Differences in regional brain volume related to the extraversion–introversion dimension—A voxel based morphometry study

Neuroscience Research 72 (2012) 59–67

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Differences in regional brain volume related to the extraversion–introversion dimension—A voxel based morphometry study Lea J. Forsman a,∗ , Örjan de Manzano a , Anke Karabanov a , Guy Madison b , Fredrik Ullén a a b

Neuropediatric Research Unit, Department of Women’s and Children’s Health, Karolinska Institutet and Stockholm Brain Institute, SE-171 76, Sweden Department of Psychology, Umeå University, Umeå, SE-901 87, Sweden

a r t i c l e

i n f o

Article history: Received 24 May 2011 Received in revised form 3 October 2011 Accepted 5 October 2011 Available online 12 October 2011 Keywords: Extraversion Voxel based morphometry Behavioral inhibition Behavioral activation Personality Anatomy

a b s t r a c t Extraverted individuals are sociable, behaviorally active, and happy. We report data from a voxel based morphometry study investigating, for the first time, if regional volume in gray and white matter brain regions is related to extraversion. For both gray and white matter, all correlations between extraversion and regional brain volume were negative, i.e. the regions were larger in introverts. Gray matter correlations were found in regions that included the right prefrontal cortex and the cortex around the right temporo–parietal junction – regions that are known to be involved in behavioral inhibition, introspection, and social-emotional processing, e.g. evaluation of social stimuli and reasoning about the mental states of others. White matter correlations extended from the brainstem to widespread cortical regions, and were largely due to global effects, i.e. a larger total white matter volume in introverts. We speculate that these white matter findings may reflect differences in ascending modulatory projections affecting cortical regions involved in behavioral regulation. © 2011 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

1. Introduction Personality refers to patterns of behaving, thinking and feeling that vary between individuals but are stable within a person across situations and time. Decades of research on the structure and mechanisms of personality has produced a number of influential models. One historically important and still widely used model from the psychometric tradition is Cattell’s 16 PF model, which attempts to describe the complete personality using 16 primary factors (Cattell et al., 1970). Later versions of the model include five global (second-order) factors, scores on which can be estimated as linear combinations of primary factor scores. While problems with replicability of the primary factors have been noted (Aluja and Blanch, 2004; Eysenck, 1991), the global factors have been confirmed in large-scale studies (Hofer et al., 1997; Ormerod et al., 1995; Rossier et al., 2004). The currently dominating psychometric model of personality is the five-factor model, originally developed mainly by McCrae and Costa (1990). This model includes five main factors – openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism – which have been shown to explain a large proportion of the variance in different personality measures. Two of the five-factor model dimensions, extraversion and neuroticism, are

∗ Corresponding author. E-mail address: [email protected] (L.J. Forsman).

closely related to the corresponding constructs in other personality models, including Cattell’s 16 PF (Rossier et al., 2004) and a third influential model: Eysenck’s three-factor PEN model (Draycott and Kline, 1995; Eysenck, 1967; McCrae and Costa, 1985; Scholte and De Bruyn, 2004). Indeed, traits identical to or closely related to extraversion are probably identified in almost all personality models (see Depue and Collins (1999), for a review). Eysenck’s approach to personality was pioneering in that he integrated psychometrical, behavioral, and neurobiological perspectives in a search for the biological basis of the main dimensions of personality (Eysenck, 1967, 1991). A fourth important and likewise biologically rooted personality model has been developed by Cloninger (1987, 1994). Cloninger’s model is based on genetic, neurobiological and psychometric considerations and currently includes four temperament traits (harm avoidance, novelty seeking, reward dependence, and persistence) and three character traits (self-directedness, cooperativeness, and self-transcendence). Each of these traits shows substantial correlations with several factors of the five-factor model (de Fruyt et al., 2000) and the PEN models (Zuckerman and Cloninger, 1996). The main interest in the present study is neuroanatomical correlates of Extraversion, which is one of the most well-studied aspects of personality and which, as mentioned, is highly consistent across various personality models. In general terms, extraverts are characterized as more sociable, behaviorally active, optimistic, and happy than introverts (Depue and Collins, 1999; McCrae and Costa, 1990). According to a historically important theory by Eysenck, extraverts

0168-0102/$ – see front matter © 2011 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved. doi:10.1016/j.neures.2011.10.001

