The bivalent side of the nucleus accumbens
Liat Levitaa,⁎, Todd A. Hareb, Henning U. Vossc, Gary Gloverd, Douglas J. Ballonc, B.J. Caseya
aSackler Institute for Developmental Psychobiology, Institute of Psychiatry, Weill Cornell Medical College, New York, NY, USA
bCalifornia Institute of Technology, Division of the Humanities and Social Sciences, Pasadena, CA, USA
cCitigroup Biomedical Imaging Center, Weill Cornell Medical College, New York, NY, USA
dLucas Magnetic Resonance Image Center, Stanford University, Palo Alto, CA, USA
a b s t r a c ta r t i c l ei n f o
Received 6 June 2008
Revised 10 September 2008
Accepted 21 September 2008
Available online 11 October 2008
An increasing body of evidence suggests that the nucleus accumbens (NAcc) is engaged in both incentive
reward processes and in adaptive responses to conditioned and unconditioned aversive stimuli. Yet, it has
been argued that NAcc activation to aversive stimuli may be a consequence of the rewarding effects of their
termination, i.e., relief. To address this question we used fMRI to delineate brain response to the onset and
offset of unpleasant and pleasant auditory stimuli in the absence of learning or motor response. Increased
NAcc activity was seen for the onset of both pleasant and unpleasant stimuli. Our results support the
expanded bivalent view of NAcc function and call for expansion of current models of NAcc function that are
solely focused on reward.
© 2008 Elsevier Inc. All rights reserved.
The role of the nucleus accumbens (NAcc) in behavior has tended
to focus largely on responses toward rewarding and appetitive stimuli
and events. However, based on evidence from both human neuroima-
ging studies and animal-based research, a broader role for NAcc
function has beenproposed in behavior modulated byaversive events.
This imaging study has been designed to test the proposed bivalent
function of the NAcc in behavior by addressing two arguments that
have been raised against this idea: 1) that activation of the NAcc to
aversive stimuli is secondary to some kind of relief as a result of the
termination of that event; and 2) that it is a consequence of
preparation and regulation of instrumental motor action.
The NAcc has been viewed as the key site for transference of
motivational and other emotional signals received from the prefrontal
cortex, amygdala and hippocampus to adaptive behavioral responses,
and dopamine has been strongly implicated in facilitating this process
(Laviolette, 2007; Meredith, 1999). However, this role has largely
focused on rewarding appetitive processes (Day and Carelli, 2007),
which has tended to overshadow work that has also demonstrated the
involvement of the NAcc and the dopaminergic system in aversive
emotional processes (Blackburn et al., 1992). For example, dopami-
nergic midbrain neurons increase firing in response to conditioned
and unconditioned aversive stimuli (Guarraci and Kapp,1999; Horvitz,
2000; but see, Ungless et al., 2004 and Schultz,1997), as well as novel
or unpredicted stimuli (Miller et al., 1981; Rasmussen et al., 1986).
Moreover, while the abuse potential of drugs such as amphetamine
and cocaine is largely attributed to their rewarding actions, the effects
of these drugs are not always hedonic, and there is evidence that their
anxiogenic and psychomimetic effects (Mathias et al., 2008; Raven
et al., 2000) might also be mediated by the NAcc and its dopaminergic
innervation (Broderick et al., 2003; Hunt et al., 2005; Koob et al.,1989;
Miczek et al., 1999).
Indeed, the function of the NAcc in gating and modulating goal-
directed action (Cardinal et al., 2002) requires the detection of both
safety and danger cues in the environment. Thus, an expanded,
bivalent view of NAcc function has been advocated, whereby the NAcc
is engaged in processing of both rewarding and aversive stimuli
(Becerra et al., 2001; Jensen et al., 2003; Reynolds and Berridge, 2002).
Consistent with the idea that the NAcc role in behavior is bivalent is
thatit is richlyinnervated, notonly by the amygdala, whichsignals the
salience of both positive and negative stimuli (Breiter et al., 1996;
Demos et al., 2008; Hamann and Mao, 2002; Hamann et al., 2002;
Paton et al., 2006), but also byother regions that process both aversive
and reward information, such as the orbitofrontal cortex, insula,
cingulate cortex, and the midline- and intra-laminar thalamic nuclei
(Bourgeais et al., 2001; Haber et al., 2006; Hsu et al., 2000; Vogt,
2005). In turn the NAcc can affect the expression of emotion via two
routes: 1) It can influence motor action in response to emotive stimuli
via its projections to the substantia nigra and ventromedial globus
pallidus, which belong to the basal ganglia system involved in motor
programming (Zahm and Heimer, 1993); and 2) It can induce
significant changes in autonomic and physiological processes to
these same stimuli, since it also projects to the lateral hypothalamus,
which participates in the autonomic and endocrine expression of
emotion (Kirouac and Ganguly, 1995).
