306
CNS & Neurological Disorders - Drug Targets, 2012, 11, 306-323
The Role of Catechol-O-Methyltransferase in Reward Processing and
Addiction
E.M. Tunbridge*, A. Huber, S.M. Farrell, K. Stumpenhorst, P.J. Harrison and M.E. Walton
University Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK
Abstract: Catechol-O-methyltransferase (COMT) catabolises dopamine and is important for regulating dopamine levels
in the prefrontal cortex. Consistent with its regulation of prefrontal cortex dopamine, COMT modulates working memory
and executive function; however, its significance for other cognitive domains, and in other brain regions, remains
relatively unexplored. One such example is reward processing, for which dopamine is a critical mediator, and in which the
striatum and corticostriatal circuitry are implicated. Here, we discuss emerging data which links COMT to reward
processing, review what is known of the underlying neural substrates, and consider whether COMT is a good therapeutic
target for treating addiction. Although a limited number of studies have investigated COMT and reward processing,
common findings are beginning to emerge. COMT appears to modulate cortical and striatal activation during both reward
anticipation and delivery, and to impact on reward-related learning and its underlying neural circuitry. COMT has been
studied as a candidate gene for numerous reward-related phenotypes and there is some preliminary evidence linking it
with certain aspects of addiction. However, additional studies are required before these associations can be considered
robust. It is premature to consider COMT a good therapeutic target for addiction, but this hypothesis should be revisited as
further information emerges. In particular, it will be critical to reveal the precise neurobiological mechanisms underlying
links between COMT and reward processing, and the extent to which these relate to the putative associations with
addiction.
Keywords: Dopamine, COMT, prediction error, nucleus accumbens, striatum, smoking.
1. MODULATION OF CORTICAL AND STRIATAL
DOPAMINE SYSTEMS BY CATECHOL-O-METHYLTRANSFERASE
As outlined in other contributions to this volume,
catechol-O-methyltransferase (COMT) activity is associated
with differences in working memory and executive function
dependent on prefrontal cortex (PFC) dopamine. However,
dopamine is also a key neurotransmitter for regulating the
processing of rewarding stimuli [1]. Studies investigating the
association of COMT with reward are at a relatively early
stage; however, this paper will review the evidence collected
to date. We examine studies in healthy people investigating
the role of COMT in reward processing and then review its
significance for addiction and related phenotypes. Finally,
we consider whether COMT inhibitors represent valid
targets for disorders in which aberrant reward function is a
feature.
The COMT gene metabolises the catecholamine
neurotransmitters. The human COMT gene contains a
functional polymorphism in its sequence (Val158Met) which
alters enzyme activity: individuals homozygous for the
ancestral valine allele (Val) have activity approximately 40%
higher than those homozygous for the methionine allele
(Met), whilst heterozygotes have intermediate activity [2].
The Val158Met polymorphism is not the only functional
variation within the human COMT gene [3]. However, few
studies investigating the role of COMT in reward processing
*Address correspondence to this author at the University Department of
Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK; Tel: +44 (0)1865
226492; Fax: +44 (0)1865 251076;
E-mail: [email protected]
1996-3181/12 $58.00+.00
and addiction have examined the impact of other
polymorphisms, and those that have focussed on
polymorphisms of unknown functional significance.
Therefore, this review considers exclusively the Val158Met
polymorphism.
Numerous studies in rodents have highlighted a particular
role for COMT for regulating dopamine (but not
noradrenaline) levels [4-7]. These studies demonstrate that
reduction of COMT activity, either genetically or
pharmacologically, leads to increased dopaminergic
transmission in the frontal cortex, presumably due to the
limited significance of the dopamine transporter in this
region [8, 9]. In contrast, dopamine levels in the striatum (at
least under basal conditions) are unaffected by modulation of
COMT activity [4, 5, 7, 10]. COMT’s importance in other
regions remains largely unexplored, although we have
recently collected data suggesting that it may also modulate
hippocampal catecholamine function [Laatikainen, Sharp,
Bannerman, Harrison and Tunbridge, in submission].
However, although COMT does not appear to directly affect
basal dopamine levels in the striatum, this does not
necessarily mean that it has no impact on the function of this
region (Fig. 1). Firstly, COMT may modulate striatal regions
downstream of its effects in the cortex, since there is an
inverse relationship between cortical and striatal dopamine
function [11, 12]. In support of this hypothesis, Val158Met
genotype is associated with midbrain dopamine synthesis,
demonstrated both in post-mortem tissue [13] and in vivo
[14]. Intriguingly, one of these studies [13] indicated that the
effect of COMT is limited to neurons in the ventral tier of
the substantia nigra, which preferentially project to the
dorsal striatum, rather than the ventral striatum (VStr;
including the nucleus accumbens) [15], suggesting that
© 2012 Bentham Science Publishers
The Role of Catechol-O-Methyltransferase in Reward Processing and Addiction CNS & Neurological Disorders - Drug Targets, 2012, Vol. 11, No. 3
Met COMT
Val COMT
PFC
Striatum
Tonic
dopamine
Tonic
dopamine
?
COMT
307
Phasic
dopamine
Midbrain
PFC
Striatum
Tonic
dopamine
Tonic
dopamine
?
COMT
Phasic
dopamine
Midbrain
Fig. (1). Effects of COMT on prefrontal and striatal dopamine function. Shown here is a highly simplified representation of the inverse
relationship between PFC and striatal dopamine activity (see Carr and Sesack [103] for a full description) in Val-COMT vs Met-COMT
homozygotes. COMT catabolises PFC dopamine, resulting in compensatory changes in midbrain dopamine synthesis. In Val-COMT
homozygotes, the relatively lower PFC dopamine function (compared with Met-COMT homozygotes) is hypothesised to result in increased
phasic dopaminergic transmission in the striatum, which may be accompanied by COMT acting locally within the striatum to reduce the
tonic pool of dopamine [18]. In Met-COMT homozygotes the relative impact of COMT on tonic vs phasic dopamine is reversed. Note that,
since COMT’s hypothesised impact on phasic striatal dopamine transmission results from compensatory changes presumably arising over
time, it is plausible that acute COMT inhibition might increase striatal tonic dopamine function, whilst leaving phasic transmission unaltered
(acute COMT inhibition is known to increase PFC dopamine efflux [6]). PFC, prefrontal cortex.
indirect effects of COMT might be more prominent in this
region. However, this hypothesis should be treated with
caution, given the complex, non-reciprocal projections
linking different regions of the midbrain dopamine
projection nuclei and striatal subregions [15]. Secondly,
although COMT does not affect basal dopamine
transmission in the striatum, it is possible that it becomes
significant under conditions of elevated transmission. For the
dorsal striatum evidence for this theory is mixed: COMT
inhibition potentiates the increase in dopamine levels
induced by dopamine transporter blockade [10, 16] but not
by amphetamine [17]. For VStr, including the nucleus
accumbens, the possibility that COMT is functionally
significant remains largely unexplored under any conditions,
although we have recently obtained preliminary evidence
that 9--tetrahydrocannabinol increases accumbal dopamine
efflux only when COMT activity is inhibited (Kaenmäki,
Stumpenhorst, Sharp, Harrison and Tunbridge, unpublished
observations).
