Scholarly article on topic 'Molecular neuroimaging of emotional decision-making'

Molecular neuroimaging of emotional decision-making Academic research paper on "Clinical medicine"

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Neuroscience Research
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Abstract of research paper on Clinical medicine, author of scientific article — Hidehiko Takahashi

Abstract With the dissemination of non-invasive human neuroimaging techniques such as fMRI and the advancement of cognitive science, neuroimaging studies focusing on emotions and social cognition have become established. Along with this advancement, behavioral economics taking emotional and social factors into account for economic decisions has been merged with neuroscientific studies, and this interdisciplinary approach is called neuroeconomics. Past neuroeconomics studies have demonstrated that subcortical emotion-related brain structures play an important role in “irrational” decision-making. The research field that investigates the role of central neurotransmitters in this process is worthy of further development. Here, we provide an overview of recent molecular neuroimaging studies to further the understanding of the neurochemical basis of “irrational” or emotional decision-making and the future direction, including clinical implications, of the field.

Academic research paper on topic "Molecular neuroimaging of emotional decision-making"

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Molecular neuroimaging of emotional decision-making

Hidehiko Takahashia b c'*

a Department of Psychiatry, Kyoto University Graduate School of Medicine, 54 Shogoin, Kawara-cho, Sakyo-ku, Kyoto 606-8507, Japan b Molecular Imaging Center, Department of Molecular Neuroimaging, National Institute of Radiological Sciences, 4-9-1 Anagawa, Inage, Chiba 263-8555, Japan c Precursory Research for Embryonic Science and Technology (PRESTO), Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan



Article history:

Received 26 December 2012

Received in revised form 27 January 2013

Accepted 29 January 2013

Available online 15 February 2013

Keywords: Emotion

Decision-making Neuroimaging Molecular imaging Monoamine Psychiatry

With the dissemination of non-invasive human neuroimaging techniques such as fMRI and the advancement of cognitive science, neuroimaging studies focusing on emotions and social cognition have become established. Along with this advancement, behavioral economics taking emotional and social factors into account for economic decisions has been merged with neuroscientific studies, and this interdisciplinary approach is called neuroeconomics. Past neuroeconomics studies have demonstrated that subcortical emotion-related brain structures play an important role in "irrational" decision-making. The research field that investigates the role of central neurotransmitters in this process is worthy of further development. Here, we provide an overview of recent molecular neuroimaging studies to further the understanding of the neurochemical basis of "irrational" or emotional decision-making and the future direction, including clinical implications, of the field.

© 2013 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

1. Introduction

With the dissemination of non-invasive human neuroimaging techniques such as fMRI and the advancement of cognitive science, neuroimaging studies regarding emotions, social cognition (Theory of Mind) and moral cognition became established from the late 1990s (Adolphs, 2002; Frith and Frith, 2003; Lamm et al., 2011; Moll et al., 2005; Takahashi et al., 2004). This general period was also an important time for the advancement of behavioral or experimental economics. In normative economics theory, decision makers are assumed to be "rational" and purely self-interested. However, we are not always rational, and sometimes show other regarding preference (e.g. charity, moral decision etc.). Laboratory and field evidence from behavioral economics has shown that decision-makers systematically depart from normative theory (Camerer and Loewenstein, 2004; Camerer and Fehr, 2006; Tversky and Kahneman, 1992). Because behavioral economics deals with the effects of emotional and social factors on economic decisions, not surprisingly, it has been merged with neuroscientific studies about emotions or social cognition, and this interdisciplinary approach is called neuroeconomics (Fehr and Camerer, 2007; Levallois et al., 2012). Since Daniel Kahneman and

* Correspondence address: Department of Psychiatry, Kyoto University Graduate School of Medicine, 54 Shogoin, Kawara-cho, Sakyo-ku, Kyoto 606-8507, Japan. Tel.: +81 75 751 3386; fax: +81 75 751 3246. E-mail address:

Vernon Smith were awarded the Nobel Prize in Economics for their contributions to the establishment of behavioral or experimental economics in 2002, neuroeconomics research has been accelerating (Fehr and Camerer, 2007; Glimcher et al., 2005; Sanfey et al., 2003; Takahashi et al., 2009). Past neuroeconomics studies have investigated the neural basis of "irrational" or "emotional" decision-making that violates normative theory, demonstrating that, in addition to cortical regions such as the prefrontal cortex (PFC), subcortical emotion-related brain structures play a major role in "irrational" decision-making (Fehr and Camerer, 2007). The next question then is how modulatory neurotransmission is involved in these central processes (Rangel et al., 2008). Here, we provide an overview of recent efforts to understand the neuro-chemical basis of "emotional" decision-making under risks.