The functional significance of the anatomical connectivity of the
NAcc is reflected at the physiological level where different neurons in
the NAcc can respond to either aversive or appetitive stimuli (Roitman
NeuroImage 44 (2009) 1178–1187
⁎ Corresponding author. The Sackler Institute for Developmental Psychobiology,
Weill Cornell Medical College, 1300 York Ave, Box 140, New York, NY 10021, USA. Fax:
+1 212 746 5755.
E-mail address: email@example.com (L. Levita).
1053-8119/$ – see front matter © 2008 Elsevier Inc. All rights reserved.
Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/ynimg
et al., 2005; Setlow et al., 2003; Wheeler et al., 2008; Wilson and
Bowman, 2005;Yanagimoto andMaeda,2003). In addition,behavioral
studies in both rodents and non-humanprimates have shown that the
NAcc plays a critical role in aversive conditioning and active avoidance
behavior (Ammassari-Teule et al., 2000; Hoebel et al., 2007; Iordanova
et al., 2006; Levita et al., 2002; Schwienbacher et al., 2004), and an
increasing number of human imaging studies have shown enhanced
activity in this region in response to both conditioned and uncondi-
tioned aversive stimuli (Becerra et al., 2001; Gottfried et al., 2002;
Jensen et al., 2003). Finally, in a manner similar to that previously
demonstrated for the amygdala (Etkin et al., 2004; Stein et al., 2007;
Straube et al., 2007), studies in non-human primates and humans
have demonstrated that the positive association between anxiety
levels and responses to aversive and anxiety-evoking stimuli is also
modulated by the degree of NAcc activation (Kalin et al., 2005; Sturm
et al., 2003).
A role for the NAcc in negative contexts is further supported by
studies implicating the NAcc in contextual Pavlovian aversive
conditioning (Haralambous and Westbrook, 1999; Levita et al., 2002;
Westbrook et al., 1997), as well as conditioned inhibition of ongoing
instrumental action (Parkinson et al., 1999). The latter is consistent
with evidence implicating the NAcc in mediating the interaction
between Pavlovian and instrumental contingencies (Hall et al., 2001).
However, in contrast to the detrimental effect of lesions of the
amygdala on fear learning (for review see, Maren, 2001; Phelps and
LeDoux, 2005), some studies have failed to find an effect of lesions or
pharmacological manipulations of the NAcc on discrete cue Pavlovian
aversive conditioning (e.g., Levita et al., 2002; Westbrook et al.,1997).
This finding is also mirrored in a number of human imaging studies
failing to show NAcc activation in response to conditioned aversive
stimuli (Chandrasekhar et al., 2008; Hamann and Mao, 2002; Phelps
et al., 2004). Moreover, it could be argued that NAcc activity observed
in anticipation of, or response to, negative events, is due to the
rewarding effects of termination of an aversive event rather than a
result of a response to the noxious stimuli (Ikemoto and Panksepp,
1999). Additionally, since the NAcc influences instrumental behavior
by allowing Pavlovian conditioned stimuli (CSs) to affect the level of
instrumental responding(Cardinal etal.,2002),the engagementof the
NAcc in some studies may reflect its role in modulating instrumental
motor actions dissociable from emotion.
To address these two possibilities, we designed an fMRI study in
which we could dissociate brain activation to the initiation and
termination of unpleasant and pleasant auditory stimuli in the
absence of learning or a motor response. Consequently, in this study
subjects were required to passively listen to pleasant and unpleasant
auditory stimuli that were randomly presented in a long-event
related design while skin conductance response (SCR) was recorded.
To dissociate activation related to onset versus offset, the duration of
positively and negatively valenced auditory stimuli were jittered and
regressors were created for onset and offset of the negative and
positive stimuli. We predicted that NAcc activation would be
observed for the initiation, but not termination of the unpleasant
sounds, results that would be consistent with the idea of bivalent
Materials and methods
Twenty right-handed adults (10 male, 10 female; age: range 20–
31, mean 25.7±0.6; IQ=116±2.7) took part in the study. Subjects
were free of any medical or neurological problems, and had no
current or previous diagnosis of psychiatric or neurological disorder.
All subjects gave informed consent in accordance with Weill Medical
College of Cornell University IRB committee, and were paid for their
Stimuli and apparatus
Auditory stimuli consisting of two unpleasant (negative tones: n1
and n2) and two pleasant (positive tones: p1 and p2) tones were used
in this study. These tones werepresented for 2, 4, and 6 s. The auditory
stimuli used were modified and generated using the digital audio
editors: Audacity 1.2.6 (http://audacity.sourceforge.net) and PRAAT
Version 4.5.08 (www.praat.org). The auditory stimuli generated were
of 2 s duration and were looped to generate 4 and 6 s segments.
