Association of COMT and SLC6A3 polymorphisms with impulsivity, response inhibition and brain function

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Association of COMT and SLC6A3 polymorphisms with impulsivity, response inhibition and brain function Anna-Maria Kasparbauer a, Natascha Merten a, D
esir
ee S. Aichert b, €stmann b, Thomas Meindl c, Dan Rujescu d and Ulrich Ettinger a,* Nicola Wo a

Department of Psychology, University of Bonn, Bonn, Germany Department of Psychiatry and Psychotherapy, University of Munich, Munich, Germany c Institute of Clinical Radiology, University of Munich, Munich, Germany d Department of Psychiatry, Psychotherapy and Psychosomatics, University of Halle Wittenberg, Halle, Germany b

article info

abstract

Article history:

Evidence of the genetic correlates of inhibitory control is scant. Two previously studied

Received 29 April 2015

dopamine-related polymorphisms, COMT rs4680 and the SLC6A3 30 UTR 40-base-pair VNTR

Reviewed 16 June 2015

(rs28363170), have been associated with response inhibition, however with inconsistent

Revised 23 June 2015

findings. Here, we investigated the influence of these two polymorphisms in a large healthy

Accepted 3 July 2015

adult sample (N ¼ 515) on a response inhibition battery including the antisaccade, stop-

Action editor Norihiro Sadato

signal, go/no-go and Stroop tasks as well as a psychometric measure of impulsivity (Bar-

Published online 15 July 2015

ratt Impulsiveness Scale) (Experiment 1). Additionally, a subsample (N ¼ 144) was studied while performing the go/no-go, stop-signal and antisaccade tasks in 3T fMRI (Experiment 2).

Keywords:

In Experiment 1, we did not find any significant associations of COMT or SLC6A3 with

Dopamine

inhibitory performance or impulsivity. In Experiment 2, no association of COMT with BOLD

Genetics

was found. However, there were consistent main effects of SLC6A3 genotype in all inhibitory

Cognitive control

contrasts: Homozygosity of the 10R allele was associated with greater frontoestriatal BOLD

Inhibition

response than genotypes with at least one 9R allele. These findings are consistent with

Impulsivity

meta-analyses showing that the 10R allele is associated with reduced striatal dopamine transporter expression, which in animal studies has been found to lead to increased extracellular dopamine levels. Our study thus supports the involvement of striatal dopamine in the neural mechanisms of cognitive control, in particular response inhibition. © 2015 Elsevier Ltd. All rights reserved.

1.

* Corresponding author. Department of Psychology, University of Bonn, Kaiser-Karl-Ring 9, 53111 Bonn, Germany. E-mail address: [email protected] (U. Ettinger). http://dx.doi.org/10.1016/j.cortex.2015.07.002 0010-9452/© 2015 Elsevier Ltd. All rights reserved.

Introduction

Cognitive control refers to general-purpose mechanisms that flexibly bias information processing in order to achieve goals, especially in the face of distracting stimuli or changes in the environment. The ability to inhibit responses that are inappropriate in a given context has been identified as a central

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function of this heterogeneous set of control mechanisms (Miyake et al., 2000). As inhibition-related processes have in turn been shown to be heterogeneous (Bari & Robbins, 2013), we focus here on only one specific aspect of inhibition, namely prepotent response inhibition, viz. the ability to override unwanted actions (Chambers, Garavan, & Bellgrove, 2009). Behavioural genetic studies (Anokhin, Heath, & Myers, 2004; Macare, Meindl, Nenadic, Rujescu, & Ettinger, 2014) as well as research on patients with attention deficit hyperactivity disorder (ADHD), obsessive-compulsive disorder, drug addiction, schizophrenia and their unaffected relatives suggests that a genetic predisposition underlies deficits in response inhibition (Aron & Poldrack, 2005; Chamberlain et al., 2007; Ersche et al., 2012; Raemaekers, Ramsey, Vink, van den Heuvel, & Kahn, 2006). Evidence from neuropharmacological and neuroimaging studies implicates dopaminergic frontoestriatal networks in prepotent response inhibition, particularly as measured by go/no-go, stop-signal and antisaccade tasks (Aron, Dowson, Sahakian, & Robbins, 2003; Criaud & Boulinguez, 2013; Hutton & Ettinger, 2006; Munoz & Everling, 2004; Nandam et al., 2011; Nandam, Hester, & Bellgrove, 2014; Simmonds, Pekar, & Mostofsky, 2008). Consequently, genetic investigations of dopaminergic cortical and subcortical influences on response inhibition might further our understanding of the mechanisms of inhibitory control and contribute to future treatment and prevention of psychiatric diseases. There are two particularly well characterised polymorphisms in dopamine-related genes that have previously been investigated in association with response inhibition. One polymorphism is located in the gene encoding the dopamine transporter (DAT), the SLC6A3 gene. DATs are primarily expressed in the midbrain and play a central role in dopamine transmission due to their functional role in extracellular dopamine clearance (Bannon, 2005). The polymorphism (reference sequence identification code rs28363170) found in the 30 untranslated region (UTR) of the gene is a 40-base-pair variable number of tandem repeats (VNTR). Repeats range from 3 to 13 copies, with 9-repeat (9R) and 10-repeat (10R) being the most frequent alleles (Kang, Palmatier, & Kidd, 1999; Vandenbergh et al., 1992). Two meta-analyses of human molecular imaging studies suggest that striatal DAT expression is higher in 9R carriers compared to 10R homozygotes (Costa, € ller, & Ettinger, 2011; Faraone, Spencer, Riedel, Mu¨ller, Mo Madras, Zhang-James, & Biederman, 2014). Regarding response inhibition, homozygosity for the 10R allele has been shown to be related to worse response inhibition (Cornish et al., 2005) and higher rates of impulsive errors in the Continuous Performance Task (CPT; Gizer & Waldman, 2012). However, opposing evidence has also been obtained (Kim, Kim, & Cho, 2006). In a recent study, 9R homozygotes performed worse on the incongruent condition of a Stroop task compared to 10R homozygotes and 9/10 heterozygotes (Schneider, Schote, Meyer, & Frings, 2014). A genetic imaging study of adolescents showed that during response inhibition, the homozygosity for the 10R allele may be associated with increased activation in frontal, medial, and parietal regions compared to non-10R carriers (Braet et al., 2011), although the opposite pattern was observed in adult ADHD patients (Dresler et al., 2010).