2. Emotional decision-making under risks

2.1. Neuroscientific studies of nonlinear probability weighting

Normative economics theory in decision-making under risks assumes that decision-makers combine probabilities and valuation (utility) of possible outcomes in some way, most typically by taking the probability-weighted expectation over possible utilities. However, our daily experiences and empirical evidence tell us that we systematically violate the normative theory. One type of systematic violation of normative economics theory is that people tend to weight objective probabilities nonlinearly. Decision-makers often overestimate low probabilities (e.g. playing lotteries) and

0168-0102/$ - see front matter© 2013 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

= 0.5 ro

0 0.25 0.5 0.75 1

Probability Probability Weighting Function

Fig. 1. Hypothesized model showing the contribution of central DA tone to nonlinear probability weighting. A smaller value of a (closer to 0) means a more nonlinear inflected weighting function and a higher value (closer to 1) means a more linear weighting function. At a = 1 the function is linear. DA tone might play a central role in distorting probability weighting function nonlinearly. Excessive DA tone might cause exaggerated overestimation of low probability and underestimation of moderate to high probabilities.

underestimate high probabilities. A leading alternative to normative theory (expected utility theory) is the prospect theory (Tversky and Kahneman, 1992). One of the important components of the prospect theory is nonlinear probability weighting, where objective probabilities, p, are transformed nonlinearly into decision weights w(p) by a weighting function (Fig. 1).

From a psychological point of view, the overweighting of low-probability gains may reflect the hope of winning, and under-weighting of high-probability gains may reflect the fear of losing a "near sure thing". In this sense, nonlinear probability weighting is called "emotional" decision-making. Experimental studies suggest that the weighting function is regressive, asymmetric, and inverse S-shaped, crossing the diagonal from above at an inflection point (around 1/3) where p = w(p). Although several functions have been proposed to express nonlinear probability weighting, the one-parameter function derived axiomatically by Prelec (1998), w(p) = exp{-(ln(1/p))a} with 0< a< 1, is widely used. In an inverse S-shaped nonlinear weighting function, low probabilities are overweighted and moderate to high probabilities are underweighted. The function neatly explains the typically observed pattern of risk-seeking for low probability gain and risk aversion toward high probability gain.

The neural correlates related to nonlinear probability transformation were investigated using fMRl with a certainty equivalent procedure (Paulus and Frank, 2006). During this procedure, a gamble's certainty equivalent, the amount of sure payoff at which a player is indifferent between the sure payoff and the gamble, was determined. lt was reported that differential anterior cingulate activation during estimation of high probabilities relative to low probabilities was positively correlated with Prelec's nonlinearity parameter a across subjects. Another fMRl study with risks of negative outcomes (electric shocks) found similar nonlinear response in brain regions including the caudate/subgenual anterior cingu-late (Berns et al., 2008). Tobler et al. (2008) reported that the dorsolateral PFC was involved in overweighting low probabilities and underweighting high probabilities, and that the ventral frontal regions showed the opposite pattern. However, more recently, the degree of nonlinearity in the striatal response to anticipated reward was shown to reflect the nonlinearity parameter as estimated behaviorally (Hsu et al., 2009). The discrepancies regarding

the loci of activation are thought to stem from differences in the task (probability range, context, etc.) and parameter estimation method. However, elucidating the role of the dopamine (DA) system in nonlinear probability weighting would seem promising, considering the fact that DA is linked to risk-seeking behavior (Leyton et al., 2002) and excessive DA release was observed in pathological gambling in Parkinson's disease patients (Steeves et al., 2009). Trepel et al. (2005) hypothesized in an insightful review that DA transmission in the striatum might be involved in shaping probability weighting. Taking advantage of in vivo molecular neuroimaging, we investigated the relationship between central DA transmission and nonlinear probability weighting by positron emission tomography (PET).

