 |
 Previous Article | Next Article 
The Journal of Neuroscience, January 1, 2003, 23(1):303-307
Differential Response Patterns in the Striatum and Orbitofrontal
Cortex to Financial Reward in Humans: A Parametric Functional
Magnetic Resonance Imaging Study
Rebecca
Elliott,
Jana L.
Newman,
Olivia A.
Longe, and
J.
F. William
Deakin
Neuroscience and Psychiatry Unit, University of Manchester,
Manchester M13 9PT, United Kingdom
 |
ABSTRACT |
Responses to monetary reward in humans have been assessed in a
number of recent functional imaging studies, and it is clear that the
neuronal substrates of financial reinforcement overlap extensively with
regions responding to primary reinforcers, such as food. Money has the
practical advantage of being an objectively quantifiable reinforcer. In
this study, we exploit this advantage using a parametric functional
magnetic resonance imaging design to look at the patterns of
responding to systematically varying reward values. Twelve healthy
volunteers were scanned during performance of a rewarded target
detection task, in which the reward value varied between task blocks.
We observed three distinct patterns of responding in different regions.
Amygdala, striatum, and dopaminergic midbrain responded to the presence
of rewards, regardless of value. In contrast, premotor cortex showed a
linear increase in response with increasing reward value. Finally,
medial and lateral foci of orbitofrontal cortex responded nonlinearly,
such that response was enhanced for the lowest and highest reward
values relative to the midrange. These results suggest functional
distinction in response patterns within a distributed reward system.
Key words:
reinforcement; reward; nucleus accumbens; amygdala; orbitofrontal cortex; fMRI
| |
Introduction |
Electrophysiological studies in
animals have revealed much about neural systems mediating reward.
Functional neuroimaging now allows these systems to be investigated in
the human brain. Studies using pleasant sensory stimuli (Rolls et
al.,1997 ; O'Doherty et al., 2001, 2002), drugs (Breiter et al., 1997;
Stein et al., 1998; Volkow et al., 1999), or financial reward (Delgado
et al., 2000; Elliott et al., 2000; Knutson et al., 2000; Breiter et
al., 2001) have demonstrated roles for distributed neural systems in mediating human reward processing. Key components include midbrain, amygdala, striatum, thalamus, and regions of prefrontal cortex, in
particular orbitofrontal and anterior cingulate cortices. These regions
parallel those identified in an extensive animal literature (Koob, 1992; Robbins and Everitt, 1992, 1996; Schultz, 2000), and it is striking that abstract rewards in humans (winning money, success in a fictitious competition, and symbolic reward) are associated with neuronal responses in the same regions that respond to
primary reinforcers.
Schultz and others have proposed detailed theoretical models of the
functional divisions within extended reward systems based on
electrophysiological and lesion evidence in animals (Schultz, 2000).
Ventral striatal neurons fire in response to actual rewards but also
during anticipation of predicted rewards (Schultz et al., 1992, 1993;
Hollerman and Schultz, 1998). In contrast, orbitofrontal cortex (OFC)
appears to code relative, rather than absolute, values of rewards
(Tremblay and Schultz, 1999; Watanabe, 1999). Other regions within the
system also play functionally distinct roles; for example, the
amygdala is critical in associative learning, relating stimuli to
rewards (Hatfield et al., 1996; Holland and Gallagher, 1999).
Dissociable functions within human reward systems are less clearly
understood, although evidence from functional magnetic resonance
imaging (fMRI) has started to suggest important distinctions. In a
previous study (Elliott et al., 2000), we used a simple gambling paradigm to show that total winnings correlated with hemodynamic response in ventral striatum. In contrast, OFC responses correlated with the most extreme outcomes, whether winning or losing, a finding also reported by Breiter et al. (2001). In a different approach, O'Doherty et al. (2001) showed that magnitude of symbolic monetary reward received in a reversal-learning task was correlated with neuronal response in medial OFC, whereas magnitude of punishment correlated with response in lateral OFC.
The aim of the present experiment was to explicitly dissociate the
responses of human reward systems, specifically foci of midbrain,
ventral and dorsal striatum, amygdala, and prefrontal cortex, to
varying magnitudes of financial reward using a parametric study design.
Parametric designs have proved valuable in exploring relationships
between systematically varying experimental parameters and
physiological responses (Buchel et al., 1998). We used a simple target
detection task in which correct responses were financially rewarded.
The size of reward was varied across blocks of the task to detect
different patterns of response in relation to reward value.