Significantly, the directionality of any effects of COMT
on striatal dopamine is predicted to differ depending on
whether they are mediated directly or downstream of its role
in the cortex. COMT acting directly in the striatum would
presumably affect dopamine in a similar manner to the
cortex: namely, higher COMT activity should predict lower
striatal dopaminergic function and vice versa. In contrast, if
striatal effects are mediated indirectly, via COMT’s impact
on dopamine transmission in the cortex, higher cortical
COMT activity would be expected to result in lower cortical
dopamine transmission, which in turn would result in an
increase in striatal dopamine transmission, due to the
reciprocal relationship between these two overlapping
systems [11, 12]. Thus, higher COMT activity would be
expected to result in higher striatal dopamine transmission,
and vice versa. Notably, these two possibilities are not
mutually exclusive and have been incorporated into a model
(Fig. 1) taking into account the two signalling modes of
dopamine in the striatum: transient, phasic dopamine release
resulting from burst firing of dopamine neurons and tonic,
background dopamine levels regulated by both basal
dopamine neuron firing and corticostriatal glutamate
projections [18]. In this model, high COMT activity is
predicted to result in higher striatal phasic dopamine
signalling, downstream of its effects in the cortex, but lower
tonic dopamine signalling, due to the direct modulation of
the tonic dopamine pool by COMT within the striatum, and
vice versa [18]. Although this model has yet to be directly
tested, it predicts that the effects of COMT on striatal
dopamine function and, therefore behaviours dependent on
striatal dopamine, are likely complex.
In summary, COMT has been shown to directly and
significantly impact dopamine function in the frontal cortex.
Its effects in the striatum are less clear, although it is
plausible that COMT is also relevant in this region, either
CNS & Neurological Disorders - Drug Targets, 2012, Vol. 11, No. 3
Tunbridge et al.
downstream of its modulation of cortical dopamine, or under
conditions of elevated dopamine transmission, or both.
novel or salient stimuli that do not necessarily have any
intrinsic rewarding properties [36, 37] and by cues that
signal the availability of information about future states [38].
Aversive stimuli, or cues predicting such stimuli, can cause
either phasic excitation or inhibition in different dopamine
neurons [39, 40], and dopamine transmission does not
consistently signal the value of an option when that value is
defined by the amount of effort an animal will need to exert
to obtain a goal [24]. Such activity might be viewed in terms
of motivational salience or a motivation to learn [41, 42].
Theories of dopamine function are also complicated by the
fact that neurotransmission in terminal regions may be
modulated by the inputs to that region and to local synaptic
processes that can facilitate or inhibit release. Therefore,
levels of dopamine release may not always correlate
precisely with the firing patterns of the midbrain dopamine
cells.
308
2. DOPAMINE AND REWARD PROCESSING
Multiple brain regions are implicated in reward
processing, notably the VStr, midbrain, orbitofrontal cortex
(OFC), anterior cingulate cortex (ACC), PFC, ventral
pallidum and the medial dorsal nucleus of the thalamus [19].
Dopamine, particularly the mesolimbic dopamine pathway
from the ventral tegmental area to the VStr, has long been
associated with reward and reinforcement. Systemic
administration of dopamine antagonists selectively reduces
the motivation of animals to pursue rewards, without
affecting their preferences for such rewards when they can
be obtained without effort [20-22]. Such manipulations have
a particularly pronounced effect on the activation of animals
induced by reward-predicting cues in the environment [23].
The phasic activity of many midbrain dopamine cells, and
dopamine transmission in the nucleus accumbens region of
the VStr, is also modulated by several parameters that
pertain to the value of an upcoming reward, including its
size, probability and the delay to its occurrence [24-28].
However, whilst there is general agreement that
dopamine plays a key role in reward processing, its precise
function remains contested. This is partly because reward is
a multifaceted concept, related to motivation, hedonia and
reinforcement, and dopamine may only be necessary for
some of these processes. One influential theory has proposed
that, rather than conveying the direct incentive or hedonic
properties of reward to an animal, midbrain dopaminergic
activity is used to guide learning by conveying the
discrepancy between past and current reward expectations,
known as reward prediction errors [29, 30] (see below). This
signal can then theoretically be used to tune predictions
about the value and timing of future rewards. In line with
this theory, it has been demonstrated in several situations
that the timing of putative dopamine cell activity and
dopaminergic transmission adapts with learning in
accordance with the predictions of reinforcement learning
models. For instance, in a Pavlovian conditioning paradigm
where animals learn that a particular cue is associated with
reward, phasic increases in dopamine activity and
transmission are initially observed only in response to
reward delivery; however, following training, these signals
progress backwards in time such that they come to be
elicited by the cue and not by the fully predicted reward [30,
31]. These changes in dopamine activity are behaviourally
relevant, as disruption of the dopamine system impairs the
expression and later consolidation of this type of learning,
particularly in situations where the cue itself comes to elicit
approach behaviour [32-34]. Similarly, either boosting or
blocking dopamine transmission in humans can also
accelerate or retard reward learning [35]. Such a precisely
defined role for dopamine in representing and updating value
estimates may explain why disturbances in this system form
an integral part of numerous pathological disorders.
In spite of the fact that the reward prediction theory of
dopamine remains one of the most replicable and formally
precise neurobiological models, there are nonetheless several
pieces of evidence that do not fit easily in with this account.
For instance, changes in dopamine activity can be elicited by
Similarly, the role dopamine plays in guiding decisions is
also contentious. While manipulations of the dopamine
system can alter the patterns of choice behaviour [43-45]
there are also several reports of maladaptive decision making
(i.e., where animals choose the lower value of two options)
at a time when dopamine signals are already consistently
reporting the high and low reward values associated with
each option [28, 42]. Therefore, rather than guiding action
selection policies directly, dopamine instead may provide an
opportunistic drive to engage with particular behaviours.
This may be crucial in uncertain environments to allow
animals to explore novel options and motivate them to learn.
3. COMT’S ROLE IN REWARD PROCESSING
Compared to the abundant literature studying its role in
working memory and executive function (stemming from the
seminal study by Egan and colleagues linking COMT with
PFC activity, working memory and executive function [46])
investigations into the significance of COMT for reward
processing are in their infancy. Nevertheless, consistent
findings are beginning to emerge. Perhaps unsurprisingly,
given the uncertain role of dopamine in reward processing
and the potential for COMT to modulate several sites within
the reward circuitry, COMT appears to be of significance for
several facets of reward processing. We therefore consider
separately the evidence implicating it in reward anticipation
and outcome, and then present the evidence for its
involvement in learning and decision-making tasks with a
reward component.
Effects of COMT on Brain Activation During the
Anticipation of Reward
As described above, the VStr is a key structure in reward
processing, and is involved in reward prediction and
anticipation [47]. Arguably the most consistent finding
linking COMT with reward processing is the demonstration
that VStr activation (i.e. the blood oxygen level dependent
[BOLD] response measured using fMRI) during reward
anticipation is modulated by COMT Val158Met genotype.
This was initially demonstrated by Yacubian and colleagues
[48], using a task in which participants were asked to gamble
a small or large reward, with a probability of winning of
either 12.5% or 50%. The authors demonstrated an allele
The Role of Catechol-O-Methyltransferase in Reward Processing and Addiction CNS & Neurological Disorders - Drug Targets, 2012, Vol. 11, No. 3
dose relationship between the COMT Met allele and VStr
activation during reward anticipation: the Met allele
predicted greater activation, compared with the Val allele.
Similar findings have since been obtained in two other
studies, despite significant differences in the tasks that they
employed to probe reward function. Firstly, a correlation
between the number of COMT Met alleles and VStr
activation was found during the anticipation of uncertain
rewards on a slot machine gambling task [49]. Secondly,
Schmack and colleagues [50] found effects of COMT on
VStr activation using a monetary incentive delay task, in
which participants were given the opportunity to gain or lose
monetary rewards of varying magnitude dependent on their
subsequent performance on a motor task. Note that one of
the ways in which this task differs from those used in the
other studies is that reward in this case is determined by the
individual’s performance, rather than by probability, which
is necessarily a property of the external world and therefore
beyond the participant’s control. Nevertheless, Schmack and
colleagues demonstrated a positive correlation between the
number of COMT Met alleles and VStr activation during
reward anticipation, although they only found this
relationship for trials in which participants could lose
money. Thus, under certain conditions the effect of COMT
on VStr activation during reward anticipation might be
limited to (or at least more prominent for) anticipated losses,
compared with gains. This could reflect a greater sensitivity
to losses, compared with gains, associated with the Met
allele, perhaps consistent with studies of emotional
processing in which it has been shown that the Met allele is
associated with greater reactivity to negative, but not
positive, emotional stimuli, compared to the Val allele [5153]. Alternatively, it could merely reflect the fact that
individuals tend to be more sensitive to losses than gains
[54]. Taken together, these findings demonstrate that low
COMT activity, encoded by the Met allele, is associated with
greater VStr activation during reward anticipation, compared
with the Val allele.