Stimuli: n1, a combined 1000 Hz tone and white noise, which was
intensity tiered for smooth onset and offset; n2, four bursts of a
1000 Hz square wave tone, duration 0.4 s, and silence 0.1 s; p1, a wind
chime recording that was modified for a smooth rise and fall; p2, a
second chime recording amplified and modified, like p1, for a smooth
rise and fall. All stimuli were modified so they would have the same
intensity (95 dB in scanner; Headphones; fMRI Devices Corporation,
Waukesha, WI). These stimuli were chosen after a pilot study was run
to select the most pleasant and unpleasant sounds from a selection of
eight. In the pilot study a randomly mixed sequence of the eight
sounds was presented three times to 11 subjects (age 26–36) who
rated them individually on a 20-point unpleasantness–pleasantness
scale. There was a significant difference in rating the pleasant and
unpleasant sounds (pb0.001). Average rating for the aversive sounds
was 3.7±0.33 and 17.1±0.31 for pleasant sounds. From these eight
sound stimuli the two sounds that were rated as most unpleasant and
the two sounds that were rated as most pleasant were selected for the
Skin conductance response
A skin conductance response (SCR) MRI compatible system
(SCR100C Biopac, Goleta, CA) together with the AcqKnowledge
(Biopac) software was used to monitor the SCR as it varied with the
eccrine sweat gland activity. The computer running AcqKnowledge
and the computer running E-prime (Psychology Software Tools, Inc,
Pittsburgh, PA) were interfaced allowing generation of digital TTL
timestamps for each stimulus on the Biopac channel recording, so that
stimuli presentations during scan were co-registered with SCR record.
The SCR was sampled at 200 Hz using disposable electrodermal gel
electrodes (Biopac model EL507) attached to the distal phalanx of the
pointer and middle fingers of the left hand. The electrodes were
connected to an MRI compatible cable set (MECMRI-TRANS) that
interfaced with the SCR100C amplifier and the control panel. The
SCR100C used a constant voltage (0.5 V) to measure skin conductance.
The SCR was digitized at the electrodes and 1 Hz filter applied (Gain
2 μmho/V). Subjects were asked to wash their hands with water and
dry them gently before the electrodes were attached. SCRs were
analyzed by subtracting the peak skin conductance response
occurring in a time window of 1–5 s after stimulus onset from a
baseline measure just prior tothe stimulus onset. The small numberof
subjects which we successfully recorded SCR from (n=7) precluded
the inclusion of the SCR measures in our fMRI analysis.
Subjects completed a passive listening task in which they heard
pleasant and unpleasant sounds. Stimuli duration varied between 2, 4,
and 6 s in order to deconvolve stimulus onset and offset BOLD
responses. The interstimulus interval was 12 s (Fig. 1A). The entire
experiment consisted of 5 runs, each lasting 212 s. A total of 60 stimuli
were presented, 30 negative and 30 positive sounds. The stimuli were
presented in a pseudorandom order, with never more than two
sounds of the same valence type following each other. Before the start
of the experiment participants were told that they would hear sounds
that were pleasant and unpleasant in nature and that no action was
required on their part except to continue to payattention to tones that
L. Levita et al. / NeuroImage 44 (2009) 1178–1187
would be presented. In addition, participants were instructed to close
their eyes throughout the experiment, and were reminded of this at
the beginning of each run. At the end of the experiment, while still in
the scanner, subjects heard the auditory stimuli presented throughout
the experiment, and rated their subjective experience of each tone on
a 5-point scale (1 as most unpleasant and 5 as the most pleasant). The
scanner was on during subjective rating so that the sounds would be
experienced under the same conditions as during the experimental
task. Subjects made their responses on a five button response glove.
Stimuli and response collection (valence ratings of stimuli) were
presented with the integrated functional imaging system (IFIS; PST,
Pittsburgh) using an LCD video display in the bore of the MR scanner
and a fiber optic response collection device. Self report ratings of state
and trait anxiety were measured using the Spielberger's State-Trait
Anxiety Inventory (Spielberger, 1983) administered following the
Subjects were scanned with a General Electric Signa Excite 3.0 T
fMRI scanner (General Electric Medical Systems, Milwaukee, WI) with
a quadrature head coil. Foam padding placed around the head was
used to reduce motion. A whole brain, high resolution, T1 weighted
anatomical scan (a 3D SPGR; 256×256 in-plane resolution, 240 mm
field of view [FOV]; 124 1.5-mm axial slices) was acquired for each
subject for transformation and localization of functional data into
Talairach space (Talairach and Tournoux, 1988). A spiral in and out
sequence (Glover and Thomason, 2004) was used to collect functional
data (TR=2000, TE=30, FOV=200 mm, Flip angle=90 and 64×64
matrix). We obtained 29, 5 mm thick coronal slices with an in-plane
resolution of 3.125×3.125 mm that covered the entire brain except for
the posterior portion of the occipital lobe.
