Nicotine–dopamine-transporter interactions during reward-based decision making

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Nicotine–dopamine-transporter interactions during reward-based decision making Joseph Kambeitza,n, Christian la Fougèreb, Natalie Wernerc, Oliver Pogarella, Michael Riedela,d, Peter Falkaia, Ulrich Ettingere a

Department of Psychiatry, Ludwig-Maximilian-University Munich, Munich, Germany Department of Nuclear Medicine, University of Tübingen, Tübingen, Germany c HSD University of Applied Sciences, Cologne, Germany d Clinic for Psychiatry, Psychotherapy, Gerontopsychiatry and Neurology, Rottweil, Germany e Department of Psychology, University of Bonn, Bonn, Germany b

Received 30 July 2015; received in revised form 4 March 2016; accepted 19 March 2016

KEYWORDS

Abstract

Cognitive modeling; Computational psychiatry; Prospect valence learning; SPECT; Iowa Gambling Task; Striatum

Our everyday-life comprises a multitude of decisions that we take whilst trying to maximize advantageous outcomes, limit risks and update current needs. The cognitive processes that guide decision making as well as the brain circuits they are based on are only poorly understood. Numerous studies point to a potential role of dopamine and nicotine in decision making but less is known about their interactions. Here, 26 healthy male subjects performed the Iowa Gambling Task (IGT) in two sessions following the administration of either nicotine or placebo. Striatal dopamine transporter (DAT) binding was measured by single-photon emission computed tomography (SPECT). Results indicate that lower DAT levels were associated with better performance in the IGT (p=0.0004). Cognitive modelling analysis using the prospect valence learning (PVL) model indicated that low DAT subjects’ performance deteriorated following nicotine administration as indicated by an increased learning rate and a decreased response consistency. Our results shed light on the neurochemistry underlying reward-based decision making in humans by demonstrating a significant interaction between nicotine and the DAT. The observed interaction is consistent with the hypothesized associations between DAT expression and extracellular dopamine levels, suggestive of an inverted U-shape relationship between baseline dopamine and magnitude in response to a pro-dopaminergic compound. Our findings are of particular interest in the context of psychiatric disorders where aberrant decision making represents a part of the core symptomatology, such as addiction, schizophrenia or depression. & 2016 Published by Elsevier B.V.

n Correspondence to: Department of Psychiatry, Ludwig-Maximilian-University Munich, Nußbaumstraße 7, 80336 Munich, Germany. Tel.: +49 89 4400 52731; fax: +49 89 4400 55776. E-mail address: [email protected] (J. Kambeitz).

http://dx.doi.org/10.1016/j.euroneuro.2016.03.011 0924-977X/& 2016 Published by Elsevier B.V.

Please cite this article as: Kambeitz, J., et al., Nicotine–dopamine-transporter interactions during reward-based decision making. European Neuropsychopharmacology (2016), http://dx.doi.org/10.1016/j.euroneuro.2016.03.011

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1.

Introduction

Decision making represents a higher-level cognitive ability involving multiple basal cognitive functions such as learning from past experiences, representing available options or internal needs, selecting actions as well as evaluating outcomes (Rangel et al., 2008). Understanding the cognitive and motivational factors that underlie decision making as well as their neurobiological underpinnings is central to understanding human behaviour, not only in healthy individuals but also in psychiatric patients where aberrant decision making frequently represents a core of the symptomatology (Maia and Frank, 2011; Montague et al., 2012; Lee, 2013). Multiple studies have implicated dopaminergic neurotransmission in reward based decision making especially in the striatum (Everitt and Robbins, 2005; Schultz, 2007). At the same time nicotine seems to assert effects on individuals’ choice behaviour (Xiao et al., 2008; Buelow and Suhr, 2014) by modulating dopaminergic circuits (Brody et al., 2004, 2009; Scott et al., 2007; Takahashi et al., 2008). However, little is known about the interplay of dopaminergic and nicotinergic circuits in the moderation on rewardbased decision making. The Iowa Gambling Task (IGT) represents a well investigated experimental paradigm to model real-life decision making in a controlled laboratory setting (Bechara et al., 1994). Subjects are instructed to select cards from four available decks while trying to maximize reward. The cognitive and motivational processes underlying decision making in the IGT can be disentangled with the help of cognitive modelling (Yechiam et al., 2005; Ahn et al., 2008). The prospect valence learning model (PVL model) represents one of the most recently formulated models of the IGT (Ahn et al., 2008, 2011). It decomposes participants' choice behaviour into four latent variables that are hypothesized to guide decision making: learning rate, sensitivity to gains, sensitivity to losses and response consistency. Importantly, cognitive models of the IGT such as PVL allow the identification of clinical populations that show specific changes in decision making such as increased sensitivity to rewards in amphetamine abusers (Ahn et al., 2014). The IGT has successfully been employed to demonstrate the role of dopamine (DA) for reward-based decision making. A recent PET study reported an association of D2/D3receptor binding potential in the striatum and IGT performance in healthy subjects, suggesting better performance in subjects with high striatal DA levels (Linnet et al., 2010). The major determinant of striatal DA levels is the presynaptically located dopamine transporter (DAT) which enables reuptake of DA from the synaptic cleft into the presynaptic neuron. A role of the DAT in reward processing has been demonstrated in subjects with attention deficit hyperactivity disorder (ADHD) (Volkow et al., 2011) as well as in a rodent model of the IGT (Van Enkhuizen et al., 2014). At the same time nicotine has been found to influence IGT performance (Xiao et al., 2008; Buelow and Suhr, 2014). Lower IGT scores have been reported in subjects who smoked in the past seven days (Xiao et al., 2008) and in smokers who stayed abstinent over night (Buelow and Suhr, 2014) but not in smokers compared to non-smokers (Businelle et al., 2008). In general, acute nicotine administration exerts positive