Using a certainty equivalent procedure, we estimated probability weighting with Prelec's one-parameter function outside the PET scanner. There was positive correlation between striatal D1 receptor binding measured by [11C]SCH23390 PET and the nonlinearity parameter a of weighting function (Fig. 2) (Takahashi et al., 2010a). No correlation was found between D2 receptor binding measured by [11C]raclopride PET and nonlinearity parameter a. That is, subjects with lower striatal D1 receptor binding tend to show more pronounced overestimation of low probabilities and underestimation of high probabilities. Although [11C]SCH23390 is a selective radioligand for D1 receptors, it also has some affinity for serotonin (5-HT) 2A receptors. 5HT2A receptor density in the striatum is negligible compared to D1 receptor density. However, 5HT2A receptor density is never negligible in extrastriatal regions, and it was reported that approximately one-fourth of the cortical signal of [11 C]SCH23390 was due to binding to 5HT2A receptors (Ekelund et al., 2007). Future studies with a more selective radioligand are recommended to test the role of extrastriatal (cortical) D1 receptors in nonlinear weighting.

Mis-estimation of probabilities, especially of low probabilities, might be related to some problematic behaviors in neuropsychi-atric disorders. Clinical studies have reported the emergence of pathological gambling in Parkinson's disease patients taking DA agonist medication (Dagher and Robbins, 2009; Gallagher et al., 2007), and such patients showed exaggerated DA release in the ventral striatum measured by [11 C]raclopride PET during gambling (Steeves et al., 2009). Although pathological gambling is a heterogeneous disorder and cannot be solely attributed to mis-estimating probability, these observations can lead to the hypothesis that excessive DA transmission might cause distortion of subjective probability weights for gains (positive outcomes) (Fig. 1). On the basis of this hypothesis, circumstantial evidence can lead us to the conjecture of a vicious-cycle mechanism for developing drug/gambling addiction as follows: Reduced striatal D1 binding (which might in part be determined by genetic information) is linked to a risk-seeking trait. The risk-seeking trait is linked to enhanced activation and DA release in the striatum during risk-seeking behavior (Leyton et al., 2002; St Onge and Floresco, 2009). Chronic exposure to unusually high release of DA might lead to down-regulation of D1 receptors (Moore et al., 1998; Yasuno et al., 2007). Further decrease in D1 receptor binding would then lead to further risk-seeking. Reduced striatal D1 binding could therefore be a gateway to a vicious cycle, creating a predisposition to drug addiction and pathological gambling. ln fact, a recent study suggested that reduced D1 receptor binding may be associated with an increased risk of relapse in drug addiction (Martinez et al., 2009).

However, nonlinear probability weighting is a combination of risk-seeking (overestimation of low probability) and risk-aversion (underestimation of high probability). In fact, a recent study reported that pathological gamblers demonstrated an overall shift toward risk, rather than excessive distortion of nonlinear probability weighting in decision-making under risks (Ligneul et al., 2012). Thus, the shape of weighting function, especially in the

Fig. 2. Relationship between striatal DA D1 receptors and nonlinear probability weighting: (A) parametric image of DA D1 receptor binding potential measured by [nC]SCH23390 is shown and (B) positive correlation between striatal D1 receptor binding and a of weighting function is shown.

high-probability portion, should be determined by multiple neuro-transmitters other than DA (Takahashi et al., 2010b), such as 5-HT (Takahashi et al., 2005) and NE (Onur et al., 2009), which are also known to modulate the emotional reaction of fear. Furthermore, the role of modulatory neurotransmitters in shaping weighting function for losses (negative outcomes) should be tested as well.

2.2. Neuroscientific studies of loss aversion

Distaste derived from losing a certain amount of money appears to be greater than the pleasure derived from gaining the equivalent amount. Imagine having a chance to participate in a coin-flip game of chance. Using a fair coin, if the result is heads, you will win $100, and if the result is tails, you will lose $100. Are you willing to participate in this gamble? Typically, most people would say "no". Well, how about the following gamble? If the winning prize is increased to $200, while the potential loss remains $100. In this case, some people would say "yes". This means that, typically, losses have at least twice the impact of equivalent gains, a property called loss aversion (Tversky and Kahneman, 1992). Many laboratory and field studies have found evidence in monkeys for food rewards, and in humans for financial outcomes, features of consumer goods, food rewards, game show winnings, and apartment sales (Camerer and Loewenstein, 2004; Chen et al., 2006; Knutson et al., 2007). In prospect theory, this is modeled by a value function of losses that is steeper than that of gains (Fig. 3).