Imaging data analysis
AFNI software package (Cox,1996). The first 4 volumes (8 s) from each
run were discarded to allow the scanner to reach magnetization
equilibrium. Following slice time correction, images were registered
to the first image volume following the high-resolution anatomical
dataset using rigid body transformations and smoothed using an
isotropic 6 mm Gaussian kernel. Head motion was examined to
confirm that all subjects had less than 2 mm of translation or 2° of
rotational movement. Time series were normalized to percent signal
change to allow comparisons across runs and individuals by dividing
signal intensity at each time point by the mean intensity for that voxel
and multiplying the result by 100. Four regressors were created for
onset and offset of negative and positive sounds by convolving the
stimulus timing files with a gamma-variant hemodynamic response
function. Linear regression modeling was performed to fit the percent
signal change time courses to each regressor. Linear and quadratic
trends were modeled in each voxel time course to control for
correlated drift. Motion parameters were included in the GLM as
covariates of no interest. The resulting regression coefficients
represent an estimate of percent signal change from the mean.
Group level analyseswere conducted on the regression coefficients
from the individual analysis after transformation into the standard
coordinate space of Talairach and Tournoux (1988), using parameters
obtained from the transformation of each subjects' high-resolution
anatomical scan. Talairached transformed images had a re-sampled
resolution of 3×3×3 mm. Normalization to Talairach space was done
using automatic Talairach transformation in AFNI, where the anato-
mical volume was warped using 12-parameter affine transform to a
template volume (TT_N27) in Talairach space. An omnibus 2 (valence;
negative/positive)×2 (time; onset/offset) way ANOVA that included
subject as a random factor was conducted to determine the main
effects of valence, time, and valence×time interaction. Correction for
multiple comparisonswas applied at the clusterlevelfollowing Monte
Carlo simulations conducted in the AlphaSim program within AFNI.
Clusterwise false-positive rates of pb0.05 corrected for multiple
comparisons were determined for whole brain analyses. Additional
simulations were restricted to the NAcc and amygdala based on the
size of these regions unilaterally derived from the Talairach atlas
included in the AFNI distribution (Volume: amygdala ∼890 mm3;
Nucleus accumbens ∼1000 mm3). Whole brain simulations were
conducted at individual voxel α probabilities set at 0.01, 0.001 and
0.0001 to allow identification of both broad and focal activations.
Individual voxel α probabilities were set at 0.025 for simulations
within the amygdala and NAcc. Only clusters with more than three
voxels were considered for analysis.
For functional region of interest (ROI) analyses, anatomical masks
of the NAcc and amygdala ROIs were defined from the Talairach atlas
included in the AFNI software distribution. Voxels within these masks
that showed activation above the threshold of pb0.025 for the
valence×time interaction at the group level were included in the
functional ROI analysis. To address the concern that our group level
functional ROI (defined by the average group level of activation)
represented the same anatomical region in different participants we
Talairach transformed individual anatomical ROIs for the NAcc and
amygdala generated with FreeSurfer to compare the individual
FreeSurfer ROIs with the Talairached group functional ROIs. Parcella-
tion of the subcortical anatomy into regions of interest was performed
using the FreeSurfer software suite (Fischl et al., 2002). These tools
delineate anatomical divisions via automatic parcellation methods in
which the statistical knowledge base derives from a training set
incorporating the anatomical landmarks and conventions described
by Duvernoy (1991). The resulting segmentation maps were viewed
and the FreeSurfer derived-segmentation of regions of interest were
Fig. 1. Behavioral task and validation of stimulus valence. (A) The paradigm consisted of presentations of negative and positive sound stimuli of variable duration (2, 4 or 6 s)
presented with a fixed inter-stimulus interval (ISI) of 12s. In total 30 negative and 30 positive auditory stimuli were presented in a pseudorandom order. (B) While in the scanner
subjects rated the sounds as either pleasant or unpleasant on a 5 point rating scale,1 being most unpleasant, and 5 being most pleasant. (C) Skin conductance response (SCR) to the
negative and positive auditory stimuli.
L. Levita et al. / NeuroImage 44 (2009) 1178–1187
evaluated and manually edited when found to be incorrect. We found
that all subjects showed overlap within each of the two regions
of interest, demonstrating that our group functional ROIs fit the
individual subject anatomical data.
To examine the time course of activation, mean BOLD responses
were plotted for selected clusters. To that end we applied functional
masks of these clusters (based on the group level analysis) to extract
individual time series averaged across voxels for each subject's fMRI
time series. From these, percent signal change for each event was
calculated relative to the mean of the two TRs prior to stimulus onset
in a time window of 0 to 12 s. We also analyzed our data using the
mean as the baseline which did not appear to change any of the
results. In addition, BOLD signal attenuation or enhancement with
repeated presentations of the negative and positive stimuli was
examined in individual subjects in two regions of interest, the NAcc
and amygdala. In this analysis the peak hemodynamic response for
each stimulus in each run was measured in the significant clusters
observed after group analysis in the right NAcc and right amygdala.