effects on cognitive functions that underlie decision making such as attention, short-term episodic memory or working memory (Heishman et al., 2010). On the molecular level nicotine binds to the nicotinic acetylcholine receptor (nAChR), although some of nicotine's cognitive effects likely result from an interplay of cholinergic agonism with downstream dopaminergic neurotransmission (Jacobsen et al., 2006; Zhu and Reith, 2008). Specifically, administration of cholinergic substances such as nicotine leads to an increase of dopamine levels in the striatum (Brody et al., 2004, 2009; Scott et al., 2007; Takahashi et al., 2008) probably via binding to nAChR of dopaminergic neurons located in the striatum and the ventral tegmental area (Nisell et al., 1994; Ferrari et al., 2002). Thus, recent results indicate a significant role of striatal DA and nicotine during reward-based decision making. While on the molecular level the interplay of nicotine and dopamine has been demonstrated, their mutual effects during reward-based decision have – to the best of our knowledge – not been investigated so far. We, therefore, administered nicotine and placebo to healthy volunteers before they performed the IGT to measure the compound's effects on decision making. We also obtained single-photon emission computed tomography (SPECT) scans of striatal DAT in order to investigate whether this key molecule in striatal dopamine neurotransmission is related to IGT performance and its modulation by nicotine.

2. 2.1.

Experimental procedures Participants

In the present study twenty-six healthy, non-smoking, male volunteers were recruited (mean age=26.68 years, SD=2.91). Participants were screened for possible psychiatric diseases by the Mini International Neuropsychiatric Interview (Sheehan et al., 1998) and excluded in case of a psychiatric condition, a first-degree relative with psychosis, a history of neurological illness or another severe medical condition, head injury with loss of consciousness of 45 min, lifetime history of alcohol or substance abuse or dependence, visual impairments, obesity (body mass index430), intake of any medications which act on the CNS. Verbal IQ was estimated with a standardized German vocabulary test, the MehrfachwahlWortschatz-Intelligenztest-B (Lehrl, 2005). Approval of the ethics committee of the Faculty of Medicine of the University of Munich was obtained. Participants provided written informed consent before inclusion.

2.2.

Experimental design

Prior to the study all participants took part in a baseline session including a health check in form of questionnaires, blood-testing, electrocardiography and electroencephalography to exclude cardiovascular or metabolic disorders that might have put participants at risk following nicotine administration. Participants were then tested in two separate sessions using a double-blind, placebocontrolled counterbalanced within-subjects design. Nicotine (NiQuitin Clear 7 mg, GlaxoSmithKline, Germany) and placebo (Fink and Walter GmbH, Germany) patches were applied on the participants’ right upper back by a research assistant who was not involved in any further testing in order to ensure double blindness. Following a 3-hour waiting period to allow for stable peak plasma levels of nicotine (Petrovsky et al., 2013; http://www.pharmazie.com/ graphic/A/35/1-23135.pdf), participants performed the IGT.

