Interaction of COMT val158met and externalizing behavior: Relation to prefrontal brain activity and behavioral performance

NeuroImage 60 (2012) 2158–2168

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Interaction of COMT val 158met and externalizing behavior: Relation to prefrontal brain activity and behavioral performance Zarrar Shehzad a, Colin G. DeYoung b, Yoona Kang a, Elena L. Grigorenko a, c, Jeremy R. Gray a,⁎ a b c

Department of Psychology, Yale University, New Haven, CT, USA Psychology Department, University of Minnesota, Minneapolis, MN, USA Child Study Center, Yale University, New Haven, CT, USA

a r t i c l e

i n f o

Article history: Received 23 October 2011 Revised 16 January 2012 Accepted 18 January 2012 Available online 28 January 2012 Keywords: COMT Externalizing fMRI Genetics Inhibitory control MSIT

a b s t r a c t A promising approach in neuroimaging studies aimed at understanding effects of single genetic variants on behavior is the study of gene–trait interactions. Variation in the catechol-O-methyl-transferase gene (COMT) is associated with the regulation of dopamine levels in the prefrontal cortex and with cognitive functioning. Given the involvement of dopaminergic neurotransmission in externalizing behavior, a trait characterized by impulsivity and aggression, especially in men, externalizing (as a trait) may index a set of genetic, environmental, and neural characteristics pertinent to understanding phenotypic effects of genetic variation in the COMT gene. In the current study, we used a gene–trait approach to investigate effects of the COMT val 158met polymorphism and externalizing on brain activity during moments involving low or high demands on cognitive control. In 104 male participants, interference-related activation depended conjointly on externalizing and val158met: stronger activation in the dorsal anterior cingulate and lateral prefrontal cortex was found for val/val individuals with high trait externalizing while stronger activation in cingulate motor areas and sensorimotor precuneus was found for met/met individuals with low externalizing. Our results suggest that the val/val genotype, coupled with high levels of trait externalizing, lowers the efficiency of stimulus conflict resolution, whereas the met/met genotype, coupled with low levels of externalizing, lowers the efficiency of response selection. © 2012 Elsevier Inc. All rights reserved.

Introduction Measures of brain activity have been increasingly used as ‘intermediate phenotypes’ to bridge our understanding of the link between genes and behavior (Green et al., 2008; Hariri and Weinberger, 2003; Meyer-Lindenberg and Weinberger, 2006; Thompson et al., 2010). The majority of studies using neuroimaging as an intermediate phenotype have examined the influence of a single genetic variant on brain activity and behavior. More recent work suggests that considering the effects of a gene in the context of additional and interacting factors such as multiple variants in the same gene (MeyerLindenberg et al., 2006), variants in related genes (Grigorenko et al., 2010; Nikolova et al., 2011; Stelzel et al., 2009), environmental conditions (Dodge, 2009; van Os et al., 2008), or psychological traits (Blasi et al., 2009; DeYoung et al., 2006) may further elucidate gene–brain– behavior relationships. The present study applied a gene–trait interaction approach to understanding the relation of variation in the catechol-O-methyltransferase gene (COMT) to cognitive control. COMT produces an ⁎ Corresponding author at: Yale University, Department of Psychology, 2 Hillhouse Ave, CT, 06511, USA. Fax: + 1 203 432 7172. E-mail address: [email protected] (J.R. Gray). 1053-8119/$ – see front matter © 2012 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2012.01.097

enzyme that breaks down catecholamines such as dopamine and norepinephrine. Although COMT is distributed throughout cortical and limbic regions (Jiang et al., 1998; Matsumoto et al., 2003), its effects are predominately localized in the prefrontal cortex (Gogos et al., 1998; Karoum et al., 1994) where COMT is the primary mechanism of dopamine (but not norepinephrine) clearance from the synapse (Chen et al., 2004; Lewis et al., 2001; Sesack et al., 1998; Tunbridge et al., 2004). Furthermore, variation in COMT-mediated prefrontal dopamine metabolism is thought to affect cognitive control abilities since dopamine levels are associated with variation in cognitive performance (Aalto et al., 2005; Congdon et al., 2009; Cools, 2008; Robbins and Arnsten, 2009) and dopamine levels influence the signal-to-noise ratio of task-specific neuronal firing and electrophysiological responses in the prefrontal cortex (Gallinat et al., 2003; Sawaguchi et al., 1990; Williams and Goldman-Rakic, 1995; Winterer et al., 2006). One commonly investigated variant in the COMT gene is the single nucleotide polymorphism val 158met or rs4680. This polymorphism occurs at codon 158 in the fourth exon of one of the COMT protein isoforms, the membrane-bound form (Lachman et al., 1996). It is substantiated by a guanine (G) to adenine (A) mutation, which results in a valine (val) to methionine (met) amino acid substitution during enzyme synthesis. The val variant of the enzyme breaks down dopamine