Specifically, it allowed us to distinguish between regions in which the
response to reward was a simple on-off function, regions in which
there was a linear response to increasing reward, and regions in which
the response related to reward value in a nonlinear manner.
| |
Materials and Methods |
Subjects. Twelve right-handed subjects, six male and
six female, were recruited to participate in this experiment. All
subjects were students at the University of Manchester (mean age of
23.6) and were not wage earners. The financial rewards used were
therefore likely to have a similar value for all subjects. Subjects
with self-reported neurological or psychiatric history were excluded, and subjects were asked not to use recreational drugs or drink excessive alcohol in the 48 hr before scanning. The Beck Depression Inventory was used to screen subjects for clinically significant depression. Subjects who were color-blind were also excluded.
fMRI scanning. Subjects were scanning using a Phillips
(Eindhoven, Holland) 1.5T Gyroscan ACS NT, retrofitted with
Powertrak 6000 gradients, operating at software level 6.1.2. One
hundred two single-shot echo-planar volume images were acquired, with a
repeat time of 5 sec and an echo time of 40 msec. Each volume comprised
40 axial slices with 3.5 mm spacing and in-plane resolution of 3 × 3 mm. The first two volumes of each run were to allow for T1
equilibration effects and were discarded before analysis. A T1-weighted
structural scan was also acquired for each subject, and these were
examined by a consultant radiologist to exclude any structural
abnormality; no such abnormality was reported for any of the 12 subjects.
Cognitive task. Subjects were scanned during performance of
a simple target detection task. Different colored squares were presented on a screen at a rate of one every 1.33 sec. Subjects were
told to respond by squeezing a pneumatic bulb with their right hand
every time they saw a green or blue square. The study was divided into
blocks 40 sec long; each block contained 22 colored squares, eight of
which were targets, interspersed randomly among nontargets. When
subjects responded to a target, they saw a reward stimulus comprising
an image of a coin with the monetary value superimposed. Each reward
stimulus was also displayed for 1.33 sec. The value of the reward was
constant within blocks, and the amount to be won for correct responses
was displayed continuously at the bottom of the screen.
Four levels of reward were used in different blocks [10p (pence), 20p,
50p, and £1 (pound)], and there were also blocks in which
responses elicited no reward. Subjects saw a blank circle after
squeezing the bulb. In between the 40 sec blocks were 10 sec rest
blocks. These were included partly to give subjects a break and partly
to allow nonspecific drift in fMRI signal to be modeled out of the data.
Data analysis. Data were analyzed using SPM99 (K. J. Friston, The Wellcome Department of Cognitive Neurology, London,
UK). Images were first realigned, using the first image as a
reference. They were then normalized into a standard stereotactic
space, using Montreal Neurological Institute templates and the
coordinate system of Talairach and Tournoux (1988), and smoothed using
an isotropic Gaussian kernel filter of 10 mm full-width
half-maximum to facilitate intersubject averaging.
Statistical analysis was performed with a random effects model. A
parametric design was used, as discussed by Buchel et al. (1998), that
allowed us to model nonlinear as well as linear hemodynamic responses
using orthogonalized polynomial expansion functions. First-level
analysis was performed on each subject to generate a single mean image
corresponding to each term of the polynomial expansion. These mean
images were then combined in a second-level analysis using one-sample
t tests to investigate group effects. Statistical maps were
thresholded at p < 0.001 uncorrected, and small volume
corrections (Worsley et al., 1996) were applied to a priori regions of
interest: amygdala, dorsal and ventral striatum, and medial and lateral OFC.
| |
Results |
Behavioral responding
All subjects correctly detected all targets. Response latencies
did not differ significantly under the different reward conditions, although there was a nonsignificant trend for subjects to respond quicker for larger rewards (p < 0.1).
fMRI results
For clarity, we focus here on positive associations between reward
size and neuronal responses. We had no clear predictions about regions
that would be more responsive for smaller rewards, and there were no
responses observed in negative contrasts that survived correction for
multiple comparisons.
Regions responding to reward compared with no reward
This represents the zeroth-order term in the parametric analysis
and corresponds to those regions in which there is an on-off or
all-or-nothing response to the presence of reward (Table
1).
Neuronal responses significant at p < 0.05, corrected
for multiple comparisons, were observed in the bilateral lingual gyrus, left postcentral gyrus (BA 3), anterior medial prefrontal cortex (BA
9), and left putamen. Responses significant at p < 0.001 uncorrected were seen in bilateral superior temporal gyrus, right
insula, right premotor cortex (BA 6), dopaminergic midbrain, right
putamen, right ventral striatum, and right amygdala. Applying a small
volume correction to the hypothesized regions of midbrain, striatum and amygdala, these regions were significant at p < 0.05 corrected (Fig. 1). There was also a
response in the left lateral OFC (BA 11), significant at
p < 0.001 uncorrected, but this did not survive small
volume correction.
View larger version (89K):
[in this window]
[in a new window]
|
Figure 1.
Blood oxygenation level-dependent (BOLD)
responses in the striatum (A), midbrain
(B), and amygdala (C) that
showed an all-or-nothing response to reward. The mean adjusted response
(no units) of ventral striatum to different reward values is shown in
D, demonstrating the all-or-nothing response pattern.
