Cortical thickness correlates with impulsiveness in healthy adults

NeuroImage 59 (2012) 824–830

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NeuroImage j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / y n i m g

Cortical thickness correlates with impulsiveness in healthy adults Christina Schilling a, b,⁎, 1, Simone Kühn a, c, 1, Alexander Romanowski a, Florian Schubert d, Norbert Kathmann b, Jürgen Gallinat a a

Department of Psychiatry and Psychotherapy, Charité University Medicine Campus Mitte, St. Hedwig Krankenhaus, Große Hamburger Str. 5-11, 10115 Berlin, Germany Department of Psychology, Humboldt-Universität zu Berlin, Rudower Chaussee 18, 12489 Berlin, Germany Department of Experimental Psychology and Ghent Institute for Functional and Metabolic Imaging, Henri Dunantlaan 2, 9000 Ghent, Belgium d Physikalisch-Technische Bundesanstalt, Abbestraße 2, 10587 Berlin, Germany b c

a r t i c l e

i n f o

Article history: Received 4 January 2011 Revised 18 July 2011 Accepted 19 July 2011 Available online 30 July 2011 Keywords: Impulsiveness Attention Structural correlate Cortical thickness Middle frontal cortex Healthy adult

a b s t r a c t Background: Impulsiveness is a central domain of human personality and of relevance for the development of substance use and certain psychiatric disorders. This study investigates whether there are overlapping as well as distinct structural cerebral correlates of attentional, motor and nonplanning impulsiveness in healthy adults. Methods: High-resolution magnetic resonance scans were acquired in 32 healthy adults to model the gray– white and gray–cerebrospinal fluid borders for each individual cortex and to compute the distance of these surfaces as a measure of cortical thickness (CT). Associations between CT and the dimensions of impulsiveness (Barratt-Impulsiveness-Scale 11, BIS) were identified in entire cortex analyses. Results: We observed a significant negative correlation between left middle frontal gyrus (MFG) CT and the attention BIS score (FDR p b .05), motor, nonplanning and total BIS score (each p b 0.001 uncorrected). In addition, CT of the orbitofrontal (OFC) and superior frontal gyrus (SFG) were inversely correlated (p b 0.001 uncorrected) with BIS total and motor score. Among other negative associations only one positive correlation (right inferior temporal with nonplanning score, p b 0.001 uncorrected) was found. Conclusions: The MFG is crucial for top-down control, executive and attentional processes. The MFG together with the OFC and SFG appears to be part of brain structures, which have previously been shown to mediate behavioral inhibition, well-planned action and attention, which are core facets of impulsiveness as measured with the Barratt-Impulsiveness-Scale. © 2011 Elsevier Inc. All rights reserved.

Introduction Impulsiveness plays a central role in modulating various aspects of ordinary life and is currently targeted by various scientific disciplines, such as personality psychology, genetics, psychiatry and neuroimaging. Impulsiveness has been described as a highly heritable personality facet (Seroczynski et al., 1999) that has been associated with eating behavior (Boschi et al., 2010), driving characteristics (Owsley et al., 2003) and the number of sexual partners (Flory et al., 2006). Besides other personality

Abbreviations: ADHD, Attention-deficit/hyperactivity disorder; BA, Brodman area; BIS, Barratt Impulsiveness Scale; CT, Cortical thickness; FDR, false discovery rate; fMRI, Functional magnetic resonance imaging; GM, Gray matter; IFC, Inferior frontal cortex; IFJ, Inferior frontal junction; IPC, Inferior parietal cortex; M.I.N.I., Mini-International Neuropsychiatric Interview; MFG, Middle frontal gyrus; OFC, Orbital frontal cortex; PMC, Primary motor cortex; SFG, Superior frontal gyrus; STG, Superior temporal gyrus; STN, Subthalamic nucleus; VBM, Voxel based morphometry. ⁎ Corresponding author at: Charité Medicine Berlin, Clinic for Psychiatry and Psychotherapy, St. Hedwig Krankenhaus, Große Hamburger Str. 5-11, 10115 Berlin, Germany. Fax: + 49 30 2311 2750. E-mail address: [email protected] (C. Schilling). 1 These authors contributed equally to this work. 1053-8119/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2011.07.058

