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The Journal of Neuroscience, December 15, 2002, 22(24):10829-10837
Appetitive and Aversive Olfactory Learning in Humans Studied
Using Event-Related Functional Magnetic Resonance Imaging
Jay A.
Gottfried,
John
O'Doherty, and
Raymond J.
Dolan
Wellcome Department of Imaging Neuroscience, Institute of
Neurology, London, WC1N 3BG, United Kingdom
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ABSTRACT |
We combined event-related functional magnetic resonance imaging
(fMRI) with olfactory classical conditioning to differentiate the
neural responses evoked during appetitive and aversive olfactory learning. Three neutral faces [the conditioned stimuli (CS+)] were
repetitively paired with pleasant, neutral, or unpleasant odors [the
unconditioned stimuli (UCS)] in a partial reinforcement schedule. A
fourth face was never paired to odor [the nonconditioned stimulus
(CS )]. Learning-related neural activity, comparing unpaired (face
only) CS+ stimuli with CS, showed valence-independent activations in
rostral and caudal orbitofrontal cortex (OFC). Medial OFC responded to
the appetitive (app) CS+, whereas lateral OFC responded to the aversive
(av) CS+. Within nucleus accumbens, neural responses showed divergent
activation profiles that increased with time in response to the appCS+
but decreased in response to the avCS+. In posterior amygdala,
responses were elicited by the appCS+, which habituated over time. In
temporal piriform cortex, neural responses were evoked by the avCS+,
which progressively increased with time. These results highlight
regional and temporal dissociations during olfactory learning and imply
that emotionally salient odors can engender cross-modal associative
learning. Moreover, the findings suggest that the role of human primary
(piriform) and secondary olfactory cortices transcends their function
as mere intermediaries of chemosensory information processing.
Key words:
olfaction; odor; emotion; conditioning; associative
learning; neuroimaging; fMRI; orbitofrontal cortex; nucleus accumbens; amygdala; piriform cortex
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INTRODUCTION |
Behavior is powerfully influenced by
the biological salience of environmental stimuli. Odors can impact on
behavioral states by virtue of their associations with threat, food,
and sex. For example, bombykol, a volatile pheromone secreted by female
silkworm moths, will drive potential mates miles upwind in hot pursuit (Carde and Mafra-Neto, 1997 ). Despite a conspicuous phylogenetic decline in olfactory prowess, humans can exhibit behavioral
vulnerability to odors in certain instances (MacFarlane, 1975;
Beauchamp and Maller, 1977; Baron, 1988; Stern and McClintock,
1998).
How the brain encodes and recalls behaviorally meaningful information
can be investigated through associative learning paradigms. Classical
(Pavlovian) conditioning is one such example in which a previously
neutral item [the conditioned stimulus (CS+)] becomes predictive of
an emotionally salient item [the unconditioned stimulus (UCS)]
through repeated pairings. In aversive conditioning the UCS is often
represented by an electric shock or loud sound, whereas foods, liquids,
or drugs commonly substitute for an appetitive (reward-based) UCS.
Animal studies indicate that the amygdala is critical to aversive
(fear) conditioning (LeDoux, 2000), whereas dopaminergic mesolimbic
circuitry has been implicated in appetitive learning (Schultz, 1998;
Kalivas and Nakamura, 1999). Human neuroimaging studies of conditioning
have essentially focused on aversive paradigms (Buchel et al., 1998;
LaBar et al., 1998; Morris et al., 2001), with few studies addressing
reward learning (O'Doherty et al., 2002).
Given the hedonic primacy of odor perception (Schiffman, 1974), odors
can act as rewarding or aversive UCS stimuli in conditioning experiments. In aversion therapy for overeaters, desserts become endowed with the aversive properties of an unpleasant odor after multiple pairings (Frohwirth and Foreyt, 1978), although this effect is
short-lived (Cole and Bond, 1983). When an odor UCS is paired with a
visual (face) CS+, both subjective preferences of the faces and
physiological indices of conditioning are significantly altered
(Todrank et al., 1995; Hermann et al., 2000). In the olfactory neuroimaging literature, few studies have explored higher-order aspects
of odor processing [although see Savic et al. (2000), Royet et al.
(2001), and Dade et al. (2002)], and none has attempted to
characterize the brain regions involved in olfactory associative learning.
