gleaned from the GLM analysis as well as
the comparison with the NPS: the constant regressors for uncon-
trollable pain and controllable pain and the time–intensity curve
for uncontrollable pain were best reflected by the signal change in
the insula. The two warm conditions (uncontrollable and con-
trollable) were also well reflected but showed lower percentage
signal changes (Fig. 8).
Discussion
Here, we identify brain regions important for driving pain facil-
itation by uncontrollability and suggest mechanisms by which
pain is facilitated or inhibited when stimulation is uncontrollable
or controllable. We show that uncontrollable pain is associated
with increased sensitization, reflected by increased activation in
pain-processing regions. At the same time, uncontrollable pain
was not associated with a higher activation magnitude in pain-
processing regions compared with controllable pain, as investi-
gated with univariate GLM analysis as well as by comparison
with the NPS, a multivariate pain activation pattern. Rather,
uncontrollability-induced pain facilitation increased the connec-
tivity of mPFC with anterior insula, ACC, and the inferior pari-
etal lobe. Anterior insula and ACC are important pain-processing
areas and the connectivity to the inferior parietal lobe might in-
clude SII, which is also involved in pain processing. There was
some indication of increased coupling between the dorsal ACC
and insula and/or SI for the uncontrollable warm condition. Al-
though this indicates that the role of the mPFC in the context of
stimulus uncontrollability might not be specific to pain, it should
be noted that the dorsal ACC does not belong to the mPFC re-
gions previously implicated in pain facilitation by negative emo-
tions (Ploghaus et al., 2001;Schweinhardt et al., 2008;Berna et al.,
2010). Consistent with this, we observed behavioral conse-
Figure 5. Brain correlates of sensitization in the uncontrollable condition. The subjective ratings in the uncontrollable painful condition correlated with activation in pain-processing areas over
and above activation related to the condition. Ins, Insula; GP, globus pallidus; THA, thalamus; ACC, anterior cingulate cortex. For details, see Table 3.
Table 2. Brain activation reflecting the additional sensitization in the
uncontrollable pain condition
Brain region
Cluster size
(no. of voxels)
Z-score
peak
MNI peak coordinates
(mm)
xyz
Cluster spanning 1446 3.61 10 28 50
Supplementary motor
area
3.61 10 28 50
ACC 3.59 10 22 32
3.37 2 38 16
Cluster spanning 1011 3.74 46 20 ⫺10
OFC 3.74 46 20 ⫺10
Inferior frontal gyrus 3.65 50 20 ⫺4
Frontal opercular cortex 3.64 44 20 0
Central opercular cortex
(BA44)
3.46 54 8 0
Insula 3.26 44 10 ⫺2
Cluster spanning 904 4.41 8 ⫺84 ⫺10
Visual cortex 4.41 8 ⫺84 ⫺10
Cluster spanning 507 3.69 ⫺44 16 ⫺2
Frontal opercular cortex 3.69 ⫺44 16 ⫺2
OFC 3.68 ⫺32 20 ⫺14
Insula 3.45 ⫺38 10 0
Cluster spanning 455 3.71 4 ⫺26
Thalamus 3.71 4 ⫺26
Globus pallidus 3.34 14 0 2
Listed are brain areas in which activation was significant on a whole-brain level (voxel-based threshold Z⬎2.3 and
cluster-based threshold p⬍0.05). Please note that local maxima are given as peaks if a significant cluster encom-
passed more than one region. ACC, anterior cingulate cortex; OFC, orbitofrontal cortex.
Table 3. Brain activation for the contrast (uncontrollable pain >uncontrollable
warm) >(controllable pain >controllable warm)
Brain region
Cluster size
(no. of voxels)
Z-score
peak
MNI peak coordinates (mm)
xyz
Cluster spanning 1165 3.95 ⫺34 ⫺26 54
MI 3.95 ⫺34 ⫺26 54
SI 3.91 ⫺38 ⫺30 52
One cluster was significant with a voxel-based threshold Z⬎2.3, cluster-corrected across a mask consisting
of pain processing brain regions at p⬍0.05. Local maxima within the cluster are given for individual
anatomical areas. MI, primary motor cortex; SI, primary somatosensory cortex.