The second polymorphism studied here is a single nucleotide polymorphism (SNP) in the gene encoding the dopamine degrading enzyme catechol-O-methyltransferase (COMT), the COMT gene. This SNP (reference sequence identification code rs4680) results in a valine-to-methionine substitution at codon 158 (val158met) of the membrane-bound isoform of the protein. This allelic variation (also known as the val158met polymorphism) is functional, as the met158 allele has about one third to one fourth of the activity of the val158 allele, resulting in less efficient catecholamine catabolism (Lachman et al., 1996; Lotta et al., 1995; Weinshilboum, Otterness, & Szumlanski, 1999). This COMT polymorphism plays a modulating role in dopamine transmission in prefrontal cortex, but not in subcortical regions (Slifstein et al., 2008). Evidence relating rs4680 to response inhibition and its neural correlates is inconsistent. Whilst there is evidence of the val allele being associated with better inhibition in the antisaccade task (Haraldsson et al., 2010), the met allele has been associated with better performance in the Stroop task (Schneider et al., 2014). Other studies have failed to observe significant associations with go/no-go and stop-signal task performance (Gurvich & Rossell, 2014; Plewnia et al., 2013; Stokes, Rhodes, Grasby, & Mehta, 2011). With regard to brain function, val carriers showed reduced prefrontal blood oxygenation level dependent (BOLD) response in the prefrontal cortex compared to non-val carriers during antisaccades in a functional magnetic resonance imaging (fMRI) study (Ettinger et al., 2008). Similarly, Congdon, Constable, Lesch, and Canli (2009) observed reduced BOLD activation in the right inferior frontal cortex in carriers of the val allele during successful inhibition in the stop-signal task. However, the opposite pattern was observed in the posterior cingulate cortex during a go/no-go task (Stokes et al., 2011). Thus, research into the associations of these dopamine system-related polymorphisms with response inhibition performance and its underlying brain activity has been inconclusive so far. A possible reason for this inconsistency may be low power due to relatively small sample sizes (behavioural studies: N ¼ 130 to N ¼ 405; fMRI studies: N ¼ 36 to N ¼ 51). Additionally, most previous studies selected only one response inhibition task, thereby failing to allow the assessment of the specificity of any observed associations within the domain of inhibitory function. Finally, the only response inhibition studies combining the two polymorphisms (Colzato, van den Wildenberg, Van der Does, & Hommel, 2010; Congdon et al., 2009; Gurvich & Rossell, 2014; Schneider et al., 2014; Stokes et al., 2011) did not investigate or report statistical interactions of the two polymorphisms with regards to inhibition performance, leaving open whether such geneegene interaction effects may exist. We, therefore, examined the associations of SLC6A3 30 UTR VNTR and COMT rs4680 with performance in a comprehensive response inhibition battery including the go/no-go, stopsignal, antisaccade and Stroop tasks in a large sample of healthy adults (N ¼ 515) (Experiment 1). Additionally, a well powered subsample (N ¼ 144) was studied while performing the go/no-go, stop-signal and antisaccade tasks in fMRI (Experiment 2). Moreover, given the conceptual (Congdon & Canli, 2008) and empirical (Aichert et al., 2012) link between (poor) response inhibition and (high) impulsivity, we also

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included a psychometric measure of impulsivity, the Barratt Impulsiveness Scale (BIS; Patton, Stanford, & Barratt, 1995). We investigated main effects of SLC6A3 and COMT as well as their interactions in order to provide a thorough assessment of the role of these dopamine genes in frontoestriatal inhibitory control mechanisms.

2.

Method Experiment 1

2.1.

Participants

containing 500 mM dNTPs (ABgene, Hamburg, Germany), 100 nM PCR primers, 1625 mM MgCl2 and 0.5 U HotStar Taq polymerase (Qiagen, Hilden, Germany). Following SAP (shrimp alkaline phosphatase) treatment the iPLEX reaction cocktail containing extension primers (7e14 mM), 1x iPLEX termination mix and 1x iPLEX enzyme was added to the PCR-products. After desalting the extension products with SpectroCLEAN resin, samples were spotted on SpecroCHIPs GenII and analysed with the MassARRAY MALDI-TOF mass spectrometer. Allele specific extension products and resulting genotypes were identified by Typer 3.4.

Ethical approval for both experiments reported here was obtained from the ethics committee of the Faculty of Medicine of the University of Munich and participants provided written, informed consent. Participants were recruited via circular emails, newspaper advertisements and advertisements placed around the local community in Munich. Potential participants were first screened for basic suitability in a telephone interview. If suitable, they attended a detailed screening interview in the laboratory to check for the following exclusion criteria: (i) any DSM-IV Axis I disorders (Mini-International Neuropsychiatric Interview; Sheehan et al., 1998), (ii) past or current diagnosis of ADHD, (iii) any diagnosis of psychotic disorders or ADHD in first-degree relatives, (iv) current or past neurological disorders, (v) current poor physical condition, (vi) current medication intake (except contraceptives) and (vii) visual impairment (other than the use of corrective lenses or glasses). Inclusion criteria were German as first language and age between 18 and 55 years.

2.3.

2.2.

2.5.

Genotyping

During the laboratory session, participants provided a sample of approximately 3 ml saliva (OG-500; DNA Genotek Inc., USA). DNA was extracted from saliva using QIAamp DNA-BloodMidi-Kit (Qiagen, Germany). For the VNTR 30 UTR of the SLC6A3, the following primers were used. Forward: 50 -TGT GGT GTA GGG AAC GGC CTG AG30 , Reverse: 50 -CTT CCT GGA GGT CAC GGC TCA AGG-30 (Vandenbergh et al., 1992). A PCR reaction containing 50 ng DNA, 10 pmoles of each primer, 5 U Taq Polymerase (Fermentas, Vilnius, Lithuania) and 100 mM dNTP Mix (Fermentas, Vilnius, Lithuania) was carried out with buffer supplied by the manufacturer in a final volume of 20 ml. Amplification was done in a Thermocycler (Eppendorf, Hamburg, Germany) using the following conditions: initial denaturation at 95  C for 5 min, 35 cycles of denaturation at 95  C for 30 sec, annealing at 61  C for 30 sec, elongation at 72  C for 90 sec following a final elongation at 72  C for 3 min. PCR products were separated on 2% agarose gel by electrophoresis and visualized by ethidium bromide staining and UV fluorescence. COMT rs4680 was genotyped using the MassARRAY platform (Sequenom, San Diego, CA) according to manufacturer's protocol. Briefly, PCR (ACG TTG GAT GTT TTC CAG GTC TGA CAA CGG and ACG TTG GAT GAC CCA GCG GAT GGT GGA TTT) and extension primers (ATGCACACCTTGTCCTTCA) were designed using the Assay Designer 4.0. 12.5 ng of genomic DNA were used for Multiplex PCR reactions with a mastermix