Please cite this article as: Kambeitz, J., et al., Nicotine–dopamine-transporter interactions during reward-based decision making. European Neuropsychopharmacology (2016), http://dx.doi.org/10.1016/j.euroneuro.2016.03.011

Nicotine–dopamine-transporter interactions during reward-based decision making At the end of each testing session patches were removed by a research assistant, participants were questioned whether they felt they had received placebo or nicotine and blood samples were provided in order to check for nicotine and cotinine plasma levels. The dose of 7 mg nicotine was used as previous studies have reported significant pro-cognitive effects and tolerable side-effects at this dosage (Petrovsky et al., 2011; Poltavski and Petros, 2006).

2.3.

Iowa gambling task

The procedure of the Iowa Gambling Task is only described briefly. For further details please see Bechara et al. (1994) and Werner et al. (2013). Initially subjects are provided with a fictitious amount of €2000 and instructed to try to maximize reward during the course of the experiment. On every trial subjects get to choose one card from four possible decks (A, B, C, D) using the keyboard numbers 1, 2, 3 and 4. Decks are presented for 3000ms. If no response is made within that time, the programme randomly chooses a card. Each selected card is associated with an immediate reward or loss of a variable amount. Decks A and B typically lead to a reward of €100 whereas decks C and D typically lead to a reward of €50. However, on average every tenth draw from deck A or B leads to a loss of €250 and every tenth draw from deck C or D lead to a reward of €250. Thus, decks C and D are associated with smaller immediate gains but higher reward over the whole course of the experiment as compared to decks A and B. After each card selection, subjects were presented with the amount of reward and loss as well as their total amount. For each subject, one experimental session consisted of 100 trials in total. The order in which decks were presented on screen was randomized across subjects and experimental session.

2.4.

Prospect valence learning model

We used the prospect valence learning (PVL) model to investigate cognitive processes underlying reward-based decision making during the IGT. When confronted with the same reward, subjects differ with respect to the utility which they assign to it. Subjects with high reward sensitivity will assign a higher utility to a certain reward than subjects with low reward sensitivity. In the PVL model this relationship between reward x(t) and utility u(t) for a given trial t is described by the utility function: uðtÞ ¼

x ðtÞa  λjx ðtÞja

if x ðtÞ Z0 if x ðtÞo0

ð1Þ

The parameter alpha determines the shape of the utility function and thus the sensitivity to reward. For example, a high alpha value for an individual subject indicates a high sensitivity to reward as compared to subjects with low alpha. Typically subjects differ also with respect to the utility assigned to losses (x(t)o0). The parameter lambda determines an individual subject's sensitivity to reward as compared to losses. Subjects with lambda o1 show higher sensitivity to losses than rewards while subjects with lambda 41 show higher sensitivity to rewards than to losses. The utility function u(t) is used to estimate the expected outcome Ej of deck j via a Rescorla–Wagner rule (Rescorla and Wagner, 2015):
 E j ðt þ1Þ ¼ E j ðtÞ þAδj ðtÞ uðtÞ  E j ðtÞ ð2Þ Here, the expected outcome of the upcoming trial Ej(t+1) is computed by the sum of the outcome of the last trial Ej(t) and the weighted prediction error of the last trial [u(t) Ej(t)]. The learning rate A (0oAo1) indicates to which extent the expectancy of the past trials influences the expected outcome of the upcoming trial. Small values of A indicate that the most recent trials have only a small relative influence on the expectancy in the current trial and thus that ‘forgetting’ happens slowly. A high learning rate A

3

indicates that ‘forgetting’ happens more rapidly and thus that the most recent trials carry the most importance for the expectancy in the current trial. δj(t) is a dummy variable indicating if deck j is chosen (δj(t)=1) or not (δj(t)=0). In this way only the expected outcome of the selected deck gets updated whereas the expected outcome of the not-selected decks remains unchanged. It is important to consider that selecting only the deck with the highest expected outcome might not be the best strategy. Leaving no room for explorative choice behaviour holds the risk of sticking with a non-optimal option. In order to allow for such initial explorative choice behaviour a softmax selection rule (Luce, 1959) has been suggested. Accordingly in the PVL model the probability that deck j is chosen in the next trial Pr[D(t+1)=j] is given by the softmax selection rule (Luce, 1959): Pr½Dðtþ 1Þ ¼ j ¼ P4

eθE j ðt þ 1Þ

k¼1

eθE j ðt þ 1Þ

ð3Þ

Here, the sensitivity parameter θ determines the relationship between expected outcomes and choice probabilities. When θ approaches zero, choices become more independent of expected outcomes. This allows more random deck selection which represents explorative behaviour to try out alternative decks. In turn high values of θ indicate more deterministic choices. This represents choice behaviour in which always the deck with the highest expectancy is selected. Sensitivity θ is assumed to be independent of the trial and was set to 3c  1 as suggested by previous reports (Ahn et al., 2008; Yechiam and Ert, 2007). Here, c is a consistency parameter representing more random responses for low values of c and more deterministic responses for higher values of c. Separate PVL models were fitted for the placebo and the nicotine condition. Models were estimated using the R statistical programming language version 2.10.1 (Core Team., 2013) using Hierarchical Bayesian Models as implemented by Ahn et al. (2011). Mean parameter estimates were extracted for each subject and significant effects of nicotine treatment and striatal DAT binding potential were investigated as described below.