aspects, it is in particular impulsiveness, which has been shown to be an important risk factor for a number of mental health problems (Martinotti et al., 2008). Impulsive symptoms have been described as a central symptom domain in major psychiatric disorders, for instance attention-deficit/hyperactivity disorder (ADHD; Solanto et al., 2009), substance abuse (Sher et al., 2000), borderline personality disorder (Berlin et al., 2005) and suicidal behavior (Dougherty et al., 2004). Impulsiveness has been described as a “predisposition toward rapid, unplanned reactions to internal or external stimuli without regard to the negative consequences of these reactions” (Moeller et al., 2001). It forms a core aspect of human personality (Barratt, 1994a; Cloninger, 1986) and becomes manifested in a multidimensional phenomenon. Although several sub-factors have been described in the literature, previous research on impulsiveness has primarily focused on disinhibition (Read et al., 2010). The differentiation of three subscales of impulsiveness (Patton et al., 1995) appears to dominate the recent literature (Congdon and Canli, 2008). Based on data collected from more than 700 participants Patton et al. (1995) described these distinct factors: attention (rapid shifts and impatience with complexity), motor (impetuous action) and nonplanning (lack of future orientation). The different dimensions of

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impulsiveness are assumed to have different neural substrates (Barratt, 1994b; Bechara and Damasio, 2005). The question of which neurobiological substrates underlie impulsiveness has stimulated a growing body of neuroimaging research, mainly comprising functional magnetic resonance imaging (fMRI) studies. The narrow set of task paradigms applied in fMRI investigations, such as the Go/No-Go and stop-signal tasks, may account for their evidence being limited to inferences on aspects of motor impulsiveness (Congdon and Canli, 2008). Those studies have put forward particularly the right inferior frontal cortex (IFC) and the subthalamic nucleus (STN) as the neural correlates of behavioral inhibition (Congdon and Canli, 2008). The inferior frontal cortex is essential for controlling behavioral inhibition and the STN plays a central role in braking ongoing motor commands (Nambu et al., 2002). Interestingly, Asahi et al.(2004) have found a negative association between prefrontal activation during response inhibition (Go/No-Go task) and the subtrait motor impulsiveness (BIS 11) in a healthy sample. Similarly, Horn et al.(2003) have described an inverse correlation between trait impulsiveness (BIS sum score) and activation in the superior frontal gyrus (SFG) as well as the temporoparietal association area during response inhibition (Go/No-Go task) in healthy adults. Only a fraction of neuroimaging studies on impulsiveness has used structural imaging. Previous structural MRI studies on impulsivenessrelated disorders have yielded heterogeneous findings. For instance, while subscales of the Barratt-Impulsiveness-Scale (BIS) were reported to be positively correlated with gray matter (GM) of the bilateral orbital frontal cortex (OFC) in a group of non-psychotic psychiatric clients (Antonucci et al., 2006), for the same region a volume deficit was observed in depressive patients with impulsiveness related suicide attempts compared to controls (Monkul et al., 2007). Furthermore, in an ADHD sample Carmona et al.(2005) have shown GM volume deficits in frontostriatal regions, the cerebellum, left cingulate, bilateral parietal and temporal cortex compared to normal controls. Both negative (left middle frontal gyrus, MFG) and positive (posterior cingulate, ventral striatum) correlations with impulsiveness have been found in recently detoxified substance users (Schwartz et al., 2010). However, studying heterogeneous clinical samples causes extra variance associated with disorder and medication specific confounds, which might also account for the divergent results reported by structural MRI studies as discussed above. Structural imaging studies on impulsiveness' morphological correlates in healthy samples are rare. To our knowledge, up to now four studies have been published on associations with GM volume, out of those only three ones focused on trait impulsiveness: A positive correlation with GM volume in superior and middle frontal regions was shown (Gardini et al., 2009), while all other studies reported an inverse relationship. External behavioral ratings of impulsiveness were found to be negatively associated with right ventromedial prefrontal cortex volume (Boes et al., 2009). In particular, the bilateral orbitofrontal cortex GM volume was reported to be inversely correlated with the BIS total score (Matsuo et al., 2009). In sum, previous structural MRI studies on the neural substrates of impulsiveness in healthy samples have either focused on broader personality concepts than the specific facet of impulsiveness (Gardini et al., 2009) or have not covered subtraits of impulsiveness (Kumari et al., 2009). Even though Matsuo et al.(2009) focused on all three empirically based subtraits of impulsiveness (Patton et al., 1995), they could not identify any structural correlate of attentional impulsiveness. Furthermore, none of those investigations has applied alternative measures such as cortical thickness (CT), albeit recent structural studies emphasize the relevance of the thinning of the cortical surface for impulsiveness-related disorders such as ADHD (Almeida et al., 2010; Batty et al., 2010) and substance use (Lawyer et al., 2010; Lopez-Larson et al., 2011). The present study aimed to explore the structural substrates of facets of impulsiveness using surface-based morphology analyses applying FreeSurfer software. This fully automated tool has success-