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Demographic assessment

Psychometric assessments included a demographics questionnaire for age and gender, the MWT-B (MehrfachwahleWortschatzeIntelligenztest; Lehrl, 1995) as a measure of verbal intelligence and the Edinburgh Handedness Inventory (EHI; Oldfield, 1971) as a measure of handedness. The MWT-B requires the identification of words amongst nonwords. Scores range from 0 to 37, with higher scores indicating better verbal intelligence.

2.4.

Impulsivity assessment

Impulsivity was measured using the Barratt Impulsiveness Scale (BIS; Patton et al., 1995), an established measure of impulsivity (Stanford et al., 2009). Here, the BIS total score was used, as factor analysis of the German version failed to replicate the original three-factor structure (Preuss et al., 2008). Higher BIS total scores indicate higher levels of impulsivity.

Response inhibition assessment

Prepotent response inhibition was assessed using the antisaccade, stop-signal, go/no-go and Stroop tasks. Participants performed these tasks in a randomized order. All tasks except € umler, 1985) were prethe paper-and-pencil Stroop task (Ba sented on a 17-inch monitor.

2.5.1.

Antisaccade task

Participants sat with their chins on a chin rest with their eyes 57 cm from the monitor. Viewing was binocular and movements of the right eye were recorded using an EyeLink 1000 (SR Research Ltd., Canada) video-based combined pupil and corneal reflection tracker with a sampling rate of 1000 Hz. A nine-point calibration was carried out before the task. The stimulus was a black circle (approximately .3 of visual angle in diameter) presented on a white background. The task comprised 60 trials. In each trial, the stimulus appeared first in the central position for a random duration of 1000e2000 msec and then appeared immediately in a peripheral position (±7.25 or ±14.5 ; each presented 15 times in random order) for 1000 msec. Participants were instructed to look at the stimulus in the centre and perform a horizontal saccade in the opposite position of the peripheral stimulus as fast and spatially accurately as possible. Saccades were identified using DataViewer (SR Research Ltd.) on the basis of minimum amplitude (1 ) and latency (80 msec) criteria. The inhibitory dependent variable was the

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percentage of directional errors, calculated as the number of trials with a first saccade in direction of the peripheral stimulus divided by the total number of included trials (excluding artefacts or no-response trials). Due to calibration problems or poor signal quality data of N ¼ 7 participants were lost.

2.5.2.

Stop-signal task

The stop-signal task (Rubia, Smith, & Taylor, 2007) was a choice reaction time task in which participants had to press the left or right arrow key to indicate the direction of a centrally presented green airplane (go-signal). In 130 go trials the airplane appeared for 1000 msec and was followed by a black screen lasting 700 msec. In 48 stop trials the airplane was followed by an exploding bomb which appeared for 300 msec (stop-signal), signalling the participants to stop their ongoing response. The direction of the airplane was balanced evenly in both go- and stop trials. The initial onset delay between go and stop stimulus was 250 msec (stop-signal delay). A tracking algorithm individually adjusted the stop-signal delay based on each participant's performance. The stop-signal delay was increased by 50 msec when more than 50% of the performed stop trials were correct, making it more difficult, or decreased by 50 msec if the percentage of correct stop trials was lower than 50%. The inhibitory dependent variable was the stop-signal reaction time (SSRT) which was calculated by subtracting the stop-signal delay from the mean response time to go trials (Logan, Schachar, & Tannock, 1997).

2.5.3.

Go/no-go task

The go/no-go task (adapted from Rubia et al., 2001) consisted of two blocks of 150 trials each. The visual stimulus was a white arrow presented centrally on a black screen. In go trials the arrow pointed to the left or right side (go-signal). The direction (left or right) of the arrow remained the same across one block. In 40 trials of each block, a no-go signal (an upwards pointing arrow) appeared instead of the go-signal. Participants were required to press a button whenever an arrow appeared that pointed to the sides, but to inhibit a response when an upwards pointing arrow occurred. In each trial, the presentation duration of the arrow was 200 msec, followed by a blank screen lasting for 800 msec. The inhibitory dependent variable was the percentage of errors on no-go trials, the so-called commission error rate.

2.5.4.

Stroop task

The Stroop task was implemented as a standardised paper€ umler, 1985) with ‘no-interference’ and and-pencil test (Ba ‘interference’ conditions. During the ‘no-interference’ conditions participants read out a black-on-white list of colour words (colour word reading) in the first part and named red, yellow, green and blue coloured bars (colour naming) in the second part. The interference condition was always the last part of the task and required participants to ignore the written colour word and name the ink colour of the word. The ink colour and the colour word were always incongruent, e.g. the word blue was shown in red ink. For each condition there were three sheets with 72 items (words/colour bars) each. Participants were asked to be as fast

and accurate as possible. The experimenter measured the time required for each sheet and counted the participant's errors and corrections. The inhibitory dependent variable was the interference score, which was calculated by subtracting the median response time of the colour naming condition from the median response time of the interference condition.

2.6.