2.5.

SPECT measurement

SPECT scans were acquired 4 h after the intravenous injection of approximately 185 MBq [123I]FP-CIT (DaTSCAN, GE Healthcare, Amersham, UK) using a Prism 3000 triple-headed gamma camera (Philips, formerly by Picker, Cleveland, Ohio) equipped with high resolution fan beam collimators (120 projections at 60 s/view; total scan time of 43 min). The projection data were checked for participant motion. The projection images were reconstructed by filtered back-projection (Butterworth 3-D post-filter; 0.60 cycles/ cm, 5th order) and corrected for attenuation according to Chang's method (Chang, 1978). The data were semiquantitatively evaluated using a modified version of the Brain Analysis Software (BRASS, version 3.5; Hermes Medical Solutions, Stockholm, Sweden) and standardized 3dimensional volumes of interest. This software has been validated previously and the procedure has been described in detail elsewhere for SPECT with [123I] FP-CIT (Koch et al., 2005). The striatal binding potential was calculated as follows: (binding in the striatum – binding in the occipital cortex/binding in the occipital cortex), in which binding in the occipital cortex represents non-specific binding. The mean time interval between acquisition of SPECT scans and the first session of the IGT was 291 days on average (SD=163.57 days) which is comparable to other studies (e.g. 501 days in Cools et al., 2009). As previous studies report only negligible changes of DAT binding potential (e.g. 0.8% per year in van Dyck et al., 1995; 0.7% per year in Volkow et al., 1994, 0.7% per year in Volkow et al., 1996), we assume that this time interval did not significantly affect our results.

Please cite this article as: Kambeitz, J., et al., Nicotine–dopamine-transporter interactions during reward-based decision making. European Neuropsychopharmacology (2016), http://dx.doi.org/10.1016/j.euroneuro.2016.03.011

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2.6.

Statistical analysis

For the analysis of the behavioural data, the primary outcome measure net score was defined as follows (Ahn et al., 2008, 2014; Fridberg et al., 2010): net score= (selected cards deck C+ selected cards deck D)– (selected cards deck A + selected cards deck B) Thus the net score represents an individual participant's amount of advantageous choices. As net scores typically increase during the course of the experiment because subjects identify advantageous decks, the 100 trials were separated into 5 equal blocks of 20 trials and net scores were calculated separately for each block (Ahn et al., 2014; Fridberg et al., 2010; Worthy et al., 2013a, 2013b). Net score data were analysed within a linear mixed-model framework in R statistical programming language version 2.10.1 (Core Team., 2013) using Drug (nicotine vs. placebo) and Block (1–5) as within-subjects factors, striatal DAT binding potential and age as between-subjects factors and net score as the outcome measure. For the computational modelling analysis, separate linear mixedmodels were generated using R for each parameter (A, c, lambda, alpha) using Drug (nicotine vs. placebo) as within-subjects factor and striatal DAT binding potential as well as age as betweensubjects factors. For all analyses, subject was treated as a randomeffect in order to account for individual differences in task performance. When reporting results from linear models it is typically recommended to report the variance explained by the model R2 as a measure of how well the model fits the data (Cohen et al., 2013). However, for mixed-effects models R2 is not clearly defined as due to the hierarchical structure of the model it is not clear at which level the explained variance should be calculated (Nakagawa and Schielzeth, 2013). Thus as recommended by Nakagawa and Schielzeth (Nakagawa and Schielzeth, 2013) we report R2GLMM(m) as the proportion of variance explained by the fixed factors, R2GLMM(c) as a measure of variance explained by the fixed and the random factors and the Akaike information criterion (AIC). The significance level for all statistical tests was set at po0.05.

3. 3.1.