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(DA) 40% more efficiently than the met variant, especially in the prefrontal cortex (Chen et al., 2004; Weinshilboum et al., 1999). As COMT is the primary mechanism of DA clearance from synaptic areas of the prefrontal cortex, the increased breakdown of DA leads individuals with the val variant to have lower sustained or tonic levels of prefrontal DA (Grace, 1991). Human neuroimaging studies have suggested that val/val individuals compensate for lower tonic levels of prefrontal DA with increased brain activity during executive cognitive tasks (Mier et al., 2010). Specifically, val/val individuals show greater activity in fronto-parietal regions such as the anterior cingulate, dorsolateral prefrontal cortex, inferior frontal sulcus, and pre-SMA during tasks involving working memory (Bertolino et al., 2004; Bilder et al., 2004; Caldu et al., 2007; de Frias et al., 2010; Egan et al., 2001; Mattay et al., 2003), attentional control (Blasi et al., 2005), and response inhibition (Congdon et al., 2009; Kraemer et al., 2007). These differences in brain activity have been found to be independent of differences in behavioral performance (Bishop et al., 2008; de Frias et al., 2010; Dennis et al., 2010). Consequently, the increased neural activity in val/val individuals may reflect lower neural efficiency as a result of lower tonic DA levels, which requires increased neural activity to stay on-task. The differences in dopamine levels and presumed neural efficiency between alternative alleles of the val 158met polymorphism are thought to underlie observed phenotypic differences in cognition and behavior (Mier et al., 2010). The val 158met polymorphism is associated with a range of psychiatric disorders often involving gender specific effects (Harrison and Tunbridge, 2008). For instance, recent work has shown increased risk of conduct disorder for val variants (Caspi et al., 2008; DeYoung et al., 2010) and increased risk of ADHD for met variants especially in male populations (Biederman et al., 2008; Cheuk and Wong, 2006; DeYoung et al., 2010). The val 158met polymorphism has also been associated with individual differences in cognitive performance. Explicitly manipulating the effects of the polymorphism in mice disrupted attentional set-shifting abilities, and impaired working and recognition memory for mice with an overexpressed val allele (Papaleo et al., 2008). In humans, individuals homozygous for the val allele have shown decrements in memory, executive functioning, and inhibitory control (Barnett et al., 2007; Bruder et al., 2005; Sheldrick et al., 2008), although not all behavioral studies support this association (Barnett et al., 2008; Tsai et al., 2003). The conflicting behavioral results on the association between val 158met and cognition suggest that additional and interacting factors may play a role in modulating behavior during cognitive control (Tunbridge et al., 2006). Recent work has therefore considered the influence of psychological traits (i.e., gene–trait interactions). A psychological trait reflects a characteristic pattern of psychological function (and, therefore, of brain function), influenced by many genetic and environmental factors. Effects of any particular genetic variation are likely to differ if they play out in the brains of people with sufficiently different levels of a given trait, especially if that trait reflects a broad pattern of psychological functioning. As one example of gene–trait interactions, DeYoung et al. (2006) found that externalizing (Krueger et al., 2007) was negatively correlated with IQ but only for individuals lacking the 7-repeat allele of the dopamine D4 receptor gene. This work suggests that considering the val 158met polymorphism in context of a trait such as externalizing might help better explain the effect of the COMT gene on behavior and brain activity. As externalizing has been related to dopamine activity (Chambers and Potenza, 2003; Pihl and Peterson, 1995; Solanto, 1998) and cognitive function (Andersson et al., 1998; Elkins et al., 1997; Koenen et al., 2006; Kuntsi et al., 2004; Séguin et al., 2004), the behavioral and neural effects of a particular val 158met allele may be systematically different among individuals with different levels of externalizing. The effects of val 158met on neural efficiency are generally assumed to be present throughout the population,

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but they might be particularly pronounced in individuals high in externalizing, who show a general tendency toward problems of disinhibition. Thus, the cognitive and neural inefficiencies in val/val individuals might be reduced for those low in externalizing but might be worse for those high in externalizing. In the present study, we investigated whether the effect of the val 158met polymorphism on prefrontal functioning varies with an individual's level of externalizing behavior. Given previous reports of a gender specific relation between variants in the COMT gene and clinical disorders related to externalizing (Biederman et al., 2008; Cheuk and Wong, 2006), we selectively recruited a male sample. We assessed externalizing using several self- and peer-report questionnaires. We then used functional magnetic resonance imaging (fMRI) to measure brain activity during the multi-source interference task (MSIT; Bush and Shin, 2006), which imposes low or high demands on cognitive control (control or interference trials, respectively). The MSIT has been shown to produce robust reaction time interference effects and robust activation of the cingulate–frontal– parietal cognitive attention network for interference relative to control trials, making it an ideal task to probe prefrontal functioning (Bush and Shin, 2006). We hypothesized a main effect of slowed reaction times and increased cognitive-interference-related brain activity for COMT val/val genotype individuals in the anterior cingulate and pre-SMA, in line with previous work on this polymorphism during attentional control and response inhibition (Blasi et al., 2005; Congdon et al., 2009; Kraemer et al., 2007). Extending these previous investigations, our critical hypothesis was an interaction effect between the val 158met polymorphism and externalizing, which would demonstrate that psychological traits might moderate genetic effects on brain activity and cognition. Specifically, participants with high externalizing behavior should show slower reaction times and increased interference related brain activity relative to participants with low externalizing behavior but only if these participants had the val/val genotype (i.e., lower tonic levels of prefrontal dopamine). Materials and methods Participants One hundred and thirteen participants were recruited from Yale University and the surrounding community. Note that these 113 participants were selected to complete the fMRI portion of the study from a larger sample of 214 participants who only completed behavioral testing, genotyping, and questionnaires. No criterion other than availability was used to select this subsample. The complete (no fMRI) sample (N = 214) has been reported elsewhere (DeYoung et al., 2011). All participants provided written consent and received financial compensation for their participation. From the 113 participants, nine participants were excluded, including three due to excessive motion (greater than 1.25 mm between contiguous time-points) during the fMRI scanning session, three due to more than five no response trials, and three due to unsuccessful genotyping. No participant reported a history of neurological or psychiatric disorder. The remaining 104 participants were all right-handed Caucasian males with a mean age of 23.9 ± 5.2 years (Table 2). The experimental protocol was approved by the Yale University Human Investigation Committee. Procedure Participants attended two separate sessions on different days. In the first session, participants (1) responded to questionnaires related to externalizing behavior, including the self- and peer-report UPPS Impulsivity Scales (Whiteside and Lynam 2001), Buss Aggression Questionnaire (BAQ; Buss and Perry 1992), Drug Screening Test,