Statistical analysis

For statistical analysis, participants were grouped according to their COMT rs4680 (Val/Val, Val/Met, Met/Met) and SLC6A3 VNTR (9/9, 9/10, 10/10) genotype. Due to the rare occurrence of other repeats in the SLC6A3 VNTR, such individuals were excluded from further analyses (see Results). To assess statistical effects of genotype on demographic variables we conducted one-way analysis of variance (ANOVA) or c2 tests as appropriate. We also investigated associations between demographic and inhibitory variables using ANOVA or Pearson correlations as appropriate. Regarding response inhibition variables, scores that were at least 3 standard deviations away from the sample mean were excluded from further analyses. Furthermore, negative SSRT scores were excluded (Congdon et al., 2012). Final sample sizes are listed in the supplementary materials (Supplementary Table 1). To increase power to detect gene effects on response inhibition we decided to use the maximum sample size available to us for each variable. Therefore, we performed three univariate analyses of variance (ANOVAs) for each dependent variable. First, each dependent variable was entered into models with a single between-subject factor of genotype, once for COMT and once for SLC6A3. Additionally, to assess geneegene interactions, each dependent variable was entered into a separate 33 factorial ANOVA with the between-subject factors COMT (Met/Met vs Val/Met vs Val/Val) and SLC6A3 (9/9 vs 9/10 vs 10/10). SPSS version 22 (IBM, USA) for Windows was used for all analyses. As we investigated two polymorphisms and four inhibitory tasks, we applied Bonferroni correction in order to counteract the Type I error inflation due to multiple analyses.

3.

Results Experiment 1

A total of 515 participants were included into Experiment 1. Participants' ages ranged between 18 and 54 years (M ¼ 26.64, SD ¼ 7.50) and 49.13% of the sample were female. Table 1 summarizes demographic characteristics and impulsivity and inhibitory variables for the entire sample. The same data grouped by genotype are presented in Supplementary Tables 2 (demographics) and 3 (impulsivity and inhibitory variables). Both genotype distributions were in HardyeWeinberg equilibrium. The SLC6A3 genotype was successfully obtained from 512 participants and the COMT genotype was successfully obtained from 485 participants. Nine participants were carriers of rare repeat genotypes (10/ 11, 5/9, 5/10, 7/10, 8/10) and excluded from further analyses involving SLC6A3.

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Table 1 e Experiment 1: Demographic, inhibitory and impulsivity variables.

Sex (female) Handedness (left-handed)

N

%

515 515

49.9 6.7 Mean ± Standard Deviation

Age (years) MWT-B score AS: directional errors (%) SST: stop-signal-reaction time (ms) GNG: commission errors (%) STROOP: interference score (s) BIS total score

515 515 504 484 510 507 515

26.64 30.57 28.86 179.91 23.02 22.95 59.14

± 7.50 ± 3.05 ± 20.44 ± 72.47 ± 13.07 ± 7.61 ± 9.26

MWT-B: verbal intelligence test; AS: antisaccade task; SST: stopsignal task; GNG: go/no-go task; STROOP: Stroop task; BIS: Barratt Impulsiveness Scale.

There were no significant main effects or interactions of genotype groups for any of the demographic variables (age, gender, verbal IQ and handedness). Age correlated positively with Stroop interference, [r(507) ¼ .122, p ¼ .006] and directional errors in the antisaccade task [r(494) ¼ .145, p ¼ .001], indicating performance decline in these inhibitory tasks with age. In contrast, there was a negative correlation of age with percentage of commission errors in the go/no-go task [r(510) ¼ .174, p < .001], indicating better performance with increasing age. MWT-B scores correlated positively with age [r(514) ¼ .176, p < .001] and negatively correlated with BIS total score [r(515) ¼ .133, p ¼ .002]. BIS total score correlated positively with directional errors in the antisaccade task [r(504) ¼ .139, p ¼ .002] and the percentage of commission errors in the go/no-go task [r(510) ¼ .110, p ¼ .013]. There was a gender effect on BIS [F(1,514) ¼ 6.74, p ¼ .01] and on commission errors [F(1,509) ¼ 7.34, p ¼ .007]. Male participants made significantly more commission errors (M ¼ 24.56, SD ¼ 13.27) than female participants (M ¼ 21.45, SD ¼ 12.68), but scored lower on the BIS (males: M ¼ 58.10, SD ¼ 8.92; females: M ¼ 60.21, SD ¼ 9.45).

3.1. Associations of genotype with impulsivity and response inhibition There were no significant associations between any of the impulsivity or inhibitory variables with COMT or SLC6A3 genotype (all p > .18), nor were there any significant two-way interactions (all p > .25). All h2 were less than .01, suggesting that only very small amounts of variance were explained. The results remained essentially the same when age was entered as a covariate.

4.

Discussion Experiment 1

In this experiment, we investigated associations of COMT, SLC6A3 and their interactions with a battery of response inhibition tasks and psychometric impulsivity. The rationale for

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this investigation was the evidence of frontoestriatal dopaminergic mechanisms of response inhibition and impulsivity (Bari & Robbins, 2013) and the roles of the SLC6A3 and COMT genes in mediating striatal and frontal dopamine turnover. However, despite this a priori evidence, we failed to observe any significant associations. One merit of this study is the relative large sample size, compared to previous behavioural studies of SLC6A3 and COMT associations with inhibitory performance. However, it is of course possible that the sample might still have been underpowered to detect subtle gene effects on behavioural variables. Whilst there is strong evidence for frontoestriatal dopaminergic influence on inhibitory processes, the COMT and SLC6A3 polymorphisms might also not be the most sensitive genetic measures of dopamine baseline signalling. For € mer et al. (2007) and Colzato et al. (2010) found example, Kra a significant influence of dopamine receptor genes (DRD4, DRD2) on SSRT, suggesting that postsynaptic receptor density might be a more sensitive measure of dopamine baseline transmission and therefore these receptor genotypes might be more prone to exhibit effects on behavioural performance. Our negative findings are, however, in agreement with other studies that failed to identify a significant influence of these polymorphisms on inhibitory control performance  , 2008; Congdon et al., 2009; (Barnett, Scoriels, & Munafo Ettinger, Merten, & Kambeitz, in submission; Ettinger et al., € mer et al., 2007; Stokes 2009; Gurvich & Rossell, 2014; Kra et al., 2011). Importantly, some of the studies with negative findings at the level of inhibitory performance found associations between neural activation during inhibition and the COMT or SLC6A3 polymorphisms (Congdon et al., 2009; Ettinger et al., 2008; Kasparbauer et al., 2015; Stokes et al., 2011), suggesting that the investigation of brain function may be a more powerful method for the detection of gene effects than the study of behavioural measures alone (Meyer-Lindenberg & Weinberger, 2006). Therefore, in Experiment 2 we used fMRI to investigate the associations of these two polymorphisms with BOLD during the antisaccade, go/no-go and stop-signal tasks. The neural mechanisms of these tasks have been described in a number of previous functional neuroimaging studies, making them ideally suited as well-characterised behavioural probes for the study of gene effects at the level of brain function. The three tasks share the recruitment of superior, middle and inferior frontal as well as striatal areas (Bari & Robbins, 2013; Criaud & Boulinguez, 2013; Jamadar, Fielding, & Egan, 2013). Therefore, and given that the primary sites of expression of COMT and DAT are in the prefrontal cortex and striatum, respectively (Ciliax et al., 1999; Matsumoto et al., 2003), we carried out both whole-brain BOLD analyses and anatomically focussed analyses of superior, inferior and middle frontal gyrus, and caudate and putamen. We expected that the SLC6A3 and COMT polymorphisms would play a functional role in the modulation of the frontoestriatal networks underlying performance on the antisaccade, go/nogo and stop-signal tasks.