Results Behavioural analysis

Linear mixed-model analysis using net score as dependent variable (R2GLMM(m) =0.1733, R2GLMM(c) =0.2829, AIC=1821.769) indicated a significant effect of Block (F(4,219)=9.1841,

po0.0001), a significant effect of DAT (F(1,22)=17.3829, p=0.0004) but no significant effect of Drug (F(1,219) =0.8216, p=0.3657). The effect of Block indicated a significant increase in net score from block 1 to 5. The effect of DAT indicated higher net scores in participants with low DAT binding potential (see Figure 1). Fitting a more complex mixed-linear model (R2GLMM 2 (m) = 0.2064, RGLMM(c) =0.3116, AIC = 1833.587) including higher-order interactions indicated no significant interactions between Block and striatal DAT binding potential, between Block and Drug, between striatal DAT binding potential and Drug and no significant three-way interaction (all p40.4). Separate, exploratory linear mixed-models for each block indicated a significant effect of striatal DAT binding potential on net score in block 3 (F(1,22) = 8.7115, p =0.0074), block 4 (F(1,22) =6.0557, p= 0.0222) and block 5 (F(1,22) =6.9078, p= 0.0154). For block 1 (F(1,22) = 0.6682, p= 0.4224) and block 2 (F(1,22) = 3.5051, p =0.0745) there was no significant effect of striatal DAT binding potential on net score. There was no significant effect of Drug on net score for any block (all p40.1).

3.2.

Computational modelling analysis

For the learning parameter A (R2GLMM(m) = 0.1541, R2GLMM (c) = 0.1541, AIC= 184.8858) there was a significant interaction between Drug and striatal DAT binding potential (F (1,23) = 4.7950, p= 0.0389) and a significant effect of Drug (F(1,23) = 4.9548, p= 0.0361) but no significant effect of striatal DAT binding potential (F(1,22) =1.9625, p= 0.1752). These results indicate that overall nicotine had an effect on subjects learning rate. Subjects showed higher learning rates during the nicotine (unadjusted log mean =  1.78) as compared to the placebo condition (unadjusted log mean = 1.93). However as indicated by the significant DAT-Drug interaction, nicotine affected participants learning rate differently depending on the individual striatal DAT binding potential. Separate analyses for the placebo and nicotine conditions indicated higher A in participants with higher striatal DAT in the placebo (F(1,22) = 6.1230, p =0.0215) but not in the nicotine condition (F(1,22) = 0.1402, p= 0.7117, see Figure 2). Correlations coefficients were calculated separately for each conditions as a measure of the effect size. Learning rate alpha showed a negative

proportion of advantageous choices

1.00 nicotine placebo 0.75

0.50

0.25

0.00 block 1

block 2

block 3

block 4

block 5

Figure 1 (A) Effect of Drug, Block and DAT on proportion of advantageous choices (net score). Linear mixed-model analysis (R2GLMM(m) =0.1733, R2GLMM(c) =0.2829, AIC =1821.769) indicated a significant effect of Block (F(4,219) =9.1841, po0.0001, left figure) and (B) a significant effect of DAT (F(1,22) =17.3829, p= 0.0004). Error bars represent the 95% confidence interval. Please cite this article as: Kambeitz, J., et al., Nicotine–dopamine-transporter interactions during reward-based decision making. European Neuropsychopharmacology (2016), http://dx.doi.org/10.1016/j.euroneuro.2016.03.011

Nicotine–dopamine-transporter interactions during reward-based decision making

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Figure 2 Effect of nicotine and striatal DAT binding potential on log parameter estimates of the PVL model. P-values indicate interactions between striatal DAT binding potential and treatment.

correlation with DAT during the nicotine condition (r=  0.20, p= 0.35) but a positive correlation (r= 0.43, p= 0.032) during the placebo condition. For the model of the parameter response consistency c (R2GLMM(m) = 0.2136, R2GLMM(c) = 0.2484, AIC = 39.732) there was a significant effect of striatal DAT binding potential (F(1,22) = 9.4589, p= 0.0055) indicating higher response consistency in participants with lower striatal DAT binding potential. There was a trend for an effect of Drug (F(1,23) =3.7403, p= 0.0655) indicating higher c in the placebo condition and a trend for a DAT  Drug interaction (F(1,23) = 3.3918, p= 0.0785). Separate analyses for the placebo and nicotine conditions indicated a significantly higher c in participants with low striatal DAT in the placebo condition (F(1,22) = 10.1713, p= 0.0042) but no such effect in the nicotine condition (F(1,22) = 1.0743, p= 0.3112, see Figure 2). Correlations coefficients were calculated separately for each conditions as a measure of the effect size. Response consistency c showed a significant negative correlation with DAT during the placebo condition (r= 0.56, p= 0.038) and a non-significant smaller correlation (r= 0.21, p= 0.30) during the nicotine condition. For the model of the parameter loss aversion lambda (R2GLMM(m) = 0.0497, R2GLMM(c) = 0.0555, AIC= 152.4737) there was no significant effect of striatal DAT binding potential, no significant effect of Drug and no Drug  DAT interaction (all p40.5). For the model of the parameter reward sensitivity alpha (R2GLMM(m) = 0.0902, R2GLMM(c) = 0.3270, AIC= 107.6142) there was no significant effect of striatal DAT binding potential, no significant effect of Drug and no Drug  DAT interaction (all p40.1).