In this report we describe the use of event-related functional magnetic
resonance imaging (fMRI) techniques to delineate the neural substrates
underlying human olfactory conditioning. By manipulating the affective
properties of the odor UCS, we indexed alternate modes of conditioning
within the same experiment: appetitive learning in the case of a
pleasant odor and aversive learning in the case of an unpleasant odor.
We predicted that variations in the emotional valence of learning would
elicit differential responses in orbital frontal cortex (OFC),
amygdala, and ventral striatum, regions strongly implicated in
appetitive and aversive learning. We also hypothesized that olfactory
structures involved in low-level sensory processing [as defined in a
companion paper in this issue (Gottfried et al., 2002)] would show
modulation of their response as a function of associative learning.
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MATERIALS AND METHODS |
The detailed methodology has been reported in the accompanying
paper (Gottfried et al., 2002), and only distinct aspects are described here.
Subjects. Informed consent was obtained from 17 healthy
right-handed subjects (10 women; mean age, 23 years), although data from 2 subjects (1 woman) was discarded because of technical problems. The study was approved by the joint National Hospital for Neurology and
Neurosurgery and Institute of Neurology Ethics Committee.
Stimuli and experiment. Subjects participated in an
olfactory form of classical conditioning that was similar to previous experiments in our laboratory (Buchel et al., 1998; Morris et al.,
2001). Here, a series of neutral faces was paired with odors that
varied in pleasantness. Four neutral faces (two male, two female)
comprised the one nonconditioned (CS) and the three conditioned (CS+)
stimuli and were projected to the subjects as they lay inside the
scanner. Three odors comprised the pleasant (8% vanillin), neutrally
valenced (0.1% phenethyl alcohol), and unpleasant (5% 4-methyl-pentanoic acid) UCS stimuli. These were delivered using a
four-channel computer-controlled olfactometer suitable for the fMRI
environment (Lorig et al., 1999). Under 50% reinforcement, only
one-half of all faces (CS+) were paired with their corresponding odor,
resulting in seven discrete conditions: (1) "appetitive" face,
paired with pleasant odor (appCS+P); (2)
appetitive face, unpaired (appCS+U); (3)
"neutral" face, paired with neutral odor (ntCS+P); (4) neutral face, unpaired
(ntCS+U); (5) "aversive" face, paired with
unpleasant odor (avCS+P); (6) aversive face, unpaired (avCS+U); and (7) face never paired with
odor (CS). This scheme allowed us to model responses to the
appetitive and aversive CS+ without interference from the odor UCS.
During the task, subjects made a forced-choice push-button response
regarding facial gender. Trial onset was marked by the appearance of a
face. After 500 msec, the brightening of a dull-red cross-hair cued
subjects to sniff, and the olfactometer was simultaneously triggered to
deliver odor or control air, depending on the condition type. In this
manner, subjects completed a sniff on each trial, regardless of odor
presence. The stimulus duration of faces, sniff cues, and odor pulses
was 750 msec in all cases. Thus, the CS+ and UCS overlapped for 250 msec. The intertrial interval was 7.5 sec, and stimulus presentation
was randomized. Each event type was repeated 25 times, except for the
CS (50 times), for an experimental duration of 25 min.
Subject-specific respiratory patterns (sniffs) were monitored on-line
via corrugated breathing belts (Siemens, Erlangen, Germany) coupled to
a differential pressure sensor (Honeywell, Morristown, NJ).
Conditioning index. Subject reaction times (RTs), which have
been used successfully in our laboratory as a reliable index of
conditioning (Critchley et al., 2002), were measured to gauge the
efficacy of learning. RTs were recorded using Cogent 2000 software
(Wellcome Department of Imaging Neuroscience, London, UK). Off-line
analysis was performed using Matlab 6.0 (The Mathworks Inc., Natick,
MA). Condition-specific RTs for each subject were averaged over three
successive temporal blocks of the experiment. Differences between
unpaired CS+ and CS conditions were calculated for each valence level
(aversive, appetitive, neutral) and pooled across subjects. RT data
from six subjects (appetitive, aversive events) and eight subjects
(neutral events) were unavailable because of technical complications.
Image acquisition. Gradient echo T2*-weighted echoplanar
images (EPI) were acquired with blood-oxygen level-dependent (BOLD) contrast on a 2-Tesla Siemens Vision MRI scanner (Siemens, Erlangen, Germany), using an optimized sequence to reduce signal dropout in
orbitofrontal and ventral temporal lobes (Deichmann and Turner, 2002).