5020 •J. Neurosci., May 4, 2016 •36(18):5013–5025 Bra¨scher, Becker et al. •Neural Correlates of Un/Controllable Pain
quences (i.e., increased sensitization) of uncontrollability only
for painful stimulation. The amygdala appeared to contribute to
uncontrollability-induced pain facilitation, but only at a lenient
statistical threshold. We also confirmed the dlPFC as being an
important region for the pain-inhibitory effects of controllability
and present data suggesting that this inhibition is achieved by
increased inverse connectivity of the dlPFC with the anterior in-
sula and thalamus during controllable pain stimulation.
Dissociation of pain perception in controllable and
uncontrollable tasks
During painful trials in the controllable task, subjects showed
sensitization as a specific form of pain facilitation indexed by
downregulation of the temperature. Such sensitization has been
shown before in similar paradigms (Kleinbo¨hl et al., 1999;Ho¨lzl
et al., 2005;Becker et al., 2011) and related to spinal wind-up
(Eide, 2000;Kleinbo¨hl et al., 2006). Subjects with a high external
locus of control showed more temperature downregulation with
controllable pain, in accordance with theories of learned helpless-
ness (Abramson et al., 1978;Mu¨ller, 2012).
Sensitization with controllable pain was not significantly as-
sociated with increased activation in any pain-processing area,
consistent with the notion that it does not involve supraspinal
mechanisms. In the uncontrollable task, the same nociceptive
inputs were reapplied, thereby accounting for peripheral and spinal
sensitization. Now, these temperature profiles were consistently
Figure 6. Connectivity between insula and medial PFC and connectivity between medial PFC and pain-processing regions. The PPI analysis shows that the connectivity of the anterior insula as
seed (A) is increased during uncontrollable painful stimulation with two clusters in mPFC (pgACC and amPFC) (B). The “reverse” PPI analyses with pgACC and amPFC (B) as seeds show increased
connectivity with right anterior insula and inferior parietal lobe (seed: pgACC) (C; inferior parietal lobe not depicted) and with the bilateral anterior insula and ACC (seed: amPFC) (D) during
uncontrollable painful stimulation. For details, see Table 4. INS, insula; pgACC, perigenual anterior cingulate cortex; amPFC, anteromedial prefrontal cortex.
Bra¨scher, Becker et al. •Neural Correlates of Un/Controllable Pain J. Neurosci., May 4, 2016 •36(18):5013–5025 • 5021
rated as increasingly painful over the course of the individual trials,
demonstrating the pain-facilitatory effects of uncontrollability. Pre-
vious imaging studies of uncontrollable pain have largely failed to
demonstrate behavioral pain-increasing effects of uncontrollability
(but see Wiech et al., 2006).
Uncontrollability leads to increased activation in pain-
processing brain regions and OFC
Insula, ACC, medial thalamus, and pallidum encoded sensitiza-
tion during uncontrollable pain, confirming indirect evidence
from earlier studies that used uncontrollable pain stimulation as
Table 4. Functional connectivity of the anterior insula with regions of interest during uncontrollable and controllable stimulation and of the “reversed” analyses based on
the prior results
Brain region Cluster size (no. of voxels) Connectivity Z-score peak
MNI peak coordinates (mm)
xyz
Uncontrollable pain; insula seed
amPFC 185 Positive 3.14 8 58 10
Perigenual ACC 243 Positive 3.22 ⫺10 38 ⫺8
Uncontrollable pain; amPFC seed (“reversed” PPI)
pgACC/dorsal ACC 577 Positive 3.57 2 40 14
Insula/frontal operculum 408 Positive 3.91 ⫺34 18 8
Insula/frontal operculum 383 Positive 3.90 34 14 12
Uncontrollable pain; perigenual ACC seed (“reversed” PPI)
Insula/frontal operculum 775 Positive 4.09 36 12 14
Inferior parietal lobule 212 Positive 4.01 62 ⫺32 36
Uncontrollable warm; insula seed
ACC 181 Positive 3.40 8 28 30
Uncontrollable warm; ACC seed (“reversed” PPI)
SI/postcentral gyrus 363 Positive 3.91 ⫺32 ⫺36 40
Controllable pain; insula seed
dlPFC 359 Negative 3.51 34 58 22
dlPFC 193 Negative 3.16 42 16 34
Controllable pain; dlPFC seed (“reversed” PPI)
Thalamus 888 Negative 3.87 8 ⫺22 16
Controllable warm; insula (seed)
dlPFC 200 Negative 3.51 34 58 22
Listed are brain areas in which functional connectivity was significant on a voxel-based threshold of Z⬎2.3, corrected for spatial extent across ROIs at a cluster level of p⬍0.05. The ROIs for the PPI analysis with the insula seed were medial
PFC, amygdala, PAG, and dlPFC. The ROIs for the “reversed” PPIs consisted of pain-processing brain regions. amPFC, anteromedial prefrontal cortex; ACC, anterior cingulate cortex; pgACC, perigenual ACC; SI, primary somatosensory cortex;
dlPFC, dorsolateral prefrontal cortex.