BOLD responses are thresholded at p < 0.001 uncorrected, but regional responses survive Bonferroni correction when
the small volume procedure (Worsley et al., 1996) is used.
|
|
Regions responding linearly to increasing reward
This represents the first-order term in the parametric analysis
and identifies those regions in which neuronal response increases monotonically with increasing reward (Table 1). The main region involved was a large cluster with the voxel of maximal response in
right premotor cortex (BA 6) (Fig.
2).
View larger version (21K):
[in this window]
[in a new window]
|
Figure 2.
BOLD responses in the premotor cortex
(A) that showed linear increase in response to
increasing reward. The mean adjusted response (no units) of premotor
cortex to different reward values is shown in B,
demonstrating the linear response pattern. BOLD responses are
thresholded at p < 0.001 uncorrected.
|
|
Regions responding nonlinearly to increasing reward
This represents the second-order term in the parametric analysis.
Because of the orthogonalization of the polynomial expansion terms, the form of the model was a U-shaped curve (Buchel et al., 1998). Thus, this actually represented regions in which the response was maximal at the lowest (zero) and highest (£1) levels of reward and
less so at the intermediate levels. The regions involved were the
anterior medial frontal cortex (BA 8), in which response survived correction for multiple comparisons, and the medial (BA 10) and lateral
orbitofrontal cortex bilaterally (BA 47) (Fig.
3A,B). The medial focus survived small volume correction at p < 0.05. Although the individual lateral OFC foci did not, the fact
that this response was bilateral and symmetrical argues against a type 2 error.
View larger version (36K):
[in this window]
[in a new window]
|
Figure 3.
BOLD responses in the medial (BA 10;
A) and lateral (BA 47; B) OFC that showed
a U-shaped response to reward value. The mean adjusted responses (no
units) of the two OFC foci to different reward values are shown in
C and D, demonstrating the response
pattern corresponding to an orthogonalized second-order polynomial
expansion. BOLD responses are thresholded at p < 0.001 uncorrected, but regional responses survive Bonferroni correction
when the small volume procedure (Worsley et al., 1996) is used.
|
|
| |
Discussion |
This study confirmed roles for dorsal and ventral striatum,
amygdala, and medial and ventral prefrontal regions in human reward processing. As in previous studies (Elliott et al., 2000; Knutson et
al., 2000; Breiter et al., 2001), it is striking that regions responsive to monetary reinforcement overlap extensively with those
responsive to primary reinforcers in animals. The key finding of the
present study was of differential patterns of responsiveness in
different regions. Amygdala and striatum showed an all-or-nothing response to reward, whereas premotor cortex responded linearly to
increasing reward and anterior medial frontal and OFC foci responded in
a more complex, nonlinear manner.
The simple on-off striatal response to reward is, at first sight,
contradictory to our previous finding (Elliott et al., 2000) of
increased ventral striatal response associated with cumulative amount
of money won. However, in that study, the amount won was confounded
with the number of reward experiences. Each individual reward had the
same value, and high accumulated winnings reflected more rewards
experienced. In the present study, the number of rewards experienced is
constant across rewarded blocks; it is the value of individual
rewards that varies. It is therefore possible that striatal signal
reflects the number of reward experiences to a greater extent than
their value. Breiter et al. (2001) also reported increases in ventral
striatal response associated with increasing reward value in a design
in which value and number of rewards were not confounded. However, it
is striking that, in their study comparing $0, $2.50, and $10 rewards,
the difference in ventral striatal signal between $0 and $2.50 appeared
much greater than the difference between $2.50 and $10, which would be
reasonably consistent with the pattern of responding observed here.
Although a role for striatum in processing monetary reward has been
reliably demonstrated, amygdala response has been less consistently
observed. Here, the pattern of amygdala response is similar to that
seen in ventral striatum. In neuropsychological studies of gambling
(Bechara et al., 1999), patients with bilateral amygdala damage fail to
observe the normal emotional responses to monetary reward, clearly
suggesting a role for this region in financial reward processing.
However, imaging studies of gambling have not always reported amygdala
activation, perhaps reflecting a relatively transient signal in this
region. An important consideration is that, in most imaging studies of
gambling, rewards are not fully predictable. For rewarded blocks in the
present study, there is a 100% contingency between target stimuli and
rewards. The task therefore has similarities with secondary
reinforcement and associative learning paradigms in animals, which
critically implicate the amygdala (Hatfield et al., 1996; Schoenbaum et
al., 1998; Holland and Gallagher, 1999). Conditioned reinforcement is
likely to occur to a greater extent here than in tasks in which
relationships between cues, responses, and rewards are not completely predictable.