In addition to an effect of COMT on the VStr, the three
studies which examined its impact during reward
anticipation also found effects of the Val158Met
polymorphism on activation of cortical regions, although the
precise localisation varied, perhaps due to differences in the
tasks employed. Thus, the Met allele was associated with
greater activation of the PFC [48, 49] and temporal pole
[50], compared with the Val allele. The authors of one of
these studies propose that the greater cortical activation
associated with the Met allele might reflect greater task
engagement [49], possibly related to the greater reactivity to
emotional stimuli observed in these subjects [51-53],
although direct evidence for this hypothesis is lacking.
Alternatively, genotype differences may reflect variation in
the top-down control of reward prediction.
Finally, it should be noted that COMT’s effects are likely
to be dependent on other factors that impact on dopamine
function. Consistent with this hypothesis, several studies
linking COMT with reward anticipation found, in addition to
main effects of COMT genotype on brain activation,
interactive effects of COMT and a polymorphism in the
dopamine transporter gene (DAT) that may be associated
with DAT expression [55] and would therefore be expected
to impact on striatal dopamine function. Interactive effects of
309
COMT and DAT have been reported in the VStr [48, 49, 56],
caudate [49] and PFC [49]. Thus, the effect of COMT on
reward anticipation-related brain activation is modulated by
functional variation in DAT. It is highly likely that effects of
COMT on all aspects of reward processing will be
modulated by genetic (and epigenetic, and environmental)
factors which impact on dopamine signalling, as has been
demonstrated for links between COMT and cognitive
function [57].
Effects of COMT on Brain Activation During Reward
Outcome
Marco-Pallarés
and
colleagues
[58]
used
electroencephalography (EEG) to investigate the neural
response to reward outcome in a gambling task. On each
trial, participants had to choose to gamble either low (5) or
high (25); they then received feedback to tell them whether
they had won or lost the corresponding amount [59].
Consistent with earlier findings [59], the authors found a
medial frontal negativity (MFN) occurring from 250-300
msec that was modulated by valence: it was greater in
magnitude for losses, compared to wins. Val homozygotes
were found to show a larger difference in MFN magnitude
for losses vs wins, compared with Met homozygotes.
Consistent with these findings, we have recently found
similar effects of COMT Val158Met on global neural
activation using magnetoencephalography [MEG; Farrell,
Tunbridge, Braeutigam and Harrison, unpublished
observations]). Furthermore, Marco-Pallarés and colleagues
[58] also found that Val homozygotes showed a greater
difference in MFN amplitude for maximum (25) vs
minimum (5) losses, compared with Met homozygotes. The
authors also examined oscillatory activity and found greater
activation in the beta range for gains than losses. This beta
activation was also modulated by COMT genotype, with Val
homozygotes showing a greater difference for gains vs losses
than Met homozygotes [58]. Thus, Val homozygotes showed
greater differences in both MFN magnitude and beta
activation for losses vs gains, compared with Met
homozygotes.
The same research group also conducted a fMRI study
using the same task, except that 20% of trials were ‘boost’
trials, in which either ‘5’ or ‘25’ was replaced by ‘125’ [60].
They found that the VStr was activated for valence (in this
case gains minus losses) for both the standard and boost
trials. The authors found a significant effect of COMT on
this valence-related activation of the VStr during boost trials
but not standard trials. Similar to their findings obtained with
EEG, this effect was due to a significantly greater difference
between VStr activation in gains trials minus loss trials in
Val homozygotes, compared with Met homozygotes. Further
examination of the data indicated that the greater difference
seen in Val homozygotes was due to a greater reduction in
BOLD response to losses, compared with Met homozygotes;
BOLD responses to gains were similar in Val and Met
homozygotes. Taken together, these studies indicate that
COMT modulates the relative activation to gains vs losses,
with the Val allele associated with greater differential
activation between win and loss conditions. The major
discrepancy between these studies is that we and MarcoPallarés and colleagues found an effect of COMT on
CNS & Neurological Disorders - Drug Targets, 2012, Vol. 11, No. 3
Tunbridge et al.
standard trials (that is, those comparing ‘5’ and ‘25’) [58],
whilst Camara et al’s effect of COMT was limited to ‘boost’
trials, in which one of the gambles was unexpectedly high
[60]. Further studies are required to clarify the reasons for
this, but a parsimonious explanation involves the difference
in temporal resolution between EEG and MEG, and fMRI:
whilst differences over a 50 msec time window can be
readily detected using EEG and MEG, fMRI can only detect
changes occurring over several seconds. Thus, it is possible
that standard trials produce only a relatively small and short
acting change in neural activation, which is insufficient to
alter BOLD response averaged across several seconds but
can be seen using EEG and MEG, whilst ‘boost’ trials
produce a greater and/or longer-lasting change in neuronal
activation that can also be detected using fMRI.
Investigation of the effect of ‘boost’ trials on neural
activation using techniques with high temporal resolution,
such as EEG or MEG, will help clarify this discrepancy.
stimulus [63] (Fig. 2). This hypothesis is consistent with
findings of increased experience of reward, in terms of
positive affect, associated with the Met allele [64].
310
In addition to the studies detailed above, which examined
the effect of COMT on neural responses using a gambling
task, Forbes and colleagues [61] investigated the effect of
several dopaminergic genes on VStr activation during
outcome of a guessing task. This task was slightly different,
in that participants did not win or lose a specific amount on a
trial-by-trial basis; however, they did receive positive or
negative feedback after each trial, and were told that their
performance would determine the amount of money they
would receive after the task was complete. Although this
task robustly activated the VStr, reward-related VStr
activation was unaffected by COMT genotype in this study.
The same group failed to find an effect of COMT on rewardrelated VStr activation in a follow-up study using the same
task [62]; however, they did report that reward-related VStr
activation in this case was modulated by a multilocus
dopaminergic genetic profile (a dopamine function ‘score’
derived from the additive effect of functional polymorphisms
in COMT and DAT, and the dopamine receptor genes,
DRD2 and DRD4), suggesting that, although not
significantly affected by COMT alone, it is under
dopaminergic modulation. These findings suggest that
effects of COMT might be less prominent during tasks in
which the relevant reward is somewhat removed from ongoing performance, since in this task participants were
unable to monitor how their performance on a trial-to-trial
basis impacted on post-task reward. They also emphasise
that the effect of COMT on brain activation during reward
outcome is likely to be moderated by polymorphisms in
other dopaminergic genes, as has also been reported for
reward anticipation [48, 49] described above. Consistent
with this hypothesis, interactive effects of COMT and DAT
on cortical activation during reward outcome have also been
reported [49].
Finally, as is the case during reward anticipation, COMT
effects do not appear to be limited to the VStr, but extend
into cortical regions. Thus, following the delivery of
uncertain rewards, Met homozygotes activate the OFC more
than Val homozygotes, with heterozygotes showing
intermediate activation [49]. The authors hypothesise that
this may reflect lower valuation of rewards associated with
the Val allele, since, amongst other things, the OFC is
involved in representing the expected reward value of a
In summary, the findings of Marco-Pallarés and
colleagues [58] and Camara and colleagues [60] demonstrate
that the COMT Val allele is associated with greater
discrimination between gains and losses, compared with the
Met allele, at least at the level of brain activation, and that
this is mediated at least in part by the VStr [60]. Although
this finding requires confirmation and extension, it suggests
that COMT may impact on the quality of the reward
prediction error, as discussed further below. The findings of
both studies suggest that the relative insensitivity to gains vs
losses associated with the Met allele might be due to
differences in processing losses between Val and Met
carriers. However, the negative findings reported by others
[61, 62] emphasise that the relative importance of COMT
during reward outcome may depend on the task employed: it
will be of interest to investigate whether COMT plays a
greater role in tasks in which winning or losing is directly
coupled to monetary gains and losses, respectively. In
addition, COMT also appears to modulate OFC activation
during the delivery of uncertain rewards, with the Met allele
associated with greater activation, compared with the Val
allele.