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Michigan Alcohol Screening Test, and Hyperactivity/Impulsivity ADHD symptoms (childhood and adult), (2) completed the Vocabulary and Performance subscales of the Wechsler Abbreviated Scale of Intelligence (Wechsler, 1997), (3) provided saliva samples for genotyping, and (4) received packets of questionnaires (including UPSS and BAQ) to give to two peers (people who knew the participant well) to complete and return by mail. In the second session, participants completed the MSIT while having their brain activity assessed with fMRI. The duration between the two sessions ranged from 1 to 108 weeks (mean 30.3 ± 27.2 weeks). Externalizing behavior For scales with both self- and peer-report ratings, we first averaged together all available ratings for each participant. We then indexed externalizing behavior as the first unrotated factor (Table 1) extracted from a maximum likelihood factor analysis of the scales listed above. Eigenvalues indicated a large first factor, consistent with work showing that much of the variance in different specific externalizing behaviors is attributable to a single latent externalizing factor (Krueger et al., 2007); the first five eigenvalues were 3.54, 1.51, 1.41, 1.08, and .95. Each individual's externalizing factor score was used for behavioral and fMRI analyses. For each genotype group, the average factor score and the average score for each contributing questionnaire is given in Table 1. The factor score for externalizing and the scores for each questionnaire did not significantly differentiate the COMT genotype groups (all ps b 0.05). Genotyping Participants filled an Oragene™ sample-collecting vial with saliva. The val 158met single-nucleotide polymorphism was genotyped using the ABI TaqMan platform (Applied Biosystems, Foster City, California, USA) with a success rate of 97.1%. In addition, the genotype distribution for our val 158met sample did not significantly differ from Hardy– Weinberg equilibrium (χ 2 = 0.32, df = 1, p = 0.57). Behavioral task The MSIT is a test of inhibitory cognitive control (Bush and Shin, 2006). During the task, participants viewed sets of three adjacent digits (drawn from 1, 2, 3, or 0) that appeared in the center of the screen (e.g., 100 or 323) shown in white against a black background. Stimulus duration was 1.75 s and the inter-trial interval was 0.25 s for 50% of trials, 2.25 s for 25% of trials, and 4.25 s for 25% of trials. Participants were instructed to use the index, middle, or ring fingers of their right hand to press buttons 1, 2, or 3, respectively, which corresponded to the number that was different from the other two numbers on the screen. During congruent (control) trials (n = 72), the target number (e.g., 1) always matched its position on the button press (e.g., 1st position), thus facilitating performance, while the

other two numbers were always 0 and did not correspond to any response. During interference trials (n = 24), the target number (e.g., 2) never matched its position on the button press (e.g., 2nd position) and the adjacent numbers were also potential targets (e.g., 1 or 3) and were sometimes larger in size, thus making them more salient and hence hindering performance. Stimuli were presented using PsyScope (Cohen et al., 1993) on a Macintosh laptop. fMRI data acquisition Imaging data were collected using a 3.0-Tesla Siemens Trio scanner at the Yale Magnetic Resonance Research Center. For each participant, a high-resolution T1-weighted anatomical image (MPRAGE, time repetition [TR] = 2500 ms; time echo [TE] = 3.34 ms; inversion time = 1100 ms; flip angle = 7°; slices = 256, voxel size = 1 × 1 × 1 mm) and 180 contiguous functional volumes (gradient-echo EPI sequence; TR = 2000 ms; TE = 25 ms; field of view [FOV] = 240 cm; flip angle = 80°; voxel size = 3.75 × 3.75 × 4 mm) were acquired. Participants viewed stimuli projected onto a screen through a mirror mounted on the head coil. Responses were made using fiber-optic response buttons, using the fingers of the right hand. Data analysis Behavior All behavioral analyses on MSIT task performance were conducted in the R statistical package, version 2.11. We conducted a three-way repeated measures analysis of variance (ANOVA) on mean RT and standard deviation of RT. We included genotype as a factor, externalizing behavior as a continuous measure, trial type as a repeated measure, and age and IQ as additional confounding covariates. Because accuracy rates were very high (~ 100% for control trials and ~96% for interference trials) and were not normally distributed according to the Shapiro–Wilk test of normality, we employed robust regression to test the relation between accuracy and the same covariates as in the previous ANOVA. fMRI preprocessing Functional MRI data analyses were performed using FSL, version 4.1.2 (FMRIB's Software Library: http://www.fmrib.ox.ac.uk/fsl/). The first three volumes (6 s) were discarded to allow for T1 equilibration. Preprocessing included motion correction to the mean image (Jenkinson et al., 2002), spatial smoothing (Gaussian kernel FWHM = 6 mm), mean-based intensity normalization of all volumes by the same factor, and high-pass temporal filtering (0.02 Hz). Functional data were registered to a common stereotaxic space (Montreal Neurological Institute 152-brain template [MNI152]). The mean functional image was first linearly registered using FLIRT to a highresolution T1 image allowing for 6 degrees of freedom. The high-

Table 1 Externalizing behavior was indexed as the first unrotated factor from the list of measures in the first column (after the second row) below. The second column indicates each measures corresponding loading on the first unrotated factor (a higher value indicates a greater contribution of that scale to our measure of externalizing). The third to fifth columns provide the mean and standard deviation for the measure indicated in the first column across participants in each specified COMT genotype group. Measure

Factor loading

val/val

met/val

met/met

Externalizing factor score Urgency—UPSS Anger—BAQ Hostility—BAQ Verbal aggression—BAQ Physical aggression—BAQ ADHD—adult hyperactivity MAST ADHD—child hyperactivity Lack of perseverance—UPSS DAST Lack of premeditation—UPSS Sensation seeking—UPSS

– 0.80 0.76 0.59 0.58 0.49 0.43 0.39 0.36 0.32 0.31 0.26 0.02

− 0.05 ± 1.08 − 0.07 ± 0.88 0.10 ± 0.97 2.63 ± 0.76 0.08 ± 1.04 0.14 ± 1.08 4.28 ± 2.54 0.27 ± 0.27 6.68 ± 4.20 2.38 ± 0.83 0.29 ± 0.30 − 0.15 ± 0.89 − 0.01 ± 0.94

0.06 ± 1.06 0.13 ± 0.98 0.12 ± 0.93 2.63 ± 0.92 − 0.06 ± 0.98 0.08 ± 1.04 5.18 ± 3.49 0.39 ± 0.31 7.39 ± 4.44 2.43 ± 0.66 0.36 ± 0.28 − 0.16 ± 0.86 − 0.09 ± 0.96