There were 33 slices per volume (1.8 mm thickness, 1.2 mm gap), which
covered ~80% of the whole brain, and they were acquired continuously
every 2.31 sec. Images were spatially realigned (Friston et al.,
1995a), slice-time corrected, normalized, and smoothed with an 8 mm
kernel. T1-weighted anatomical images were also obtained from each
subject and co-registered to the mean normalized EPI.
Analysis of learning-evoked responses. Event-related fMRI
data were analyzed in SPM99 (Wellcome Department of Imaging
Neuroscience) (Friston et al., 1995b) using a random-effects model as
described in the companion paper (Gottfried et al., 2002), with the
following exceptions. Regressors of interest included the six CS+
conditions, plus the CS condition divided randomly into two time
series of 25 events each (designated "appCS" and "avCS").
The estimation of two independent CS baselines was necessary to
implement conjunction analysis (see below), although it is important to
note that appCS and avCS represented experimentally identical event
types. To model learning, the design incorporated condition × time interactions, using a time constant of one-quarter session
length (372 sec) similar to that used previously in neuroimaging
studies of conditioning from our laboratory (Buchel et al., 1998;
Morris et al., 2001). Neural responses specific to learning were
isolated by contrasting the unpaired CS+ events with their designated
CS baselines.
Activations common to both appetitive and aversive learning were tested
using conjunction analysis (Price and Friston, 1997). Subject-specific parameter estimates were calculated for four effects
of interest: appCS+U,
avCS+U, appCS, and avCS. (This process was
repeated for the four time-modulated regressors.) These estimates from
all 15 subjects were then entered into a repeated-measures ANOVA, using
nonsphericity correction. By testing appropriate pairs of linear
contrasts, we were able to identify learning-related responses
independent of specific valence. As detailed in the accompanying paper
(Gottfried et al. 2002), a descriptive model of the data was also
constructed to depict time courses of activation within significant
(accumbens) voxels derived from the primary model.
We report activations surviving a threshold of p < 0.001 uncorrected in regions predicted a priori, including
brain areas implicated in associative learning (OFC, amygdala, nucleus
accumbens, ventral tegmental area) (O'Doherty et al., 2002a). In
medial temporal sites involved in olfactory sensory processing
(piriform cortex) and learning (hippocampus), the threshold was set at
p < 0.005 uncorrected. Correction for multiple
comparisons was performed across small volumes of interest (Worsley et
al., 1996) by constructing binary anatomical masks of amygdala, nucleus
accumbens, OFC (including medial, lateral, anterior, and posterior
subdivisions), and piriform cortex to limit the effective search space.
The statistical threshold used to report these activations was set at
p < 0.05 corrected for small volumes of interest or,
where indicated, for whole-brain volume. All voxels of interest are
reported in Montreal Neurological Institute (MNI) coordinate space.
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RESULTS |
Behavioral
Subjects responded more slowly to the avCS+U
face compared with the CS during early phases of aversive learning,
but this differential effect habituated later in the experiment (Fig.
1A). The main effect
across experimental blocks approached significance (n = 9 subjects; F = 3.52; df = 2; p = 0.054; one-way repeated-measures ANOVA). Post hoc paired
t tests revealed that RTs were significantly slower in the
first compared with the second block (p < 0.05) and approached significance in a comparison of first and third blocks
(p = 0.068). Similar differential responses were
seen during appetitive learning in response to the
appCS+U face (Fig. 1B). There
was a significant main effect across the three sessions (n = 9 subjects; F = 3.69; df = 2;
p < 0.05; repeated-measures ANOVA), and post
hoc comparisons revealed the RTs were significantly more delayed
in the first than the second block (p < 0.05).
By comparison, there was no significant main difference of RTs across experimental blocks for neutral learning (n = 7 subjects; F = 3.21; df = 1.17; p = 0.114; repeated-measures ANOVA, Greenhouse-Geisser adjusted).
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Figure 1.
Differential reaction times. Condition-specific
RTs were averaged across subjects over each of three successive blocks
of the experiment. A, RT differences between an aversive
CS+U and CS (mean ± SEM). Significant between-block
differences were set at p < 0.05 and are indicated
by asterisk. B, RT differences between an
appetitive CS+U and CS. Significance,
p < 0.05.