Perhaps the most striking finding of this study is of dissociable
patterns of responding in striatum-amygdala compared with OFC. This
corroborates studies in animals (Tremblay and Schultz, 1999; Watanabe,
1999) that suggest that patterns of neuronal firing associated with
reward are different in striatal and OFC neurons. Although both regions
contain neurons that respond during the expectation and detection of
reward, OFC neurons additionally code relative values of different
rewarding stimuli. The on-off pattern of striatal response observed
here is clearly consistent with the proposal that this region is
involved in expectation and detection of rewards. Rewards are expected
with the same probability and detected with the same frequency in all
of the rewarded blocks; what varies is the value of the reward. The
exact pattern of responding in OFC regions is such that response is
maximal to the zero reward and £1 reward conditions and lowest to the
midrange values. A region that responds to extremes of the reward range
may be best equipped to code relative values.
In a previous study (Elliott et al., 2000), we reported that OFC
regions (although exclusively lateral ones in that study) responded
under the most extreme situations of winning or losing in a gambling
task. Similarly, Breiter et al. (2001) demonstrated OFC responses (both
medial and lateral) that reflected either the worst or best possible
outcomes in a probabilistic task, including foci that coded both
extremes rather than intermediate situations. Neuropsychological
studies (Bechara et al., 1999, 2000) in patients with OFC lesions
suggests that the deficits in decision making shown by these patients
are attributable to impaired ability to weigh up consequences of
actions rather than hyposensitivity or hypersensitivity to good or bad
outcomes. Again, this suggests a more relative than absolute coding of
reward value in the OFC.
The finding of a U-shaped relationship between reward value and OFC
function is not, however, consistent with the results of O'Doherty et
al. (2001), demonstrating a positive correlation between medial OFC
response and reward value but a negative correlation between lateral
OFC and reward value (expressed as a positive correlation with
punishment). Although (with the eye of faith) there is some evidence
from our study (Fig. 3) that the U-shaped function observed is skewed
toward the positive extreme in medial OFC and the negative extreme
(actually zero) in lateral OFC, the dissociation observed by O'Doherty
et al. is not borne out here. A possible explanation for the
discrepancy is that we only used rewards, whereas both rewards and
punishments were used by O'Doherty et al. Lateral orbitofrontal
responses have been particularly associated with behavioral inhibition
and perceptual set shifting (Dias et al., 1996; Bechara et al., 2002),
and negative outcomes may act as cues to elicit such behavioral change.
Financial penalties were not included in the present design, and it is
possible that the prospect of negative outcomes may have led to a
clearer functional dissociation between medial and lateral regions.
The differential pattern of responding in OFC relative to
limbic-striatal structures observed here was predictable on the basis
of previous research. More surprising was the linear pattern of
responding in premotor cortex. This finding should be interpreted with
caution because the observed response did not survive correction for
multiple comparisons, and, because it was not predicted a priori, use
of a region of interest approach was not appropriate. However, response
in this region was spatially extensive, and we therefore believe that
it is likely to represent a genuine effect. It is interesting that the
linear relationship between increasing reward value and premotor
response is paralleled by a trend toward a linear decrease in reaction
time. Subjects tend to respond quicker when targets predict higher
reward value. Premotor responses may reflect increased motor
preparedness to respond to stimuli predicting larger rewards. In a
framework proposed by Schultz (2000), dorsal and lateral prefrontal
regions, including premotor cortex, are suggested to be particularly
involved in using information about expected rewards to mediate the
goal-directed behavior that elicits reward delivery.
Unlike several recent studies (Knutson et al., 2000; Breiter et al.,
2001; Critchley et al., 2001; O'Doherty et al., 2001), we adopted here
a blocked rather than event-related approach. An event-related study in
which reward magnitudes are varied would inevitably have introduced an
element of unpredictability. Our approach allowed us to look at
responses to reward magnitudes that were fully predictable within
blocks and thus unaffected by the confound of expectation. This is an
important point, because Breiter et al. (2001) have shown that
responses to reward value are critically modulated by subjects'
expectancy. However, by choosing the blocked approach, we are unable to
specify whether the responses observed reflect reward anticipation,
reward detection, or a combination of the two. It is possible that
differential responses to reward value in these regions would be
accompanied by differential temporal patterning of response in relation
to cues, responses, and rewards, as previous studies in both animals (Schultz, 2000) and humans (Breiter et al., 2001) would predict.
This discussion has focused on the responses of amygdala, striatum,
premotor cortex, and OFC. Other regions in which there were significant
reward-related responses included occipital areas, showing an
all-or-nothing response and perhaps reflecting more varied visual input
in the reward conditions in which colored squares were interspersed
with coin images. Also, a dorsomedial prefrontal region above the
anterior cingulate showed a similar pattern of responding to the OFC. A
corresponding region, with sensitivity to reward value, was reported by
O'Doherty et al. (2001). This region has been implicated in studies of
internal generation of emotional states (Reiman et al., 1997),
independent of emotional valence, and may reflect enhanced emotive
responses to the best and worst outcomes.
In conclusion, this study has shown that different components of human
reward processing systems respond differentially to monetary value.