Figure 7. Connectivity between insula and dlPFC. The PPI analysis shows that the inverse connectivity of the anterior insula (seed; A) with two clusters in the dlPFC (B) is increased during
controllable painful stimulation. INS, insula; dlPFC, dorsolateral prefrontal cortex.
5022 •J. Neurosci., May 4, 2016 •36(18):5013–5025 Bra¨scher, Becker et al. •Neural Correlates of Un/Controllable Pain
a control condition (Salomons et al., 2004;Mohr et al., 2005;
Wiech et al., 2006). Each of these regions contains nociceptive
neurons encoding stimulus intensity (Kenshalo et al., 1980;Sikes
and Vogt, 1992;Chudler and Dong, 1995;Frot et al., 2007). The
signal in these regions scales linearly with the perceived intensity
of different stimulus intensities in brain-imaging studies (Coghill
et al., 1999;Seminowicz and Davis, 2007;Loggia et al., 2012),
although nonlinear relationships between applied intensity and
BOLD response have been observed (Johnstone et al., 2012).
Therefore, the result that the additional sensitization in uncon-
trollable pain scales with the activation in these regions (com-
pared with controllable pain) indicates that the loss of control
leads to amplified processing of the nociceptive input.
The lateral OFC also showed increased activation with sensi-
tization during uncontrollable pain. This is not a typical pain-
encoding region, but has been associated with uncontrollable
(Wiech et al., 2006) as well as unpredictable (Carlsson et al., 2006)
pain and the fear of pain (Ochsner et al., 2006). Because the lateral
OFC is implicated in the processing of punishers (O’Doherty et
al., 2001), its activation in the present study may reflect negative
affective processing induced by uncontrollable pain.
mPFC might drive additional sensitization during
uncontrollable pain stimulation
Our results suggest brain regions that mediate pain facilita-
tion during uncontrollable stimulation, namely pgACC and
amPFC as parts of the mPFC. In demonstrating stronger cou-
pling of mPFC to the anterior insula, ACC, and inferior parietal
lobe during uncontrollable pain, we extend previous work that
observed increased activation in these regions during uncontrol-
lable compared with controllable painful stimulation (Salomons
et al., 2004;Wiech et al., 2006). Our results link the activity to
behavioral pain facilitation and suggest that the mPFC drives the
additional sensitization, perhaps via direct connections to the
pain-processing regions anterior insula and ACC (Mesulam and
Mufson, 1982;Mufson and Mesulam, 1982). The mPFC is sug-
gested to play a major role in emotional
processing, with regulatory functions of
the perigenual and subgenual ACC and
mPFC especially for negative emotions
(Etkin et al., 2011). Regarding pain,
mPFC has been shown to mediate pain
facilitation by negative emotions
(Ploghaus et al., 2001;Schweinhardt et al.,
2008;Berna et al., 2010). In subacute back
pain patients, increased functional con-
nectivity between mPFC and nucleus ac-
cumbens predicted transition to chronic
pain (Baliki et al., 2012), suggesting the
clinical significance of this region. Be-
cause the mPFC is a large area, the ques-
tion arises whether different subregions
assume different pain-facilitatory roles.
