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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">The role of dopamine in risk taking: a specific look at Parkinson’s disease and gambling</title><author><name sortKey="Clark, Crystal A" sort="Clark, Crystal A" uniqKey="Clark C" first="Crystal A." last="Clark">Crystal A. Clark</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Dagher, Alain" sort="Dagher, Alain" uniqKey="Dagher A" first="Alain" last="Dagher">Alain Dagher</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">24910600</idno><idno type="pmc">4038955</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4038955</idno><idno type="RBID">PMC:4038955</idno><idno type="doi">10.3389/fnbeh.2014.00196</idno><date when="2014">2014</date><idno type="wicri:Area/Pmc/Corpus">000C14</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000C14</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">The role of dopamine in risk taking: a specific look at Parkinson’s disease and gambling</title><author><name sortKey="Clark, Crystal A" sort="Clark, Crystal A" uniqKey="Clark C" first="Crystal A." last="Clark">Crystal A. Clark</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Dagher, Alain" sort="Dagher, Alain" uniqKey="Dagher A" first="Alain" last="Dagher">Alain Dagher</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author></analytic><series><title level="j">Frontiers in Behavioral Neuroscience</title><idno type="eISSN">1662-5153</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>An influential model suggests that dopamine signals the difference between predicted and experienced reward. In this way, dopamine can act as a learning signal that can shape behaviors to maximize rewards and avoid punishments. Dopamine is also thought to invigorate reward seeking behavior. Loss of dopamine signaling is the major abnormality in Parkinson’s disease. Dopamine agonists have been implicated in the occurrence of impulse control disorders in Parkinson’s disease patients, the most common being pathological gambling, compulsive sexual behavior, and compulsive buying. Recently, a number of functional imaging studies investigating impulse control disorders in Parkinson’s disease have been published. Here we review this literature, and attempt to place it within a decision-making framework in which potential gains and losses are evaluated to arrive at optimum choices. We also provide a hypothetical but still incomplete model on the effect of dopamine agonist treatment on these value and risk assessments. Two of the main brain structures thought to be involved in computing aspects of reward and loss are the ventral striatum (VStr) and the insula, both dopamine projection sites. Both structures are consistently implicated in functional brain imaging studies of pathological gambling in Parkinson’s disease.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Abler, B" uniqKey="Abler B">B. Abler</name></author><author><name sortKey="Walter, H" uniqKey="Walter H">H. Walter</name></author><author><name sortKey="Erk, S" uniqKey="Erk S">S. Erk</name></author><author><name sortKey="Kammerer, H" uniqKey="Kammerer H">H. Kammerer</name></author><author><name sortKey="Spitzer, M" uniqKey="Spitzer M">M. Spitzer</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Albin, R L" uniqKey="Albin R">R. L. Albin</name></author><author><name sortKey="Young, A B" uniqKey="Young A">A. B. Young</name></author><author><name sortKey="Penney, J B" uniqKey="Penney J">J. B. Penney</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Alexander, G E" uniqKey="Alexander G">G. E. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Front Behav Neurosci</journal-id><journal-id journal-id-type="iso-abbrev">Front Behav Neurosci</journal-id><journal-id journal-id-type="publisher-id">Front. Behav. Neurosci.</journal-id><journal-title-group><journal-title>Frontiers in Behavioral Neuroscience</journal-title></journal-title-group><issn pub-type="epub">1662-5153</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">24910600</article-id><article-id pub-id-type="pmc">4038955</article-id><article-id pub-id-type="doi">10.3389/fnbeh.2014.00196</article-id><article-categories><subj-group subj-group-type="heading"><subject>Neuroscience</subject><subj-group><subject>Review Article</subject></subj-group></subj-group></article-categories><title-group><article-title>The role of dopamine in risk taking: a specific look at Parkinson’s disease and gambling</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Clark</surname><given-names>Crystal A.</given-names></name><xref ref-type="aff" rid="aff1"></xref><uri xlink:type="simple" xlink:href="http://community.frontiersin.org/people/u/96273"></uri></contrib><contrib contrib-type="author"><name><surname>Dagher</surname><given-names>Alain</given-names></name><xref ref-type="aff" rid="aff1"></xref><xref ref-type="author-notes" rid="fn001"><sup>*</sup></xref><uri xlink:type="simple" xlink:href="http://community.frontiersin.org/people/u/45344"></uri></contrib></contrib-group><aff id="aff1"><institution>Montreal Neurological Institute, McGill University</institution><country>Montreal, QC, Canada</country></aff><author-notes><fn fn-type="edited-by"><p>Edited by: Paul Vezina, The University of Chicago, USA</p></fn><fn fn-type="edited-by"><p>Reviewed by: Walter Adriani, Istituto Superiore di Sanita, Italy; Andrew David Lawrence, Cardiff University, UK</p></fn><corresp id="fn001">*Correspondence: Alain Dagher, Montreal Neurological Institute, McGill University, 3801 University St., Montreal, QC H3A 2B4, Canada e-mail: <email xlink:type="simple">alain.dagher@mcgill.ca</email></corresp><fn fn-type="other" id="fn002"><p>This article was submitted to the journal Frontiers in Behavioral Neuroscience.</p></fn></author-notes><pub-date pub-type="epub"><day>30</day><month>5</month><year>2014</year></pub-date><pub-date pub-type="collection"><year>2014</year></pub-date><volume>8</volume><elocation-id>196</elocation-id><history><date date-type="received"><day>16</day><month>3</month><year>2014</year></date><date date-type="accepted"><day>12</day><month>5</month><year>2014</year></date></history><permissions><copyright-statement>Copyright © 2014 Clark and Dagher.</copyright-statement><copyright-year>2014</copyright-year><license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/3.0/"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>An influential model suggests that dopamine signals the difference between predicted and experienced reward. In this way, dopamine can act as a learning signal that can shape behaviors to maximize rewards and avoid punishments. Dopamine is also thought to invigorate reward seeking behavior. Loss of dopamine signaling is the major abnormality in Parkinson’s disease. Dopamine agonists have been implicated in the occurrence of impulse control disorders in Parkinson’s disease patients, the most common being pathological gambling, compulsive sexual behavior, and compulsive buying. Recently, a number of functional imaging studies investigating impulse control disorders in Parkinson’s disease have been published. Here we review this literature, and attempt to place it within a decision-making framework in which potential gains and losses are evaluated to arrive at optimum choices. We also provide a hypothetical but still incomplete model on the effect of dopamine agonist treatment on these value and risk assessments. Two of the main brain structures thought to be involved in computing aspects of reward and loss are the ventral striatum (VStr) and the insula, both dopamine projection sites. Both structures are consistently implicated in functional brain imaging studies of pathological gambling in Parkinson’s disease.