0.05 ± 0.83 0.14 ± 1.07 0.00 ± 0.86 2.74 ± 0.95 − 0.03 ± 0.76 − 0.10 ± 0.82 4.80 ± 2.73 0.39 ± 0.26 7.67 ± 3.59 2.55 ± 0.64 0.36 ± 0.27 0.16 ± 0.89 0.06 ± 0.98

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resolution T1 image was registered to the MNI152 brain with a 2 mm 3 resolution allowing for 12 degrees of freedom (Jenkinson and Smith, 2001; Jenkinson et al., 2002) and further refined with nonlinear warping using ART (Ardekani et al., 1995; 2005). fMRI statistical analysis Individual subject analyses were carried out in functional space using the General Linear Model (GLM) as implemented in FILM (FMRIB's Improved Linear Model) with local autocorrelation correction (Woolrich et al., 2001). Each participant's model included regressors for correct control trials, correct interference trials, incorrect control trials, incorrect interference trials, no-response trials, reaction-time orthogonalized to the timing of control and interference trials (i.e., reaction-time independent of trial type), and 6 motion parameters. We high-pass filtered (0.02 Hz) each regressor as was done with the functional data and included the temporal derivative of each regressor except for RT and no-response trials. We then convolved each input in the model except for the motion parameters with a double-gamma hemodynamic response function (HRF), which is a mixture of two Gamma functions—a standard positive function and a delayed, inverted Gamma that models the late undershoot of the HRF. Statistical maps were generated through contrasts of these regressors focusing only on correct trials for control trials > fixation, interference trials > fixation, all trials > fixation, interference > control trials, as well as the inverse of these four comparisons. In the present study, we will focus on only two of these contrasts: interference > control trials and control > interference trials. The resulting statistical maps were transformed from functional to standard space as detailed previously allowing for comparison across individuals. A group analysis was then carried out in standard space using a mixed-effects model as implemented in FLAME (FMRIB's Local Analysis of Mixed-Effects) (Beckmann et al., 2003). In our model, we included three regressors of interest: val 158met genotype modeling load effects of the val or met allele with three distinct values for each genotype group, externalizing, and the interaction between val 158met and externalizing, as well as two covariates of noninterest: age and IQ. In a separate analysis, we controlled for drug and alcohol use to assess if substance abuse was confounded with externalizing; the results were nearly identical in this separate analysis, thus have not been shown. To further understand differences between high and low externalizing, we ran an additional model with the same covariates of non-interest but with three different regressors to represent high externalizing val/val individuals, low externalizing val/val individuals, high externalizing met/met individuals, and low externalizing val/val individuals (see Fig. 6). All the resulting group-level statistical images were thresholded at Z > 2.3 and corrected for multiple comparisons at a cluster significance threshold of p b 0.05 using Gaussian random field theory (GRF). Peaks of brain activity were determined using AFNI's 3dmaxima with a minimum distance of 10 voxels (20 mm) between peaks. To further assess the robustness of our results, we also visualized the group-level statistical images at different voxel-level thresholds (Z > 2, 2.6, 2.8, or 3.3) given a fixed cluster-level correction of p b 0.05 (Supplementary Fig. 4). To explore any significant whole-brain results, we conducted additional post-hoc analyses. These generally involved calculating the mean parameter estimates from a region of interest (ROI; e.g.,

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using peak activations) for interference relative to control trials and then conducting an analysis of variance (ANOVA) or t-test with a regressor of interest (e.g., genotype or trial type). It should be noted that given the use of a significance test in selecting these ROIs, any subsequent significance testing on results derived from these ROIs is biased by non-independence (Kriegeskorte et al., 2009). Thus, we use results from any post-hoc ROI analyses only to characterize and understand the gene–trait interaction effect on cognitive-control related brain activity, not to determine the significance of the effect. Results Behavioral data Table 2 presents descriptive statistics for the three behavioral measures collected in the fMRI scanner: accuracy, mean RT, and standard deviation of RT. For accuracy (percent correct), we observed significant deviations from normality for control (W = 0.2, p b 0.001) and interference (W = 0.8, p b 0.001) trials. Therefore, we used robust regression and only found a significant main effect of trial type on accuracy, F(1,98) = 18.8, p b 0.0001. Participants were more accurate during control (M = 100%) relative to interference (M = 96.3%) trials. For mean RT, we found a significant main effect of genotype (F[2,96] = 4.3, p b 0.05) and trial type (F[1,98] = 1643.8, p b 0.001). Participants with the met/met genotype (M = 764.3 ms) were on average faster at responding than those with either the val/met genotype (M = 820.7 ms) or val/val genotype (M = 824.1 ms) (Fig. 1). All participants were also on average faster at responding during control (M = 632.6 ms) relative to interference (M = 977.9 ms) trials. Since the genotype groups differed in mean RT, we included mean RT as a covariate of non-interest when we assessed behavioral differences for the standard deviation of RT. For standard deviation of RT, we only found a significant main effect of trial type (F[1,98] = 236.8, p b 0.001). fMRI data For each individual, we identified brain regions in which BOLD activity differed between interference and control trials, independent of head motion, errors, and response time (RT). Task related activity in the cingulate–frontal–parietal cognitive attention (CFP) network As expected across all participants we found significant activations for interference greater than control trials in the cingulate–frontal– parietal cognitive attention network and significant deactivations in the default-mode network (Fig. 2; Supplementary Table 1 for peaks). No significant main effects of COMT val 158met genotype or externalizing We did not observe any significant linear effects of genotype or externalizing for interference versus control trials. In an exploratory analysis, we noted effects at a more liberal voxel threshold of Z > 1.7 (GRF corrected; Supplementary Fig. 1, Supplementary Table 2). In particular, we observed a positive linear effect of the val/val genotype in the pre-SMA such that val/val individuals displayed increased