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fully been applied in a wide range of empirical research (Greene and Killiany, 2010; McCauley et al., 2010; Raj et al., 2010). Particularly CT has been suggested to be a more sensitive parameter with a higher signal-to-noise ratio compared to voxel based morphometry (Choi et al., 2008; Dickerson et al., 2008; Hutton et al., 2009; Salat et al., 2004). Although, there is some evidence for frontal structures playing a decisive role in impulsiveness, previous results in healthy samples are heterogeneous and lack findings on CT. Hence, our aim was to identify structural correlates of impulsiveness by means of an exploratory approach using CT in entire cortex analyses in healthy adults. In addition, smoking status was controlled for, since it has been shown to affect brain morphology in otherwise healthy subjects (Gallinat et al., 2006; Kuhn et al., 2010). Operationalizing impulsiveness by means of the BIS scale provided the chance to reflect both the common trait, thought to underlie this construct (Barratt, 1994b; Cloninger, 1986), and its multidimensional character (attentional, nonplanning and motor impulsiveness). Material and methods Participants We recruited subjects by means of advertisements in local newspapers. Thirty-two adults (M 35.2 years, SD 10.5, 18 females) took part in the present study. A psychiatrist screened participants for exclusion criteria using the Mini-International Neuropsychiatric Interview (M.I.N.I.; Sheehan et al., 1998). Any axis I disorder including drug abuse or dependence led to exclusion from the study. In addition, any volunteer with a family history (first degree) of Axis I disorders or medical conditions was excluded. Besides smoking (11 current smokers, 5 former smokers) no significant substance use was reported. All procedures of this study were approved by the ethics committee of the Charité University Medicine Berlin. Prior to testing, all participants were provided with a complete description of the assessment. Subsequently their written consent was obtained. Impulsiveness measure Participants were asked to fill in the BIS 11, a self-report questionnaire designed to measure impulsiveness (German version, Preuss et al., 2008). Besides the total score of all 30 items three subscale scores can be computed, which have previously been identified by means of factor analysis: attentional, motor and nonplanning impulsiveness (Patton et al., 1995). All items are answered on a 4-point Likert-scale (Rarely/Never; Occasionally; Often; Almost always/Always). Higher scores signify higher impulsiveness. MR imaging Structural MRI was performed on a 3-Tesla scanner (MEDSPEC 30/100, Bruker Biospin, Ettlingen, Germany). T1-weighted images were acquired using MDEFT (modified driven equilibrium Fourier transform, with TE = 3.8 ms, TR = 20.53 ms; TI = 550 ms, nominal flip angle 30°; 128 contiguous slices, 1.5 mm thick; 1-mm in-plane (x–y) resolution). Image analysis Anatomical images were visually inspected for motion artifacts and gross structural abnormalities. CT was estimated from the T1weighted magnetic resonance images using FreeSurfer software, a set of automated tools for reconstruction of brain cortical surface (Fischl and Dale, 2000). Accordingly, the T1-weighted images were used to segment cerebral white matter (Dale et al., 1999) and to estimate the gray–white matter interface. This gray–white matter estimate was