3.3. Analysis of nicotine plasma levels and participants’ blindness for patch treatment Plasma levels of nicotine and cotinine showed a significant increase in the nicotine condition as compared to the placebo condition (for all po0.0001, Figure 3). χ2-test indicated that subjects did not differentiate whether they

Figure 3 Effect of nicotine administration on nicotine and cotinine plasma concentration levels.

had received nicotine or placebo patches at a statistically significant level (χ2(1)= 1.5, p =0.22).

4.

Discussion

We investigated the roles of the neurotransmitters acetylcholine and dopamine during reward based decision making in the IGT. As expected, overall performance (as measured by net scores) increased from blocks 1 to 5 indicating that subjects learnt to identify advantageous decks. Striatal DAT binding potential was related to overall task performance, suggesting that participants with lower DAT binding potential showed higher net scores. There was no effect of nicotine treatment on net score and no nicotineDAT interaction. On the basis of these findings, it is hypothesized that subjects with lower DAT binding potential adopted a more advantageous strategy that was associated with higher outcomes whereas subjects with high DAT binding potential showed worse performance. In order to dissociate the cognitive and motivational processes that might underlie this effect, we followed a computational modelling approach using the prospect valence learning (PVL) model. This approach allowed us to separately test for effects of nicotine and striatal DAT

Please cite this article as: Kambeitz, J., et al., Nicotine–dopamine-transporter interactions during reward-based decision making. European Neuropsychopharmacology (2016), http://dx.doi.org/10.1016/j.euroneuro.2016.03.011

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binding potential on subjects’ learning rate, response consistency, loss aversion and reward sensitivity. There was a significant interaction of nicotine treatment and DAT on learning rate. In the placebo condition, higher DAT was associated with higher learning rates, implying higher levels of attention to the most recent outcomes. In the nicotine condition, no such relation was apparent. Similarly, there was a non-significant trend for a nicotineDAT interaction on the parameter of response consistency. Higher DAT was associated with lower response consistency in the placebo but not in the nicotine condition. Taken together, these results indicate that in the placebo condition subjects with high DAT exhibit higher attention on most recent outcomes and low consistency of responses with respect to outcome expectancies. In contrary, subjects with low DAT presented high response consistency and attention to a larger range of past outcomes. Considering the higher net outcome in low DAT subjects, high consistency and low learning rate might indicate an advantageous strategy in the IGT. This is supported by reports of a significant positive correlation between consistency and net score as well as a significant negative correlation between learning rate and net score in healthy subjects (Worthy et al., 2013a, 2013b; Kester et al., 2006). The overall effect of nicotine was dependent on the participants’ individual DAT binding potential. Specifically, the data point to an intriguing pattern: nicotine adversely affected learning rate a and consistency c in low-DAT subjects whereas it appears that no such effect was present in high-DAT subjects. The implications of this interaction with respect to its neurobiological basis are discussed as follows.

4.1.

Association of striatal DAT with net score

In the present analysis subjects with lower DAT binding potential and therefore possibly higher extracellular striatal DA levels (Scheffel et al., 1997; Giros et al., 1996; Rao et al., 2013) showed higher net scores during the IGT across drug conditions. Thus DA seemed to exert positive effects on participants’ performance during reward-based decision making. This finding is compatible with previous evidence that higher striatal DA levels as indexed by radioligand displacement is associated with higher IGT performance (Linnet et al., 2011). Similarly, decreasing DA levels via DA depletion showed a (trend-level significant) negative effect on IGT performance (Sevy et al., 2006). Also in line with our findings, animal studies show that acute administration of dopaminergic substances such as nicotine or amphetamine affect decision making by decreasing risky choices (Mitchell et al., 2011; Simon et al., 2011). A further way to investigate the role of DA during decision making is to use naturally occurring variants in genes coding for molecular structures involved in dopaminergic neurotransmission. Using this approach it has been shown that genetic polymorphisms of the DAT gene (SLC6A3) are related to striatal activity during reward processing as measured by fMRI (Dreher et al., 2009). The absence of a main effect of nicotine on the net score during the IGT is also compatible with previous findings. In rodent studies, the acute administration of nicotine

(Silveira et al., 2015) or the administration of a DA reuptake inhibitor (Baarendse et al., 2013) did not alter decision making during the IGT. Additionally, smoking status in male patients with schizophrenia was not related to IGT performance (Yip et al., 2009).