60 Table 1 Summary of structural imaging studies demonstrating relations between brain anatomy and extraversion and related traits. Each row shows positive correlations between a trait and regional brain size (volume or cortical thickness). Abbreviations: Tests: NEO FFI, NEO PI-R, SR, sensitivity to reward; TCI, temperament and character inventory. Traits: E, extraversion; I, introversion; N, neuroticism; S, stability; HA, harm avoidance; NS, novelty seeking; P, persistence; SD, self-directedness; C, cooperativeness; ST, self-transcendence. Anatomical localizations: L, left; R, right. AntTPo, anterior temporal pole; AG, angular gyrus; Am, amygdala; CaS, calcarine sulcus; Caud, caudate nucleus; GP, globus pallidus; Hipp, hippocampus; IFG, inferior frontal gyrus; FG, fusiform gyrus; MFG, middle frontal gyrus; MTG, middle temporal gyrus; OFC, orbitofrontal cortex; PaHG, parahippocampal gyrus; PreCG, precentral gyrus; Put, putamen; SFG, superior frontal gyrus; SMG, supramarginal gyrus; SPL, superior parietal lobule; STG, superior temporal gyrus. Study

N

Age

Test

Trait

Method

Frontal

Parietal

Temporal

Occipital

Subcortical

41 (22 F)

mean 23.8

NEO PI-R

E

VBM (density)

L OFCa , R OFCa

–

R CaSa

–

L PreCGa , R PreCGa R OFC

–

L Am, R Ama , L FGa , L PaHGa , R STGa –

–

–

N/A

N/A

N/A

N/A

–

N/A

N/A

N/A

N/A

–

–

–

–

–

I Rauch et al. (2005)

14 (6 F)

Wright et al. (2006)

28 (17 F)

20–34

NEO FFI

R IFG, R MFG

–

R FG

–

–

Wright et al. (2007)

29 (17 F)

61–84

NEO FFI

E I

Cortical thickness

R SFG, L MFG –

– –

– –

– –

– –

20–40

TCI

HA

Manual morphometry of cingulate region

R ACG

N/A

N/A

N/A

N/A

L PCR –

N/A N/A

N/A N/A

N/A N/A

N/A N/A

R SFG

–

–

–

–

–

–

–

L Caud, Put L GP R Caud, Put –

21–34

NEO FFI

E

Cortical thickness in OFC

I E

Cortical thickness Manual morphometry of Am

I

Extraversion-related traits 100 (50 F) Pujol et al. (2002)

NS RD, P, SD, CO, ST

BarrósLoscertales et al. (2006) Iidaka et al. (2006)

50 (0 F)

56 (26 F)

mean 22.4

TCI

HA NS RD P

VBM (volume)

L OFCa L MFG – –

R AGa – – –

L Am R, MTGa – R ITGa , R STGa –

– – – –

– – R Caud –

Yamasue et al. (2008)

183 (66 F)

20–40

TCI

Low HA

VBM (volume)

L PFC (females only)

–

R Hipp

–

–

a

18–34

SR

Low SR

VBM (volume)

High SR

A trend at p < 0.001 (uncorrected) was observed.

L.J. Forsman et al. / Neuroscience Research 72 (2012) 59–67

Extraversion Omura et al. (2005)