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Neuroimaging
Valence-independent olfactory learning
Sustained responses. To highlight brain regions
involved in olfactory learning, regardless of specific valence, we
tested for regions showing a common response during both forms of
learning based on the conjunction of (appCS+U appCS) and (avCS+U avCS). This analysis
revealed confluent activations in OFC with distinct peaks
that encompassed medial, anterior,
posterior, and lateral orbital gyri (Fig. 2A, Table
1). The most significant peaks were in
the right rostral OFC [medial: x = 14, y = 46, z = 18; Z = 5.85; and anterior: 28, 46, 6; Z = 4.79; both
p < 0.05 corrected for whole-brain volume), although
activations were also detected on the left (medial: 18, 46, 12;
Z = 3.45; p < 0.001 uncorrected; and
anterior: 28, 50, 12; Z = 4.36; p < 0.05 small-volume corrected (SVC) over the region of interest].
Learning-evoked responses in bilateral centroposterior OFC (24, 30, 12; Z = 4.41; p < 0.05 SVC; and 22, 32, 16; Z = 3.65; p = 0.052 SVC)
closely overlapped caudal OFC regions defined previously in our
companion study (Gottfried et al., 2002) as "secondary olfactory"
cortex (at 20, 30, 20, and 24, 34, 16) (Fig.
2B). Significant effects were also observed in
lateral orbital gyri (right: 34, 36, 10; Z = 4.38;
and 38, 34, 12; Z = 4.28; left: 30, 54, 10;
Z = 4.41; and 34, 46, 12; Z = 3.87;
p < 0.05 SVC). Outside of OFC, the only other neural responses elicited by the learning conjunction were in retrosplenial cortex, which closely bordered significance (0, 48, 10;
Z = 4.75; p = 0.051 whole-brain
corrected).
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Figure 2.
Valence-independent learning. A,
Rostral orbitofrontal cortex. The SPM (threshold, p < 0.001) is overlaid on the axial (i) and
coronal (ii) sections of a subject's normalized
T1-weighted scan and depicts extensive bilateral activations in rostral
areas of orbitofrontal cortex. In this and all subsequent figures, the
right side of the brain corresponds to the right side of
the image. B, Caudal orbitofrontal cortex.
i, Neural activations in caudal orbitofrontal cortex are
superimposed on a normalized T1-weighted coronal image (threshold,
p < 0.001). ii, The learning-evoked
activations depicted in i are illustrated in
red (threshold, p < 0.001) and
overlap the odor-evoked activations derived from the companion study
(Gottfried et al., 2002), which are shown in blue
(threshold, p < 0.005).
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Time-dependent responses. We also performed conjunctions
between condition × time interactions [namely,
(appCS+U × time appCS × time) and
(avCS+U × time avCS × time)] to
detect time-dependent changes in neural activity common to appetitive
and aversive learning. Common learning-related activations that showed
progressive increases over time (response potentiation) were evident in
ventromedial prefrontal cortex (PFC) (4, 34, 14; Z = 3.20; p < 0.001 uncorrected), anterolateral OFC (32, 46, 12; Z = 3.17; p < 0.001 uncorrected), and cerebellum (12, 40, 46; Z = 4.81; p < 0.05 whole-brain corrected) (Table 1). No
significant time-dependent decreases were found in this analysis.
Appetitive olfactory learning
Sustained responses. We next tested for the main effect
of appetitive learning in the contrast (appCS+U appCS), which showed several activation foci in medial OFC (18, 44, 16; Z = 4.03; p < 0.05 SVC; 16, 50, 10; Z = 3.80; p < 0.001 uncorrected; and 10, 36, 14; Z = 3.56;
p < 0.001 uncorrected) (Fig.
3A). These responses extended
into gyrus rectus and medial PFC. An additional focus of activation was
observed in anterior OFC (30, 42, 10; Z = 3.60;
p < 0.001 uncorrected; p = 0.058 SVC).
The direct contrast (appCS+U avCS+U) demonstrated bilateral activity in
ventral striatum (Fig. 3B). Neural activations in the right
hemisphere bordered the ventral caudate head (14, 6, 2;
Z = 3.12; p < 0.001 uncorrected),
whereas left-sided responses were localized to nucleus accumbens (8,
4, 4; Z = 3.07; p = 0.051 SVC).
Bilateral responses were observed in dorsomedial areas of posterior
amygdala, bounded superiorly and posteriorly by ventral pallidum (left:
20, 14, 12; Z = 3.37; p < 0.001 uncorrected; and right: 16, 10, 12; Z = 2.94;
p < 0.005 uncorrected), and also in a more ventral
region of the right medial temporal lobe (14, 12, 22; Z = 3.07; p < 0.005 uncorrected) that extended anteriorly
into amygdala (Fig. 3C, Table
2).