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5.

Method Experiment 2

5.1.

Participants

Participants from Experiment 1 were invited to take part in a neuroimaging experiment (Experiment 2). In addition to above inclusion and exclusion criteria, participants had to be righthanded and were required to meet the MRI inclusion criteria of containing no metal in the body and having no history of claustrophobia.

5.2.

fMRI data acquisition

The antisaccade, go/no-go and stop-signal tasks were conducted in a single visit following at least more than one day after participation in Experiment 1. For each of the tasks, T2*weighted whole-brain MR echo planar images (EPI) of the BOLD response were collected on a Siemens Verio MR scanner at 3 T field strength. Functional images were acquired in a single run for each task with a repetition time of 1800 sec for the go/no-go and stop-signal tasks and 2000 msec for the antisaccade task. For each task, the first 4 volumes were discarded to allow for establishment of steady-state longitudinal magnetization. Each image volume compromised 28 axial slices, each 4 mm thick with an inter-slice gap of 0.8 mm and an in-plane resolution of 3  3 mm. Flip angle was 80 and echo time was 30 msec for all tasks. Slices were always acquired inferior to superior parallel to the AC-PC line. Breaks were allowed between tasks. For co-registration with functional images a highresolution isotropic Magnetization Prepared Rapid Gradient Echo (MPRAGE) sequence was acquired with following parameters: TR ¼ 14 msec, TE ¼ 7.6 msec, flip angle 20 , spatial resolution .8*.8*.8 mm isotropic voxels, FOV 256*256, acquisition time ¼ 10 min.

appeared to remind the participants of the task (antisaccade: “look away”; prosaccade: “follow the dot”; fixation: “centre”). Horizontal movements of the left eye were recorded using an MRI-compatible limbus tracker (MR-Eyetracker, CRS Ltd., Rochester, UK). Fibre optic cables guided the infrared light between the emitter/detector array mounted on the head coil beneath the participant's left eye and the hardware in the control room. Signals were digitised using a 12-bit analogueto-digital converter (Data Translation DT9802) and sampled at 500 Hz. A three-point calibration (±8 , 0 ) was performed before each task. Recordings were analysed offline using Eyemap (AMTech GmbH, Heidelberg, Germany). Saccades were identified on criteria of minimum latency (100 msec) and amplitude (1 ). The inhibitory dependent variable was the percentage of directional errors (saccades to the peripheral stimulus) in the antisaccade task.

5.3.2.

Go/no-go task

5.3.

fMRI response inhibition tasks

The event-related go/no-go task was adapted from Chikazoe et al. (2009) and comprised 200 go trials, 30 oddball trials, and 30 no-go trials. We decided to include oddball trials in order to use these trials as baseline and subtract the attentional capture confounding infrequent no-go trials (Criaud & Boulinguez, 2013). Each trial consisted of a coloured circle presented for 500 msec on black background in the middle of the screen, followed by 1300 msec (jittered 1100e1500 msec) of black background. Go trials were indicated by a grey circle. Nogo and oddball trials were indicated by yellow or blue circles, counterbalanced across participants. Trial order was quasirandomized with 3, 4 or 5 go trials between no-go and oddball trials and between no-go trials. Participants had to press a button with their right index finger on go and oddball trials, but had to withhold the response on no-go trials. Trials with RTs smaller than 200 msec and higher than the individual mean RT plus 2.5 standard deviations were excluded from behavioural and neuroimaging analyses. The inhibitory dependent variable was the percentage of no-go commission errors.

5.3.1.

Antisaccade task

5.3.3.

The antisaccade task (Ettinger et al., 2009) comprised five blocks of antisaccades, five blocks of prosaccades, and five blocks of fixation, presented in the same quasi-random order for each participant. Each block lasted 30 sec and each saccade block comprised 10 trials. Each saccade trial started with the stimulus, a dot, in the centre of the screen for a duration of 1100e1900 msec, followed by a peripheral stimulus, a black dot of the same size presented horizontally at ±8 , for a duration of 1100e1900 msec such that each trial was 3000 msec in total (no gap or overlap). The screen background colour was grey. The colour of the central stimulus indicated whether participants had to perform an antisaccade (red) or a prosaccade (green). The peripheral target was presented with equal frequency on each side during one block. During fixation blocks a black central stimulus remained stationary at the centre of the screen for 30 sec. Participants were instructed to make a saccade to the peripheral stimulus during prosaccade blocks and to the mirror image location of the peripheral stimulus during antisaccade blocks. Before each block an instruction lasting for 2000 msec

Stop-signal task

The event-related stop-signal task (Rubia, Halari, Mohammad, Taylor, & Brammer, 2011) involved 234 go trials and 60 stop trials. Each trial began with the presentation of a white arrow pointing right or left for 500 msec in the centre of a black screen, followed by a black screen for a jittered duration between 1100 and 1500 msec. On average, trials were 1800 msec in duration. During go trials, participants responded by pressing the corresponding button as fast as possible. In 20% of the trials the go signal was followed by the stop-signal, a white arrow pointing upwards, which indicated participants to inhibit their response. A tracking algorithm adjusted the delay from go-to stopsignal according to each participant's performance. In order to allow a constant performance of approximately 50% correct stop-signals, the percentage of correct stop trials was calculated after each stop trial. The first delay between go- and stop-signal (stop-signal delay) was 250 msec. In subsequent trials the stop-signal delay was increased by 50 msec when the participant's inhibition performance was higher than 50% to make the task more difficult or decreased by 50 msec when

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the percentage of correct stops signals was less than 50% to make the task easier. The inhibitory dependent variable was the SSRT. Participants with negative SSRT were excluded from further behavioural and neuroimaging analyses (Congdon et al., 2012).