</p></abstract><kwd-group><kwd>impulse control disorders</kwd><kwd>impulsivity</kwd><kwd>reward</kwd><kwd>loss aversion</kwd><kwd>insula</kwd><kwd>ventral striatum</kwd></kwd-group><counts><fig-count count="3"></fig-count><table-count count="0"></table-count><equation-count count="0"></equation-count><ref-count count="161"></ref-count><page-count count="12"></page-count><word-count count="11235"></word-count></counts></article-meta></front><body><sec id="s1"><title>Gambling as a disorder of reward and punishment processing</title><p>Pathological gambling can be conceptualized as a disorder of reward and punishment processing, whereby the gambler selects an immediate but risky opportunity to obtain money over the larger, more probable opportunity to save money (Ochoa et al., <xref rid="B94" ref-type="bibr">2013</xref>). Indeed, gambling is typically conceptualized as a disorder of impulsivity, in which decision-making is rash and relatively uninfluenced by future consequences. Pathological gamblers demonstrate increased impulsivity and increased delayed discounting on laboratory measures (Verdejo-Garcia et al., <xref rid="B139" ref-type="bibr">2008</xref>). The coupling of increased reward seeking behavior with insensitivity to negative consequences may explain the persistence of gambling in the face of overall monetary losses (Vitaro et al., <xref rid="B141" ref-type="bibr">1999</xref>; Petry, <xref rid="B99" ref-type="bibr">2001b</xref>; Cavedini et al., <xref rid="B20" ref-type="bibr">2002</xref>). This conceptual framework is similar to that used in drug addiction, where seeking immediate gains while minimizing potential risks is ubiquitous. Hallmarks of addiction include cravings or compulsions, a loss of control, and continued engagement in behaviors that maintain the addiction despite repeated negative consequences (American Psychiatric Association, <xref rid="B4" ref-type="bibr">2000</xref>). Similarly, pathological gambling can be referred to as a behavioral addiction because it shares many common features with drug-addiction, such as compulsion and loss of control over one’s behavior, as well as continuation of the behavior in the face of negative consequences (Grant et al., <xref rid="B58" ref-type="bibr">2006</xref>; Goodman, <xref rid="B54" ref-type="bibr">2008</xref>). Pathological gamblers exhibit uncontrollable cravings, tolerance, habituation, and withdrawal symptoms, similar to those of drug addicts (Wray and Dickerson, <xref rid="B155" ref-type="bibr">1981</xref>; Castellani and Rugle, <xref rid="B19" ref-type="bibr">1995</xref>; Duvarci and Varan, <xref rid="B42" ref-type="bibr">2000</xref>; Potenza et al., <xref rid="B104" ref-type="bibr">2003</xref>). Moreover, both pathological gambling and substance abuse are associated with the same specific personality traits, namely sensation seeking and impulsivity (Zuckerman and Neeb, <xref rid="B159" ref-type="bibr">1979</xref>; Castellani and Rugle, <xref rid="B19" ref-type="bibr">1995</xref>), which index heightened arousal to potential rewards and reduced self-control and inhibitory function. The high comorbidity between substance dependence (drugs and alcohol) and pathological gambling (Petry, <xref rid="B98" ref-type="bibr">2001a</xref>; Petry et al., <xref rid="B97" ref-type="bibr">2005</xref>), and evidence for common genetic factors, point to the two disorders having overlapping etiologies (Slutske et al., <xref rid="B124" ref-type="bibr">2000</xref>; Goodman, <xref rid="B54" ref-type="bibr">2008</xref>).</p><p>One useful model views reward and punishment learning as inherent components in the decision-making process. Decision-making can be broken down to the weighing of the probability and value of reward against potential costs (e.g., negative consequences). Other factors such as outcome ambiguity and variance (sometimes referred to as risk) also affect individual choices (Huettel et al., <xref rid="B63" ref-type="bibr">2006</xref>), but here we will only consider potential gains and losses as determinants of decision-making while gambling. We will also take “risk” to mean the potential loss attached to any choice. Risk, as so defined, increases with the magnitude and probability of potential losses. In fact, risk-taking may be seen as an indicator of the balance existing between computations of potential gains and losses. Two of the main brain structures thought to be involved in these computations are the ventral striatum (VStr) and the insula, both dopamine projection sites. Both have been linked to computations of value, with the VStr being especially responsive to reward prediction error (RPE), encoding gain anticipation positively and loss anticipation negatively (Rutledge et al., <xref rid="B115" ref-type="bibr">2010</xref>; Bartra et al., <xref rid="B7" ref-type="bibr">2013</xref>), and the insula responding predominantly to losses and loss anticipation in some studies (Knutson and Greer, <xref rid="B69" ref-type="bibr">2008</xref>) or to both positive and negative outcomes in others (Campbell-Meiklejohn et al., <xref rid="B17" ref-type="bibr">2008</xref>; Rutledge et al., <xref rid="B115" ref-type="bibr">2010</xref>). Bartra et al.’s meta-analysis (Figure <xref ref-type="fig" rid="F1">1</xref>) suggests that the insula encodes arousal or salience as opposed to value, as it responds positively to both gains and losses. This meta-analysis also raises the possibility of a greater role for the insula in the assessment of risk and losses than gains (compare panels A and B in Figure <xref ref-type="fig" rid="F1">1</xref>). Alteration of the balance between these gain and loss anticipation systems may underlie the inappropriate choice behaviors that occur in disorders such as addiction, gambling and impulse control disorders.</p><fig id="F1" position="float"><label>Figure 1</label><caption><p><bold>Meta-analysis of fMRI studies of value (taken from Bartra et al., <xref rid="B7" ref-type="bibr">2013</xref>)</bold>. The authors extracted peak coordinates of activation from 206 published fMRI studies that investigated value computations. <bold>(A)</bold> Significant clustering of positive responses. <bold>(B)</bold> Significant clustering of negative responses. <bold>(C)</bold> Conjunction maps, showing regions with significant clustering for both positive and negative responses. <bold>(D)</bold> Results of a between-category comparison, showing regions with significantly greater clustering for positive than negative effects. <bold>(E)</bold> Detail of the striatum, illustrating overlap between the conjunction map (<bold>Panel C</bold>) and the difference map (<bold>Panel D</bold>). These data demonstrate the relative response of anterior insula, striatum and ventromedial PFC to positive and negative value.</p></caption><graphic xlink:href="fnbeh-08-00196-g0001"></graphic></fig><p>Recent research suggests that differences in brain function, structure, and biochemistry are present in those who develop gambling problems, with dopamine being a common etiological factor. Imaging studies have demonstrated an increase in mesolimbic dopamine release during gambling tasks in healthy subjects (Thut et al., <xref rid="B131" ref-type="bibr">1997</xref>; Zald et al., <xref rid="B158" ref-type="bibr">2004</xref>; Hakyemez et al., <xref rid="B60" ref-type="bibr">2008</xref>). However it should be noted that unpredictable reward tasks have the ability to cause a suppression and enhancement of dopamine transmission in different regions of the striatum (Zald et al., <xref rid="B158" ref-type="bibr">2004</xref>; Hakyemez et al., <xref rid="B60" ref-type="bibr">2008</xref>). Earlier research on pathological gamblers suggested altered dopaminergic and noradrenergic systems, as found through a decrease in concentration of dopamine and an increase in cerebrospinal fluid levels of 3,4-dihydroxyphenyl-acetic acid and homovanilic acid (Bergh et al., <xref rid="B8" ref-type="bibr">1997</xref>). Pathological gamblers have also been reported to have higher cerebrospinal fluid levels of 3-methoxy-4-hydroxyphenylglycol, a major metabolite of norepinephrine, as well as significantly greater urinary outputs of norepinephrine in comparison to controls (Roy et al., <xref rid="B114" ref-type="bibr">1988</xref>), indicative of a functional disturbance of the noradrenergic system. In addition there is evidence that genetic polymorphisms affecting dopaminergic neurotransmission act as risk factors for problem gambling (Lobo and Kennedy, <xref rid="B81" ref-type="bibr">2006</xref>).