The Impact of COMT on Learning and Decision-Making
with a Reward Component
As outlined elsewhere in this volume, numerous human
and animal studies have implicated COMT in cognitive
function, particularly working memory. Generally speaking,
these studies have demonstrated that lowered COMT
activity, mediated either genetically or pharmacologically is
associated with improved working memory and other aspects
of executive function [53]. These cognitive effects of COMT
are generally attributed to its direct action on dopamine in
the frontal cortex, consistent with robust associations
between the Val158Met polymorphism and activation of the
PFC during working memory tasks [65]. However, although
low COMT activity, for example as determined by the Met
allele, is generally considered beneficial for working
memory performance, which requires the stable
representation of items in memory, it has also been proposed
that it might be detrimental in tasks where flexibility is
required [18] (although this remains to be formally
demonstrated). Given that COMT impacts on cognitive
function and on processing of reward anticipation and
outcome, it seems likely that it should also impact on
learning and decision-making tasks with a reward
component. Several studies have reported positive findings
in this respect, reviewed below. However, given the impact
of COMT on several inter-related but somewhat separable
components, its involvement in reward-related learning and
decision-making is inevitably complex and as outlined
below, is exquisitely task-dependent.
Frank and colleagues [66] investigated the impact of
COMT during performance of a probabilistic reward
learning task, in which participants were presented pairs of
stimuli which had complementary probabilities of winning
or losing (e.g. stimulus A associated with 80% chance of
The Role of Catechol-O-Methyltransferase in Reward Processing and Addiction CNS & Neurological Disorders - Drug Targets, 2012, Vol. 11, No. 3
dlPFC
Maintenance
Biasing
MetCOMT o
ndopamine
nactivation during
reward anticipation
311
VStr
Prediction error
Met COMT
p phasic dopamine
n tonic dopamine
nactivation during
reward anticipation
ACC
Action-reward associations
Cost-benefit
COMT effects unknown
OFC
Reward value
Delay costs
Credit assignment
VTA/SN
Dopamine projection regions
Met COMT
p dopamine synthesis
Met COMT o
nactivation during uncertain
reward delivery
Fig. (2). Effects of COMT on the reward circuitry. The location of key structures in the reward circuitry, and their functions and regulation
by COMT during reward processing, is shown. The dorsolateral prefrontal cortex (dlPFC) is involved in the maintenance of reward
contingencies and biasing the representation of reward information based on prior information. The Met COMT allele, compared to the Val
COMT allele, is presumed to increase dopamine in this region and is associated with increased activation during reward anticipation. The
anterior cingulate cortex (ACC) is critical for encoding the effort required to obtain a reward (cost-benefit) and action-reward associations.
The impact of COMT on this structure during reward processing is unknown (although COMT genotype does modulate cingulate activation
during attentional processing [104]). The orbitofrontal cortex (OFC) represents reward value, delay costs and credit assignment, and
demonstrates relatively greater activation during the delivery of uncertain rewards in Met COMT, compared with Val COMT, carriers. The
ventral striatum (VStr) is thought to encode a prediction error (see main text). Met COMT is hypothesised to decrease phasic, but increase
tonic, dopamine transmission in this region, relative to Val COMT [18]. Met COMT carriers show greater activation in this region during
reward anticipation, compared with Val COMT carriers. Dopaminergic projections arise in the ventral tegmental area (VTA) and substantia
nigra (SN) and their function is impacted by COMT genotype, presumably downstream of its effects in the cortex.
being ‘correct’ whilst stimulus B is associated with 20%
chance of being ‘correct’) and were required, using trial-bytrial feedback, to learn whether to select A or B to maximise
success. Once these associations had been learnt, different
combinations of stimuli could then be presented to probe not
only how well these associations had been learnt, but also
the strategies used to learn them. According to reinforcement
learning theory, dopamine is expected to contribute to three
separable aspects of task performance: learning to choose a
stimulus associated with a positive outcome (positive
reinforcement), associated with striatal dopamine signalling;
to avoid a stimulus associated with a negative outcome
(avoidance learning), possibly also associated with striatal
dopamine signalling [67]; and with the maintenance of
associations between each stimulus and outcomes on a trialby-trial basis, supported by dopamine’s actions in the PFC.
The COMT Met allele was associated with greater lose-shift
behaviour, that is, Met carriers were more likely to switch
responding to the alternative stimulus in a given pair after
receiving negative feedback during the process of learning
associations, but the allele was not associated with either
positive reinforcement or avoidance learning. These results
are consistent with a main effect of COMT on PFC
dopamine, and a lack of effect on striatal dopamine
signalling in this task.
A recent study from the same group [68] extended these
findings, by investigating the effect of explicit instructions
about reward contingencies on performance. The task used
was the same as in their previous study [69], except that
participants were instructed that “Stimulus X has the best
chance of being correct”. These instructions were randomly
assigned, so that they could be either correct or incorrect.
The authors found that COMT significantly influenced the
extent to which subjects persisted in responding to prior
instruction when these instructions were inaccurate: Met
carriers showed significantly greater persistence compared
with Val homozygotes. They hypothesise that the PFC is
capable of biasing striatal-dependent learning based on prior
instructions, so that incorrect responses are rated as being
more correct if subjects have previously been told this is the
case. These findings are therefore consistent with the
hypothesis of the Val allele being associated with more
flexible responding, compared with the Met allele, as
described above (Fig. 2).
A similar COMT Val allele advantage associated with a
need for flexible responding was found in a probabilistic
learning task in which reward contingencies switched once
participants learnt to select the high-probability rewarding
stimulus to a given criterion [70]. Val homozygotes won
more money and reached criterion more often than Met
CNS & Neurological Disorders - Drug Targets, 2012, Vol. 11, No. 3
Tunbridge et al.
homozygotes. The authors used a reinforcement learning
computational model to demonstrate that the Val
homozygotes’ better performance was determined by a
stronger increase in learning rate following reversal of
reward contingencies, compared with the Met homozygotes.
This model assumes that on each trial the expected reward
for a given action is compared to the actual reward and that
learning is driven when there is a difference between the
observed and expected reward (known as the prediction
error), which is encoded by phasic dopamine transmission
[30]. Prediction errors can encode both a greater than
expected reward (a positive prediction error) and a less than
expected reward (a negative prediction error). The authors
looked at the neural correlates of this prediction error: Val
homozygotes showed greater VStr activation to both positive
and negative prediction errors, compared with Met
homozygotes. In addition, they demonstrated that the
magnitude of the prediction error correlated with activation
in the right putamen, and that this activation was affected by
COMT genotype. In Val homozygotes, the right putamen
activation was positively correlated with positive prediction
errors and negatively correlated with negative prediction
errors. In contrast, the correlation between prediction error
magnitude and activation in Met homozygotes was positive
irrespective of the directionality of prediction errors. Taken
together, these findings suggest that the striatal response to
prediction errors is more adaptive in Val homozygotes,
compared with Met homozygotes, which, in turn, leads to
better updating of expected reward contingencies on a trialby-trial basis, and therefore better learning.
have been employed for this research (Fig. 2). During reward
anticipation, the COMT Met allele has been associated with
greater activation of the VStr and cortical regions across
several different tasks [48-50]. However, the behavioural
correlates of this enhanced activation are not yet clear.
312
Finally, two studies suggest that COMT impacts on delay
discounting, that is the likelihood of choosing a more
immediate but smaller reward over a larger, delayed reward.