Regions including midbrain, striatum, and amygdala were more responsive
to the presence or occurrence of reward than its value. Premotor cortex
responded linearly to increasing reward value, perhaps reflecting the
increasing potency with which larger rewards control goal-directed
behavior. Finally, a more subtle pattern of responding was seen in
medial and lateral parts of OFC, whereby response was greatest for the
lowest and highest rewards. This is consistent with a role for
orbitofrontal cortex in coding relative, rather than absolute, values
of rewards.
| |
FOOTNOTES |
Received June 3, 2002; revised Oct. 4, 2002; accepted Oct. 8, 2002.
This work was supported by the University of Manchester Research
Support Fund and the Medical Research Council.
Correspondence should be addressed to Dr. Rebecca Elliott,
Neuroscience and Psychiatry Unit, Room G907, Stopford Building, University of Manchester, Oxford Road, Manchester M13 9PT, UK. E-mail:
rebecca.elliott{at}man.ac.uk.
| |
References |
-
Bechara A,
Damasio H,
Damasio AR,
Lee GP
(1999)
Different contributions of the human amygdala and ventromedial prefrontal cortex to decision making.
J Neurosci
19:5473-5481[Abstract/Free Full Text].
-
Bechara A,
Tranel D,
Damasio H
(2000)
Characterization of the decision-making deficit of patients with ventromedial prefrontal lesions.
Brain
123:2189-2202[Abstract/Free Full Text].
-
Bechara A,
Dolan S,
Hindes A
(2002)
Decision-making and addiction. II. Myopia for the future or hypersensitivity to reward?
Neuropsychologia
40:1690-1705[Web of Science][Medline].
-
Breiter HC,
Gollub RL,
Weisskoff RM,
Kennedy DN,
Makris N,
Berke JD,
Goodman JM,
Kantor HL,
Gastfriend DR,
Riorden JP,
Mathew RT,
Rosen BR,
Hyman SE
(1997)
Acute effects of cocaine on human brain activity and emotion.
Neuron
19:591-611[Web of Science][Medline].
-
Breiter HC,
Aharon I,
Kahneman D,
Dale A,
Shizgal P
(2001)
Functional imaging of neural responses to expectancy and experience of monetary gains and losses.
Neuron
30:619-639[Web of Science][Medline].
-
Buchel C,
Holmes AP,
Rees G,
Friston KJ
(1998)
Characterizing stimulus-response functions using nonlinear regressors in parametric fMRI experiments.
NeuroImage
8:140-148[Web of Science][Medline].
-
Critchley HD,
Mathias CJ,
Dolan RJ
(2001)
Neural activity in the human brain relating to uncertainty and arousal during anticipation.
Neuron
29:537-545[Web of Science][Medline].
-
Delgado MR,
Nystrom LE,
Fissell C,
Noll DC,
Fiez JA
(2000)
Tracking the haemodynamic response to reward and punishment in the striatum.
J Neurophysiology
84:3072-3077[Abstract/Free Full Text].
-
Dias R,
Robbins TW,
Roberts AC
(1996)
Dissociation in prefrontal cortex of affective and attentional shifts.
Nature
380:69-72[Medline].
-
Elliott R,
Friston KJ,
Dolan RJ
(2000)
Dissociable neural responses associated with reward, punishment and risk-taking behaviour.
J Neurosci
20:6159-6165[Abstract/Free Full Text].
-
Hatfield T,
Han J-S,
Conley M,
Gallagher M,
Holland P
(1996)
Neurotoxic lesions of basolateral, but not central, amygdala interfere with Pavlovian second-order conditioning and reinforcer devaluation effects.
J Neurosci
16:5256-5265[Abstract/Free Full Text].
-
Holland PC,
Gallagher M
(1999)
Amygdala circuitry in attentional and representational processes.
Trends Cogn Sci
3:65-73[Web of Science][Medline].
-
Hollerman JR,
Schultz W
(1998)
Dopamine neurons report an error in the temporal prediction of reward during learning.
Nat Neurosci
1:304-309[Web of Science][Medline].
-
Knutson B,
Westdorp A,
Kaiser E,
Hommer D
(2000)
fMRI visualization of brain activity during a monetary incentive delay task.
NeuroImage
12:20-27[Web of Science][Medline].
-
Koob GF
(1992)
Dopamine, addiction and reward.
Semin Neurosci
4:139-148.
-
O'Doherty J,
Kringelbach ML,
Rolls ET,
Hornak J,
Andrews C
(2001)
Abstract reward and punishment in the human orbitofrontal cortex.
Nat Neurosci
4:95-102[Web of Science][Medline].
-
O'Doherty JP,
Deichmann R,
Critchley HD,
Dolan RJ
(2002)
Neural responses during anticipation of a primary taste reward.
Neuron
33:815-826[Web of Science][Medline].
-
Reiman EM,
Lane RD,
Ahern GL,
Schwartz GE,
Davidson RJ,
Friston KJ,
Yun LS,
Chen K
(1997)
Neuroanatomical correlates of externally and internally generated human emotion.