</p></sec><sec id="s2"><title>Dopamine in reinforcement</title><p>Considerable evidence from animal studies, implicating dopamine in behavioral reinforcement, provides a neurobiological substrate that could encompass processing of natural rewards, such as food and sex, as well as drugs of abuse and pathological gambling (Di Chiara and Imperato, <xref rid="B37" ref-type="bibr">1988</xref>; Wise and Rompre, <xref rid="B152" ref-type="bibr">1989</xref>; Wise, <xref rid="B153" ref-type="bibr">1996</xref>, <xref rid="B154" ref-type="bibr">2013</xref>). The observations of Schultz and others (Schultz et al., <xref rid="B118" ref-type="bibr">1998</xref>; Schultz, <xref rid="B119" ref-type="bibr">2002</xref>) confirmed a role for dopamine neurons in response to rewards; however the current model of dopamine signaling can be traced to a seminal paper by Montague, Dayan and Schultz (Schultz et al., <xref rid="B117" ref-type="bibr">1997</xref>), where it was argued that the firing pattern of dopamine neurons did not signal reward <italic>per se</italic>, but a RPE signal, similar to those used in machine learning. This finding, along with evidence that dopamine could modulate synaptic plasticity (Calabresi et al., <xref rid="B14" ref-type="bibr">2007</xref>; Surmeier et al., <xref rid="B128" ref-type="bibr">2010</xref>) led to the theory that dopamine acts as a learning (or reinforcement) signal that shapes future motivated behavior. Subsequent research has shown that dopamine may also encode predictions about upcoming rewards and reward rate, thus acting as a value signal in the mesocortical and mesolimbic dopaminergic pathways (Montague and Berns, <xref rid="B89" ref-type="bibr">2002</xref>).</p><p>The main projection site of dopamine neurons is the striatum, whose connectivity to frontal, limbic and insular cortex, provides a mechanism whereby dopamine can act as a prediction error signal driving both “Go” learning, which relates to actions with positive outcomes, and “No Go” or avoidance learning, which relates to actions that lead to punishment or an absence of reward. First, dopamine signaling operates in two modes (Grace, <xref rid="B57" ref-type="bibr">2000</xref>): slow constant release of dopamine regulates tonic levels, which mostly signal via dopamine D<sub>2</sub> receptors on striatal medium spiny neurons; phasic bursts of dopamine firing lead to large increases in synaptic dopamine which signal via both the D<sub>1</sub> and D<sub>2</sub> receptor systems. D<sub>1</sub> receptors have low affinity for dopamine (Marcellino et al., <xref rid="B84" ref-type="bibr">2012</xref>) and only respond to large increases in synaptic dopamine released during phasic dopamine neuron bursts that reflect positive RPEs, supporting learning to approach rewarding stimuli (Frank, <xref rid="B50" ref-type="bibr">2005</xref>). Dopamine D<sub>2</sub> receptors, on the other hand, have a higher affinity for dopamine, allowing them to respond to tonic dopamine signaling, and to detect transient reductions in tonic dopamine levels that follow pauses in dopamine neuron firing during negative RPEs. This facilitates learning to avoid negative outcomes (Frank, <xref rid="B50" ref-type="bibr">2005</xref>). The cortico-striatal system can be divided into a direct and an indirect pathway (Figure <xref ref-type="fig" rid="F2">2</xref>), which have opposite effects on the thalamus and hence cortex (Albin et al., <xref rid="B1" ref-type="bibr">1989</xref>). In the dorsal striatum, receptors are segregated, with the D<sub>1</sub> receptors within the direct pathway, related to action selection, while the D<sub>2</sub> receptors control response inhibition within the indirect pathway (Mink, <xref rid="B87" ref-type="bibr">1996</xref>). This separation allows dopamine to drive both reward (increases in dopamine signaling a better outcome than expected) and punishment (reductions in tonic dopamine indicated a worse outcome than expected). Frank proposed a model in which phasic dopamine bursts following rewards promote positive reinforcement while reductions in tonic dopamine levels lead to negative reinforcement, each controlled by the D<sub>1</sub>/direct pathway and the D<sub>2</sub>/indirect pathway, respectively (Cohen and Frank, <xref rid="B27" ref-type="bibr">2009</xref>). This computational model suggests that the RPE dopamine signal promotes learning from positive outcomes via stimulation of D<sub>1</sub> receptors, whereas learning to avoid negative outcomes is mediated via disinhibition of indirect pathway striatal neurons secondary to a reduction of D<sub>2</sub> receptor stimulation during dopamine pauses (Cohen and Frank, <xref rid="B27" ref-type="bibr">2009</xref>). A negative outcome (punishment or lack of an expected reward) leads to pause in the firing of dopamine neurons, which then leads to a transient reduction in tonic dopamine. It should also be noted that D<sub>2</sub> receptor stimulation reduces excitability of neurons in the indirect pathway (Hernandez-Lopez et al., <xref rid="B61" ref-type="bibr">2000</xref>), therefore, reductions in D<sub>2</sub> receptor signaling have the effect of activating the inhibitory “No Go” pathway. This allows for bidirectional positive and negative reinforcement signaling by dopamine neurons. Support for this model has been provided by numerous experiments. Parkinson’s disease patients show enhanced positive learning when on their medications, but improved negative learning while off medication (Frank et al., <xref rid="B49" ref-type="bibr">2004</xref>). Pharmacological manipulations also support the model (Frank and O’Reilly, <xref rid="B47" ref-type="bibr">2006</xref>; Pizzagalli et al., <xref rid="B100" ref-type="bibr">2008</xref>). The striatal release of dopamine is linked to associative learning and habit formation via control of corticostriatal synaptic plasticity, which is affected in an opposite manner by D<sub>1</sub> and D<sub>2</sub> signaling (Shen et al., <xref rid="B123" ref-type="bibr">2008</xref>). D<sub>1</sub> dopamine receptor signaling promotes long-term potentiation (Reynolds et al., <xref rid="B111" ref-type="bibr">2001</xref>; Calabresi et al., <xref rid="B14" ref-type="bibr">2007</xref>), whereas D<sub>2</sub> receptor signaling promotes long-term depression (Gerdeman et al., <xref rid="B52" ref-type="bibr">2002</xref>; Kreitzer and Malenka, <xref rid="B74" ref-type="bibr">2007</xref>). Note that this model has been tested most thoroughly at the level of the striatum. Multivariate analysis of fMRI data shows that reinforcement and punishment signals are ubiquitous in the brain, most notably in the entire frontal cortex and striatum (Vickery et al., <xref rid="B140" ref-type="bibr">2011</xref>). Less is known about the information signaled by dopamine projections to brain areas other than the striatum, such as frontal cortex, insula, hippocampus and amygdala, or how the RPE signal is used by these areas.</p><fig id="F2" position="float"><label>Figure 2</label><caption><p><bold>Basal ganglia model.