5.4.

Statistical analysis of behavioural data

Due to the smaller sample size of Experiment 2, SLC6A3 9/9 homozygotes and 9/10 heterozygotes were grouped together into a group of 9R carriers, as done previously (Costa et al., 2011; Kasparbauer et al., 2015). As in Experiment 1, statistical effects of genotype on demographic variables were assessed using one-way analysis of variance (ANOVA) or c2 tests as appropriate. Regarding response inhibition variables, scores that were at least 3 standard deviations away from the sample mean were excluded from further analyses. As before, we aimed to increase power to detect gene effects on response inhibition performance during fMRI by using the maximum sample size available for each variable. Therefore we performed three univariate analyses of variance (ANOVAs) for each dependent variable. First, each dependent variable was entered into models with a single betweensubject factor of genotype, once for COMT and once for SLC6A3 in independent models. Additionally, to assess geneegene interactions, each dependent variable was entered into a separate 32 factorial ANOVA with the between-subject factors COMT (Met/Met vs Met/Val vs Val/Val) and SLC6A3 (9R vs 10/10). SPSS version 22 (IBM, USA) for Windows was used for all analyses. As in Experiment 1, we applied Bonferroni correction in order to counteract the Type I error inflation due to multiple analyses.

5.5.

fMRI data analysis

5.5.1.

Image preprocessing

Data were processed using SPM8 (http://www.fil.ion.ucl.ac.uk/ spm/) running in MATLAB R2012b (The MathWorks Inc.). Images were aligned to the first image in the time series and subsequently realigned to the mean image of all images in the timeseries. Next the mean functional images were coregistered to the individual structural images. Functional images were normalized to the Montreal Neurological Institute (MNI) template with parameters obtained from the segmentation of the structural image using the default tissue probability maps, and finally spatially smoothed using an 8 mm fullwidth-at-half-maximum Gaussian filter. Data were high-pass filtered (128 sec).

5.5.2.

Statistical analysis

In the antisaccade task, the model included at the singlesubject level two task regressors containing the on- and offset of the respective blocks and a regressor of no interest with the onset and duration of task instructions. Fixation blocks were not included in the model and served as implicit baseline. In the go/no-go task, the following conditions were modelled using a synthetic canonical hemodynamic response

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function: (1) correct no-go trials, (2) incorrect no-go trials, (3) correct oddball trials, (4) incorrect oddball trials, and (5) incorrect go trials. For all conditions, we modelled the onsets of the stimuli and not the responses, if any occurred. Similarly, in the stop-signal task the task regressors consisted of (1) correct stop trials, (2) incorrect stop trials, (3) incorrect go trials and (4) premature response trials (in stop and go trials). Correct go trials on both tasks were not included in the model and served as implicit baseline (see e.g. Chamberlain et al., 2009; Chikazoe et al., 2009). As above, we modelled the onsets of the stimuli and not the responses, if any occurred. In all first-level models, six additional regressors were included to account for subject movement. Single-subject maps of the main inhibitory contrasts were combined at the group level in a random effects analysis to produce statistical parametric maps of group activation. The contrasts antisaccade>prosaccade blocks, correct no-go>correct odd trials and correct stop>incorrect stop trials were chosen as main inhibitory contrasts in group analysis for the antisaccade, go/no-go and stop-signal tasks, respectively (Supplementary Fig. 1). For the main effects of each genotype ANOVA with three groups for COMT (Val/Val, Val/Met, Met/Met) and two groups for SLC6A3 genotype (9R, 10/10) was used. To assess geneegene interaction effects a full-factorial model was applied with two factors: (1) SLC6A3 genotype with two levels (9R, 10/ 10) and (2) COMT genotype with three levels (Val/Val, Val/Met, Met/Met). Group analyses were conducted both for the whole brain and theoretically informed (Bari & Robbins, 2013; Criaud & Boulinguez, 2013; Jamadar et al., 2013), anatomically defined regions of interests (ROI), which were extracted from the automated anatomical labelling (aal) atlas of the WFU PickAtlas tool (v3.05). ROI analysis included five bilateral ROIs: (1) caudate, (2) putamen, (3) middle frontal gyrus, (4) inferior frontal gyrus and (5) superior frontal gyrus. For all of the above analyses, the voxel threshold was set to P < .05 corrected for multiple comparisons. Reported coordinates represent MNI space.

6.

Results Experiment 2

A total of 144 participants returned for Experiment 2. For N ¼ 2 participants, neither SLC6A3 nor COMT genotype was available. For a further N ¼ 4 participants, the SLC6A3 genotype and for another N ¼ 11 participants their COMT genotype was missing. In the go/no-go task, N ¼ 5 participants had to be excluded due to technical problems with the response device and a further N ¼ 5 participants were excluded because they did not comply with the task instructions. In the stop-signal task, N ¼ 2 participants had to be excluded due to technical problems. Another N ¼ 34 were excluded because they showed a negative SSRT, suggesting they applied a strategy to avoid commission errors (Congdon et al., 2012). For the antisaccade task, behavioural and neuroimaging data of N ¼ 2 participants was missing, and for 15 participants behavioural data were not available. For sake of consistency, we did not include these

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participants in the antisaccade neuroimaging analysis, however including the participants for whom behavioural data were missing, produced similar results. Final sample sizes for each task are listed in supplementary materials (Supplementary Table 4). After exclusion of participants with negative SSRT, the number of participants in the Val/Val and 9R carrier groups was N ¼ 3 (Supplementary Table 4). Therefore, we excluded Val/Val participants from the analysis of SLC6A3  COMT interactions in the stop-signal task. Table 2 summarizes demographic characteristics and inhibitory performance variables for the entire sample. The same data grouped by genotype are presented in Supplementary Tables 5 (demographics) and 6 (inhibitory variables). Both genotype distributions were in HardyeWeinberg equilibrium. The final sample sizes per task and gene ranged from N ¼ 92 to N ¼ 127.