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used as the starting point of a deformable surface algorithm searching for the pial surface. The entire cortex of each individual subject was visually controlled for inaccuracies in segmentation. All images passed quality control, none of the segmentations needed to be manually corrected or excluded. Local CT was measured based on the difference between the position of equivalent vertices in the pial and gray–white matter surfaces. The surface of the gray–white matter border was inflated and differences between subjects in the depth of gyri and sulci were normalized. Each subject's reconstructed brain was morphed and registered to an average spherical surface (Fischl et al., 1999). In order to obtain CT difference maps the data were smoothed on the surface using a Gaussian smoothing kernel with a full-width half maximum of 20 mm. Statistical analysis Entire cortex analyses were computed to explore the association of CT and impulsiveness (BIS total score; subscale scores) by means of multiple regressions. In order to control for nuisance effects arising from interindividual differences in nicotine consumption (Kuhn et al., 2010) the factor smoking (smokers versus nonsmokers) was included as a covariate of no interest besides age and gender (Good et al., 2001). We used a threshold of p b .001 uncorrected. Such a liberal approach has been shown to be sensitive for detecting subtle structural differences. In addition, we applied a multiple comparison correction by means of False Discovery Rate (FDR) thresholded at p b .05 to all four exploratory multiple regressions in order to decrease the likelihood of obtaining false positives relative to true positives (Genovese et al., 2002). Results Descriptive data on the impulsiveness measures The participants' BIS scores were normally distributed and comparable to both the total score reported for the original BIS version 11 (Patton et al., 1995) and scores of the subscales characterizing healthy adults of the same age (Matsuo et al., 2009). No significant differences between female and male participants were found for the BIS subscales (Table 1). Entire cortex analyses — correlation between GM thickness and impulsiveness The entire cortex analysis revealed inverse correlations (uncorrected) between BIS total score and GM thickness of the left frontal cortex, namely the middle frontal (Brodmann area, BA, 8, 46), orbitofrontal (BA 11/15) and superior frontal (BA 9) cortex (Table 2). The attention BIS score was inversely correlated with GM thickness of the bilateral middle frontal (left BA 8/44, right BA 9), bilateral superior temporal gyrus (STG; left BA 22/52, right BA 22/40), left superior fronto median cortex (BA 10) and left transverse occipital cortex (BA 19). The more conservative, multiple comparison corrected threshold confirmed the negative correlation for BIS attention and the cluster in the left MFG/inferior frontal junction (IFJ; BA 8/44; FDR Table 1 Descriptive data on the impulsiveness measures (BIS-11) (N = 32).

BIS BIS BIS BIS

total score attention score motor score nonplanning score

Total sample

Females

Males

M (SD)

M (SD)

M (SD)

60.81 14.61 21.97 24.84

61.72 14.89 21.72 25.67

59.54 14.23 22.31 23.69

Barratt-Impulsiveness-Scale 11 (BIS).

(7.74) (1.82) (3.50) (4.47)

(8.91) (1.91) (4.06) (4.83)

(5.85) (1.69) (2.66) (3.82)

Table 2 Summary of clusters associated with the impulsiveness measures (BIS-11) (N = 32).