</bold> A possible model whereby basal ganglia compute the utility of gains and losses via two segregated pathways in the corticostriato-thalamocortical circuit. Striatal output neurons of the direct pathway express D1 receptors and project to the internal globus pallidus (GPi) and the substantia nigra pars reticulata (SNr), and has an action selection effect on cerebral cortex. Striatal output neurons in the indirect pathway express D2 receptors and reduce the tonic inhibition of the external globus pallidus (GPe) on the GPi/SNr, which leads to action inhibition in the cortex. D1 receptors respond mainly to phasic (high concentration) dopamine signaling due to their low affinity for dopamine. D2 receptors have high affinity for dopamine and respond to lower tonic dopamine levels. Excitatory projections in green, inhibitory in red.</p></caption><graphic xlink:href="fnbeh-08-00196-g0002"></graphic></fig></sec><sec id="s3"><title>Striatum and monetary reward</title><p>In human functional neuroimaging studies, changes in brain activation have been demonstrated consistently in response to monetary rewards (Thut et al., <xref rid="B131" ref-type="bibr">1997</xref>; Elliott et al., <xref rid="B44" ref-type="bibr">2000</xref>; Knutson et al., <xref rid="B72" ref-type="bibr">2000</xref>; Breiter et al., <xref rid="B11" ref-type="bibr">2001</xref>; O’Doherty et al., <xref rid="B92" ref-type="bibr">2007</xref>). Further, studies have teased apart the different brain areas involved in the various components of monetary reward, such as anticipation, feedback, winning and losing. There seems to be a specialization within dopamine projection sites in relation to monetary reward: anticipation of monetary reward increases activation in the VStr, which includes the nucleus accumbens, while rewarding outcomes increase activation in the ventral medial prefrontal cortex, dorsal striatum, and posterior cingulate, with deactivation in the aforementioned regions during reward omission (Elliott et al., <xref rid="B44" ref-type="bibr">2000</xref>; Breiter et al., <xref rid="B11" ref-type="bibr">2001</xref>; Knutson et al., <xref rid="B71" ref-type="bibr">2001b</xref>; Tricomi et al., <xref rid="B134" ref-type="bibr">2004</xref>). Neuroimaging experiments in humans suggest that VStr activity strongly correlates with expected value, as well as magnitude and probability (Breiter et al., <xref rid="B11" ref-type="bibr">2001</xref>; Knutson et al., <xref rid="B68" ref-type="bibr">2001a</xref>, <xref rid="B161" ref-type="bibr">2005</xref>; Abler et al., <xref rid="B160" ref-type="bibr">2006</xref>; Yacubian et al., <xref rid="B163" ref-type="bibr">2006</xref>; Rolls et al., <xref rid="B162" ref-type="bibr">2008</xref>). Work by D’Ardenne et al. (<xref rid="B33" ref-type="bibr">2008</xref>) supports a role for the mesolimbic dopamine system in monetary RPE signaling. Activation of the ventral tegmental area, the origin of the mesolimbic dopamine circuit, reflected positive RPEs, whereas the VStr encoded positive and negative RPEs. Similarly, Tom et al. (<xref rid="B133" ref-type="bibr">2007</xref>) showed that VStr activity reflected potential monetary gains and losses bidirectionally. This study also demonstrated that these neural signals reflected individual variations in loss aversion, the tendency for losses to be more impactful than potential gains. Finally, the influential actor-critic model (Sutton and Barto, <xref rid="B129" ref-type="bibr">1998</xref>) proposes that the VStr uses prediction errors to update information about expected future rewards while the dorsal striatum uses this same prediction error signal to encode information about actions that are likely to lead to reward. This distinction has found support from fMRI experiments (O’Doherty et al., <xref rid="B91" ref-type="bibr">2004</xref>; Kahnt et al., <xref rid="B66" ref-type="bibr">2009</xref>). Interestingly, the ability to update behavior in response to RPE was shown to correlate with functional connectivity between dorsal striatum and dopaminergic midbrain (Kahnt et al., <xref rid="B66" ref-type="bibr">2009</xref>). The imaging studies mentioned here support the theory of dopamine as a RPE signal, at least in its striatal projection.</p></sec><sec id="s4"><title>Insula and risk</title><p>The insula is frequently activated in functional neuroimaging experiments (Duncan and Owen, <xref rid="B41" ref-type="bibr">2000</xref>; Yarkoni et al., <xref rid="B156" ref-type="bibr">2011</xref>). Functionally it can be divided into three distinct subregions: a ventroanterior region associated with chemosensory (Pritchard et al., <xref rid="B106" ref-type="bibr">1999</xref>) and socio-emotional processing (Sanfey et al., <xref rid="B116" ref-type="bibr">2003</xref>; Chang and Sanfey, <xref rid="B21" ref-type="bibr">2009</xref>), a dorsoanterior region associated with higher cognitive processing (Eckert et al., <xref rid="B43" ref-type="bibr">2009</xref>), and a posterior region associated with pain and sensorimotor processing (Craig, <xref rid="B30" ref-type="bibr">2002</xref>; Wager et al., <xref rid="B147" ref-type="bibr">2004</xref>). Different functional insular areas project to different striatal targets: the VStr receives insular projections primarily related to food and reward, whereas the dorsolateral striatum receives insular inputs related to somatosensation (Chikama et al., <xref rid="B22" ref-type="bibr">1997</xref>).</p><p>The insular cortex is involved in decision-making processes that involve uncertain risk and reward. Specifically, fMRI studies have reported insular cortex involvement in risk-averse decisions (Kuhnen and Knutson, <xref rid="B75" ref-type="bibr">2005</xref>), risk avoidance and the representation of loss prediction (Paulus et al., <xref rid="B96" ref-type="bibr">2003</xref>), monetary uncertainty (Critchley et al., <xref rid="B31" ref-type="bibr">2001</xref>), and encoding a risk prediction error (Preuschoff et al., <xref rid="B105" ref-type="bibr">2008</xref>). Patients with insular cortex damage place higher wagers in comparison with healthy participants and their betting is less sensitive to the odds of winning, with high wagers even at unfavorable odds (Clark et al., <xref rid="B26" ref-type="bibr">2008</xref>). Other research suggests that optimum decisions involving risk depend on the integrity of the insular cortex, showing that insula lesion patients have altered decision-making involving both risky gains and risky losses (Weller et al., <xref rid="B150" ref-type="bibr">2009</xref>) (However see Christopoulos et al., <xref rid="B23" ref-type="bibr">2009</xref>). Specifically, insula damage was associated with a relative insensitivity to expected value differences between choices. Previous research has shown that there is a dissociation between insula and VStr, with VStr activation preceding risk-seeking choices, and anterior insula activation predicting risk-averse choices (Kuhnen and Knutson, <xref rid="B75" ref-type="bibr">2005</xref>) suggesting that the VStr represents gain prediction (Knutson et al., <xref rid="B68" ref-type="bibr">2001a</xref>), while anterior insula represents loss prediction (Paulus et al., <xref rid="B96" ref-type="bibr">2003</xref>). While imaging studies also demonstrate a more general role of the anterior insula in signaling the valence (positive or negative) of potential rewards (Litt et al., <xref rid="B80" ref-type="bibr">2011</xref>; Bartra et al., <xref rid="B7" ref-type="bibr">2013</xref>) the lesion data argue that the anterior insular cortex has a role in risk evaluation, specifically in making risk-averse decisions. Indeed, in healthy subjects, the insula is part of a value network that appears to track potential losses in a way that correlates with individual loss aversion level (Canessa et al., <xref rid="B18" ref-type="bibr">2013</xref>). It is possible that an imbalance between prefrontal-striatal circuitry and insular-striatal circuitry may lead to suboptimal choices when weighing potential gains and losses, as observed in pathological gamblers (Petry, <xref rid="B98" ref-type="bibr">2001a</xref>; Goudriaan et al., <xref rid="B56" ref-type="bibr">2005</xref>).