There were no significant differences in BOLD signal between SLC6A3 genotypes nor between COMT genotypes for any of the inhibitory contrasts in whole-brain analysis. When using frontal and striatal ROIs, there were significant main effects of SLC6A3 genotype in each inhibition contrast, revealing greater activation for 10/10 homozygotes compared to 9R carriers (Fig. 1). For the antisaccade task and the stop-signal task we found the significant main effect of SLC6A3 genotype in the left caudate (x ¼ 8, y ¼ 12, z ¼ 12, Z ¼ 3.74, cluster size k ¼ 13) and right putamen (x ¼ 34, y ¼ 2, z ¼ 8, Z ¼ 3.69, cluster size k ¼ 12), respectively. For the go/no-go task we found the significant main effect in the right middle frontal gyrus (x ¼ 40, y ¼ 24, z ¼ 50, Z ¼ 4.24, cluster size k ¼ 37). All clusters survived peak-level family-wise error (FWE) correction for multiple comparison at p < .05. Adding age as a covariate in the second level analysis did not alter these results.

6.1. Associations of genotype with measures of response inhibition There were no significant main effects or interactions after correction for multiple testing. The results were similar when age was entered as a covariate. However, for sake of completeness it should be noted that there was a main effect of COMT on percentage of commission errors in the go/no-go task that was significant at uncorrected level [F(2,112) ¼ 3.54, p ¼ .03, h2 ¼ .06]. Post-hoc t-tests showed that the Met/Val group made significantly more commission errors than Met/Met individuals (p ¼ .04). The result was similar when age was entered as a covariate. There were no other associations of any of the inhibitory variables with either SLC6A3 or COMT (all p > .26), and no statistical interactions between the two polymorphisms (all p > .69).

6.2.

Associations of genotype with BOLD

Results of significant BOLD activation during each of the main inhibitory contrasts can be found in Supplementary Figure 1 and Supplementary Table 3.

Table 2 e Experiment 2: Demographic, inhibitory and impulsivity variables. N Sex (female)

143

% 49.7 Mean ± Standard Deviation

Age (years) MWT-B score AS: directional errors (%) SST: stop-signal-reaction time (ms) GNG: commission errors (%) BIS total score

143 143 125 107 139 143

26.27 30.42 21.75 140.42 16.49 59.51

± 7.38 ± 2.98 ± 15.42 ± 95.35 ± 12.70 ± 8.74

MWT-B: verbal intelligence test; AS: antisaccade task; SST: stopsignal task; GNG: go/no-go task; STROOP: Stroop task; BIS: Barratt Impulsiveness Scale. Please note that only right-handed participants were included in Experiment 2.

Fig. 1 e Main Effect of SLC6A3 on BOLD. Main effect of SLC6A3 polymorphism for go/no-go (top) in right middle frontal gyrus (y ¼ 24), stop-signal (middle) in right putamen (y ¼ ¡2) and antisaccade (bottom) task in left caudate (y ¼ 12). For display purposes, maps are thresholded at P < .01, uncorrected, but results are significant at P < .05, family-wise error (FWE). The y-axis represents parameter estimates. Error bars depict standard error of the mean (SEM).

c o r t e x 7 1 ( 2 0 1 5 ) 2 1 9 e2 3 1

There was no main effect of COMT or a COMT x SLC6A3 interaction effect in any of the ROIs.

7.

Discussion Experiment 2

In Experiment 2, we followed the approach of imaging genetics to characterise gene effects at the level of brain function (Meyer-Lindenberg & Weinberger, 2006). We utilised three inhibition tasks and studied the largest sample to date in fMRI investigations of COMT or SLC6A3 effects on brain function during inhibition. Given previous evidence (Congdon et al., 2009; Ettinger et al., 2008; Stokes et al., 2011), we hypothesised that COMT and SLC6A3 effects may be more penetrant at the level of brain function than at the level of cognitive task performance. Our results support this assumption with regards to SLC6A3. As in Experiment 1, we did not find any significant associations of COMT or SLC6A3 with inhibitory performance. However, ROI analysis of BOLD data revealed consistent main effects of SLC6A3 genotype in all inhibitory contrasts, indicating that homozygosity of the 10R allele is associated with greater frontoestriatal BOLD response during response inhibition than genotypes with at least one 9R allele. These findings are consistent with previous evidence (Braet et al., 2011). For example, a qualitatively similar pattern was found during reward anticipation and memory in the striatum and hippocampus, respectively (Wittmann, Tan, Lisman, Dolan, & Du¨zel, 2013). Additionally, in a pharmacogenetic study of the moderating role of SLC6A3 in the effects of methylphenidate on brain function during go/no-go task performance we recently observed in an independent sample, using the same task as in the current study, that 10R homozygotes had higher BOLD than 9R carriers in an extended cortico-subcortical network in the placebo condition (Kasparbauer et al., 2015), compatible with the current findings. An important question concerns the mechanisms by which the SLC6A3 VNTR exerts its effects on BOLD. Previous evidence from meta-analyses implies lower striatal DAT expression in 10R homozygotes (Costa et al., 2011; Faraone et al., 2014). Given the primary role of the DAT in transporting extracellular dopamine back into the presynaptic neuron, a genotype with fewer DAT may thus be associated with increased extracellular and reduced intracellular DA (Scheffel et al., 1997). This interpretation is supported by studies of knockout (KO) and knockdown (KD) mice, which either lack the DAT (KO) or show drastically reduced expression (KD). These animals show increased extracellular and reduced intracellular DA as well as decreased amplitude of evoked DA release (Giros, Jaber, Jones, Wightman, & Caron, 1996; Rao, Sorkin, & Zahniser, 2013), overall suggesting a pattern of increased tonic and reduced phasic DA signalling (Gowrishankar, Hahn, & Blakely, 2014). Mice with enhanced DAT expression on the other hand show reduced extracellular dopamine levels (Salahpour et al., 2008). Assuming then that 10R homozygotes show reduced DAT expression in striatum (Costa et al., 2011; Faraone et al., 2014) we conclude that these subjects may show a similar pattern of increased extracellular dopamine. This may in turn lead to