Middle frontal gyrus Orbital frontal cortex Superior frontal gyrus Superior temporal gyrus Transverse occipital cortex Precentral cortex Inferior parietal cortex Inferior temporal cortex

Sum score

Attention score

Motor score

Nonplanning score

Left Left Left – – – – –

Bilateral – Left Bilateral Left – – –

Left Right Left – – Right Left –

Left Right – – – – – Right1

Barratt-Impulsiveness-Scale 11 (BIS); right1 indicates the only positive association, while all other correlations were negative.

p b .05; Fig. 1). None of the other correlations reported survived correction for multiple comparisons (Fig. 2). Furthermore, negative correlations (uncorrected) were found for the motor BIS score and GM thickness of the frontal cortex (left middle frontal BA 6/9, right orbitofrontal BA 11, left superior frontal BA 8), of the right precentral cortex (BA 4) and of the left inferior parietal cortex (BA 40). The nonplanning BIS score was both negatively (left MFG BA 8/44, 46; right orbitofrontal cortex BA 11) and positively (right inferior temporal cortex BA 38) correlated (uncorrected) with GM thickness. Discussion This is the first study on structural correlates of impulsiveness focusing on cortical thickness in a healthy sample. For the first time, structural neural correlates of all three dimensions of impulsiveness have been identified in healthy subjects (uncorrected results). In particular, neural correlates of attentional impulsiveness have been detected (FDR p b .05). Furthermore, so far none of the four MRI studies in healthy samples on this subject has controlled for smoking, even though there is strong evidence showing the effects of smoking on brain structures (Gallinat et al., 2006; Kuhn et al., 2010). Compared to voxel based morphometry (VBM), CT has been suggested to be a more sensitive parameter with a higher signal-to-noise ratio (Choi et al., 2008; Hutton et al., 2009; Salat et al., 2004). While VBM has been shown to be sensitive to a combination of changes in gray matter thickness, intensity, cortical surface area as well as cortical folding (Hutton et al., 2009; Voets et al., 2008), surface-based morphology analysis allows to assess the contributions of gray matter thinning independently of regional surface area (Dale et al., 1999; Fischl et al., 1999). Moreover, VBM is especially susceptible to the degree of smoothing, differences in registration and choice of normalization template (Bookstein, 2001; Jones et al., 2005). Since the days of Phineas Gage in the 19th century much literature has been concerned with identifying the neural basis of impulsiveness, while struggling with a wide range of methodological difficulties such as heterogeneous clinical samples. Currently, there are only a few comparable volumetric MRI studies on the neural correlates of impulsiveness, which are based on healthy samples unbiased by psychiatric diagnoses (Boes et al., 2009; Gardini et al., 2009; Kumari et al., 2009; Matsuo et al., 2009). Even though all of these studies have reported frontal clusters, none of the regions described earlier has yet been replicated except for the OFC. Thus our results extend previous findings, since we could confirm associations with frontal structures reported earlier in healthy samples (MFG; OFC; SFG). In addition, our findings provide preliminary evidence for neural correlates exclusively associated with respectively each of all three dimensions of impulsiveness (uncorrected; Patton et al., 1995). With regard to overlapping neural substrates of different aspects of impulsiveness the MFG, OFC and SFG appear to be of particular relevance. We found the BIS total score to be exclusively associated with these structures. Additionally, each of the MFG, OFC and SFG was

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P-Value

Fig. 1. BIS attention score inversely (blue) correlated with the left middle frontal gyrus/ inferior frontal junction (BA 8/ 44; FDR p b .05; N = 32); co-varied for age, gender and smoking.

correlated with, at least, two more BIS subscales in our study (Table 2). Furthermore, these areas comprise those ones, which were already reported as structural correlates of impulsiveness in prior MRI studies in healthy subjects (Gardini et al., 2009; Kumari et al., 2009; Matsuo et al., 2009) as well as found in the present study. Our findings on the left MFG appear most striking. This structure was the only one correlating with the BIS total score as well as with each of all three subscores. Further, the association between the left MFG/IFJ and the BIS attention score was the sole correlation surviving a correction for multiple comparisons (FDR p b .05). General impulsiveness has already

been described as negatively correlated with the left MFG in clinical MRI studies on disorders comprising impulsive symptoms (Lyoo et al., 2006b; Sasayama et al., 2010). Though, yet no structural correlates specific for attentional impulsiveness have been shown in healthy samples. However, GM density decrease in the MFG has been found to be related to general cognitive functioning requiring attention (Kim et al., 2006). In particular, the MFG has been described as part of an executive attention network (Andersson et al., 2009), mediating executive as well as attentional processes (Solanto et al., 2009) and further playing an associated role in inhibitory control/motor