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Figure 3.
Neural activations evoked by appetitive olfactory
learning. A, In the main effect of appetitive learning
(appCS+U appCS), BOLD activity was significantly
increased within medial and anterior orbitofrontal cortex. The SPMs are
superimposed on coronal (i) and axial
(ii) sections of a T1-weighted scan (threshold,
p < 0.001). B, C, Neural
activations in bilateral nucleus accumbens (B)
and posterior amygdala/medial temporal lobe (C)
were revealed in the direct contrast of (appCS+U avCS+U) and are depicted in coronal
(i) and axial (ii) formats
(threshold, p < 0.005).
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Time-dependent responses. Time-dependent modulations of
neural activity specific to appetitive learning were examined using condition × time interactions [that is,
(appCS+U × time) relative to (appCS × time)]. Learning-related increases (response potentiation) were
evident in nucleus accumbens (14, 16, 12; Z = 3.82;
p < 0.05 SVC) (Fig. 4)
and ventromedial PFC (4, 34, 16; Z = 3.24; p < 0.001 uncorrected). Activations in right
cerebellar hemisphere were also elicited (26, 46, 34;
Z = 5.06; p < 0.05 whole-brain corrected). A direct comparison between (appCS+U × time) and (avCS+U × time) revealed
significant habituating responses in bilateral anterior hippocampus
(right: 36, 14, 20; Z = 3.12; p < 0.001 uncorrected; and left: 32, 14, 16; Z = 3.07; p < 0.005 uncorrected) and in a region of right
medial temporal lobe (16, 12, 24; Z = 3.05;
p < 0.005 uncorrected) that was contiguous with
posterior ventromedial amygdala (at y = 10) (Table
2).
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Figure 4.
Time-dependent response enhancements in nucleus
accumbens elicited by appetitive learning. The main effect of
(appCS+U × time) relative to (appCS × time)
demonstrates increasing activity in right nucleus accumbens over the
course of the experiment. SPMs are displayed on coronal
(A) and axial (B) slices of
a T1-weighted scan (threshold, p < 0.005).
C, The contrasts of parameter estimates from the peak
voxel within right nucleus accumbens are plotted for each subject, and
the mean value (0.17) is indicated by a short horizontal
bar.
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Aversive olfactory learning
Sustained responses. By testing the contrast
(avCS+U avCS), we determined neural activity
evoked by aversive olfactory learning. This revealed significant
activations in rostral OFC with peaks in left lateral (32, 52, 12;
Z = 4.19; p < 0.05 SVC) (Fig.
5A) and right medial (14, 46, 18; Z = 3.88; p < 0.001 uncorrected) orbital gyri. No significant responses were observed in the direct comparison of avCS+U and
appCS+U conditions.
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Figure 5.
Neural activations evoked by aversive
olfactory learning. A, The main effect of aversive
learning (avCS+U avCS) showed responses in left
lateral orbitofrontal cortex. The SPM is overlaid on coronal
(i) and axial (ii) sections of a
T1-weighted image and thresholded at p < 0.001. B, i, In the contrast of condition × time
interactions (avCS+U × time avCS × time),
significant response increases were seen in anteromedial temporal lobe
extending into temporal piriform cortex and periamygdaloid areas
(threshold, p < 0.005). ii, Similar
time-dependent enhancements of activity were detected in the direct
comparison between (avCS+U × time) and
(appCS+U × time) (threshold, p < 0.001). C, i, Time-dependent response
decreases were observed in nucleus accumbens in the contrast of
(avCS+U × time avCS × time) (threshold,
p < 0.005). ii, The
contrasts of parameter estimates from the peak accumbens voxel are
plotted for each subject, along with the mean value (0.12).
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Time-dependent responses. Time-dependent neural responses
during aversive learning were examined by contrasting condition × time interactions between avCS+U and its baseline
(avCS). Time-dependent increases in activity (response potentiation)
were observed in left OFC (18, 22, 14; Z = 3.99;
p = 0.058 SVC) and in left temporopolar cortex (26,
6, 38; Z = 3.26; p < 0.001 uncorrected) (Fig. 5B,i, Table
3). This latter response extended
caudally into periamygdaloid and posterior (temporal) piriform cortices
(y = 2). Similar response enhancements in
anterior mesiotemporal structures (peak: 30, 6, 38;
Z = 4.74; p < 0.001 uncorrected) were
identified in the direct contrast of time-modulated aversive
(avCS+U × time) and appetitive
(appCS+U × time) learning conditions (Fig.