Am J Psychiatry
54:918-925.
-
Robbins TW,
Everitt BJ
(1992)
Functions of dopamine in the dorsal and ventral striatum.
Semin Neurosci
4:119-127.
-
Robbins TW,
Everitt BJ
(1996)
Neurobiobehavioural mechanisms of reward and motivation.
Curr Opin Neurobiol
6:228-236[Web of Science][Medline].
-
Rolls ET,
Francis S,
Bowtell R,
Browning D,
Clare S,
Smith T,
McGlone F
(1997)
Taste and olfactory activation of the orbitofrontal cortex.
NeuroImage
5:S199.
-
Schoenbaum G,
Chiba AA,
Gallagher M
(1998)
Orbitofrontal cortex and basolateral amygdala encode expected outcomes during learning.
Nat Neurosci
1:155-159[Web of Science][Medline].
-
Schultz W
(2000)
Multiple reward systems in the brain.
Nat Rev Neurosci
1:199-207[Web of Science][Medline].
-
Schultz W,
Apicella P,
Scarnati E,
Ljungberg T
(1992)
Neuronal activity in monkey ventral striatum related to the expectation of reward.
J Neurosci
12:4595-4610[Abstract].
-
Schultz W,
Apicella P,
Ljungberg T,
Romo R,
Scarnati E
(1993)
Reward-related activity in the monkey striatum and substantia nigra.
Prog Brain Res
99:227-235[Web of Science][Medline].
-
Stein EA,
Pankiewicz J,
Harsch HH,
Cho KK,
Fukker SA,
Hoffman RG,
Hawkins M,
Rao SM,
Bandettini PA,
Bloom AS
(1998)
Nicotine-induced limbic-cortical activation in the human brain: a functional MRI study.
Am J Psychiatry
155:1009-1015[Abstract/Free Full Text].
-
Talairach J,
Tournoux P
(1988)
In: Co-planar stereotactic atlas of the human brain. New York: Thieme.
-
Tremblay L,
Schultz W
(1999)
Relative reward preference in primate orbitofrontal cortex.
Nature
398:704-708[Medline].
-
Volkow ND,
Fowler JS,
Wang G-J
(1999)
Imaging studies on the role of dopamine in cocaine reinforcement and addiction in humans.
J Psychopharmacol
13:337-345.
-
Watanabe M
(1999)
Attraction is relative not absolute.
Nature
398:661-662[Medline].
-
Worsley KJ,
Marrett S,
Neelin P,
Vandal AC,
Friston KJ,
Evans AC
(1996)
A unified statistical approach for determining significant signals in images of cerebral activation.
Hum Brain Mapp
4:58-73[Web of Science].
Copyright © 2003 Society for Neuroscience 0270-6474/03/231303-05$05.00/0
Related articles in J. Neurosci.:
- This Week in The Journal
J. Neurosci. 2003 23: 0.
[Full Text]
This article has been cited by other articles:
|
|
|
|
 
K. N. Spreckelmeyer, S. Krach, G. Kohls, L. Rademacher, A. Irmak, K. Konrad, T. Kircher, and G. Grunder
Anticipation of monetary and social reward differently activates mesolimbic brain structures in men and women
Soc Cogn Affect Neurosci,
June 1, 2009;
4(2):
158 - 165.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
T. M. C. Lee, C. C. H. Chan, A. W. S. Leung, P. T. Fox, and J.-H. Gao
Sex-Related Differences in Neural Activity during Risk Taking: An fMRI Study
Cereb Cortex,
June 1, 2009;
19(6):
1303 - 1312.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
G. Xue, Z. Lu, I. P. Levin, J. A. Weller, X. Li, and A. Bechara
Functional Dissociations of Risk and Reward Processing in the Medial Prefrontal Cortex
Cereb Cortex,
May 1, 2009;
19(5):
1019 - 1027.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
Y. Minagawa-Kawai, S. Matsuoka, I. Dan, N. Naoi, K. Nakamura, and S. Kojima
Prefrontal Activation Associated with Social Attachment: Facial-Emotion Recognition in Mothers and Infants
Cereb Cortex,
February 1, 2009;
19(2):
284 - 292.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
B. H. Schott, L. Minuzzi, R. M. Krebs, D. Elmenhorst, M. Lang, O. H. Winz, C. I. Seidenbecher, H. H. Coenen, H.-J. Heinze, K. Zilles, et al.