</p></sec><sec id="s5"><title>Pathological gambling among patients with Parkinson’s disease</title><p>Pathological gambling was first reported in the context of Parkinson’s disease and dopamine replacement therapy in 2000 (Molina et al., <xref rid="B88" ref-type="bibr">2000</xref>). The lifetime prevalence of pathological gambling in the general public is approximately 0.9 to 2.5% (Shaffer et al., <xref rid="B122" ref-type="bibr">1999</xref>). In Parkinson’s disease, the prevalence rates are higher, from 1.7 to 6.1% (Ambermoon et al., <xref rid="B3" ref-type="bibr">2011</xref>; Callesen et al., <xref rid="B15" ref-type="bibr">2013</xref>). The risk factors associated with the occurrence of pathological gambling in Parkinson’s disease are young age of Parkinson’s disease onset, a personal or family history of drug or alcohol abuse, depression, and relatively high impulsivity and novelty seeking personality scores (Voon et al., <xref rid="B146" ref-type="bibr">2007b</xref>). Interestingly, these are similar to the risk factors for drug addiction and pathological gambling in the general population. Also, there have been reports of addiction to L-dopa in certain patients (e.g., Giovannoni et al., <xref rid="B53" ref-type="bibr">2000</xref>), a phenomenon that had already been noted in the 1980s. It was perhaps initially surprising to find that Parkinson’s disease patients can become addicted to their own medication or develop behavioral addictions because they were thought to not possess the personality type typical of addicted individuals. They are generally described as industrious, punctual, inflexible, cautious, rigid, introverted, slow-tempered, with lack of impulsiveness and novelty seeking, and they have low lifetime risks for cigarette smoking, coffee drinking, and alcohol use predating Parkinson’s disease onset (Menza et al., <xref rid="B85" ref-type="bibr">1993</xref>; Menza, <xref rid="B86" ref-type="bibr">2000</xref>).</p><p>Dopamine replacement therapy has been implicated in the development of pathological gambling in Parkinson’s disease (Gschwandtner et al., <xref rid="B59" ref-type="bibr">2001</xref>; Dodd et al., <xref rid="B39" ref-type="bibr">2005</xref>) and a remission or reduction of pathological gambling is typically noted after reduction or cessation of dopamine agonist medication (Gschwandtner et al., <xref rid="B59" ref-type="bibr">2001</xref>; Dodd et al., <xref rid="B39" ref-type="bibr">2005</xref>). A broader set of behavioral addictions termed impulse control disorders, including but not limited to pathological gambling, compulsive sexual behavior, and compulsive buying, have been reported in association with dopamine replacement therapy (Weintraub et al., <xref rid="B149" ref-type="bibr">2006</xref>; Voon et al., <xref rid="B144" ref-type="bibr">2007a</xref>; Dagher and Robbins, <xref rid="B34" ref-type="bibr">2009</xref>). Dopamine agonists (pramipexole, ropinirole and pergolide) appear to pose a greater risk than L-Dopa monotherapy (Seedat et al., <xref rid="B120" ref-type="bibr">2000</xref>; Dodd et al., <xref rid="B39" ref-type="bibr">2005</xref>; Pontone et al., <xref rid="B103" ref-type="bibr">2006</xref>). Reducing the dopamine agonist and increasing L-Dopa to achieve same motor response abolished pathological gambling in affected individuals (Mamikonyan et al., <xref rid="B83" ref-type="bibr">2008</xref>), while a cross-sectional study of over 3000 Parkinson’s disease patients found that taking a dopamine agonist increased the odds of developing an impulse control disorder by 2.72 (Weintraub et al., <xref rid="B148" ref-type="bibr">2010</xref>). Finally, these side-effects of dopamine agonist therapy have been recently noted in other diseases, such as restless leg syndrome, fibromyalgia and prolactinomas (Davie, <xref rid="B35" ref-type="bibr">2007</xref>; Driver-Dunckley et al., <xref rid="B40" ref-type="bibr">2007</xref>; Quickfall and Suchowersky, <xref rid="B107" ref-type="bibr">2007</xref>; Tippmann-Peikert et al., <xref rid="B132" ref-type="bibr">2007</xref>; Falhammar and Yarker, <xref rid="B46" ref-type="bibr">2009</xref>; Holman, <xref rid="B62" ref-type="bibr">2009</xref>). It should be noted however that some studies have reported behavioral addictions and/or impulsivity and compulsivity in association with high-dose L-Dopa monotherapy (Molina et al., <xref rid="B88" ref-type="bibr">2000</xref>), deep brain stimulation for Parkinson’s disease (Smeding et al., <xref rid="B125" ref-type="bibr">2007</xref>), and in drug naïve Parkinson’s disease patients (Antonini et al., <xref rid="B5" ref-type="bibr">2011</xref>), all in the absence of dopamine agonists. Nonetheless, the clinical evidence overwhelmingly supports the theory that dopamine agonism at the D<sub>2</sub> receptor family is sufficient to cause impulse control disorders.</p></sec><sec id="s6"><title>Brain imaging studies</title><sec id="s6-1"><title>Neurotransmitter imaging</title><p>Positron emission tomography (PET) imaging allows for changes in endogenous levels of dopamine to be inferred from changes in the binding of the [<sup>11</sup>C]raclopride to the dopamine D<sub>2</sub> receptors. The first [<sup>11</sup>C]raclopride PET study in this area was on Parkinson’s patients with dopamine dysregulation syndrome. Dopamine dysregulation syndrome is characterized by the compulsive taking of dopaminergic drugs, which is often comorbid with impulse control disorders (Lawrence et al., <xref rid="B78" ref-type="bibr">2003</xref>). Patients with dopamine dysregulation syndrome exhibited enhanced L-Dopa induced VStr dopamine release compared to similarly treated Parkinson’s disease patients not compulsively taking dopaminergic drugs (Evans et al., <xref rid="B45" ref-type="bibr">2006</xref>). This was the first study to provide evidence for sensitization of mesolimbic dopamine circuitry in Parkinson’s disease patients prone to compulsive drug use. Subsequent studies have supported a relative hyperdopaminergic state in Parkinson’s disease patients with pathological gambling. Three studies mapping the concentration of dopamine reuptake transporters (DAT) have shown reduced levels in the VStr of Parkinson’s disease patients with impulse control disorders compared to unaffected patients (Cilia et al., <xref rid="B24" ref-type="bibr">2010</xref>; Lee et al., <xref rid="B79" ref-type="bibr">2014</xref>; Voon et al., <xref rid="B145" ref-type="bibr">2014</xref>). Unfortunately the finding is non-specific, as reduced DAT concentration can index either reduced nerve terminals (and reduced dopamine signaling) or reduced DAT expression (and therefore increased tonic dopamine levels). Supporting the latter hypothesis, impulse control patients demonstrate reduced [<sup>11</sup>C]raclopride binding in the VStr compared to Parkinson’s controls (Steeves et al., <xref rid="B127" ref-type="bibr">2009</xref>), which is also consistent with elevated tonic dopamine in this group. Note, however that this result failed to be replicated in a similar study (O’Sullivan et al., <xref rid="B93" ref-type="bibr">2011</xref>).