(A) Impulsiveness total score

(B) Impulsiveness attention

(C) Impulsiveness motor

(D) Impulsiveness nonplanning

P-Value

Fig. 2. The regions inversely (blue) and positively (red) correlated with BIS scores; co-varied for age, gender and smoking; (p b .001 uncorrected; N = 32).

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impulsiveness (Simmonds et al., 2008). For instance, CT of the bilateral MFG has been found to be positively associated with cognitive manipulation (Ehrlich et al., 2011). Moreover, lesion studies have shown that the extent of damage to the left MFG was reliably correlated with the degree of top-down control (Aron et al., 2004). Thus, the MFG, known to be central for cognitive control (Goghari and MacDonald, 2008), might have an impact on both cognitive processes, such as attention (Derrfuss et al., 2005) as well as planning (Lawrence et al., 2009), and on motor impulsiveness via attention-related top-down control. However, so far neuroimaging literature on motor impulsiveness has favored a more direct association with the right IFC based on fMRI data (Buchsbaum et al., 2005; Congdon and Canli, 2008), lesion studies (Aron et al., 2003; Rieger et al., 2003) as well as transcranial magnetic stimulation (Chambers et al., 2006). Yet, considering BIS subscales' overlap as regards content in terms of control processes (Patton et al., 1995), it appears reasonable to assume common neural substrates of the discussed dimensions of impulsiveness such as the MFG. Another structure suggesting overlapping structural correlates is the OFC. This region is the only one, which had already been shown by previous MRI studies in healthy samples (Kumari et al., 2009; Matsuo et al., 2009). In line with our findings, Matsuo et al.(2009) have found OFC GM volumes to be inversely correlated with the very same BIS scores (total, motor and nonplanning based on (Patton et al., 1995)). Clinical MRI studies generally support a negative association between OFC and impulsiveness (Hesslinger et al., 2002), even though Antonucci et al.(2006) have found bilateral OFC GM volume to be positively associated with BIS motor impulsiveness in a heterogeneous group of non-psychotic psychiatric patients. Moreover, fMRI studies provide evidence for a link between OFC and nonplanning impulsiveness as a lack of future orientation; difficulties with self-control and cognitive complexity (Lee et al., 2008). Integrating our findings on the OFC in previous research results on impulsiveness, this region might be understood as involved in several aspects of impulsiveness, namely motor and nonplanning impulsiveness. The third structure, which might be of relevance to various dimensions of impulsiveness, is the SFG. Similar to the inverse correlations with the BIS total, attention and motor score in our study, MRI literature suggests a negative association between SFG and general impulsiveness (Almeida et al., 2010) rather than a positive one (Gardini et al., 2009). Except for some studies on inhibitory control/motor impulsiveness (Chen et al., 2009) the kind of association between SFG and impulsiveness has not been specified, which might suggest a more generic link between SFG and different aspects of impulsiveness. In addition to potentially common neural substrates of different dimensions of impulsiveness, more distinct structural correlates were identified in our study, which were exclusively associated with respectively each of the BIS subscales. In line with the BIS attention score negatively correlating with CT of the STG in our study, this structure has been associated with general impulsiveness (Lyoo et al., 2006a) and further with attention regulation (Lee et al., 2010). Similar to our finding on attentional impulsiveness and the left transverse occipital cortex, significantly decreased CT in this area has been described in subjects characterized by an impulsive pathology (Lyoo et al., 2006a). In general, the visual area near the junction of the left intraparietal and transverse occipital sulci has been found to be involved in a wide variety of tasks requiring attention (Itoh et al., 2008). Exclusively for the BIS motor score we found negative correlations with CT of primary motor cortex (PMC; BA 4) and inferior parietal cortex (IPC). Conversely, a positive association with GM volumes in the IPC and impulsive pathology (ADHD) has been reported (Brieber et al., 2007). Though, both the IPC (Solanto et al., 2009) and the PMC (Swann et al., 2009) have been described as associated with motor inhibition, suggesting a link with motor impulsiveness.