Boettiger et al. [71] showed that Val homozygotes were
more likely to choose impulsively, that is to choose a
smaller, immediate reward, compared to waiting for a larger,
delayed reward, when compared with Met carriers. COMT
genotype was also associated with activity in the dorsal PFC
and the posterior parietal cortex, two regions in which
activation was positively correlated with the likelihood of
choosing impulsively. As would be predicted given their
more impulsive decision-making, Val carriers showed
greater activation in the dorsal PFC and posterior parietal
cortex, compared with Met homozygotes. However, these
findings contrast with those of a more recent study in which
the Met allele was associated with significantly steeper
discounting rates, i.e. more impulsive choices, compared
with the Val allele [72]. A possible explanation for the
discrepancy between the studies is that Boettiger et al. [71]
studied adults, whilst Paloyelis and colleagues’ [72]
participants were adolescents, since numerous aspects of
PFC function, including the expression and activity of
COMT [73], change over the course of adolescence [74].
However, further studies are needed to clarify the role of
COMT in discounting behaviour.
Summary of COMT’s Role in Reward Anticipation,
Outcome and Learning
In summary, although studies of COMT’s role during
reward processing are limited, consistent findings are
beginning to emerge, despite the broad range of tasks that
COMT genotype also impacts on brain activation during
reward outcome: the Val allele is associated with greater
differences in brain activation patterns between losses and
wins, compared with the Met allele [58, 60, 70], indicating
that the neural response to wins vs losses might be more
informative for driving reward learning in Val carriers,
compared with Met carriers. Consistent with these findings,
the Val allele predicts better reward learning than the Met
allele under conditions where reward contingencies are
changing [70]. The VStr appears to be a key site for
mediating these effects, and it is presumed that COMT
effects in this region lie downstream of its impact in the PFC
(which would be consistent with the directionality of the
relationship – see introduction and Fig. 1). However,
precisely how COMT effects in the VStr and PFC interact
during the performance of these tasks is currently unclear.
The role of COMT depends on the nature of the task
employed: as detailed above, when reward contingencies are
constantly changing, COMT effects in the VStr appear to
modulate how adaptive the prediction error signal is, thereby
impacting on reward learning. However, when reward
contingencies are fixed, the PFC appears to be the key site
for COMT’s actions: under conditions where stability of
reward representations is favourable the Met allele appears
to be beneficial [66]; however, this beneficial effect of the
Met allele can be negated if incorrect prior information is
given about the expected reward value of a given stimulus
[68].
Taken together, these findings suggest that, as is also the
case with cognition [53], neither the Val allele nor the Met
allele of COMT is inherently detrimental or beneficial in
terms of reward processing; rather, COMT impact depends
on the demands of the task and the underlying circuitry.
Given these complexities, we might expect the relationship
between COMT and addiction phenotypes to also be
complex.
4. COMT AND
PHENOTYPES
ADDICTION
AND
RELATED
Drugs of abuse increase mesolimbic dopamine
transmission [75], thereby mimicking the physiological
effect of natural rewards. This hijacking of the reward
circuitry by drugs of abuse is widely considered to represent
a pathological mechanism underlying addictive disorders
[76] which can, in turn, be seen as disorders of reward
processing. Given its importance for regulating dopamine
function and its role in modulating brain activation during
reward processing, COMT is an attractive candidate gene for
addictive disorders. However, any association between
COMT and addiction is likely to be complex, since based on
the evidence outlined above both low and high COMT
activity can be detrimental to reward learning and decisionmaking in different contexts.
COMT has been well-studied as a candidate gene in
various forms of addiction and substance abuse (Tables 1-3).
The Role of Catechol-O-Methyltransferase in Reward Processing and Addiction CNS & Neurological Disorders - Drug Targets, 2012, Vol. 11, No. 3
Table 1.
Association Studies of COMT Val158Met with Alcoholism
Reference
Phenotype
a
Ishiguro et al.,
1999 [105]
Alcoholism Japanese
Tiihonen et al.,
1999 [106]
Type 1
alcoholism Population
Number of Participants (M=Males,
F=Females, where Available)
Controls: n =345
Cases: M154/F21 Case controls: M267/F0
Finnish
General population control group: n
=3140
Cases: M123/F0
Finnish
Henderson et al.,
2000 [107]
Alcohol
misuse Caucasian
Australian Nakamura et al.,
2001 [108]
Alcohol
withdrawal
symptoms Japanese
No association Met allele associated with Type 1
alcoholism in men.
Associated
Allele
---
Met
Alcoholism
German
General population control group: n
=3140
No association
---
Cases: M62/F0
n = 848
Controls: n = M112/F0
Cases: n = M91/F0
No association ---
Met allele associated with more severe
withdrawal syndrome, but not with
alcoholism per se.
---
140 parents
b
Wang et al.,
2001 [109]
Finding Relating to Val158 Met
Case controls: M267/0
Type 2
alcoholism in
violent
offenders Hallikainen et
al., 2000 [106]
M54/F16 alcoholic offspring
Met allele is associated with Type 2
alcoholism.
Met
(32 type 2; 38 type 1)
Köhnke et al.,
2003 [110]
Alcoholism
German
Kweon et al.,
2005 [111]
Alcoholism Korean
Controls: n =101
Cases: M178/F65
No association ---
No association ---
Val allele associated with alcoholism in
both men and women.
Val
No association ---
Val allele is associated with alcoholism
in men.
Val
No association
---
No association
---
M187/F0
Controls: n = 91
Cases: n = 97
Enoch et al.,
2006 [112]
Alcoholism Plains Indians Samochowiec et
al., 2006 [113]
Alcoholism Polish
Sery et al., 2006
[114]
Alcoholism
Czech
Foroud et al.,
2007 [115]
Alcohol
dependence
and early
onset alcohol
dependence EA
Alcoholism
Polish
Alcoholism
and
interaction
with family
history
Russian
Alcoholism
Caucasian
Croatians
Alcohol
dependence
Caucasian
Australians
b
c
Samochowiec et
al., 2008 [116]
c
Kibitov et al.,
2010
[117]
c
Nedic et al.,
2011 [118]
c
313
Voisey et al.
2011[119]
Non-alcoholics: M33/F100
Alcoholics: M95/F81
Controls: n = 196
Cases: M88/F12
Controls: M151/F249
Alcoholics: M279/F120
Non-alcoholic: M680/F790
Alcoholic: M826/F381
Controls: M120/F30
Cases: M99/F23 Controls: M326/F0
Cases: M395/F0
Controls: M487/F122
Cases: M312/ F81 (NB. multiple
DSM-IV disorders included) Controls: M148/F102
Cases: M157/F74
a: AA = African American; EA = European American; HA = Hispanic American; CA = Caucasian American.
b: excluded from meta-analysis of Tammimäki and Mannisto.
c: published since meta-analysis of Tammimäki and Mannisto.
Trend association with Met allele in
overall sample. Met association
significant in men with a high density
family history of alcoholism.
No association.
Association with Val allele at allele but
not genotype level. Significant
association when Val158Met-rs165774
haplotype considered Met
---
Val
CNS & Neurological Disorders - Drug Targets, 2012, Vol. 11, No. 3
Tunbridge et al.
These associations have recently been examined by metaanalysis [77] and so only an overview of the association
literature is provided in the main text. We also consider
relevant preclinical data where appropriate, although it
should be noted that people use drugs for a wide variety of
reasons (e.g. in some cases to relieve boredom or social
anxiety, or to seek mystical experiences) [78] and therefore
preclinical models can only model aspects of addictive
disorders.
Despite being beyond the scope of the current review, it
should be noted that the COMT Val allele reportedly
interacts with cannabis and -9-tetrahydrocannabinol use to
precipitate symptoms of psychosis and cognitive dysfunction
[87-89], consistent with our preliminary findings of
interactive effects of COMT and -9-tetrahydrocannabinol
on nucleus accumbens dopamine levels (Stumpenhorst,
Kaenmäki, Sharp, Harrison and Tunbridge, unpublished
observations), although this association, like all gene X
environment interactions in psychiatry, is controversial [90].