</p><p>However, these two [<sup>11</sup>C]raclopride PET studies reported a greater reduction of VStr binding potential (an index of dopamine release) during gambling (Steeves et al., <xref rid="B127" ref-type="bibr">2009</xref>) and following reward-related cue exposure (images of food, money, sex) compared to neutral cues (O’Sullivan et al., <xref rid="B93" ref-type="bibr">2011</xref>) in Parkinson’s disease patients with impulse control disorders compared to unaffected patients. This suggests an increased responsiveness of striatal reward circuitry to gambling and reward-related cues in those patients with impulse control disorders. In O’Sullivan et al. (<xref rid="B93" ref-type="bibr">2011</xref>) dopamine release was only detected in the VStr and only when subjects received a dose of oral L-Dopa just prior to scanning, consistent with post-mortem data in Parkinson’s disease showing that brain dopamine levels are much lower in dorsal than VStr (Kish et al., <xref rid="B67" ref-type="bibr">1988</xref>). These results are therefore consistent with the sensitization hypothesis proposed by Evans et al. (<xref rid="B45" ref-type="bibr">2006</xref>). More recently it was reported that Parkinson’s disease patients with pathological gambling have a reduced concentration of dopamine autoreceptors in the midbrain (Ray et al., <xref rid="B109" ref-type="bibr">2012</xref>), which is known to correlate with elevated dopaminergic responsivity and increased impulsivity (Buckholtz et al., <xref rid="B13" ref-type="bibr">2010</xref>). Finally, in Parkinson’s disease patients, dopamine synthesis capacity, as measured by [<sup>18</sup>F]DOPA PET, correlates with a personality measure of disinhibition, itself a risk factor for pathological gambling and other addictions (Lawrence et al., <xref rid="B77" ref-type="bibr">2013</xref>). In summary, PET studies provide converging evidence of heightened dopaminergic tone and increased dopamine response to reward cues as the underlying vulnerability in Parkinson’s disease patients who develop pathological gambling during dopamine agonist treatment.</p></sec><sec id="s6-2"><title>Functional magnetic resonance imaging</title><p>Parkinson’s disease patients with pathological gambling show enhanced hemodynamic responses to gambling-related visual cues in the bilateral anterior cingulate cortex, left VStr, right precuneus and medial prefrontal cortex (Frosini et al., <xref rid="B51" ref-type="bibr">2010</xref>). This is in line with similar experiments in pathological gambling without Parkinson’s disease (Crockford et al., <xref rid="B32" ref-type="bibr">2005</xref>; Ko et al., <xref rid="B73" ref-type="bibr">2009</xref>) and drug addiction (Wexler et al., <xref rid="B151" ref-type="bibr">2001</xref>), supporting the view that impulse control disorders in Parkinson’s disease may be conceptualized as behavioral addictions.</p><p>Parkinson’s disease patients with an impulse control disorder show diminished BOLD activity in the right VStr during risk taking and significantly reduced resting cerebral blood flow in the right VStr compared to their healthy disease counterparts (Rao et al., <xref rid="B108" ref-type="bibr">2010</xref>). Similarly, it was found that Parkinson’s disease patients with impulse control disorders showed a bias toward risky gambles compared to control patients, and that dopamine agonists enhanced risk taking while decreasing VStr activity (Voon et al., <xref rid="B142" ref-type="bibr">2011</xref>). The authors suggested that dopamine agonists may decouple brain activity from risk information in vulnerable patients, thus favoring risky choices. Another fMRI study reported that, relative to Parkinson’s controls, impulse control disorder Parkinson’s patients had decreased anterior insular and orbitofrontal cortex RPE signals. They also showed that dopamine agonists increased the rate of learning from gain outcomes, and increased striatal RPE activity, suggesting that dopamine agonists may skew neural activity to encode “better than expected” outcomes in Parkinson’s disease patients susceptible to impulse control disorders (Voon et al., <xref rid="B143" ref-type="bibr">2010</xref>).</p><p>While differences in striatal dopamine signaling may distinguish Parkinson’s disease patients who do and do not develop pathological gambling, the mechanism of action by which dopamine agonists change risk assessment remains unclear. Dopamine agonists change the way in which the brains of healthy individuals respond to the anticipation and feedback of rewards. During reward feedback, administration of a single dose of pramipexole to healthy adults caused decreased VStr activity in a lottery game (Riba et al., <xref rid="B112" ref-type="bibr">2008</xref>). Similarly, there was reduced VStr activation when Parkinson’s patients received a dose of L-Dopa compared to placebo (Cools et al., <xref rid="B29" ref-type="bibr">2007</xref>). This pattern of hypoactivation is reminiscent of that found in pathological gamblers without Parkinson’s disease (Reuter et al., <xref rid="B110" ref-type="bibr">2005</xref>): during a simulated gambling task, pathological gamblers showed decreased activation with respect to controls in the ventromedial prefrontal cortex and the VStr. Severity of gambling was negatively correlated with the BOLD effect in the VStr and ventromedial prefrontal cortex, suggesting that hypoactivity is a predictor of gambling severity. As noted above, impulse control disorder Parkinson’s patients were found to have diminished resting perfusion as well as diminished BOLD activity during risk taking in the VStr compared to Parkinson’s controls (Rao et al., <xref rid="B108" ref-type="bibr">2010</xref>). These studies suggest that dopamine agonists cause individuals to seek rewards and make risky choices (Riba et al., <xref rid="B112" ref-type="bibr">2008</xref>), in the face of suppressed VStr response to rewards.</p><p>It should be noted however that reduced VStr activation in fMRI experiments does not necessarily indicate reduced dopaminergic signaling. There is evidence to support relatively spared mesolimbic dopamine signaling as the risk factor for pathological gambling in Parkinson’s disease. First, the repeated taking of a dopaminergic medication for the treatment of Parkinson’s disease could lead to sensitization of dopamine signaling. VStr sensitization has been shown following repeated amphetamine administration in humans (Boileau et al., <xref rid="B9" ref-type="bibr">2006</xref>). Moreover, in Parkinson’s disease the ventral portion of striatum is relatively spared by the disease compared to the dorsal areas (Kish et al., <xref rid="B67" ref-type="bibr">1988</xref>), and thus dopamine replacement therapy, while correcting the dopamine deficiency in the dorsal striatum to normal levels, has the potential to raise dopamine levels in the VStr circuit to higher than optimal levels (Cools et al., <xref rid="B29" ref-type="bibr">2007</xref>). This “overdose” theory was first proposed by Gotham et al. (<xref rid="B55" ref-type="bibr">1988</xref>) to explain the fact that L-Dopa administration to Parkinson’s disease patients, while improving some cognitive deficits, could also cause specific impairments in other fronto-striatal cognitive tasks. In the case of impulse control disorders, we propose that excessive dopaminergic stimulation in the VStr obscures the dips in dopamine signaling related to negative prediction errors.</p><p>The insula has also been implicated in imaging studies of pathological gambling in Parkinson’s disease. In an fMRI study, Ye et al. (<xref rid="B157" ref-type="bibr">2010</xref>) found that during the anticipation of monetary rewards, a single dose of pramipexole (compared to placebo) increased the activity of the VStr, enhanced the interaction between the VStr and the anterior insula, but weakened the interaction between the VStr and the prefrontal cortex, leading to increased impulsivity. Cilia et al. (<xref rid="B25" ref-type="bibr">2008</xref>) found Parkinson’s patients with pathological gambling showed resting over-activity in brain areas in the mesocorticolimbic network, including the insula. In an fMRI study, relative to Parkinson’s controls, impulse control disorder patients had decreased anterior insular and orbitofrontal cortex activity (van Eimeren et al., <xref rid="B136" ref-type="bibr">2009</xref>; Voon et al., <xref rid="B143" ref-type="bibr">2010</xref>). Finally, in a study of Parkinson’s disease patients with and without hypersexuality, a single dose of L-Dopa abolished the normal insular deactivation seen in response to erotic pictures, only in the hypersexual patients (Politis et al., <xref rid="B102" ref-type="bibr">2013</xref>). Taken together these results may suggest an imbalance between the prefrontal-striatum connectivity and insula-striatum connectivity, favoring the influence of potential gains over that of potential risks (losses) in decision-making.