Mean of peak BOLD signal in early runs (1 and 2) and late runs (3 and
4) in these regions was calculated and a slope of best fit for early
versus late trial peak response was generated. The gradient of the
slope was taken as a measure of either habituation or sensitization to
repeated presentation of stimuli across the experiment. Positive
slopes are indicative of an increase in activation (sensitization) with
time, whereas negativeslopes areindicativeof a decrease in activation
(habituation) to the repeatedly presented stimuli.
Moreover, the interaction of the activity in the NAcc and amygdala
in response to the onset of the negative and positive stimuli with the
activity of other brain regions was characterized by performing a
functional connectivity analysis. This analysis was computed in AFNI
and performed by using the right amygdala functional ROI (derived
from group analysis results valence×time). Individual time series
were extracted from the amygdala cluster for each subject's fMRI time
series and averaged across voxels. Linear trend removal was first
conducted on the entire time series. Deconvolution was then run on
the seed time series, and an interaction regressor created [decon-
volved seed time series×events of interest−the onset of the negative
(ni) and positive (pi) stimuli]. Single subject GLM was then run exactly
as fora regularanalysis, but here, adding the two additionalregressors
of interest. In addition, all of the original regressors of interest (events)
and no interest (motion parameters) were included to account for all
sources of variability in the dataset. The resulting correlation
coefficients for the interaction regressors were transformed to a
normal distribution using Fisher's Z transformation before group
analyses on these values.
All statistical analysis of the data was conducted in SPSS 15.0 (SPSS
Inc. Chicago, IL).
Validation of stimulus valence
At the end of the experimental session, while still in the scanner,
subjects rated the auditory stimuli presented with respect to their
positive and negative stimulus valence. There was a significant
difference in valence ratings between the positive and negative
stimuli presented (Fig. 1B), where positive sounds were rated as
pleasant and negative sounds were rated as unpleasant (Z=−3.9,
p≤0.001). No gender differences were found in valence rating, nor did
valence ratings differ with respect to the duration of the stimuli
presentation (2, 4, or 6 s).
Skin conductance response
Dissociation between the positive and negative sound stimuli was
also found at the physiological level. Significantly greater skin
conductance response (SCR) was observed to the negative versus
the positive auditory stimuli (Fig. 1C; Z=−2.67, p=0.008). Measure-
ment noise caused by the scanner environment prohibited reliable
SCR in the majority of subjects tested, consequently the small number
of subjects with robust SCR (n=7) precluded the inclusion of the SCR
measures in our fMRI analysis. No evidence was found of a SCR to the
Whole brain analysis
The fMRI data were analyzed using a generalized linear model
(GLM) that evaluated BOLD responses to the initiation and termina-
tion of the auditory stimuli. While this study was focused on NAcc
activation, we first performed a whole-brain analysis to determine
regions activated by negative versus positive stimuli at onset and
offset using a 2 (valence; negative versus positive)×2 (time; onset
versus offset) ANOVA. The completelistof brain regions showingmain
effects and interactions is given in Table 1 and in Supplementary
Tables 1 and 2. Whole-brain contrast analysis probing the main effect
of valence revealed greater activation to negative rather than positive
auditorystimuliin thegreater partof thestriatalcomplex, as wellasin
the right amygdala (Table 2, and supplemental Fig. 1). Moreover,
whole brain contrast analysis for the main effect of time (stimulus
offset minus onset) for the negative as well as the positive stimuli did
Interaction of valence (positive vs. negative)×time (onset vs. offset)
Brain region Talairach coordinates (CM)
Side RL AP ISSize
F statsOnset Offset
Medial frontal gyrus
Inferior frontal gyrus
Group analysis ANOVA. CM, center of mass; L, Left; R, Right. Whole brain p=0.0001,
corrected to pb0.05.
⁎ Region of interest correction p=0.025, corrected to pb0.05.
Whole-brain contrast analysis probing the main effect of valence
Talairach coordinates (CM)
SideRLAP ISSize (mm3)
Striatum, insula, globus pallidus,
thalamus and brainstem
Posterior cingulate gyrus
Insula and Inferior frontal gyrus
Contrast Analysis: positive minus negative. CM, center of mass; L, Left; R, Right.
Whole brain pN0.001, corrected to pN0.05.
aNo brain regions with greater activation to the positive tones were found.
L. Levita et al. / NeuroImage 44 (2009) 1178–1187
not reveal any brain regions showing activation on termination of
either of these stimuli (data not shown).