Table 2 Listed are the number of participants (N) in each genotype group (val158met) along with the percent (%) accuracy, mean reaction-time (RT) in milliseconds (ms), and standard deviation of RT (averaged across participants) for control or interference trials during the MSIT task. val158met

N

val/val met/val met/met

25 49 30

Accuracy (%)

Mean RT (ms)

Standard deviation RT (ms)

Control

Interference

Control

Interference

Control

Interference

100 100 100

96 ± 1 97 ± 1 95 ± 1

641 ± 17 653 ± 12 593 ± 13

1007 ± 29 988 ± 19 936 ± 22

112 ± 7 121 ± 7 103 ± 8

187 ± 8 177 ± 5 179 ± 10

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Fig. 1. Mean ± standard error reaction time (RT) during the MSIT task. Val/val individuals had significantly slower mean RTs relative to met/met individuals during control and interference trials (two sample t-test, all ps b 0.05). Met/val individuals also had significantly slower mean RTs relative to met/met individuals during control trials (two sample t-test, p b 0.05).

conflict-related brain activity relative to met/val individuals. The met/ val group displayed activity levels that were intermediate between the two homozygous genotype groups. Nearly identical results for the main effect of genotype and externalizing were found if the interaction term was excluded from the regression model. Externalizing moderates the association of COMT with brain activity during cognitive interference Of most interest, we found a significant interaction between COMT genotype and externalizing behavior across a wide array of brain regions for interference versus control trials, including the dorsal anterior cingulate, precuneus, superior medial frontal gyrus, and lateral prefrontal cortex (Fig. 3a; Table 3). The interaction effect was predominately in or around areas that were significantly activated across participants for interference relative to control trials (Fig. 3b). To understand the effect of externalizing for each of the genotype groups, we computed mean parameter estimates for each individual from voxels found significant in the previous interaction effect (i.e., interference > control trials). For interference relative to control trials, the relation between mean parameter estimates and externalizing behavior was strongly positive for val/val individuals (r = 0.79), weakly negative for met/val individuals (r = −0.11), and strongly negative for met/met individuals (r = −0.54) (Fig. 4a). For interference trials relative to fixation, the same relation for each genotype group between mean parameter estimates and externalizing behavior was found, namely a positive relation for val/val individuals (r = 0.53), a weakly negative relation for met/val individuals (r =

Fig. 2. Thresholded maps (Z > 2.3, GRF corrected p b 0.05) of BOLD activations (red– yellow) and deactivations (blue–cyan) for interference greater than control trials.

−0.11), and a negative relation for met/met individuals (r = −0.40) (Fig. 4b). In contrast, for control trials relative to fixation, we observed opposite albeit weaker effects. That is, the relation between mean parameter estimates and externalizing behavior was negative for val/val individuals (r = −0.37), negligible for met/val individuals (r = −0.03), and positive for met/met individuals (r = 0.20) (Fig. 4c). To summarize, in regions showing an interaction effect, individuals with the val/val genotype showed increased conflictrelated activity if they were high in externalizing but decreased conflict-related activity if they were low in externalizing. Individuals with the met/met genotype demonstrated the reverse relationship— that is, decreased conflict-related activity with high externalizing but increased conflict-related activity with low externalizing. The inverse brain–behavior effect observed for met/met versus val/val individuals appeared to be strongest in different brain regions for each homozygous genotype group (Supplementary Fig. 2). The met/met group showed a stronger brain–behavior relationship (i.e., relationship between externalizing and conflict-related brain activity) in mid/posterior cingulate regions and in the superior medial frontal gyrus. The val/val group showed a stronger brain–behavior relationship in the dorsal ACC, lateral PFC, and subcortical areas. Both groups show regions of overlap in lateral parietal, mid-cingulate, and subcortical areas. As the directional relation between brain activity and externalizing levels can be driven by increases or decreases in brain activity between conditions, we examined activity levels separately for control and interference trials within val/val and met/met individuals (Fig. 5). For each individual, we computed mean parameter estimates from four spherical ROIs (4 mm radius) along the medial wall that were among the peaks of activity in the previous genotype by trait interaction (Table 3) and that displayed differences in brain–behavior relationships between met/met and val/val individuals (Supplementary Fig. 2). We also examined other ROIs in lateral cortical and subcortical areas that showed similar results as the ROIs along the medial wall (Supplementary Fig. 3). For each ROI, in order to visualize the interaction effect, we plotted mean parameter estimates across val/val and met/met individuals with high externalizing (top 40% of scores) and low externalizing (bottom 40% of scores). The first three ROIs were all near the cingulate cortex. In cingulate motor areas, high externalizing val/val individuals and low externalizing met/ met individuals showed slight increases in conflict related activity, whereas low externalizing val/val individuals and high externalizing met/met individuals both showed large conflict related deactivations (Fig. 5a). In the dorsal anterior cingulate, all participants showed increased conflict related activity, although high externalizing val/val and low externalizing met/met individuals showed larger differences between interference and control trials (Fig. 5b). In the paracingulate cortex, high externalizing val/val individuals and low externalizing met/met individuals showed large increases in cognitive conflict related activity in comparison to the other participant groups. For the last ROI in the superior medial frontal gyrus, high externalizing val/ val and low externalizing met/met individuals showed deactivation in this region during both control and interference trials, unlike low externalizing val/val individuals and high externalizing met/met who selectively deactivate this default-mode region during interference relative to control trials (Fig. 5d). Given that both high externalizing val/val and low externalizing met/met individuals showed increases in conflict-related activity, we sought to examine possible differences between these two subgroups and the two subgroups showing decreased conflict-related activity (i.e., low externalizing val/val and high externalizing met/ met individuals). High externalizing relative to low externalizing val/val individuals showed significant increases in conflict-related activity within the dorsal ACC whereas low externalizing relative to high externalizing met/met individuals showed significant increases in conflict-related activity within more posterior portions of the