</p></sec></sec><sec id="s7"><title>Risk taking and loss aversion</title><p>An influential framework for studying risky decision making is prospect theory, developed by Kahneman and Tversky (<xref rid="B65" ref-type="bibr">1979</xref>). A key finding of their work is loss aversion, a tendency for losses to loom larger than potential gains, and for individuals to typically forego risky choices when less valuable safer alternatives exist. For example most people will reject the offer of a coin flip unless the potential gain is considerably larger than the potential loss. Impulsiveness, at least in a gambling context, can be characterized as a reversal of loss aversion, and an over-weighing of potential rewards relative to losses. It remains to be seen whether loss aversion results from asymmetrical weighting of gains and losses along a single value axis (Tom et al., <xref rid="B133" ref-type="bibr">2007</xref>), or from a competitive interaction between separate systems for gains and losses (Kuhnen and Knutson, <xref rid="B75" ref-type="bibr">2005</xref>; De Martino et al., <xref rid="B36" ref-type="bibr">2010</xref>). Possibly, both models are correct: recent fMRI evidence (Canessa et al., <xref rid="B18" ref-type="bibr">2013</xref>) shows bidirectional responses to losses and gains in the VStr and ventromedial prefrontal cortex (positive for gains) and the amygdala and insula (positive for losses). In both cases, there is greater activation to potential losses, correlating with individual loss aversion measured using prospect theory (Kahneman and Tversky, <xref rid="B65" ref-type="bibr">1979</xref>). However, there are also brain regions that respond uniquely to potential losses, namely the right insula and the amygdala, once again reflecting individual variation in loss aversion (Canessa et al., <xref rid="B18" ref-type="bibr">2013</xref>). In sum, a network of regions centered on VStr, insula and amygdala seems to compute gain and loss anticipation in a way that typically results in loss aversion. Interestingly these structures, along with dorsal anterior cingulate, form an intrinsic connectivity network as identified by resting state fMRI. This network is thought to be involved in detecting and processing emotionally salient events (Seeley et al., <xref rid="B121" ref-type="bibr">2007</xref>).</p><p>Loss aversion can be explained on an emotional basis, with both potential gains and losses influencing behavior via different emotions (Loewenstein et al., <xref rid="B82" ref-type="bibr">2001</xref>), namely motivation on the gain side and anxiety for losses. Such a model might tie the former to the nucleus accumbens and the latter to the amygdala and insula. In either case, it is conceivable that individuals who are relatively less loss averse may also be at risk for impulsive behaviors such as drug addiction and gambling, due to relative under valuation of losses, although surprisingly this has yet to be formally tested.</p><p>There is some evidence implicating the striatum in reversal of normal loss aversion in pathological gamblers. Loss of striatal dopamine neurons in Parkinson’s disease is associated with reduced risk-taking behavior compared to control subjects (Brand et al., <xref rid="B10" ref-type="bibr">2004</xref>; Labudda et al., <xref rid="B76" ref-type="bibr">2010</xref>), while chronic administration of dopamine agonists, especially in high doses, reverses this tendency and promotes risky behavior and impulsivity (Dagher and Robbins, <xref rid="B34" ref-type="bibr">2009</xref>). In the healthy brain, acute administration of D<sub>2</sub> dopamine agonists may also cause an increase in risky choices in humans (Riba et al., <xref rid="B112" ref-type="bibr">2008</xref>) and rats (St Onge and Floresco, <xref rid="B126" ref-type="bibr">2009</xref>). Acute D<sub>2</sub>/D<sub>3</sub> receptor stimulation has been found to produce complex changes in the value of losses judged worth chasing (chasing being the continued gambling to recover losses) (Campbell-Meiklejohn et al., <xref rid="B16" ref-type="bibr">2011</xref>). Taken together, this suggests dopamine, acting on the striatum and possibly other mesolimbic structures, may modulate loss aversion. Two studies in Parkinson’s disease patients not affected by impulse control disorders found that a single dose of the dopamine agonist pramipexole reduced loss prediction error coding in the orbitofrontal cortex in one case (van Eimeren et al., <xref rid="B136" ref-type="bibr">2009</xref>) and the orbitofrontal cortex and insula in the other (Voon et al., <xref rid="B143" ref-type="bibr">2010</xref>). In sum, tonic dopamine activity appears to reduce loss prediction signaling, and may therefore reduce loss aversion.</p><p>We propose a general framework based on prospect theory, in which the anticipation of potential losses and rewards is computed, possibly in separate brain regions initially, and integrated to compute a decision value (Figure <xref ref-type="fig" rid="F3">3</xref>). We speculate that gain anticipation might be computed in the ventral medial prefrontal cortex, based on numerous imaging studies implicating this area in computation of value (Kable and Glimcher, <xref rid="B64" ref-type="bibr">2007</xref>; Plassmann et al., <xref rid="B101" ref-type="bibr">2007</xref>; Bartra et al., <xref rid="B7" ref-type="bibr">2013</xref>). As reviewed above, the amygdala and insula may be involved in computing loss anticipation. A possible site for the final computation of value, at least for the purpose of updating choices and action plans, is the striatum, which has fairly direct access to brain regions involved in action planning (van der Meer et al., <xref rid="B135" ref-type="bibr">2012</xref>). The striatum has inherent roles in both response-reward associations (dorsal striatum) (Alexander and Crutcher, <xref rid="B2" ref-type="bibr">1990</xref>) and creating stimulus-reward contingencies (VStr), which afford it the unique opportunity for computation of value (Packard and Knowlton, <xref rid="B95" ref-type="bibr">2002</xref>). Striatal value signals can promote reinforcement processes leading to the updating of future actions, strategies and habits, mediated by the dorsal striatum, while also driving appetitive reward seeking behavior via the VStr. For a review of the role of the striatum in value coding see Knutson et al. (<xref rid="B70" ref-type="bibr">2008</xref>); Bartra et al. (<xref rid="B7" ref-type="bibr">2013</xref>). The balance between gain and loss evaluation systems may be modulated at least in part by dopamine. We propose a model in which tonic dopamine, acting via the indirect basal ganglia pathway (Figure <xref ref-type="fig" rid="F2">2</xref>) regulates inhibitory control manifesting as loss aversion. Here lower levels of tonic dopamine would be associated with increased loss aversion. Conversely, phasic dopamine, acting via the direct pathway, would increase the value of gains. This is based on the finding that young healthy subjects given a single dose of the dopamine agonist cabergoline show reduced learning in response to gains (positive feedback), due presumably to a presynaptic effect (in low doses, cabergoline, a D<sub>2</sub> agonist, reduces phasic dopamine neuron firing via actions on the high affinity D<sub>2</sub> autoreceptor, located pre-synaptically on dopamine neurons) (Frank and O’Reilly, <xref rid="B47" ref-type="bibr">2006</xref>). Conversely, haloperidol, a D<sub>2</sub> antagonist, increased learning from gains, probably due to its ability to enhance phasic dopamine firing. With respect to Parkinson’s disease, if a patient has an individual vulnerability to undervalue losses, then dopamine agonist therapy, which tonically stimulates D<sub>2</sub> receptors and blocks sensing of the phasic dopamine dips associated with negative rewards, (Frank et al., <xref rid="B49" ref-type="bibr">2004</xref>, <xref rid="B48" ref-type="bibr">2007</xref>), could result in even lower loss aversion. One interpretation is that the intensity of phasic activity sets the gain on the value of potential rewards, while the tonic stimulation of D<sub>2</sub> receptors blocks the negative feedback associated with losses.