A fMRI study reported that the PFC and striatum are involved in loss aversion (Tom et al., 2007). Brain lesion studies have reported that amygdala lesion patients showed diminished loss aversion (De Martino et al., 2010). Sokol-Hessner et al. (2009) have shown that physiological arousal response (skin conductance response) to losses was greater than to equivalent gains on average. This means that losses are more emotionally laden and salient than equivalent gains. The study also reported that individuals with greater arousal response to losses versus gains tend to be more loss-aversive. More recently, the same research team, using fMRI, revealed that behavioral loss aversion was correlated with amygdala activation in response to losses relative to gains (Sokol-Hessner et al., 2012).

It is widely acknowledged that 5-HT plays a major role in emotional response or affective state, and enhancing central 5-HT transmission decrease amygdala activation in response to aversive stimuli (Takahashi et al., 2005). Although there have been no PET studies on the relationship between 5-HT transmission and loss aversion, circumstantial evidence suggests that central 5-HT tone

might be associated with loss aversion. Enhancing 5-HT transmission by tryptophan load reduced the "reflection effect" (Murphy et al., 2009), which refers to the fact that decision-makers tend to prefer the guaranteed $50 gain to a 50/50 gamble to win $100 or no gain at all, showing risk-aversion. However, decision-makers tend to prefer a 50/50 gamble to lose $100 or no loss at all to the guaranteed $50 loss, showing risk-seeking. "Reflection effect" and "framing effect" can be partially explained using loss aversion. De Martino et al. (2006) reported that susceptibility to the framing effect was associated with amygdala activation. They also reported that genetic variation in the promoter region of the 5-HT transporter gene (5-HTTTLPR) predicted susceptibility to the framing effect. Homozygosity for s allele showed greater amygdala activation during decision-making and stronger framing effect than l carriers (Roiser et al., 2009). More recently, large-sample behavioral economics studies in a Chinese sample also showed that

Loss /A/ Gain

Value Function

Fig. 3. Hypothesized model showing the contribution of central 5HT and NE tone to loss aversion. 5-HT and NE might contribute to shaping the slope of value function for loss. 5-HT might ease the slope of value function for loss (loss tolerance: green), and NE might intensify the slope (loss aversion: red). The value function is usually assumed to be a power function v(x) = x", but we used common simplifying assumptions that " is 1 for both value functions in gain and loss domains. The ratio (loss/gain) of the slope of linear functions was indicated as k.

Thalamic MET binding

Fig. 4. Relationship between NET in the thalamus and loss aversion: (A) average of spatially normalized summed PET image of (S,S)-[18F]FMeNER-D2 is shown and (B) negative correlation between NET binding in the thalamus and loss aversion parameter X is shown.

homozygosity for s allele showed higher loss aversion than l carriers (He et al., 2010). Although it is difficult to estimate pre-and post-synaptic (and net) 5-HT transmission by genetic variation in 5-HTTTLPR (Shioe et al., 2003), 5-HT neurotransmission seems to ease the aversive reaction to financial loss (Fig. 3).

In addition to 5-HT, a line of evidence suggests that norepi-nephrine (NE) might be involved in loss aversion. The role of NE in arousal is well established (Berridge and Waterhouse, 2003), and physiological arousal response was reported to be associated with behavioral loss aversion (Sokol-Hessner et al., 2009). Central NE blockade by propranolol attenuated the sensitivity to the magnitude of possible losses at gambles (Rogers et al., 2004). Lack of an appropriate PET radioligand has prevented us from investigating the role of central NE transmission in cognition, emotion and decision-making in vivo. However, (S,S)-18F-FMeNER-D2 has recently been developed as a radioligand for the measurement of norepinephrine transporter for PET (Arakawa et al., 2008; Schou et al., 2004). (S,S)-18F-FMeNER-D2 is a reboxetine analog and has high affinity and high selectivity for norepinephrine transporter (Fig. 4A). We utilized PET scans with (S,S)-[18F]FMeNER-D2 to investigate the relationship between central NET and loss aversion (Takahashi et al., 2013). Based on previous literatures, we were interested in the amygdala and PFC, but the relatively low expression of NET prevented reliable measurement of their NET binding with this radiologand. A NET-rich region available to PET imaging with this ligand is the thalamus. Therefore, we investigated the relationship between thalamic NET binding and loss aversion.