4.2. rate

Effect of nicotine and striatal DAT on learning

In general, the IGT requires participants to memorize which decks lead to advantageous outcomes and to update this information continuously as the task progresses. Working memory is a crucial cognitive component during the IGT (Fridberg et al., 2010). It has been demonstrated that inducing a working memory load during the IGT leads to a decrease in performance (Fridberg et al., 2010). Also, working memory deficits are associated with disadvantageous choices in the IGT in subjects with ADHD and comorbid methamphetamine abuse (Duarte et al., 2012). Here, we find higher learning rate during the nicotine as compared to the placebo condition. This effect is consistent with findings from a recent meta-analysis indicating that acute administration of nicotine is associated with increased working memory performance (Heishman et al., 2010). Smoking-status on the other hand seems not to be associated with a significant change of learning rate during the IGT (Xiao et al., 2013). The role of nicotine in the context of the IGT might therefore be related to acute rather than long-term effects. A significant effect of DAT on learning rate during the placebo condition leads us to hypothesize that higher extracellular DA levels in subjects with low DAT binding potential (Scheffel et al., 1997; Giros et al., 1996; Rao et al., 2013) are associated with lower learning rate. This relationship between DA levels and learning rate is also supported by a recent study by Sevy et al. (2006) who reported that a pharmacologically induced depletion of DA levels can increase learning rate in the IGT. DA's role in the IGT is also indicated by the finding that schizophrenic patients who exhibit aberrant dopaminergic neurotransmission as measured by PET and SPECT (Howes et al., 2012; Kambeitz et al., 2014) also show reduced response consistency during the IGT (Kester et al., 2006; Brambilla et al., 2013; Premkumar et al., 2008). Similarly Set et al. (2014) reported a significant alteration of learning rate during decision making by multiple single-nucleotide polymorphisms in the gene coding for DAT. Most importantly, participants' individual differences in the effect of nicotine on learning rate – as indicated by a significant nicotine-DAT interaction – was predicted by striatal DAT binding potential. Given the mutual effects of nicotine and DAT on striatal DA levels at the molecular level, it is hypothesized that these results might be interpreted in line with the postulated inverted-U shaped relationship between DA and cognitive performance (Cools and D’Esposito, 2011). Participants with low DAT binding potential are expected to exhibit high striatal DA levels (Scheffel et al., 1997; Giros et al., 1996; Rao et al., 2013; Gowrishankar et al., 2014; Salahpour et al., 2008) and therefore show optimal cognitive performance in the IGT as indexed by a low learning rate in the placebo condition.

Please cite this article as: Kambeitz, J., et al., Nicotine–dopamine-transporter interactions during reward-based decision making. European Neuropsychopharmacology (2016), http://dx.doi.org/10.1016/j.euroneuro.2016.03.011

Nicotine–dopamine-transporter interactions during reward-based decision making In those participants, increasing striatal DA by nicotine administration might shift individuals beyond the optimal DA level on the hypothesized inverted-U shape. Thus nicotine administration shows a negative effect as indicated by a increase in learning rate. It might be hypothesized that the contra-cognitive effects of nicotine allowed those participants to represent a smaller amount of past outcomes in working memory. This effect of nicotine was not observed (opposite effect by trend) in participants with high striatal DAT binding potential and therefore presumably low DA levels. These individuals' DA level might be lower than the optimal level. By administering nicotine an increase of DA is induced which might shift their DA level closer to the peak of the optimal DA level. This is supported by a decrease in learning rate following nicotine administration in those participants. In line with our results Brody et al. (2006) report that carriers of genes associated with lower baseline DA levels, show more pronounced DA release following nicotine administration. It needs to be noted that animal studies investigating the role of DA in decision making by pharmacologically challenging DA levels have led to heterogeneous results. Administration of a DAT inhibitor has been shown to decrease decision making in some (Van Enkhuizen et al., 2014; Young et al., 2011; Van Enkhuizen et al., 2013) but not all studies (Baarendse et al., 2013). These results appear to be in contradiction to our present findings as DAT inhibition is assumed to increase striatal DA levels. However it needs to be noted that in two (Van Enkhuizen et al., 2014; Van Enkhuizen et al., 2013) of the above mentioned studies, the detrimental effect of DAT inhibition on decision making was mainly present in animals that showed superior decision making performance at baseline. One potential mechanism underlying this might be that depending on the baseline firing of striatal neurons acute nicotine administration can facilitate or inhibit DAT function (Middleton et al., 2004; Hart and Ksir, 1996). The results of the animal studies might thus in fact point to a similar inverted-U shape relationship of striatal DA levels and performance in decision making. Individuals that show high performance at baseline might be associated with optimally tuned DA levels. Further increase of DA might push the DA beyond the optimum level in those individuals, leading to a drop in decision making performance.