In addition, genetic variation in COMT appears to modulate
sensitivity to opioids in terms of pain relief [91-93],
presumably mediated via its effect on -opiod responses to
painful stimuli [94]. Thus, although the association between
COMT Val158Met and abuse of illicit drugs does not appear
to be robust, this does not preclude an effect on
physiological responses to these drugs.
314
Associations Between COMT and Alcoholism
As shown in Table 1, association studies between COMT
and alcoholism have produced mixed results, with reports of
significant associations with both the Val and Met alleles, as
well as negative findings. Consistent with these mixed
associations, a recent meta-analysis found no significant
association between the Val158Met polymorphism and
alcoholism [77] (notably, the studies excluded from this
meta-analysis and those that were subsequently published
proved similarly mixed, as indicated in Table 1). However,
these studies are typically small and have been conducted in
a range of ethnic populations, precluding separate metaanalyses in distinct ethnic groups (Val158Met allele frequency
differs dramatically between different populations [79]).
Thus, although preliminary studies do not indicate an
association between COMT and alcoholism, evidence is
insufficient for this possibility to be excluded
Ethanol preference has also been examined in COMT
knockout mice: when given the free choice between water
and ethanol solution, male COMT knockout mice and
heterozygotes were found to consume significantly more
ethanol than their wild-type littermates; however, no
differences in ethanol consumption were detected between
female COMT knockout mice and wild-type controls [80].
These findings are consistent with a study of alcohol
consumption in male social drinkers, in which the Val allele
was associated with lower weekly alcohol consumption [81].
Associations Between COMT and Illicit Drug Abuse
Relatively few studies have examined associations
between COMT and illicit drug abuse (Table 2) and, similar
to the studies linking COMT and alcoholism, findings are
mixed and have been obtained in a variety of ethnic groups.
Consequently, meta-analysis found no association between
COMT Val158Met and abuse of stimulants, opiates or
cannabis [77]. However, it is notable that the two studies
conducted in the Han Chinese population [82, 83], both of
which tested for association between COMT Val158Met and
methamphetamine abuse and which are the largest studies
conducted to date, found significant association with the Val
allele, suggesting that it might be a risk factor in this
population.
Studies in COMT knockout mice have found no evidence
of altered cocaine consumption, compared with wild types
[80] (although male COMT knockout mice show some
altered behavioural responses to amphetamine [84], cocaine
[85] and -9-tetrahydrocannabinol [86], the primary
psychoactive compound in cannabis, compared with wild
types).
Associations Between COMT and Smoking
Similar to the associations of COMT with alcohol and
illicit drug use, reports of association with both smoking and
smoking cessation have been conducted in a number of
different ethnic populations. However, sample size for these
studies has tended to be larger than those for alcohol and
drug use. Perhaps reflecting this fact, despite the mixed
results obtained by these studies, there is a significant
association between COMT and smoking by meta-analysis
[77], with the Val allele representing the risk allele. In
contrast, no association was found between COMT and
smoking cessation by meta-analysis [77]. However, two out
of three of the studies examining the association between
COMT and smoking in patients given nicotine replacement
therapy (NRT) were excluded from the meta-analysis, since
they did not have control groups. Notably, all three studies
found that, in those given NRT, the Val allele was associated
with a poorer prognosis in terms of likelihood of quitting
[95, 96] (at least in women [96]) and speed of relapse [97].
Therefore, although there does not appear to be an overall
association between COMT and smoking cessation, the Met
allele may be associated with a better response to NRT,
compared with the Val allele.
Summary
In summary, results from association studies linking
COMT with addiction are mixed and larger studies in each
of the ethnic populations are required before firm
conclusions can be drawn. From currently-available data
there is little evidence linking COMT and alcoholism.
However, the Val allele appears to be associated with an
increased likelihood of smoking and perhaps a poorer
prognosis for smoking cessation in those treated with NRT.
The potential link between COMT and smoking phenotypes
is particularly intriguing, given the key role that the nicotinic
acetylcholine receptor plays in filtering dopaminergic input
into the striatum [98]. It is also possible that the Val allele is
associated with methamphetamine abuse in the Han Chinese
population.
Interestingly, Val homozygotes reportedly show a greater
decrease in VStr dopamine D2 receptor binding after
The Role of Catechol-O-Methyltransferase in Reward Processing and Addiction CNS & Neurological Disorders - Drug Targets, 2012, Vol. 11, No. 3
Table 2.
Association Studies of COMT Val158Met with Illicit Drug Use (Only Studies with a Non-Drug User, Non-Psychiatric
Control Group Are Included)
Reference
Phenotype
Caspi et al., 2005
[120]
Cannabis use
Baransel-Isir et al.,
2008 [121]
Finding
M412/F391
Controls: n = 610
Cases: n = 193
No association.
---
M110/F20
Controls: n = 75
Cases: n = 55
Val allele is associated with
cannabis dependence
Val
British
Caucasian
Non-users: n = 2403
Cannabis users: n = 164
Trend to association of Met with
cannabis use
---
Heterozygotes (Het) overrepresented in cannabis users
(although Hardy-Weinberg
equilibrium is violated in this
group)
(Het)
Population
New
Zealand
Caucasian
Cannabis dependence Turkish
c
Zammit et al., 2011
[90]
Cannabis use
Associated
Allele
Number of participants (M=Males,
F=Females, where Available)
a
d
Cuyas et al.
2011[122]
Cannabis use
Spanish
Caucasian
Non-users: M49/F44
Cannabis users: M69/F42d
Li et al. 2004 [123]
Methamphetamine
abuse Han Chinese Controls: M248/F187
Cases: M263/F153
Val allele is associated with
methamphetamine abuse.
Val
Suzuki et al., 2006
[124]
Methamphetamine
dependence Japanese
Controls: M155/F45
Cases (all have methamphetamine
psychotic disorder): M117/F26
No association.
---
Caucasian,
Hispanic and
d
AA Controls: M102/0
Cases: M90/F0
No association. ---
Controls: M76/F0
Cases: M117/F0
No association
---
Val/Val genotype associated with
methamphetamine abuse.
Val
d
Bousman et al., 2010a Methamphetamine
[125]
dependence c
Bousman et al.,
2010b [126]
Methamphetamine
dependence
Caucasian
American
c
Methamphetamine
abuse
Han Chinese in Controls: M248/F86
Taiwan Cases: M335/F214 Vandenbergh et al.,
1997 [128]
Polysubstance abuse CA M250/F59
Controls: n = 124
Cases: n = 185
Val allele is associated with
lifetime substance use and
substance abuse in men.
Val
Conner et al., 2010
[129]
Drug use in
adolescence CA M57/F54
No association
---
Horowitz et al., 2000
[130]
Heroin addiction Israeli Study 1 (family-based): M38 heroin
Val allele is associated with heroin
addicts and their parents
addiction in study 1 but not study
Study 2: (case-control): controls: n = 126; 2. cases: n = 101
Oosterhuis et al.,
2008 [131]
Heroin addiction
CA, HA, AA
CA controls: M40/F35; cases: M64/F34
HA controls: M11/F18; cases: M50/F43;
AA controls: M25/F17; cases: M30/F28; Met associated with opiate
addiction in Hispanic women but
not men (not significant in other
racial groups).
Demetrovics et al.,
2010 [132]
Heroin addiction Hungarian
Controls: M33/F91
Cases: M82/F35
No association.
---
Heroin dependence Russian
Controls: M326/F0
Heroin addicts: 243M/F0
No association. ---
Cocaine dependence AA Controls: 74M/181F
Cocaine users: 233M/91F
Met allele associated with
increased risk for cocaine
dependence.
Opiate dependence
Caucasian
Australian
Controls: M148/F102
Cases: M70/F50
No association
MDMA use
Spanish
Caucasian
Non-users: M49/F44
MDMA users: M33/F27
(light users: n = 32
heavy users: n = 28)
No association overall, but Val
allele associated with ‘heavy’ use
(>100 tablets lifetime use)
Jugurnauth et al.,
2011 [127]
b
c
Kibitov et al., 2010
[117]
Lohoff et al., 2008
[133]
c
Voisey et al.