</p><fig id="F3" position="float"><label>Figure 3</label><caption><p><bold>A model of decision-making based on prospect theory. (A)</bold> The utility of potential gains and losses is given by the following equation: <italic>u</italic>(<italic>x</italic>) = (<italic>x</italic>)<sup><italic>α</italic></sup> for potential gains and <italic>u</italic>(<italic>x</italic>) = −<italic>λ</italic> · (−<italic>x</italic>)<sup><italic>β</italic></sup> for losses (Kahneman and Tversky, <xref rid="B65" ref-type="bibr">1979</xref>). When the loss aversion parameter <italic>λ</italic> is greater than 1 the function is steeper in the loss domain, implying loss aversion. In this model the utility of gains and losses is computed by different neural networks and combined at some point. We list regions that may be implicated in the calculation. <bold>(B)</bold> Dopamine may influence the shape of the utility function for gains and losses, by affecting any of the parameters <italic>α</italic>, <italic>β</italic> or <italic>λ</italic> to regulate the degree of loss aversion. Tonic and phasic dopamine may modulate gain and loss calculation via the direct and indirect basal ganglia pathways (Figure <xref ref-type="fig" rid="F2">2</xref>). The balance of tonic and phasic dopamine signaling could regulate the balance between action selection and inhibition, regulating the current level of loss aversion.</p></caption><graphic xlink:href="fnbeh-08-00196-g0003"></graphic></fig><p>Parkinson’s disease patients show enhanced positive learning when on dopaminergic medications, and improved negative learning while off medication, compared to age-matched controls (Frank et al., <xref rid="B49" ref-type="bibr">2004</xref>). Treatment with dopamine D<sub>2</sub> agonists is now accepted as the cause of impulse control disorders in Parkinson’s disease, in which problem gambling is phase locked to medication use. In the model proposed here, D<sub>2</sub> stimulation would reduce loss aversion via the indirect corticostriatal pathway. We suggest that under D<sub>2</sub> agonist treatment, these patients have a tendency to undervalue losses and be more risk seeking. This is consistent with the observation that Parkinson’s disease patients’ deficits in risky decision making is dominated by impaired ability to use negative feedback (Labudda et al., <xref rid="B76" ref-type="bibr">2010</xref>). The effect on gain, risk, and loss processing of dopamine signaling in other parts of the mesolimbic and mesocortical system, notably the vmPFC, OFC, insula and amygdala, remains to be investigated in greater depth.</p><p>Loss tolerance profile may also be affected by norepinephrine signaling. In healthy volunteers, a single dose of the centrally acting beta blocker propranolol reduced the perceived magnitude of losses (Rogers et al., <xref rid="B113" ref-type="bibr">2004</xref>) and normal variations in norepinephrine reuptake transporter in the thalamus, as assessed by PET, correlate with loss aversion (Takahashi et al., <xref rid="B130" ref-type="bibr">2013</xref>). An explanation for this is that norepinephrine increases the arousal response to potential losses, and low norepinephrine signaling may therefore reduce loss aversion. While norepinephrine neurons are also affected in Parkinson’s disease, their role in the motivational and impulsive aspects of the disease have yet to be investigated (Vazey and Aston-Jones, <xref rid="B138" ref-type="bibr">2012</xref>).</p></sec><sec sec-type="conclusions" id="s8"><title>Conclusion</title><p>The causal association between dopamine D<sub>2</sub> receptor agonism and impulse control disorders in Parkinson’s disease has implications for addiction more generally. First, not all individuals develop addictive syndromes following dopamine replacement therapy; those who do appear to have relatively preserved dopamine signaling in the mesolimbic pathway, possibly through a combination of their specific pattern of neurodegeneration, sensitization and pre-morbid vulnerability (as evidenced by the fact that a family history of addiction is a risk factor). It is conceivable that enhanced mesolimbic transmission is also a risk factor in the general population (Buckholtz et al., <xref rid="B13" ref-type="bibr">2010</xref>). Second, it is clear that D<sub>2</sub> receptor agonism alone is sufficient for the development of the addictive syndrome. While combined D<sub>1</sub>/D<sub>2</sub> agonists such as L-Dopa may themselves be addictive (Lawrence et al., <xref rid="B78" ref-type="bibr">2003</xref>), D<sub>2</sub> agonists are not typically administered compulsively; rather, they have the ability to promote other addictions such as pathological gambling (O’Sullivan et al., <xref rid="B93" ref-type="bibr">2011</xref>). This is supported by animal experiments (Collins and Woods, <xref rid="B28" ref-type="bibr">2009</xref>), computational neuroscience models (Cohen and Frank, <xref rid="B27" ref-type="bibr">2009</xref>), and molecular biology evidence (Shen et al., <xref rid="B123" ref-type="bibr">2008</xref>) suggesting that D<sub>1</sub> receptor stimulation is reinforcing while D<sub>2</sub> receptor stimulation inhibits the inhibitory indirect pathway. We suggest that D<sub>2</sub> agonism, in vulnerable individuals, has the effect of “releasing the brake” on reinforcement systems, thus facilitating the development of impulse control disorders. The time-locked nature of the D<sub>2</sub> effect, and the fact that addictive behaviors typically resolve upon discontinuation of the dopamine agonist, is consistent with the theory that tonic dopamine has an invigorating effect on reward seeking behavior (Niv et al., <xref rid="B90" ref-type="bibr">2007</xref>; Dagher and Robbins, <xref rid="B34" ref-type="bibr">2009</xref>).</p><p>We note however that other mechanisms besides dopamine-mediated disruption of responses to reinforcing events and stimuli may play a role. For example, Averbeck et al. (<xref rid="B6" ref-type="bibr">2014</xref>) have proposed that Parkinson’s disease patients with impulse control disorders are uncertain about using future information to guide behavior, which could lead to impulsivity (a tendency to privilege immediate action). Also, frontal lobe deficits (Djamshidian et al., <xref rid="B38" ref-type="bibr">2010</xref>) could also lead to impulsivity through impaired self-control. These mechanisms need not be mutually exclusive.</p></sec><sec id="s9"><title>Conflict of interest statement</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ack><p>This work was supported through grants from the Canadian Institutes of Health Research and Parkinson Society Canada to Alain Dagher and fellowships from the National Sciences and Engineering Research Council of Canada to Crystal A. 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