227

increased BOLD signal, given that multi-modal neuroimaging research has shown positive associations between dopamine release and BOLD signal (Knutson & Gibbs, 2007; Schott et al., 2008). Additionally, a combined SPECT-fMRI study observed that lower DAT availability in the basal ganglia was associated with increased BOLD in left supplementary motor area during a motor task (Fazio et al., 2011), again compatible with our interpretation that 10R homozygotes show increased BOLD due to lower striatal DAT availability. Interestingly, DAT KO and KD mice also show increased impulsive behaviours (Giros et al., 1996; Rao et al., 2013) and the 10R allele has in humans been associated with ADHD (Gizer, Ficks, & Waldman, 2009) and impulsivity (Loo et al., 2003; Mata, Hau, Papassotiropoulos, & Hertwig, 2012). It should be noted, however, that DAT appears to be especially important for removing dopamine after phasic but not tonic firing (Gowrishankar et al., 2014): Increases in phasic burst firing are counteracted by DAT and there are no increases in extracellular dopamine unless the DAT is blocked (Floresco, 2013). On the other hand, dopamine levels due to tonic firing appear less dependent on DAT (Gowrishankar et al., 2014), although there is evidence to the contrary (Cagniard et al., 2006). Overall, we conclude that the increased frontoestriatal BOLD in 10R homozygotes may be due to increased extracellular DA levels following phasic burst firing. In contrast to our finding of increased BOLD in 10R homozygotes, however, several previous imaging studies showed greater activation in 9R carriers during reward-related (Aarts et al., 2010; Dreher, Kohn, Kolachana, Weinberger, & Berman, 2009; Forbes et al., 2009) and response inhibition (Congdon et al., 2009) tasks. It is unclear what may cause these inconsistencies across studies, although variations in task protocols may play a role. Such protocol differences may affect the precise cognitive mechanisms required or may influence task difficulty and motivational factors. In the context of this study, however, SLC6A3 genotype had a consistent effect on cortical and striatal regions during inhibitory performance across the antisaccade, go/no-go and stop-signal tasks. The current findings from a well powered sample, therefore, support the involvement of SLC6A3 and, accordingly, dopamine in inhibitory control mechanisms at the neural level independent of task. The location of the SLC6A3 genotype effects in striatum and right middle frontal gyrus warrants further discussion, especially as in the latter region DATs are scarce (Ciliax et al., 1999). However, several imaging studies confirm the existence of modulatory effects of SLC6A3 genotype in non-striatal areas (Arnold et al., 2015; Bertolino et al., 2009; Braet et al., 2011; Dreher et al., 2009; Kasparbauer et al., 2015). It has been suggested that these neuroimaging findings result from downstream effects of phasic dopamine firing in subcortical areas (Arnold et al., 2015). The failure to obtain a COMT effect in the present study is unexpected, given findings from a meta-analysis suggesting that the val allele is significantly associated with increased BOLD during executive cognition tasks (Mier, Kirsch, & MeyerLindenberg, 2010). The failure to observe a significant effect in our study might be due to the particular cognitive domain we investigated. Although response inhibition is of course a facet of cognitive control (Miyake et al., 2000), COMT has previously

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been linked particularly to manipulation and updating of information (Goldman, Weinberger, Malhotra, & Goldberg, 2009) and cognitive flexibility (Colzato, van den Wildenberg, & Hommel, 2014). In contrast, the current inhibition tasks demanded the inhibition of inappropriate responses and might therefore not be linked to this dopamine related polymorphism. However, this interpretation contrasts with a finding from a previous study of ours, in which we observed an association between rs4680 and BOLD during antisaccades in a smaller sample (N ¼ 36) using the same task (Ettinger et al., 2008). Given that the current sample was significantly larger and as we imaged brains at 3T rather than at 1.5T as in our previous study, we must interpret our failure to replicate our previous finding with improved methods as a challenge to the robustness of that earlier finding. Clearly, more studies on COMT associations with BOLD during inhibitory task performance are needed. Finally, it should of course be emphasised that we only investigated inhibitory tasks and, therefore, cannot rule out potential COMT influences on BOLD during other cognitive or emotional tasks (Mier et al., 2010).

8.

Limitations

There are limitations to the interpretation of our findings that need to be addressed. Firstly, we still lack a full understanding of the mechanistic links between the SLC6A3 VNTR polymorphism, dopaminergic transmission and BOLD signalling. Therefore, and in the absence of a measure for DAT availability, our interpretations remain speculative and other levels of explanation, such as the role of effort (Braet et al., 2011), may also need to be considered. However, based on the findings of two independent meta-analyses that associated 10R carriers with lower DAT (Costa et al., 2011; Faraone et al., 2014) and our recent study confirming this pattern with a medium effect size in a new sample (Kasparbauer et al., 2015), future studies combining genetic and molecular imaging methods might further elucidate our interpretations. A second limitation concerns the sample size in the respective genotype groups. Although our sample may be considered large by imaging genetics standards, the imaging results would benefit from larger and more evenly distributed genotype groups in order to detect possible gene effects of small magnitude.

9.

Overall conclusions

In the largest study of the associations of COMT rs4680 and SLC6A3 rs28363170 with response inhibition to date, we failed to observe significant genetic effects with behavioural performance in four response inhibition tasks. We also failed to find associations with psychometric impulsivity. In contrast, neuroimaging data revealed an effect of SLC6A3 genotype on frontoestriatal BOLD during response inhibition. Our findings thus present evidence for the involvement of striatal dopamine in cognitive control, in particular response inhibition. Genetic imaging studies of individual differences in inhibitory control to date have produced inconsistent

findings. Here, we also could not replicate some of our own results (see Ettinger et al., 2009). Some of these inconsistencies may be attributed to small effect sizes of individual genes. The current data suggest that neuroimaging of large samples will be able to detect subtle genetic effects in the absence of behavioural effects. These findings will inform future assumptions on neurophysiological models of cognition (Greene, Braet, Johnson, & Bellgrove, 2008).

Funding and disclosure This work was supported by the DFG Emmy Noether Programme (Et 31/2-1). The authors declare no conflict of interest.

Acknowledgements We gratefully acknowledge Maximilian Reiser for facilitating access to fMRI, Ute Coates for MR support, Annette Hartmann for genotyping and Anna Costa and Christine Macare for assistance in participant recruitment and assessment. We thank Ulrich Preuss for providing the German items of the BIS.

Supplementary data Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.cortex.2015.07.002.

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