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Fig. 3. Brain regions showing a significant interaction effect between the COMT val158met genotype and externalizing behavior (externalizing). (a) Z-score thresholded maps of BOLD signal that demonstrated a significant interaction (Z > 2.3, GRF corrected p b 0.05). (b) Direction of BOLD activity for interference relative to control trials on average across all participants (from Fig. 2) within regions showing a significant interaction effect. Across all participants, the regions in red show positive BOLD differences between interference and control trials (i.e., activation), regions in green show variable or non-significant BOLD differences in activity between interference and control trials, and regions in blue show negative BOLD differences between interference and control trials (i.e., deactivation).

brain including pre-motor areas, mid/posterior cingulate, and precuneus (Fig. 6). The reverse contrast, low externalizing relative to high externalizing val/val participants and high externalizing relative to

low externalizing met/met participants did not show significant patterns of activity. Discussion

Table 3 Listed are the peaks of brain activity for the interaction between COMT genotype and externalizing behavior during interference relative to control trials. Peaks were found using AFNI's 3dmaxima and setting a minimum distance of 20 mm between peaks. Abbreviations: H, hemisphere; R, right; L, left; B, bilateral; BA = Brodmann Area. Brain region

Cerebellum, crus II Parahippocampal gyrus Cerebellum, vermis crus II Anterior cingulate cortex Superior frontal gyrus Inferior frontal gyrus, pars orbitalis Cingulate motor area Middle frontal gyrus Postcentral gyrus Middle frontal gyrus Globus pallidus Anterior cingulate cortex Cerebellum, VI Fusiform gyrus Superior frontal gyrus Thalamus Caudate Cuneus Middle occipital gyrus Fusiform gyrus Insula Fusiform gyrus Middle frontal gyrus Anterior cingulate cortex Anterior cingulate cortex Cerebellum, crus I Superior frontal gyrus Middle frontal gyrus Postcentral gyrus Supramarginal gyrus Precuneus

H

R L R B B L L L L R L L L R R R R R R L L L L L L L L R L L L

BA

30 24 9 47 31 10 2 8 24 37 8

18 19 37 13 19 10 32 32 10 3 40 7

Coordinates

In the current study, we took a novel approach to understanding gene–brain–behavior relationships. We investigated the effect of a gene–trait interaction, between the val 158met polymorphism and

Z-score

x

y

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24 − 14 2 0 0 − 40 −2 − 32 − 54 26 − 20 −6 − 34 48 8 6 4 2 40 − 44 − 42 − 48 − 36 −8 −2 − 46 − 12 38 − 40 − 60 −6

− 80 − 36 − 74 −6 56 30 − 26 58 − 24 42 −2 34 − 58 − 52 40 − 16 12 − 100 − 94 − 38 − 28 − 76 56 16 36 − 60 64 30 − 22 − 46 − 54

− 36 −4 − 34 36 34 −4 46 −6 34 38 −6 4 − 22 − 38 52 −6 2 0 −2 − 18 14 − 22 14 28 28 − 38 20 24 50 26 58

4.27 4.22 3.92 3.86 3.76 3.72 3.63 3.63 3.63 3.62 3.59 3.58 3.56 3.55 3.53 3.50 3.46 3.45 3.28 3.24 3.21 3.20 3.18 3.12 3.08 2.99 2.93 2.70 2.67 2.66 2.66

Fig. 4. Each point represents mean parameter estimates for a participant across regions showing a significant genotype by trait interaction effect (from Fig. 3a) for (a) interference relative to control trials, (b) interference trials relative to fixation, and (c) control trials relative to fixation.

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Fig. 5. Mean parameter estimates across four subgroups (low externalizing val/val, high externalizing val/val, low externalizing met/met, and high externalizing met/met individuals) for control > fixation and interference > fixation contrasts from four spherical ROIs with radius 4 mm: (a) cingulate motor area [x = − 2, y = − 26, z = 46], (b) dorsal ACC [x = − 8, y = 16, z = 28], (c) paracingulate [x = − 2, y = 36, z = 28], and (d) superior medial frontal gyrus [x = − 12, y = 64, z = 20].

externalizing behavior, on cognitive conflict-related brain activity. The interaction between val 158met and externalizing was predominately in regions that showed increased activity during interference relative to control trials such as in the ACC, LPFC, and LPC (Fig. 3). The gene × trait interaction was robust to varying voxel-level thresholds (Supplementary Fig. 4) and varying the specific subjects included in the analysis (Supplementary Fig. 5). On average across regions demonstrating an interaction effect, val/val individuals with high externalizing and also, unexpectedly, met/met individuals with low externalizing both displayed increases in conflict-related brain activity (Fig. 4). Within brain areas demonstrating an interaction effect, val/val individuals showed a positive relation between externalizing and brain activity primarily in the dorsal ACC, LPFC, and subcortical areas while met/met individuals showed a negative relation between externalizing and brain activity primarily in the superior MPFC, cingulate motor areas, and anterior precuneus (Fig. 6; Supplementary Fig. 2). These results suggest that other factors, as indexed with externalizing behavior, can moderate the effects of the val158met polymorphism on the efficiency of neural processing. For val/val individuals, low levels of externalizing may buffer the lower efficiency in cognitive control commonly observed with the val/val genotype. For

Fig. 6. Cognitive conflict-related brain activity (interference > control trials) for low externalizing met/met and high externalizing val/val individuals (Z > 2.3, GRF correct p b 0.05). In purple are brain regions where low externalizing met/met individuals displayed significant conflict-related activity that was also significantly greater than activity for high externalizing met/met individuals. In orange are brain regions where high externalizing val/val individuals displayed significant conflict-related activity that was also significantly greater than activity for low externalizing val/val individuals.