2011[119]
Cuyas et al.
2011[122]
315
a: AA = African American; EA = European American; HA = Hispanic American; CA = Caucasian American;
b: excluded from meta-analysis of Tammimäki and Mannisto
c: published since meta-analysis of Tammimäki and Mannisto
d: data violate assumption of Hardy-Weinberg equilibrium
(Val)
(Met)
Met
---
(Val)
CNS & Neurological Disorders - Drug Targets, 2012, Vol. 11, No. 3
Tunbridge et al.
smoking (thought to reflect increased dopamine release),
compared with those that carry the Met allele [99]. A greater
decrease in VStr D2 binding following smoking was also
associated with greater alleviation of cravings following
smoking [99]. These findings hint that Val homozygotes
might obtain greater relief from cravings after smoking, and
perhaps even finding nicotine more rewarding in the first
place, potentially underlying the association between the Val
allele and nicotine dependence. This hypothesis is also
consistent with the demonstration that smokers with the
Val/Val genotype show a greater benefit of smoking on
cognitive performance, compared to an abstinence challenge,
than Met carriers [100]. Thus, although the results of
association studies should be considered preliminary, the
data collected to date support the association the Val allele
with certain addiction phenotypes. It is possible that this
tentative link between COMT and addiction might be
mediated in part by Val158Met-mediated differences in VStr
dopamine release in response to addictive substances.
However, further studies are needed to determine to what
extent the data linking COMT with smoking-induced
dopamine release can be generalised to other substances.
drawn at this stage should be considered preliminary. Larger
association studies are required, particularly in the case of
illicit drug abuse, before firm conclusions can reasonably be
drawn about the robustness of associations between COMT
and addiction phenotypes. For example, a lack of data
precludes meta-analysis in distinct subgroups. This is
particularly pertinent with respect to differences between
ethnic populations, given the dramatic ethnic diversity in the
distribution of COMT alleles [101]. Furthermore, it will be
important to examine associations between COMT and
addiction phenotypes in males vs females separately, since
COMT’s effects can be strikingly sexually dimorphic [102].
Secondly, as noted above, the mechanisms underlying the
potential association of COMT with addictive phenotypes
are currently unclear, which makes it very difficult to predict
what effect COMT inhibition would have on reward
processing and addiction. In the case of smoking, these
associations are likely complicated further by the nicotinic
receptor’s role in modulating dopaminergic input to the
striatum [98]. Most relevant from a therapeutic point of
view, it is not clear whether COMT inhibition (given to Val
allele carriers in order to potentially ameliorate addictive
phenotypes by making them more ‘Met-like’) administered
in adulthood would alter brain function in the same manner
as the genetically-encoded differences in COMT activity
resulting from the Val158Met polymorphism, which
presumably pertains throughout development and into
adulthood. As outlined in the introduction, effects of COMT
in the striatum are generally considered to be indirect and to
result from adaptive mechanisms downstream of its effects
in the cortex (although this remains to be conclusively
demonstrated). Assuming that this is correct, if the striatum
is a key site for mediating links between COMT and
addictive phenotypes, it would need to be shown that COMT
inhibition was capable of modifying adaptive changes in the
neuronal circuitry resulting from the COMT Val158Met
polymorphism, since it is plausible that the balance between
striatal and PFC dopamine transmission (and therefore the
effects of COMT) is established during development [11].
316
5. CONCLUSIONS: LINKING COMT, REWARD AND
ADDICTION
In conclusion, emerging evidence from studies in healthy
volunteers indicates that the COMT Val158Met
polymorphism impacts on reward-guided learning. Similar to
COMT’s effects on executive function [53], whether it is the
Val allele or the Met allele which is beneficial appears to
depend on the task in hand. COMT modulates striatal and
cortical function during several different stages of reward
processing and currently available data implicate the striatum
and PFC as key sites in which COMT exerts its influence on
reward learning. Studies linking COMT with addiction have
produced mixed findings; however, there is evidence that the
Val allele may be associated with an increased likelihood of
smoking, and possibly with methamphetamine abuse in the
Han Chinese population and with a poorer response to NRT
during smoking cessation.
Given the complex psychological and physiological
processes that are aberrant in addiction, and the involvement
of COMT in several aspects of reward processing, it is
difficult to yet extrapolate from non-clinical findings to
addictive disorders. Studies of COMT and reward processing
in patients with addictive disorders, compared with controls,
as well as a better understanding of the neurobiological
mechanisms underlying the COMT-related differences in
brain activation during reward processing, are required
before informed hypotheses linking COMT, reward and
addiction can be generated.
The association of the COMT Val allele with smoking
and possibly with a poorer response to NRT and with
methamphetamine abuse in certain populations suggests that
COMT inhibition, at least in those with high COMT activity
(e.g. Val homozygotes), might be a therapeutic strategy for
treating or preventing certain addictive disorders, and
perhaps even a useful adjunctive treatment for NRT.
However, this hypothesis is premature for several reasons.
Firstly, the association literature is mixed and so conclusions
Despite the emerging evidence that COMT modulates
reward processing, and perhaps the risk of certain addiction
phenotypes, a number of questions remain outstanding. For
example, several studies have demonstrated that COMT
impacts on brain activation during reward processing, but it
is not clear what the underlying biological mechanisms are.
Studies in animal models, particularly those which allow for
monitoring of fast and slow changes in dopamine
transmission, will be crucial to elucidate these mechanisms.
Furthermore, it is not currently clear what (if any) the
behavioural correlates of these activation differences are.
The associations between brain activation during reward
processing and behavioural output are complicated further
by the suggestion that the impact of COMT on reward
learning is dependent on the precise task employed. As well
as the future combination of reward learning paradigms with
computational modelling approaches, it would be of interest
to expand these studies to utilise neuroimaging techniques
with greater temporal resolution, such as MEG, to allow a
finer dissection of the contribution of different components
of the reward circuitry to different aspects of reward
processing. Finally, in addition to determining how robust its
The Role of Catechol-O-Methyltransferase in Reward Processing and Addiction CNS & Neurological Disorders - Drug Targets, 2012, Vol. 11, No. 3
Table 3.
Associations Between COMT and Smoking and Smoking Cessation
a
Reference
b
David et al.,
2002 [134]
b
Colilla et al.,
2005 [135]
Phenotype
Smoking
initiation,
persistent
smoking or
smoking
cessation a
Population
Number of Participants (M=Males,
F=Females, where Available)
270 former smokers,
EA, AA AA: M0/F241 (former smokers: n =
124;current smokers: n = 117)
Study 1: M155/F187
Redden et al.,
2005 [136]
b
Beuten et al.,
2006 [137]
Nicotine
dependence EA
d
Study 2: M231/F212
Smokers only EA controls: M515/F156; smokers:
M205/F466
Nicotine
dependence Associated
Allele
No association. ---
Met/Met genotype associated with
successful smoking cessation in EA
women and abstinence from smoking with
nicotine replacement therapy in both EA
and AA women. Val
266 current smokers EA: M0/F541 (former smokers: n =
381; current smokers: n = 160)
Smoking
cessation Finding
265 never-smokers,
British
NRT: EA: M0/F178; AA: M0/F112
EA, AA
AA controls: M1053/F313; smokers:
M463/F903
Study 1: Val allele associated with nicotine
dependence.
(Val)
d
Study 2: failed to replicate this finding.
Val allele present in protective haplotypes
in AA women and EA men.