L.J. Forsman et al. / Neuroscience Research 72 (2012) 59–67

have a lower baseline level of cortical arousal than introverts, due to differences in the baseline activity of ascending reticular pathways (Eysenck, 1967). Extravert behavior, on this account, is an active search for external stimulation in order to raise cortical arousal level. An important modification and development of Eysenck’s model has been provided by Gray and co-workers as part of their reinforcement sensitivity theory. One key idea of this model is that differences in a person’s internal and external response patterns (including arousal), to stimuli with different emotional valence, are more important for personality than is baseline arousal (Gray and McNaughton, 2000; Matthews and Gilliland, 1999). Specifically, extraversion is assumed to be dependent on two sets of neural circuitry: the behavioral activation system (BAS) and the behavioral inhibition system (BIS). Both the BAS and the BIS can increase arousal and attention to relevant sensory cues, but the BAS activates approach behavior while the BIS inhibits behavior. Key regions in the BAS include left dorsolateral and medial prefrontal regions and the basal ganglia, while the BIS involves right prefrontal regions, the amygdala, the basal ganglia and the hypothalamus (Hewig et al., 2006). The causal influences of BAS and BIS are assumed to be oblique in relation to extraversion: extraversion is due to a strong BAS and a weak BIS and introversion, on the contrary, is characterized by a weak BAS and strong BIS. The models summarized above make it important to investigate whether extraversion is in fact related to structural variability in cortical and subcortical regions that are implied in inhibition or activation of behavior, as well as white matter fibre tracts involved in control of cortical arousal. Table 1 summarizes existing studies on neuroanatomical correlates of extraversion, as well as related traits. These related traits include sensitivity to reward, which is a direct measure of activity of the BAS system (Barrós-Loscertales et al., 2006), as well as four of Cloninger’s dimensions that consistently show substantial relations to extraversion (de Fruyt et al., 2000; Zuckerman and Cloninger, 1996), i.e. harm avoidance, which is negatively correlated with extraversion and self-directedness, novelty seeking and reward dependence, all of which are positively related to extraversion. As can be seen in Table 1, the overall pattern is far from consistent. For instance, one study indeed found extraversion to be negatively related to cortical thickness in frontal regions in young adults (Wright et al., 2006). This is consistent with another study finding a relation between a weak BAS and prefrontal regional volume (Barrós-Loscertales et al., 2006). However, other studies using older participants, have demonstrated different patterns of correlations (Wright et al., 2007). These studies have reported positive relations, in various areas, between extraversion or extraversionrelated traits and cortical thickness (Rauch et al., 2005; Wright et al., 2007) or regional volume (Kaasinen et al., 2005; Yamasue et al., 2008). This inconclusive picture is likely to depend on many things, including heterogeneity with regard to personality measures, morphometrical techniques, and demographical variables such as age and sex. Notably, no previous study has to our knowledge studied relations between extraversion in Cattell’s personality model and white matter or gray matter regional volume. In order to reduce variance in non-personality related variables that are known to affect brain anatomy, we used a sample of all right-handed males. Since previous studies have found anatomical correlates of personality traits in widespread regions, with relatively low consistency across studies (Table 1), we present findings from a whole-brain analysis. However, a specific aim was to investigate if differences in extraversion are related to regional brain volume differences in regions that are implied in the BIS and BAS systems. Secondly, we were interested in exploring relationships between extraversion and white matter, since the latter has not been studied in relation to personality. Relations with regional density, i.e.

61

unmodulated brain images, were investigated for exploratory purposes. 2. Materials and methods 2.1. Participants Thirty-four healthy, right-handed (Oldfield, 1971) males participated in the study (age 19–49 yrs; mean age 33.2 ± 7.8 yrs). No participant reported being diagnosed with a psychiatric or neurological condition or disease and all denied any injury to the head. The participants were recruited from the Stockholm area with a newspaper advertisement. Two participants were excluded due to head movement artifacts in the imaging data; in other words, 32 participants were included in the analyses. The experimental procedures were in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki), ethically approved by the Regional Ethical Review Board in Stockholm (Dnr 2005/320–32), and performed with the understanding and written consent of each participant. 2.2. Personality testing Personality was measured with the Swedish version (Psykologiförlaget AB) of the 16PF, 5th version (Institute for Personality and Ability Testing, Inc.). The test was administered individually, and typically completed within 40–50 min. Scores on the global personality factors were calculated as linear combinations of the scores on primary personality factors, according to the standard procedure described in the test manual. 2.3. Magnetic resonance imaging Imaging was performed using a 1.5-T scanner (Signa Horizon Echospeed, General Electric Medical Systems, WI, USA) with a standard 8-channel head coil. One three-dimensional, highresolution T1-weighted anatomical image volume was acquired from each subject, using the following parameters: acquisition matrix, 256 mm × 256 mm; field of view, 25 cm; repetition time, 24 ms; echo time, 6 ms; flip angle, 30◦ ; number of slices, 150; slice thickness, 1 mm; and voxel size, 1 mm × 1 mm × 1 mm. The MR images were processed for voxel based morphometry using the VBM2 toolbox (Cuadra et al., 2005) within the SPM2 software package (Wellcome Department of Cognitive Neurology, London, UK). The preprocessing of the images was performed as previously described, using study-specific prior probability maps (Good et al., 2001). All images were thus spatially normalized to standardized anatomical space and segmented into separate images of gray matter, white matter, and cerebrospinal fluid. The segmentation procedure was optimized by utilizing the hidden Markov random field (HMRF) based algorithm implemented in VBM2. This procedure removes isolated voxels of one tissue class, which are unlikely to be true members of this tissue type, judging from the tissue class of neighbouring voxels. A HMRF weighting of 0.3 was employed. Images were modulated, i.e. voxel values were multiplied with Jacobian determinants from the normalization procedure, so that they reflected regional differences in absolute amount (volume) of gray and white matter (Good et al., 2001). Both modulated and unmodulated images were smoothed with a Gaussian kernel of 12 mm full width half maximum before analysis. Modulated and unmodulated gray and white matter images were regressed, using the general linear model, on scores on the global extraversion factor of the 16PF. Age was included as a nuisance covariate in all regressions. In addition, analyses with both age and total gray or white matter volume as nuisance covariates