met/met individuals, low levels of externalizing may require increased recruitment of neural resources to select the relevant response. We had also hypothesized a main effect of COMT genotype on cognitive conflict-related brain activity and behavior. Although we observed a significant genotype effect on mean reaction time (Fig. 1), we observed only marginally significant results in relation to brain activity. Specifically, val/val individuals showed increased BOLD signal in the pre-SMA relative to met/met individuals (Supplementary Fig. 1). The direction of our genotype effect, though not significant, was in line with previous findings that suggest val/val individuals show increased PFC activity across cognitive control tasks to compensate for reduced prefrontal efficiency due to lower sustained levels of prefrontal DA (Mier et al., 2010; Winterer and Weinberger, 2004). It is also interesting that this main effect and the gene–trait interaction were in the ACC/pre-SMA and more motor regions rather than the DLPFC, suggesting that our results reflect conflict at the response, rather than the representational, level. Relation of dopamine to conflict-related brain activity Our finding of a gene–trait interaction extends previous work that suggests alternate val 158met alleles can have different effects on brain activity and cognition depending on other factors that affect prefrontal and striatal dopamine levels (Bellgrove et al., 2004; Caldu et al., 2007; Congdon et al., 2009; Foltynie et al., 2004; Williams-Gray et al., 2008). In the present work, we considered the moderating influence of a psychological trait, externalizing, on the effects of the val 158met polymorphism. Externalizing behavior is suggested to have a positive relation with the sensitivity of striatal dopamine release (Buckholtz et al., 2010) and a negative relation with prefrontal dopamine levels due in part to inhibitory connections that project from the PFC to the striatum (Chambers and Potenza, 2003; Dalley et al., 2011; Frank and O'Reilly, 2006; Pattij and Vanderschuren, 2008; Pihl and Peterson, 1995). This relation between externalizing and DA levels is consistent with our observation of greater conflictrelated activity for val/val individuals with high relative to low externalizing in the dorsal anterior cingulate cortex (dACC), a region often involved in cognitive conflict resolution and error monitoring (Botvinick et al., 2004; Gray et al., 2005; Koski and Paus, 2000). As val/val individuals have lower levels of PFC DA that decreases the signal-to-noise for task-relevant responses and individuals with

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high externalizing have higher levels of striatal DA that increase automatic/pre-potent responses, val/val individuals with high externalizing could be more prone to errors during interference trials. Thus, the increased activity in the dACC for interference relative to control trials may reflect a compensatory response to maintain high levels of accuracy during the task. Such an interpretation suggests that certain factors related to low levels of externalizing may buffer the negative effects found in previous studies for val/val individuals when performing cognitive control tasks. Relation of serotonin to conflict-related brain activity Externalizing has been linked to other systems beyond dopamine, including serotonin (Dalley et al., 2011; Pattij and Vanderschuren, 2008). Externalizing-related differences in serotonin levels and sensitivity might be another mechanism for explaining our gene–trait interaction. Individuals with the met/met genotype and low levels of externalizing showed cognitive conflict-related brain activity in the sensorimotor precuneus, pre-motor cortex, and cingulate motor area. These three motor regions are all interconnected (Margulies et al., 2009) and play a prominent role in the selection of motor responses (Chen et al., 1995; Paus, 2001). These regions are also innervated by serotonin (5-hydroxytryptamine, 5-HT) in addition to DA neurons (Berger et al., 1986; 1988). Increased 5-HT transmission has been associated with reduced externalizing (Masaki et al., 2006; Soubrié, 2010; Winstanley, 2007), and 5-HT inhibits DA functioning (Daw et al., 2002; Spoont, 1992). As the pre-motor cortex and cingulate motor area project to subcortical regions such as the thalamus and basal ganglia (Margulies et al., 2009; Paus, 2001), higher levels of 5-HT in these cortical areas may inhibit subcortical DA activity. Thus, the increased conflict-related activity observed for low externalizing met/met individuals might reflect a release of inhibition on brain regions involved in motor responses. Given the inability of fMRI to distinguish between inhibitory and excitatory connections, investigating variants of 5-HT genes is needed to confirm these speculative conclusions. Activity in the default network A region in the superior MPFC was also part of the gene–trait interaction and showed decreases in task related activity. This region selectively deactivated only during interference trials for low externalizing val/val and high externalizing met/met individuals. High externalizing val/val and low externalizing met/met individuals, on the other hand, deactivated this region during both control and interference trials. Increased deactivation of the ‘default-mode’ network, which encompasses the superior MPFC, has been associated with decreases in attentional lapses and increases in performance of cognitive control tasks (Kelly et al., 2008; Weissman et al., 2006). The reduction of ongoing cortical activity during attention-demanding tasks is suggested to be one aspect of improving the neural signalto-noise for task relevant responses (Mitchell et al., 2009). The premature deactivation of the superior MPFC during control trials for high externalizing val/val and low externalizing met/met individuals may then reflect a preparatory response that increased the signal-tonoise for selecting the appropriate motor response. Relevance to psychopathology Our findings suggest that the neurophysiology of individuals with externalizing related psychopathology (e.g., conduct disorder or ADHD) may vary according to COMT genotype. Assuming that externalizing related disorders represent an extreme version of high levels of externalizing (as in our study), individuals with an externalizing related disorder would be protected, if they have the met allele, against deficits in task performance (Fig. 2) and brain activity