Valence×time interaction (Fig. 2) revealed greater activation for
the negative auditory stimuli in the inferior frontal gyrus, cingulate
cortex (Fig. 2B), anterior and posterior insula (Fig. 2C), globus pallidus,
and cerebellum, all associated with greater relative deactivation on
the offset of the negative stimulus. In contrast, the peak magnitude of
onset BOLD activation was equivalent for both valence types in
clusters observed in areas of the thalamus (Fig. 2D) and the superior
temporal gyrus (STG). However, these thalamic and STG activations
were associated with greater relative deactivation on the offset of the
Functional region of interest analysis
We conducted a functional region of interest (ROI) analysis on the
NAcc to test our prior hypothesis regarding activation in response to
positively and negatively valenced stimuli. Voxels within the NAcc
that showed activation at the group level for the valence×time
interaction were included in this functional ROI. For this analysis, an
anatomical mask of the NAcc was defined from the Talairach atlas
included in the AFNI software distribution. Voxels within the NAcc
were corrected for multiple comparisons at the pb0.05 level using
cluster thresholds determined by AlphaSim. The AlphaSim Monte
Carlo simulations were run using an individual voxel threshold of
pb0.025 within an anatomical mask of the NAcc taken from the
AFNI Talairach atlas.
Within the NAcc there was a main effect of time, whereby bilateral
activation of the NAcc was observed in response to the onset of the
positive and negative stimuli. No main effect of valence was observed.
However, there was a significant interaction between valence and
stimulus onset and offset in the right NAcc (Figs. 3A and B, Table 1),
with significantly greater response to unpleasant than pleasant sound
stimuli (t=−3.61 p=0.002) at onset. Moreover, no evidence for
activation of NAcc activity on offset of the positive or negative stimuli
was found (Fig. 3B). Further analysis of activation of the NAcc revealed
a positive correlation between the mean BOLD activation to the onset
of negative and positive stimuli, such that subjects who showed a high
response magnitude to the positive stimuli, also showed a heightened
response to the negative stimuli (Fig. 3D; Pearson's r=0.881;
pb0.001; n=20). We also found that NAcc activation to the negative
and positive stimuli remained largely constant throughout the
experiment, as measured by comparing mean response magnitude
Fig. 2. Activation of nociceptive/emotive brain regions. (A) Coronal sections illustrating regions that showed significant activation in group analysis of valence×time (pb0.0001,
corrected to pb0.05). Activations are displayed over a Talairach-normalized coronal templates in radiological convention (right is left). Greater activation was observed for the
negative aversive stimuli in the (B) cingulate cortex (CIG), (C) insular cortex (IN), and cerebellum (CB). (D) No difference in activation to negative and positive sounds was observed in
thalamus (TH). Line plots represent mean±standard error of the mean (SEM) across participants. Anterior/posterior y-coordinates are specified below the coronal sections.
L. Levita et al. / NeuroImage 44 (2009) 1178–1187
during early, middle and late trials (valence, F1,38=2.71; time
F1,38=0.43, valence×time, F1,38=0.001; Supplemental Fig. 2).
To examine regional specificity in response to aversive and
pleasant stimuli, we investigated the response of the amygdala, a
region implicated in affective processes and which sends significant
projections to the NAcc. This analysis was performed in the same
manner in which we conducted the functional ROI analysis for the
NAcc. The amygdala functional ROI was defined by voxels showing a
significant valence×time interaction using small volume correction
within an anatomical mask of the amygdala at pb0.05. Within the
amygdala there was a main effect of time as well as valence×time
interaction, but no main effect of valence. Post hoc t-tests on the
main effect of time (onset vs. offset) revealed bilateral activation of
the amygdala to both the negative and positive stimuli, as did
valence×time interaction (Fig. 4, Table 1, and Supplementary Table 2).
We also found sensitization of the amygdala, as indicated by
increased amygdala activity to repeated presentations of the negative
stimuli, but not positive stimuli. This increase was associated with
self-ratings of anxiety, such that an increased amygdala activity with
repeated presentations of the negative stimuli predicated greater
state and trait anxiety in individual subjects (State; Pearson's
r=0.674; p=0.001; Trait; Pearson's r=0.642, p=0.003, Fig. 4C). No
such association was found for the NAcc (State; Pearson's r=0.051;
Trait; Pearson's r=−0.094).
Based on anatomical data demonstrating NAcc-amygdala connec-
tivity, we also conducted functional connectivity analysis with the
right amygdala cluster set as the seed region (Fig. 4A). We found a
positive correlation between activity in the right amygdala and the
right NAcc during the presentation of the negative, but not positive
stimuli (Figs. 4D and E; and Supplemental Table 3).
Notably in this study auditory stimuli of different durations were
presented to allow us to deconvolve stimulus onset versus offset.
Plots of the hemodynamic response to the positive and negative
stimuli of different durations revealed that only regions such as the
superior temporal gyrus, which is involved in auditory perception,
were sensitive to stimulus duration. In contrast, this was not the
case in our regions of interest, the NAcc or amygdala, where the
BOLD hemodynamic response was independent of stimulus duration
(Fig. 5, and Supplemental Fig. 3).