Interestingly the recent study found that CT of the right inferior temporal cortex was positively correlated solely with the BIS nonplanning score (uncorrected). Apart from Gardini et al.(2009) none of the other non-clinical MRI study on impulsiveness has reported positive associations with impulsiveness. However, there is some evidence for temporal cortices to be involved in complex, anticipatory cognition (Lavric et al., 2008), which might support our exploratory identified association with nonplanning aspects of impulsiveness. Some methodological limitations of this study shall be noted. The sample size was modest, which may limit the generalization of our findings, nevertheless the number of participants is in the range of comparable previous studies (Kumari et al., 2009). Furthermore, except for one cluster (FDR p b .05) the results of the entire cortex analysis did not survive correction for multiple comparisons. Possibly, the correlations found might have reached significance in a larger sample. However, the exploratory approach of our study, by for the first time using CT as measure of structural correlates of impulsiveness, accounts for our sample size. Furthermore, defining a priori regions of interest did not seem suitable either, since recent neuroimaging studies on morphological correlates of impulsiveness reported very heterogeneous findings. According to this exploratory approach the Gaussian smoothing kernel of 20 mm has been chosen. Even though, there is a chance that smaller effects might have been missed, this kernel is within the recommended range for FreeSurfer analyses (http://www.fmrib.ox.ac.uk/fslcourse/lectures/freesurfer. groupanalysis.short.pdf) and has been applied in comparable previous studies (Lehmann et al., 2010; Mueller et al., 2009; Rohrer et al., 2009). Yet in sum, in the past studies with an exploratory approach have provided stimulating directions for future research. Conclusions In our study both overlapping and exclusive neural correlates of different dimensions of impulsiveness have been identified. In accordance with previous morphological studies predominantly negative correlations with impulsiveness and frontal clusters have been found. For the first time, structural correlates of attentional impulsiveness in a healthy sample have been reported (Matsuo et al., 2009); one cluster in MFG/IFJ survived correction for multiple comparisons (FDR p b .05). Future studies might benefit from applying the novel approach as outlined above in order to further explore neurobiological substrates of impulsiveness. Acknowledgments We are grateful to the Studienstiftung des Deutschen Volkes for funding CS's PhD-project. SK is a Postdoctoral Fellow of the Research Foundation Flanders (FWO). References Almeida, L.G., Ricardo-Garcell, J., Prado, H., Barajas, L., Fernandez-Bouzas, A., Avila, D., Martinez, R.B., 2010. Reduced right frontal cortical thickness in children, adolescents and adults with ADHD and its correlation to clinical variables: a cross-sectional study. J. Psychiatr. Res. 44 (16), 1214–1223. Andersson, M., Ystad, M., Lundervold, A., Lundervold, A.J., 2009. Correlations between measures of executive attention and cortical thickness of left posterior middle frontal gyrus — a dichotic listening study. Behav. Brain Funct. 5, 41. Antonucci, A.S., Gansler, D.A., Tan, S., Bhadelia, R., Patz, S., Fulwiler, C., 2006. Orbitofrontal correlates of aggression and impulsivity in psychiatric patients. Psychiatry Res. 147, 213–220. Aron, A.R., Fletcher, P.C., Bullmore, E.T., Sahakian, B.J., Robbins, T.W., 2003. Stop-signal inhibition disrupted by damage to right inferior frontal gyrus in humans. Nat. Neurosci. 6, 115–116. Aron, A.R., Monsell, S., Sahakian, B.J., Robbins, T.W., 2004. A componential analysis of task-switching deficits associated with lesions of left and right frontal cortex. Brain 127, 1561–1573.

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