5B,ii.). Conversely, the only brain region that showed a
significant decline in activity over time (response habituation) was
nucleus accumbens (10, 16, 12; Z = 3.10;
p < 0.05 SVC) (Fig. 5C). This habituating profile was opposite to the temporal pattern evoked by appetitive learning in nucleus accumbens, where neural activity increased progressively over time (Fig. 4). These divergent responses are depicted in Figure 6.
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Figure 6.
Divergent temporal profiles in nucleus
accumbens. A, C, Neural activity in nucleus accumbens
increased over time during appetitive olfactory learning.
A, The percentage signal change of the fitted
hemodynamic response in nucleus accumbens elicited by an
appCS+U stimulus (minus the CS baseline) is plotted
against repetition number for one subject and shows that repeated
presentations of appCS+U evoke progressive increases in the
response signal. C, The contrasts of parameter estimates
(appCS+U appCS) (at 14, 16, 12) pooled across
all subjects (means ± SEM) are depicted across successive thirds
of the experiment and confirm an increasing temporal response profile
at the group level. B, D, Activity in nucleus accumbens
habituated over time during aversive olfactory learning.
B, The plot of the percentage signal change elicited by
avCS+U (minus CS) reveals a pattern in nucleus accumbens
that habituates over repeated stimulus presentations (same subject as
in A). D, Similarly, the group response
profile of the contrast of parameter estimates (at 10, 16, 12)
shows a steady decline over time.
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DISCUSSION |
Our results indicate that emotionally salient odors can drive
associative learning processes. We identified regional and temporal dissociations between appetitive and aversive conditioning within several key areas implicated in studies of associative learning, including OFC, nucleus accumbens, and amygdala. In addition, the activation of brain areas previously found to participate in low-level odor processing, such as piriform cortex and caudal OFC, implies that
these regions play an active role in the transfer of affective value
between olfactory and visual modalities.
The behavioral data (Fig. 1) show that to the extent that
conditioning occurs, it is mostly completed by the second block of
trials. This temporal pattern resembles those obtained in previous imaging investigations using autonomic markers, whereby learning-based differences were expressed in the early phase of conditioning (Buchel
et al., 1998; LaBar et al., 1998). In addition, these behavioral
profiles mirror the time course (exponential function, one-quarter time constant) used to model learning-specific
responses in the fMRI data. The fact that significant differential
responses to the neutral
(ntCS+U) stimulus
were not seen supports the idea that successful olfactory learning is
contingent on odor valence rather than the presence of odor per
se. We should point out that the absence of a significant
behavioral effect in the third block of trials suggests that other
processes (e.g., fatigue, boredom) may be interfering with olfactory
conditioning. By itself this does not invalidate the responses that are
seen, but raises the possibility that other learning-specific responses
might have been missed.
Neural responses common to both appetitive and aversive learning
were evident in widespread regions of rostral and caudal OFC (Fig. 2).
The extent of these activations indicates that OFC is critical to the
learning of olfactory-visual associations, regardless of specific
valence. These findings concur with animal studies implicating OFC in
many higher-order aspects of olfactory learning (Schoenbaum and
Eichenbaum, 1995; Critchley and Rolls, 1996; Schoenbaum et al., 1998).
Although studies of human patients with orbitofrontal lesions have
advanced the idea that OFC is essential to odor discrimination (Potter
and Butters, 1980; Zatorre and Jones-Gotman, 1991), the impact of these
lesions on odor learning has not been addressed. Likewise, although OFC
is consistently detected in olfactory neuroimaging studies (Zald and
Pardo, 2000), its role in more complex processes has received limited
attention (O'Doherty et al., 2000; Savic et al., 2000; Dade et al.,
2001, 2002; Royet et al., 2001). Our study provides evidence that
higher-order olfactory computations, specifically those related to
learning, are engaged by human OFC.