We found that the NAcc responds to the onset of both positive and
negative stimuli. Onset and offset analysis of activation of the NAcc to
pleasant and unpleasant sounds in a passive listening paradigm
confirmed a direct activation of this region by aversive stimuli, rather
than an effect secondary to some kind of relief, or a result of
preparation and regulation of instrumental motor action. These
results support the expanded view of NAcc function, whereby the
NAcc plays a key role in modulating behavior to aversive and painful
stimuli, and not just to stimuli that are rewarding in nature. Our
findings are consistent with several studies that have reported striatal
activity, including the NAcc, for primary and conditioned aversive
stimuli (Blazquez et al., 2002; Ravel et al.,1999; Williams et al.,1993),
as well as enhanced dopamine release in this region in response to
similar events (Horvitz, 2002; Salamone et al., 2005). Moreover,
consistent with the idea that the BOLD activations observed in this
study did not reflect a simple sensory percept but rather valence, we
found that neither the amygdala nor NAcc were sensitive to stimulus
duration, unlike the superior temporal gyrus, a region involved in
Fig. 3. Bivalent activation of the nucleus accumbens. (A) NAcc activation map in the coronal plane (pb0.05, corrected). (B) Activation in right NAcc on onset but not offset of both
negative and positive events. (C) Time course for the hemodynamic response in the right NAcc cluster. Image is in radiological format (right is left). (D) Scatter plot of the positive
correlation of individual subject's meanpeak activation of the NAcctothe positiveand negativestimuli throughoutthe experiment. Peak activationwas defined foreach individualby
plotting the bold response for each event, and taking the peak value observed in resulting hemodynamic response function. Bar and line plots represent mean±standard error of the
L. Levita et al. / NeuroImage 44 (2009) 1178–1187
auditory perceptual processes (Pandya, 1995). Bivalent activation of
the NAcc in this study is further supported by the clear dissociation in
subjects' subjective valence rating of the positive and negative stimuli
as being pleasant and unpleasant, respectively, which was also
reflected in dissociable physiological response (SCR) to these stimuli.
Nevertheless, it is possible that the NAcc was responding to the
arousing or attention-grabbing quality of the stimuli presented rather
than their valence, which would be consistent with studies that have
suggested that the NAcc maybe responding to stimulus salience (Zink
et al., 2006), or with other studies which find that both valence and
salience are critical for NAcc activation (Cooper and Knutson, 2008).
However, in this study, the responses we observe in the NAcc are not
Fig. 4. Bivalent amygdala activation. (A) Coronal slice showing right and left amygdala clusters. (B) Time course for the hemodynamic response in the right amygdala cluster. Images
are in radiological format (right is left). Line plot represent mean±standard error of the mean (SEM) across participants. (C) Scatter plot of the correlation between trait anxiety and
change in right amygdala activation through time. Trait anxiety scores were positivelycorrelated with increased amygdala activity on repeated presentations of the negative auditory
stimuli. The y-axis represents rate of change in amygdala activation, positive values indicate sensitization, and negative scores indicate habituation. The x-axis represents trait
anxiety score. D&E. Functional connectivity analysis with the right amygdala fROI cluster set as the seed region; Significant functional connectivity was observed between the right
NAcc and right amygdala during presentation of the negative (D) but not positive tones (E). FC, functional connectivity; R, right; L, left.
Fig. 5. Nucleus accumbens and amygdala activation are not sensitive to stimulus duration. Time course of the hemodynamic response on presentation of the different duration
negative (n) stimuli (2, 4, and 6 s) in the right superior temporal gyrus (A), right nucleus accumbens (B) and right amygdala (C) clusters from group analysis valence×time interaction.
Line plots represent mean±standard error of the mean (SEM).
L. Levita et al. / NeuroImage 44 (2009) 1178–1187
solely a reflection of stimuli's salience, since in auditory sensory areas
as well as the thalamus we see equivalent activation to the positive
and negative tones presented tothe participants, suggesting matching
of stimuli in terms of salience.
In this study we did not find activation of the NAcc on the offset of
an aversive event. However, our negative result on offset of an
aversive event needs to be interpreted with caution. It is possible that
subjects would never have felt relief on the offset of the aversive
sound, since the scanner environment may in itself have been an
Notably, in this study we did not observe a dissociable temporal
activation profile for NAcc activation in response to positive and
negative primary auditory stimuli, as previously reported for condi-
tioned aversive stimuli (Gottfried et al., 2002). Gottfried et al (2002)
found that the NAcc showed significant activation to the aversively
conditioned stimulus (CS+) early but not late in learning, the reverse
being the case for the appetitive CS+. These temporal differences may
relate to cue learning rather than unconditioned stimuli (US)
responses as investigated in this study. They may also explain the
failure to observe NAcc activation in some human imaging aversive
conditioning studies (Chandrasekhar et al., 2008; Delgado et al., 2006;
Hamann and Mao, 2002), since activation of the region in response to
aversive CS+ may be masked if time is not a factor in the analysis.