4.3.

Effect of nicotine and DAT on consistency

Participants presented higher response consistency during the placebo condition indicating that nicotine led to a discrepancy between participants’ expectancies of outcomes and their selection of decks. In case of low response consistency deck selection happens independently of expectancies and can thus be considered more random or impulsive (Premkumar et al., 2008; Bishara et al., 2009). It has been suggested that the lower consistency of drug abusers in the IGT is related to their impulsive personality (Bishara et al., 2009). This is supported by the finding that drug abusers have higher scores on personality scales of impulsivity (De Wit, 2009) and that impulsivity ratings are inversely correlated with IGT task performance (Bishara et al., 2009). Low response consistency has also been

7

demonstrated in further populations associated with impulsive behaviour. For instance, patients with bipolar disorder during an affective episode (Yechiam et al., 2008) but not for bipolar patients in the euthymic state (Yechiam et al., 2008; Edge et al., 2013) show reduced response consistency. It has been reported that chronic abusers of cocaine (Stout et al., 2004), cannabis (Fridberg et al., 2010; Bishara et al., 2009) or simulant drugs (Bishara et al., 2009) show decreased response consistency in the IGT which might be related to their aberrant dopaminergic neurotransmission (Yeh et al., 2012; Yuan et al., 2014).

4.4.

Limitations

The strength of the present analysis lies in the withinsubjects design which is particularly sensitive to detect effects of the pharmacological intervention. However, it must be noted that only the nicotine administration was manipulated experimentally. The effect of DA was investigated by measuring naturally occurring differences of DAT binding potential in healthy subjects. This to some extents limits the interpretability of our findings. In general we consider our analysis exploratory and thus not correct the test statistics for multiple testings. Additionally, it needs to be acknowledged that our study only investigated a small number of young, male subjects. Studies indicate that gender (Lavalaye et al., 2000) and age (Volkow et al., 1996) are important factors that affect DAT density. Thus further investigations are needed to replicate our results in larger samples, to validate our results and to generalize findings to female individuals or different age groups.

4.5.

Summary

The present study demonstrates the role of dopaminenicotine interplay for reward-based decision making. In general our findings are compatible with the inverted-U shape hypothesis of DA and cognitive performance. Most importantly our analysis demonstrates that the heterogeneous findings of nicotine in the context of reward-based decision making may result to a significant part from individual differences in DAT expression. This has important implications for the interpretation of human and animal studies and might guide future designs to investigate the neurobiological basis of reward-based decision-making.

Role of funding source Ulrich Ettinger acknowledges funding from the DFG (Et 31/2-1), the Friedrich-Baur-Stiftung (74/10) and GE Healthcare. Joseph Kambeitz acknowledges funding from the Friedrich-Baur Stiftung (70/ 14), the Förderung-Forschung und Lehre of the Ludwig-Maximilians University Munich (881/856) and the Universität Bayern e.V.

Contributors Ulrich Ettinger and Joseph Kambeitz designed the study, recruited and tested participants, analysed the data and wrote the manuscript. Christian La Fougère conducted the SPECT measurements and the SPECT analysis. Peter Falkai and Oliver Pogarell contributed

Please cite this article as: Kambeitz, J., et al., Nicotine–dopamine-transporter interactions during reward-based decision making. European Neuropsychopharmacology (2016), http://dx.doi.org/10.1016/j.euroneuro.2016.03.011

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J. Kambeitz et al.

to the written manuscript. Natalie Werner and Michael Riedel were involved in the design of the study and the decision task.

Conflict of interest The authors declare no conflict of interest.

Acknowledgement We would like to thank Christine Macare, Anna Costa, Nicola Wöstmann and Desiree Aichert for their help during the acquisition of the data.

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Please cite this article as: Kambeitz, J., et al., Nicotine–dopamine-transporter interactions during reward-based decision making. European Neuropsychopharmacology (2016), http://dx.doi.org/10.1016/j.euroneuro.2016.03.011