In our companion paper (Gottfried et al., 2002) on olfactory sensory
processing, we showed that odor-evoked activity in OFC was restricted
specifically to caudal areas, corresponding anatomically to secondary
olfactory cortex in animals (Yarita et al., 1980; Carmichael et al.,
1994). This closely overlapped with posterior OFC activations evoked by
olfactory learning (Fig. 2B). In contrast, significant effects within rostral OFC were associated exclusively with
olfactory learning (Fig. 2A) but not with sensory
odor processing. On the basis of these observations, we argue that the
learning-evoked responses in caudal OFC may reflect modulation of
secondary olfactory cortex by higher-order centers. Such influences
might help increase the gain in structures that are primarily involved
in unimodal olfactory discrimination, thereby promoting more efficient
processing of behaviorally salient odors. Conversely, higher-order
operations pertaining to learning would be specifically executed in
rostral OFC. This idea is bolstered by animal findings suggesting an
anatomical hierarchy of orbitofrontal specialization, whereby caudal
regions (such as olfactory OFC) converge on medial and anterior
territories to permit more complex information processing (Carmichael
and Price, 1996; Ongur and Price, 2000). In this regard, neural
activity in anterior OFC has been observed in recent neuroimaging
studies of olfactory working memory [at 32, 44, 18 (Dade et al.,
2001)] and short- and long-term odor recognition [at 27, 44, 14,
and 20, 48, 18 (Dade et al., 2002)], lending further credence to the
notion that this region is an important functional component of human
olfactory learning.
Recent neuroimaging studies indicate that the neural substrates of
reward and punishment may be functionally compartmentalized within
medial and lateral OFC, respectively (O'Doherty et al., 2001, 2003;
Small et al., 2001). Moreover, our companion study (Gottfried et al.,
2002) showed that these dissociations were preserved in response to
pleasant and unpleasant odors. In the present context, evidence for
medial-lateral distinctions is less compelling but exhibits a
comparable trend. Thus, in the main effect of appetitive olfactory
learning, multiple activation foci were elicited in medial OFC (Fig.
3A), whereas responses in lateral OFC were not observed. By
comparison, the main effect of aversive learning was associated with
neural responses in lateral OFC (Fig. 5A). Although
rostromedial OFC was also identified in this contrast, the fact that
this area emerged both in the main effect of appetitive learning and in
the conjunction analysis suggests that it is probably more responsive
to nonpunishing aspects of olfactory conditioning.
We found neural responses in nucleus accumbens in the direct contrast
between appCS+U and avCS+U
(Fig. 3B), implying that these activations were specifically
related to the learning of an olfactory reward. In addition, this
differential activity progressively increased with successive
appCS+U presentations (Figs. 4, 6). Although
human neuroimaging studies confirm that nucleus accumbens is modulated
by reward-based stimuli (Delgado et al., 2000; O'Doherty et al., 2002;
Pagnoni et al., 2002), we believe that our findings are the first to
demonstrate that odors drive conditioned reward responses within human
mesolimbic structures.
Although nucleus accumbens is a major target of midbrain dopaminergic
afferents (Kalivas and Nakamura, 1999), the characterization of
dopamine-related effects on evoked BOLD activity is unclear. Nevertheless, a survey of the literature suggests parallels between our
data and animal studies of odor learning. For example, single-unit accumbens activity increased in male rats conditioned to associate novel smells with sexually receptive females (West et al., 1992). Similarly, rats administered intra-accumbens cocaine concurrently in
the presence of neutral odors developed conditioned place preference for these stimuli (Barr and Wang, 1992).
In primate electrophysiological studies, the output signals of dopamine
neurons in the ventral tegmentum are proposed to transmit reward-prediction errors to facilitate appetitive learning (Schultz, 1998). Recent human neuroimaging studies of taste-reward anticipation are broadly consistent with this proposal (O'Doherty et al., 2002; Pagnoni et al., 2002). Interestingly, the learning curves generated by
temporal-difference models of the dopamine signal (Schultz, 1998) are
reminiscent of the temporal profiles observed here in response to
olfactory appetitive learning, although our study was not specifically
devised to test such hypotheses. On the other hand, conflicting data
from lesion, pharmacological, and in vivo microdialysis
studies indicate that the animal literature is far from resolved, and
whether our findings are ultimately compatible with reward-prediction
error or other models (Berridge and Robinson, 1998; Horvitz, 2000)
warrants further investigation.
Although neural activity in nucleus accumbens progressively increased
in response to appCS+U, an opposite (decreasing)
pattern was seen in response to avCS+U (Figs.
5C, 6). Thus, it appears that the valence of olfactory
learning evokes divergent temporal profiles in the same structure. That
nucleus accumbens is differentially responsive to positive and negative
modes of learning is supported by accumulating evidence highlighting a
role for mesolimbic dopamine in aversive processing (Horvitz, 2000).