Previous studies have suggested that the NAcc may play a role in
the expression of anxiety (Kalin et al., 2005; Sturm et al., 2003).
However, here we did not find an association between the rate of
habituation of the NAcc to the negative auditory stimuli and subjects'
self-rating of anxiety. Yet, consistent with previous studies (Etkin et
al., 2004; Hare et al., 2008) the change in amygdala activity to the
negative stimuli over time was associated with subjects' anxiety
levels. The amygdala, specifically the basolateral nucleus, sends
significant projections to the NAcc (Nauta, 1982) and hence if high
anxiety levels enhance output from the amygdala, it might be that
target sites like the NAcc would also show the same phenotype.
Indeed, functional connectivity analysis revealed a positive coupling
between the amygdala and the NAcc when subjects were exposed to
the aversive, but not positive stimuli. However, while amygdala input
can affect NAcc function (Cardinal et al., 2004; Setlow et al., 2002),
other pathways to the NAcc can act to offset or change the degree by
which the amygdala can drive this region (Everitt et al., 1999; Grace,
2000; Jackson and Moghaddam, 2001; Setlow et al., 2002). More-
over, a lack of correlation between levels of anxiety and NAcc
activation may be a result of the passive nature of our task. Thus,
while our task design enabled us to examine the response of the
NAcc to emotive stimuli in the absence of possible confounds
stemming from motor responses, it did not allow us to examine fully
the functional significance of these activations in modulation of
behavior. Tasks that involve instrumental approach–avoidance
behavior may be more likely to demonstrate correlations between
individual anxiety levels and both task performance and the degree
of brain activation in the NAcc.
In this study, the amygdala, like the NAcc, showed a bivalent
pattern of activation, consistent with a large body of evidence
demonstrating that while the amygdala responds most reliably to
negative stimuli (Hariri et al., 2000; Phelps et al., 2001; Whalen et al.,
1998), it also performs operations such as signaling the salience of
positive stimuli (Breiter et al., 1996; Demos et al., 2008; Hamann and
Mao, 2002; Hamann et al., 2002). However, while the NAcc and
amygdala respond to similar types of bivalent information, and are
intimately connected, they belong to functionally dissociable neural
circuits: 1) An amygdala-centered circuit that acts as a rapid response
module that can engage affective response units even prior to
conscious stimulus identification (Morris et al., 2001); and 2) A
NAcc-centered circuit that can only fullyengage down-stream sitesfor
action-selection once stimulus identity has been established and its
significance evaluated. Thus, while NAcc neurons respond to emotion-
eliciting stimuli, they do so in a manner that is largely dependent on
individual stimulus identity, i.e., object-specific, rather than respon-
ding to a single common physical or psychological property of these
stimuli (Roitman et al.,2005; Setlowet al., 2003; Wilsonand Bowman,
2005; Yanagimoto and Maeda, 2003). This is in sharp contrast to
neurons in the amygdala which tend to respond to a single common
psychological property (Belova et al., 2007; Maeda et al., 1993; Paton
et al., 2006; Salzman et al., 2007).
The stimulus-identity dependency of NAcc neurons is consistent
with the NAcc being a part of an approach–avoidance behavior
network. Such a system must first be able to process information
about the identity and value of unconditioned stimuli that can act
either as rewards or punishers, and that once these events occur,
motor systems must re-direct behavior to gain maximal utility from
rewarding events (Day and Carelli, 2007), or be engaged in a way that
will allow the organism to avoid threat and aversive outcomes (Faure
et al., 2008; Reynolds and Berridge, 2001). This idea is consistent with
the role of the NAcc in both negative and positive reinforcement
processes, for example in humans anticipating monetary gain and loss
(Cooper and Knutson, 2008), and the damaging effect of NAcc lesions
and pharmacological manipulations in tasks that require behavioral
inhibition and modulation of instrumental action to optimize reward
gain and avoid risk (Cardinal et al., 2004; Christakou et al., 2004;
Martinez et al., 2002; Salamone et al., 1997; Wadenberg et al., 1990).
In this study we were able to show that just as the amygdala is not
solely responsive tonegative events, the NAccis notonly responsiveto
anticipated positive rewards, but also aversive events. These results
support models of NAcc function that are not solely focused on
reward. This broader bivalent role for the NAcc is consistent with the
anatomical connectivity of the NAcc that allows it to integrate a
substantial amount of information from regions that process both
positive and negative valence. Future work needs to investigate the
precise role of this integration in emotional regulation via outputs of
the NAcc to motor, cognitive and autonomic centers.
We would like to thank Bruce McCandliss and Jason Zevin for their
thoughtful discussions about this work. This research was supported
in part by the National Institute of Drug Abuse Grant R01 DA018879
(BJC), NIH P50 MH52196 and MH079513, the Mortimer D. Sackler
family and Dewitt-Wallace Reader's Digest Foundation.
Appendix A. Supplementary data
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