The studies discussed above have either
described relevant activation in peri-
genual or subgenual ACC (Ploghaus et al.,
2001;Berna et al., 2010) or the amPFC
(Schweinhardt et al., 2008;Baliki et al.,
2012), the latter being two patient studies.
Interestingly, here, both pgACC and
amPFC showed increased connectivity
with the anterior insula during uncontrol-
lable pain. It seems important to probe
specific mPFC subregions in future studies of pain facilitation.
Although below the significance threshold, the amygdala also
showed stronger coupling to the anterior insula during uncontrol-
lable pain, consistent with the notion that a lack of control increases
the threatening value of pain (Bowers, 1968;Arntz and Schmidt,
1989). Contrary to our hypothesis, PAG did not contribute to pain
facilitation. This might indicate that pain facilitation by uncontrol-
lability is not mediated via descending pathways, in contrast to many
pain-inhibitory processes (Basbaum and Fields, 1984). However,
negative findings of course need to be interpreted with caution.
dlPFC might mediate controllability-induced pain inhibition
We confirmed dlPFC activation during controllable pain, consis-
tent with a previous study on pain controllability (Wiech et al.,
2006) and other forms of cognitive– emotional hypoalgesia (Wa-
ger et al., 2004). Furthermore, our study showed an increased
inverse connectivity between insula and dlPFC and between
dlPFC and thalamus during controllable pain. This means that, as
activity in the dlPFC increases, activity in the insula and thal-
amus decreases. Interestingly, although the anterior insula was
inversely connected to the dlPFC also during controllable warm
stimulation, the “reversed PPI” with a dlPFC seed did not reveal
any significantly connected regions for the controllable warm
condition. Therefore, it appears that downregulation of regions
processing pain and warmth occurs predominantly with painful
stimulation. Animal studies provide further insight into possible
mechanisms by showing that having control is conveyed via sig-
naling from vmPFC, the homologous region in the rat to the
human dlPFC (Uylings et al., 2003), to the dorsal raphe nucleus.
During controllable stress, stress-induced activation of the dorsal
raphe nucleus is inhibited by the vmPFC and behavioral conse-
quences of uncontrollable stress (potentiated fear conditioning,
escape deficits) are blocked, which is reversed by pharmacologi-
cal inactivation of vmPFC (Amat et al., 2005).
Figure 8. Time courses of BOLD signal change in the anterior insula. Modeled time courses for the conditions controllable pain,
uncontrollablepain,controllable warm, uncontrollable warm, andtheuncontrollable pain time–rating regressorfromtheanterior
insula; shaded area: stimulation interval after the target temperature was reached. Percentage signal change was calculated
relative to the mean of each time series.
Bra¨scher, Becker et al. •Neural Correlates of Un/Controllable Pain J. Neurosci., May 4, 2016 •36(18):5013–5025 • 5023
Clinical implications
Chronic pain is in many instances uncontrollable. Experiencing
uncontrollable pain can lead to hypervigilance (Aldrich et al.,
2000), learned helplessness, and depression, resulting in a self-
amplifying vicious circle (Arntz and Schmidt, 1989). Further,
losing control over pain potentially increases fear of pain and
interferes with task performance (Crombez et al., 2008). In con-
trast, perceived control decreases pain and discomfort in acute
pain (Thrash et al., 1982) and is associated with better function-
ing in chronic pain patients (Tan et al., 2002). Understanding the
endogenous mechanisms underlying pain modulation by uncon-
trollability and controllability might inform clinical approaches
of pain management and therapy. Further, the brain regions that
drive pain facilitation or inhibition by uncontrollability or con-
trollability could be targeted with methods such as transmagnetic
stimulation or neurofeedback with the aim to reduce clinical
pain.
The finding that the perceptual sensitization induced by un-
controllability was not reflected by a higher activation magnitude
in pain-processing regions indicates that a lot has to be learned
about pain processing in the brain before pain biomarkers based
on activation patters should be considered (Davis et al., 2012). In
particular, it needs to be established whether different types of
pain augmentation and facilitation (e.g., by increased nociceptive
input or different types of cognitive– emotional pain modula-
tion) are associated with consistent (connectivity) patterns in the
brain or if the brain can construct the sensation of “pain” via
different mechanisms.
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