Loss aversion parameters were determined outside the PET scanner using a 50:50 mixed gamble (gain-loss). This parameter X is similar to the parameter in prospect theory but makes the common simplifying assumptions of a linear rather than curvilinear value function (Fig. 3), and identical decision weights for a 0.5 probability of a gain or loss. The study revealed that there was a negative correlation between X and NET binding in the thalamus (Fig. 4B). That is, individuals with low thalamic NET tend to show pronounced aversive reaction to financial losses. ln other words, individuals with high thalamic NET tend to show more fearless decision-making. Although NE has been implicated in arousal, it was reported that NE also affects processing of salient information (Berridge and Waterhouse, 2003). Neurons of the locus coeruleus (LC), the major source of NE in the brain, are phasi-cally evoked by salient or emotional stimuli (Aston-Jones et al., 1994), and phasic LC activation leads to NE release in target sites (Berridge and Waterhouse, 2003). Enhancing NE tone by NE re-uptake inhibitor improves detection of emotional stimuli

(De Martino et al., 2008), and blockade of central NE by propra-nolol predominantly impairs processing of negatively emotional stimuli (Cahill et al., 1994). Thus, PET findings suggest that individuals with low NET in the thalamus might show exaggerated or prolonged effect of NE released by salient stimuli due to low re-uptake, and consequently show pronounced emotional or arousal response to losses relative to gains. Thalamic NET might be an indirect mediator of the relationship between NE transmission and loss aversion. Similarly to 5-HT transmission, Rasch et al. (2009) reported that a genetic variation of ADRA2B, the gene encoding the a2b-adrenergic receptor, predicted amygdala responsivity to negative emotional stimuli. Future studies with a more appropriate radioligand for measuring NET in the amygdala and PFC, which are implicated in loss aversion, are recommended. For the present, it is not unreasonable to suppose that central NE transmission contributes to shaping the slope of the value function in the loss domain (Fig. 3).

ln a clinical setting, NET blocker, atomoxetine, is used in the pharmacotherapy of Attention-Deficit Hyperactivity Disorder (ADHD). ADHD patients are known to show impulsive and reckless decision-making and have high comorbidity rates of drug addiction and gamble addiction (pathological gambling) (Breyer et al., 2009; Pattij and Vanderschuren, 2008). Our finding suggests that NET blockers might shift ADHD patients' decision-making from reckless (less loss-aversive) to more cautious (more loss-aversive) by reducing NET binding. Based on intuitive assumption that pathological gamblers show diminished aversive responses to financial losses, along with ADHD, one can make a prediction that NET inhibitors might be beneficial for pathological gambling. However, pathological gambling seems to be a heterogeneous disorder with various social and biological backgrounds. Diagnostic criteria of pathological gambling are similar to drug addiction, but one characteristic feature of pathological gambling is chasing (American Psychiatric Association, 1994). Pathological gamblers chase their losses and keep gambling in order to get even (but they end up piling up even more losses, and often debts, in reality). Chasing is phenomenon reflecting the unwillingness to accept losses and is similar to the disposition effect or reflection effect, which can be explained by loss aversion. Thus, contrary to intuitive prediction, some types of pathological gamblers might show exaggerated loss aversion. Compared to the DA system, the role of the NE system in reward processing has been less studied, and specifically, the research field that would elucidate the role of NE in decision-making in normal and pathological populations is worthy of further development.

3. Conclusion and future direction

The PET technique is a powerful tool for investigating the relationship between neurotransmitters and decision-making in vivo in human. However, standard PET studies tell us only the correlational relationship. Complementary pharmacological studies as well as animal studies are needed for a full understanding of the causal relationship. Another challenge is the translation of lab evidence into daily-life decision-making and behavior. Laboratory studies are typically conducted in a controlled and simplified environment. Just how well neurochemical information improves the predictability of decision-making model in a more naturalistic setting should be tested (Levallois et al., 2012). Mis-estimating risk could lead not only to drug/gamble addiction but also to other forms of neuropsychiatric disorders such as schizophrenia and depression. An interdisciplinary approach combining molecular imaging techniques, cognitive neuroscience, economics and clinical psychiatry will provide new perspectives for understanding the neurobiology of impaired decision-making in neuropsychiatric disorders as well as their drug development (Takahashi et al., 2012b; Takahashi, 2012a).

Conflict of interest

The author declares no conflict of interest.


A part of this study is the result of "Integrated Research on Neuropsychiatric Disorders" carried out under the Strategic Research Program for Brain Sciences by the Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT), a Grant-in-Aid for Scientific Research on Innovative Areas: Prediction and Decision Making (23120009), a Grant-in-Aid for Young Scientist A (23680045), a research grant from Takeda Science Foundation, a research grant from Brain Science Foundation, a research grant from Casio Science Foundation and a research grant from Senshin Medical Research Foundation.


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