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(Fig. 3) during sustained attention. Such a distinction in physiology and behavior based on genetic variation may also help partition different types of externalizing disorders. That is, we may expect that individuals with the val/val versus met/met genotype are susceptible to particular types of externalizing disorders, which can be measured as differences in task-related brain activity (Fig. 6). Although we did not distinguish between different kinds of externalizing behavior, other studies have found such a relationship. For instance, DeYoung et al. (2010) found that the val/val genotype in the val 158met polymorphism was associated with conduct disorder whereas the met/met genotype was associated with ADHD. This susceptibility to different disorders would also fit with divergent effects of val 158met on cognitive versus affective phenotypes; individuals with the val allele display an advantage during emotional tasks, whereas those with the met allele display an advantage during executive control tasks (Mier et al., 2010; Stein et al., 2006). Future work examining task-related brain activity and performance in relation to different externalizing disorders might elucidate the mechanisms that give rise to such dysfunctional behavior. Furthermore, as indicated by our findings, an easy and important step in this direction will be the inclusion of individual difference measures such as externalizing behavior in other genetics and fMRI studies. Lack of behavioral effects for gene–trait interaction In the current study, we focused on the significant relation between a gene–trait interaction and brain activity. We did not, however, observe the hypothesized gene–trait interaction on mean RT, although we did observe a significant main effect of genotype on mean RT. One previously suggested possibility is that larger sample sizes are required to reliably detect effects of a single gene upon behavioral measures, whereas smaller sample sizes suffice for detecting effects of the same gene upon neural activity (Hariri and Weinberger, 2003). Another related possibility is that our task was not sufficiently difficult to produce observable differences in behavior (indeed accuracy measures were at ceiling) between individuals with different val 158met and externalizing profiles. Thus, future studies using larger sample sizes and more difficult tasks will be necessary to firmly extend our neuroimaging findings to behavior. Reproducibility and open science Although we have used a large sample for a neuroimaging study (N = 104), reproducibility remains a concern (Glahn et al., 2007; Meyer-Lindenberg and Weinberger, 2006). A sample size of 60–70 has previously been recommended for neuroimaging studies of single nucleotide polymorphisms such as for the COMT (Mier et al., 2010) and serotonin transporter (Munafò et al., 2008) genotypes. Since we examined an interaction effect (gene × trait), an even larger sample size might be required to detect an effect. Given the cost and timeconsuming nature of replication with MRI data, open data sharing offers an immediate and feasible approach to addressing issues of small sample sizes and spurious results. The ability to pool data across samples reduces the burden on any individual laboratory and allows the creation of large datasets with sufficient power to both discover and confirm gene–brain–behavior relationships. In addition, such large datasets allow for novel exploratory analyses such as genome-wide associations in context of task-based neuroimaging (Hodgkinson et al., 2010). With the aim of fostering open science and future replication, we will make the data used in the current study publicly available at www.openfmri.org. Conclusion We found a significant gene–trait interaction predicting neural activity during a cognitive conflict task. We used the psychological trait

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of externalizing as a broad index of other genetic, neural, and environmental factors that may influence the phenotypic effects of the COMT val 158met polymorphism. The typical association of the val/ val genotype with neural inefficiency was found only for subjects with high externalizing, illustrating the fact that specific genetic effects might not be as consistent in the population as is often assumed. High externalizing val/val and low externalizing met/met individuals displayed increased conflict-related BOLD activity especially in the cingulate cortex relative to low externalizing val/val and high externalizing met/met individuals. Presumably, the increased BOLD activity reflects increases in underlying neural activity as a result of lower efficiency in cortical functioning. To further clarify the relation between the COMT gene and externalizing behavior upon neural functioning, future studies should also consider factors affecting baseline levels of prefrontal functioning. Including psychological traits as an additional factor in neuroimaging genetics studies is a new avenue that may provide more comprehensive understanding of the phenotypic effects for a broad array of genetic variations beyond that of COMT. Funding This work was supported by the National Institute of Mental Health (F32 MH077382 to C.G.D.), the National Science Foundation (DRL 0644131 to J.R.G.), and a Natural Sciences and Engineering Research Council of Canada Post-Graduate Scholarship (to Z.S.). Conflict of interest None declared. Supplementary materials related to this article can be found online at doi:10.1016/j.neuroimage.2012.01.097 Acknowledgments The authors would like to thank Dr. Ranjani Prabhakaran for her assistance in the preparation of the manuscript. References Aalto, S., Brück, A., Laine, M., Nagren, K., Rinne, J.O., 2005. Frontal and temporal dopamine release during working memory and attention tasks in healthy humans: a positron emission tomography study using the high-affinity dopamine D2 receptor ligand [11C]FLB 457. J. Neurosci. 25, 2471–2477. Andersson, H.W., Sonnander, K., Sommerfelt, K., 1998. Gender and its contribution to the prediction of cognitive abilities at 5 years. Scand. J. Psychol. 39, 267–274. Ardekani, B.A., Braun, M., Hutton, B.F., Kanno, I., Iida, H., 1995. A fully automatic multimodality image registration algorithm. J. Comput. Assist. Tomogr. 19, 615–623. Ardekani, B., Guckemus, S., Bachman, A., Hoptman, M., Wojtaszek, M., Nierenberg, J., 2005. Quantitative comparison of algorithms for inter-subject registration of 3D volumetric brain MRI scans. J. Neurosci. Methods 142, 67–76. Barnett, J.H., Jones, P.B., Robbins, T.W., Müller, U., 2007. Effects of the catechol-Omethyltransferase Val158Met polymorphism on executive function: a metaanalysis of the Wisconsin Card Sort Test in schizophrenia and healthy controls. Mol. Psychiatry 12, 502–509. Barnett, J.H., Scoriels, L., Munafò, M.R., 2008. Meta-analysis of the cognitive effects of the catechol-O-methyltransferase gene Val158/108Met polymorphism. Biol. Psychiatry 64, 137–144. Beckmann, C., Jenkinson, M., Smith, S., 2003. General multilevel linear modeling for group analysis in FMRI. NeuroImage 20, 1052–1063. Bellgrove, M.A., Dockree, P.M., Aimola, L., Robertson, I.H., 2004. Attenuation of spatial attentional asymmetries with poor sustained attention. Neuroreport 15, 1065–1069. Berger, B., Trottier, S., Gaspar, P., Verney, C., Alvarez, C., 1986. Major dopamine innervation of the cortical motor areas in the cynomolgus monkey. A radioautographic study with comparative assessment of serotonergic afferents. Neurosci. Lett. 72, 121–127. Berger, B., Trottier, S., Verney, C., Gaspar, P., Alvarez, C., 1988. Regional and laminar distribution of the dopamine and serotonin innervation in the macaque cerebral cortex: a radioautographic study. J. Comp. Neurol. 273, 99–119. Bertolino, A., Caforio, G., Blasi, G., De Candia, M., Latorre, V., Petruzzella, V., Altamura, M., Nappi, G., Papa, S., Callicott, J., Mattay, V., Bellomo, A., Scarabino, T., Weinberger, D., Nardini, M., 2004. Interaction of COMT Val(108/158) met genotype

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