Relevant to our results is the demonstration that dopamine release in
ventral striatum decreases as a result of conditioned odor aversion
(Abercrombie et al., 1989; Doherty and Gratton, 1992; Besson and
Louilot, 1995). In addition, microdialysis studies performed in rodents
in vivo indicate that accumbens dopamine increases or
decreases in response to rewarding or aversive stimuli, respectively
(Di Chiara et al., 1999). However, further work is needed to establish
how synaptic dopamine levels relate to fMRI signal change.
Discrete subregions of posterior amygdala were associated exclusively
with appetitive learning (Fig. 3C). Responses in dorsomedial areas were sustained over the course of the experiment, whereas ventromedial regions exhibited both sustained and habituating response
patterns. The demonstration of different activation loci within the
amygdala may reflect functional segregation, particularly with respect
to habituating and nonhabituating profiles, as shown previously (Morris
et al., 2001). Although current fMRI techniques lack sufficient spatial
resolution to discern individual amygdala subnuclei, the dorsomedial
activation may correspond to the central nucleus of the amygdala (Mai
et al., 1997), an area linked to appetitive Pavlovian
conditioning in animal lesion studies (Gallagher et al., 1990;
Parkinson et al., 2000). In addition, this same region closely overlaps
activations evoked by anticipation of pleasant taste (O'Doherty et
al., 2002). This anatomical convergence, based on different types of
natural rewards, implicates a broad role for posterior dorsal amygdala
in appetitive processing. More generally, our data extend recent
findings from human studies demonstrating the importance of amygdala to
various reward-related learning processes (Hamann et al., 1999;
Johnsrude et al., 2000).
We found no significant amygdala activity during aversive olfactory
learning or in anticipation of aversive tastes (O'Doherty et al.,
2002). These findings are intriguing given its reliable involvement in
animal (LeDoux, 2000) and human (Buchel et al., 1998; LaBar et al.,
1998) studies of fear conditioning. Indeed, one might have predicted
the reverse pattern of results, in which the amygdala was a selective
participant in aversive, rather than appetitive, learning. As Cahill
and McGaugh (1990) have elegantly shown with aversive taste
reinforcers, it is possible that the use of unpleasant odors simply
might not be arousing enough to engage amygdala-dependent conditioning
(although one might expect this to apply to the pleasant odor as well).
Alternately, qualitative differences in the aversive UCS may elicit
variable activation patterns. Loud sounds or electric shocks are more
likely to produce affective responses approximating fear or startle,
shown in diverse species to be potent activators of amygdala (LeDoux,
2000), whereas unpleasant odors may provoke reactions closer to
disgust. To this end, faces expressing disgust (as opposed to fear)
seem to activate amygdala relatively poorly (Phillips et al., 1997) and
may help explain why aversive olfactory learning did not involve this structure.
A remaining possibility is that functional disparities may reflect a
unique organization of olfaction, whereby aversive learning would be
implemented by other brain regions. Potential candidates include areas
of temporal piriform and periamygdaloid cortices, which showed
increasing neural activations over time in the main effect of
avCS+U (relative to avCS) and in the direct
comparison with appCS+U (Fig. 5B). The
idea that primary olfactory cortex helps establish salient cross-modal
associations is complementary to animal studies indicating that
piriform cortex supports learning-related processes (Schoenbaum and
Eichenbaum, 1995; Saar et al., 1999; Datiche et al., 2001). Indeed, the
detection of learning-evoked activations in both human primary
(piriform) and secondary (caudal OFC) olfactory projection sites
suggests that these areas should not be considered simply as unimodal
sensory relays but as active participants in more complex aspects of
odor information processing.
| |
FOOTNOTES |
Received July 16, 2002; revised Sept. 23, 2002; accepted Sept. 27, 2002.
This research was supported by a Physician Postdoctoral Fellowship from
the Howard Hughes Medical Institute (J.A.G.) and by a Wellcome Trust
Programme Grant (R.J.D.). We thank P. Aston, E. Featherstone, O. Josephs, and J. S. Winston, as well as the radiology staff of the
Wellcome Department of Imaging Neuroscience.
Correspondence should be addressed to Dr. Jay A. Gottfried, Wellcome
Department of Imaging Neuroscience, Functional Imaging Laboratory, 12 Queen Square, London WC1N 3BG, UK. E-mail:
j.gottfried{at}fil.ion.ucl.ac.uk.
| |
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TOP
ABSTRACT
INTRODUCTION