Mesolimbic Functional Magnetic Resonance Imaging Activations during Reward Anticipation Correlate with Reward-Related Ventral Striatal Dopamine Release
J. Neurosci.,
December 24, 2008;
28(52):
14311 - 14319.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
W. Schultz, K. Preuschoff, C. Camerer, M. Hsu, C. D Fiorillo, P. N Tobler, and P. Bossaerts
Explicit neural signals reflecting reward uncertainty
Phil Trans R Soc B,
December 12, 2008;
363(1511):
3801 - 3811.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
E. D. Huey, R. Zahn, F. Krueger, J. Moll, D. Kapogiannis, E. M. Wassermann, and J. Grafman
A Psychological and Neuroanatomical Model of Obsessive-Compulsive Disorder
J Neuropsychiatry Clin Neurosci,
November 1, 2008;
20(4):
390 - 408.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
K. Koch, C. Schachtzabel, G. Wagner, J. R. Reichenbach, H. Sauer, and R. Schlosser
The neural correlates of reward-related trial-and-error learning: An fMRI study with a probabilistic learning task
Learn. Mem.,
October 2, 2008;
15(10):
728 - 732.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
H. Takahashi, M. Kato, M. Matsuura, M. Koeda, N. Yahata, T. Suhara, and Y. Okubo
Neural Correlates of Human Virtue Judgment
Cereb Cortex,
August 1, 2008;
18(8):
1886 - 1891.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
E. Z. Wheeler and L. K. Fellows
The human ventromedial frontal lobe is critical for learning from negative feedback
Brain,
May 1, 2008;
131(5):
1323 - 1331.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
J. A. Weiler, C. Bellebaum, and I. Daum
Aging affects acquisition and reversal of reward-based associative learning
Learn. Mem.,
April 1, 2008;
15(4):
190 - 197.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
J. M. Simmons and B. J. Richmond
Dynamic Changes in Representations of Preceding and Upcoming Reward in Monkey Orbitofrontal Cortex
Cereb Cortex,
January 1, 2008;
18(1):
93 - 103.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
C. A. Boettiger, J. M. Mitchell, V. C. Tavares, M. Robertson, G. Joslyn, M. D'Esposito, and H. L. Fields
Immediate Reward Bias in Humans: Fronto-Parietal Networks and a Role for the Catechol-O-Methyltransferase 158Val/Val Genotype
J. Neurosci.,
December 26, 2007;
27(52):
14383 - 14391.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
M. Pessiglione, L. Schmidt, B. Draganski, R. Kalisch, H. Lau, R. J. Dolan, and C. D. Frith
How the Brain Translates Money into Force: A Neuroimaging Study of Subliminal Motivation
Science,
May 11, 2007;
316(5826):
904 - 906.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
B. Seymour, N. Daw, P. Dayan, T. Singer, and R. Dolan
Differential Encoding of Losses and Gains in the Human Striatum
J. Neurosci.,
May 2, 2007;
27(18):
4826 - 4831.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
P. N. Tobler, J. P. O'Doherty, R. J. Dolan, and W. Schultz
Reward Value Coding Distinct From Risk Attitude-Related Uncertainty Coding in Human Reward Systems
J Neurophysiol,
February 1, 2007;
97(2):
1621 - 1632.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
R. Z. Goldstein, N. Alia-Klein, D. Tomasi, L. Zhang, L. A. Cottone, T. Maloney, F. Telang, E. C. Caparelli, L. Chang, T. Ernst, et al.
Is Decreased Prefrontal Cortical Sensitivity to Monetary Reward Associated With Impaired Motivation and Self-Control in Cocaine Addiction?
Am J Psychiatry,
January 1, 2007;
164(1):
43 - 51.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
H. E Fisher, A. Aron, and L. L Brown
Romantic love: a mammalian brain system for mate choice
Phil Trans R Soc B,
December 29, 2006;
361(1476):
2173 - 2186.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
K. Blair, A. A. Marsh, J. Morton, M. Vythilingam, M. Jones, K. Mondillo, D. C. Pine, W. C. Drevets, and J. R. Blair
Choosing the Lesser of Two Evils, the Better of Two Goods: Specifying the Roles of Ventromedial Prefrontal Cortex and Dorsal Anterior Cingulate in Object Choice.
J. Neurosci.,
November 1, 2006;
26(44):
11379 - 11386.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
P. L. Remijnse, M. M. A. Nielen, A. J. L. M. van Balkom, D. C. Cath, P. van Oppen, H. B. M. Uylings, and D. J. Veltman
Reduced orbitofrontal-striatal activity on a reversal learning task in obsessive-compulsive disorder.
Arch Gen Psychiatry,
November 1, 2006;
63(11):
1225 - 1236.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
S. N. Haber, K.-S. Kim, P. Mailly, and R. Calzavara
Reward-Related Cortical Inputs Define a Large Striatal Region in Primates That Interface with Associative Cortical Connections, Providing a Substrate for Incentive-Based Learning.
J. Neurosci.,
August 9, 2006;
26(32):
8368 - 8376.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
R. Levy and B. Dubois
Apathy and the Functional Anatomy of the Prefrontal Cortex-Basal Ganglia Circuits
Cereb Cortex,
July 1, 2006;
16(7):
916 - 928.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
L. Becerra, K. Harter, R. G. Gonzalez, and D. Borsook
Functional magnetic resonance imaging measures of the effects of morphine on central nervous system circuitry in opioid-naive healthy volunteers.