Met
Plains
Indians Enoch et al.,
2006 [112]
Smoking Berrettini et
al., 2007 [138]
Smoking
cessation with
bupropion EA, AA Smoking
behaviour
Han Chinese M52/F116 non-smokers
M76/F65 smokers
Val allele associated with smoking in
women
Val
d
d
Guo et al.,
2007 [139]
EA: M200/F236 (placebo: n = 201;
bupropion: n = 235)
AA: M23/F61 (placebo: n =
47Bupropion: n = 37) Former smokers: M66/F0
Not habitual smokers: M139/F260
Foroud et al.,
2007 [115]
Habitual
smoking Johnstone et
al., 2007 [140]
Smoking
cessation with
or without
NRT European
British Placebo: M144/F217
Nicotine
EA,
dependence
European
M733/F1196 (controls: n = 879; cases:
n = 1050)
Australian
Tochigi et al.,
2007 [142]
Smoking
behavior Japanese
Ton et al.,
2007 [143]
Smoking
cessation with
or without d,lfenfluramine American
(93%
Caucasian)
Han et al.,
2008 [144]
Smoking
cessation with
bupropion
Korean Smoking
cessation
European
M382/F414
British
All received NRT
b
Saccone et
al., 2007 [141]
Munafo et
al., 2008[145]
No association.
---
Never-smokers: M102/F0
Current smokers: M203/F0
b
317
with NRT
EA
Habitual smokers: M481/ F348
No association with smoking initiation,
persistence or cessation. ---
No association. ---
No main effect of Val158Met on smoking
cessation.
Interactive effect with treatment type:
greater benefit of active NRT treatment
compared with placebo on likelihood of
abstinence in the Met/Met genotype group.
---
No association.
---
Cases: M99/F98
Val/Val genotype associated with current
heavy smoking in men.
Val
M0/F593
No association.
Abstinent: M90/F0
Val allele associated with better treatment
outcome with bupropion therapy.
NRT: M152/F211
Controls: M89/F165
Non-abstinent: M135/F0
Val allele associated with shorter time of
abstinence and increased rate of relapse
into smoking during and after nicotine
replacement therapy.
No association with withdrawal symptoms.
---
Met
Val
318
CNS & Neurological Disorders - Drug Targets, 2012, Vol. 11, No. 3
Tunbridge et al.
(Table 3) contd…..
a
Reference
Phenotype
a
d
Smoking
behavior Caucasian
American Amstadter et
al., 2009 [147]
Extent of
smoking after
hurricane
exposure American
Breitling et
al., 2009 [148]
Smoking
cessation German
Shiels et al.,
2008 [146]
Population
Number of Participants (M=Males,
F=Females, where Available)
Never-smokers: M1507/F3829
Former smokers: M1681/F1342
Current smokers: M683/F1017
M220/F394
(EA: n = 555; AA: n = 23; Other: n =
33)
Finding
Associated
Allele
Val/Val genotype associated with initiation
of smoking in women under 54 years of
age and with persistence of smoking in
men who smoke <10 cigarettes per day.
Val
Met allele associated with increased
smoking after hurricane exposure in whole
sample, and in particular in EA males. Met
M1126/F317
(former smokers: n = 925
No association.
---
No association.
---
current smokers: n = 518)
M371/F0
b
Tang et al.,
2009 [149]
Smoking
behavior Han Chinese (never-smokers: n = 102
former smokers: n = 66
current smokers: n = 203)
Never-smokers: M1989/F198
Omidvar et
al., 2009 [150]
De Ruyck et
al., 2010 [151]
Smoking
cessation Dutch
Nicotine
dependence,
withdrawal,
and smoking
cessation Belgian
Current smokers : M646/F549
Han Chinese
Greenbaum
et al., 2010
[153]
Nicotine
dependence
Israeli Jewish Nedic et al.,
2010 [154]
Smoking
behavior Croatian
Gu et al.,
2010 [152]
c
c
Munafo et
al., 2011 [155]
c
Voisey et al.
2011[119]
M158/F75 (fewer individuals in
smoking cessation group)
Val allele associated with higher odds for
successful quitting. ---
No association.
---
No association. ---
No association. ---
Val/Val genotype associated with smoking
in males, and at trend level in the whole
sample.
Val
Smoking and
Parkinson’s
disease
c
Former smokers: M1185/F548
No association with current smoking
status.
Controls: M188/F166 (nonsmokers: n
= 184; smokers: n = 170)
PD cases: M115/F 61 (nonsmokers: n
= 134; smokers: n = 42;) Controls: 108F/M0
Smokers: 90F/M0 Controls: M398/F56
Smokers: M180/F23
No association with smoking status.
Tobacco
smoking
behaviour in
pregnant
women
European
Nicotine
dependence
Caucasian
Australian
Non-smokers: M0/F 4,226
Smokers: M0/F 2,001
Controls: M148/F102
Cases: M68/F79
Met allele associated with increased
heaviness of smoking before pregnancy
but not with smoking cessation during
pregnancy. No association
(Met)
---
a: AA = African American; EA = European American; HA = Hispanic American; CA = Caucasian American; ND = Nicotine dependence, NRT = Nicotine Replacement Therapy.
b: excluded from meta-analysis of Tammimäki and Mannisto.
c: published since meta-analysis of Tammimäki and Mannisto.
d: data violate assumption of Hardy-Weinberg equilibrium.
reported associations with addictive phenotypes are,
COMT’s therapeutic candidacy would be significantly
advanced by a better understanding of the mechanisms
underlying such associations (assuming that they are indeed
genuine). Elucidating these mechanisms will likely require
the combination of animal models, to understanding the role
of COMT in the pathophysiological processes of addiction,
with studies in healthy human volunteers and patients, to
help clarify its role in the complex physiological and
psychological processes underlying addiction and the extent
to which associations of COMT with reward processing
contribute. As detailed above, in order for COMT inhibition
to be a viable treatment or adjunctive therapy for addiction,
it is critical to understand how COMT impact on the corticalstriatal systems, particularly the subcortical components of
this circuitry arise and whether their function can be
successfully modulated by pharmacological agents
administered in adulthood. Again, animal models will be
critical for these studies, since they will involve the
manipulation of neuronal circuitry and investigation of the
neurobiological and behavioural consequences of these
manipulations.
In conclusion, COMT modulates reward-related brain
activation during both anticipation and delivery of reward,
The Role of Catechol-O-Methyltransferase in Reward Processing and Addiction CNS & Neurological Disorders - Drug Targets, 2012, Vol. 11, No. 3
and impacts on reward-related learning and its underlying
neural circuitry. There are also hints that COMT is
associated with at least some addiction-related phenotypes,
but more evidence is required before firm conclusions can be
drawn. Several clinical trials investigating the impact of
COMT inhibition on reward-related phenotypes are currently
underway (including pathological gambling, nicotine
dependence and withdrawal, and cocaine-related disorders;
http://clinicaltrials.gov/ct2/results?term=tolcapone).
However, given the limited information currently available,
we think it premature to consider COMT an actionable drug
target for addictive disorders; however, this hypothesis
should be revisited once more data are available. In
particular, the absence of animal studies investigating the
role of COMT in basic reward processing and its links with
addiction is striking. Such studies will be essential in order
to fully understand the involvement of COMT in reward
processing and addiction.
ACKNOWLEDGEMENTS
This review article was supported by a Royal Society
Research fellowship awarded to EMT. Additional support
was provided by the Wellcome Trust (studentships awarded
to KS and SMF and a Research Career Development
Fellowship awarded to MW) and the UK Medical Research
Council (studentship awarded to AH, and a project grant to
PJH, EMT and others).
CONFLICT OF INTEREST
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
Declared none.
ABBREVIATIONS
BOLD
= Blood oxygen level dependent
COMT
= Catechol-O-methyltransferase
DAT
= Dopamine transporter
EEG
= Electroencephalography
fMRI
= Functional magnetic resonance imaging
MEG
= Magnetoencephalography
Met
= Methionine
MFN
= Medial frontal negativity
NRT
= Nicotine replacement therapy
OFC
= Orbitofrontal cortex
PFC
= Prefrontal cortex
Val
= Valine
VStr
= Ventral striatum
[12]
[13]
[14]
[15]
[16]
[17]
[18]
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Revised: February 20, 2012
PMID: 22483300
323
Accepted: March 6, 2012