Anesth. Analg.,
July 1, 2006;
103(1):
208 - 216.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
A. Galvan, T. A. Hare, C. E. Parra, J. Penn, H. Voss, G. Glover, and B. J. Casey
Earlier development of the accumbens relative to orbitofrontal cortex might underlie risk-taking behavior in adolescents.
J. Neurosci.,
June 21, 2006;
26(25):
6885 - 6892.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
S. F. Taylor, B. Martis, K. D. Fitzgerald, R. C. Welsh, J. L. Abelson, I. Liberzon, J. A. Himle, and W. J. Gehring
Medial Frontal Cortex Activity and Loss-Related Responses to Errors
J. Neurosci.,
April 12, 2006;
26(15):
4063 - 4070.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
D. M. Small, D. Gitelman, K. Simmons, S. M. Bloise, T. Parrish, and M.-M. Mesulam
Monetary Incentives Enhance Processing in Brain Regions Mediating Top-down Control of Attention
Cereb Cortex,
December 1, 2005;
15(12):
1855 - 1865.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
L. K. Tremblay, C. A. Naranjo, S. J. Graham, N. Herrmann, H. S. Mayberg, S. Hevenor, and U. E. Busto
Functional Neuroanatomical Substrates of Altered Reward Processing in Major Depressive Disorder Revealed by a Dopaminergic Probe
Arch Gen Psychiatry,
November 1, 2005;
62(11):
1228 - 1236.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
A. Galvan, T. A. Hare, M. Davidson, J. Spicer, G. Glover, and B. J. Casey
The Role of Ventral Frontostriatal Circuitry in Reward-Based Learning in Humans
J. Neurosci.,
September 21, 2005;
25(38):
8650 - 8656.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
A. Aron, H. Fisher, D. J. Mashek, G. Strong, H. Li, and L. L. Brown
Reward, Motivation, and Emotion Systems Associated With Early-Stage Intense Romantic Love
J Neurophysiol,
July 1, 2005;
94(1):
327 - 337.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
S. M. L. Cox, A. Andrade, and I. S. Johnsrude
Learning to Like: A Role for Human Orbitofrontal Cortex in Conditioned Reward
J. Neurosci.,
March 9, 2005;
25(10):
2733 - 2740.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
S. H. Mitchell
Measuring Impulsivity and Modeling Its Association With Cigarette Smoking
Behav Cogn Neurosci Rev,
December 1, 2004;
3(4):
261 - 275.
[Abstract]
[PDF]
|
|
|
|
|
|
|
D. M. Small, J. Voss, Y. E. Mak, K. B. Simmons, T. Parrish, and D. Gitelman
Experience-Dependent Neural Integration of Taste and Smell in the Human Brain
J Neurophysiol,
September 1, 2004;
92(3):
1892 - 1903.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
D. J.-F. de Quervain, U. Fischbacher, V. Treyer, M. Schellhammer, U. Schnyder, A. Buck, and E. Fehr
The Neural Basis of Altruistic Punishment
Science,
August 27, 2004;
305(5688):
1254 - 1258.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
A. R. Aron, D. Shohamy, J. Clark, C. Myers, M. A. Gluck, and R. A. Poldrack
Human Midbrain Sensitivity to Cognitive Feedback and Uncertainty During Classification Learning
J Neurophysiol,
August 1, 2004;
92(2):
1144 - 1152.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
N. Camille, G. Coricelli, J. Sallet, P. Pradat-Diehl, J.-R. Duhamel, and A. Sirigu
The Involvement of the Orbitofrontal Cortex in the Experience of Regret
Science,
May 21, 2004;
304(5674):
1167 - 1170.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
D. H. Zald, I. Boileau, W. El-Dearedy, R. Gunn, F. McGlone, G. S. Dichter, and A. Dagher
Dopamine Transmission in the Human Striatum during Monetary Reward Tasks
J. Neurosci.,
April 28, 2004;
24(17):
4105 - 4112.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
I. K. Goerendt, A. D. Lawrence, and D. J. Brooks
Reward Processing in Health and Parkinson's Disease: Neural Organization and Reorganization
Cereb Cortex,
January 1, 2004;
14(1):
73 - 80.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
C. F. Zink, G. Pagnoni, M. E. Martin, M. Dhamala, and G. S. Berns
Human Striatal Response to Salient Nonrewarding Stimuli
J. Neurosci.,
September 3, 2003;
23(22):
8092 - 8097.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
J. O'Doherty, H. Critchley, R. Deichmann, and R. J. Dolan
Dissociating Valence of Outcome from Behavioral Control in Human Orbital and Ventral Prefrontal Cortices
J. Neurosci.,
August 27, 2003;
23(21):
7931 - 7939.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
TOP
ABSTRACT
Introduction