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Fig. 1. Gray matter regions in which regional volume correlated negatively with extraversion. Surface rendering of clusters significant at 0.05 (corrected). Scatter plots show correlations between regional volume scores (first eigenvariate of the cluster) and extraversion for the three indicated clusters (1–3). Abbreviations: AG, angular gyrus; MFG, middle frontal gyrus; SFG, superior frontal gyrus; SMG, supramarginal gyrus. Detailed data for all clusters is given in Table 2.

were performed. Regressions on the four other global personality factors of the 16PF were performed for exploratory purposes. All covariates were corrected to have a zero mean. Voxels with a value of less than 0.2 were excluded to avoid edge effects around the border between gray and white matter (Mühlau et al., 2006). Statistical significance was determined using a voxel height threshold of p < 0.05, corrected for multiple comparisons using false discovery rate (FDR) (Genovese et al., 2002). For the major gray matter clusters showing a significant relation to extraversion, a region of interest analysis was performed where the first eigenvariate of the cluster was used as a mean intensity measure and plotted against extraversion (see Fig. 1).

3. Results The sample had a mean extraversion score of 4.8 ± 1.7 (mean ± SD; range 1–8) or, expressed as standard scores using the Swedish norms (Russell and Karol, 2002), −0.31 ± 0.87. The sample was thus slightly more introverted and showed a smaller variability than the normative sample. There was no correlation between age and extraversion (r = 0.13; n.s.). We first investigated correlations between extraversion and gray matter (GM) volume, controlling for age. No positive correlations were found in any brain regions, even when investigating non-significant trends using more liberal thresholds (p = 0.2). Negative correlations between GM volume and extraversion were widespread (Fig. 1, Table 2). Scatterplots in Fig. 1 show the relation

between the first eigenvariate of the cluster, which corresponds to the mean voxel intensity, and extraversion for three major clusters (1–3 in Fig. 1 and Table 1). In the frontal lobe, clusters were found in the superior and middle frontal gyri on both the left (cluster 1, Fig. 1) and right (cluster 2, Fig. 1) sides. In the parietal lobe, larger clusters were located around the right parieto–temporal junction in the supramarginal and angular gyri (cluster 3, Fig. 1). Smaller clusters were found in the temporal and occipital lobes and subcortically in the caudate nucleus and thalamus; for further details, see Table 2. When controlling for both age and total GM volume, trends (p-values between 0.07 and 0.14) were still found in all clusters (Table 2). No correlation was found between total GM volume and extraversion (r = -0.22; n.s.; see Table 3). Neither were any positive or negative relation found between GM density and extraversion. Next, we examined relations between regional white matter (WM) volume and extraversion, controlling for age. As for gray matter, no positive correlations were found, even at liberal thresholds (p = 0.2). Negative correlations between extraversion and WM volume were extensive and bilateral, and included the corpus callosum and large portions of the frontal, parietal and cerebellar WM, extending inferiorly into the rostral brainstem and superiorly in the internal capsule to the superior frontal lobe (Fig. 2, Table 4). Correlations were less extensive in the temporal and occipital WM. For a detailed description, see Table 4. When controlling for both age and total WM volume, only weak trends (p-values between 0.15 and 0.2) were still found for a few clusters. Total WM volume showed a significant negative correlation with extraversion

L.J. Forsman et al. / Neuroscience Research 72 (2012) 59–67

63

Table 2 Gray matter regions with a negative correlation between extraversion and regional volume. Regions with an extent of more than five voxels are reported, sorted after peak voxel t-values for each brain lobe. The coordinates in Montreal Neurological Institute (MNI) space (x, y, z) and t-values of the three most significant peak voxels of each cluster are given. All correlations were significant (p < 0.05, FDR corrected) when controlling for age (left t-value column). When controlling for both age and total gray matter volume (right t-value column) strong trends with p-values between 0.07 and 0.14 were seen. Clusters 1–3 in Fig. 1 are identified with superscripts 1–3 in the size column. Abbreviations: g, gyrus; sup, superior; inf, inferior; L, left; and R, right. Brain region

Side

Frontal lobe Sup/middle frontal g Middle frontal g Sup frontal g Middle frontal Sup g frontal sulcus Inf frontal prefrontal cortex Ventrolateral g

L

R R R R R R L

Parietal lobe Angular g Supramarginal g

R

t-value

Size (mm3 )

Peak coordinate

Age controlled

Age and total GM volume controlled

x

y

z

5.59 4.89 4.55 4.05 4.22 4.01 3.66 3.76 3.93 3.86 3.91

5.22 4.54 4.12 3.60 3.76 3.61 3.29 3.31 3.48 3.45 3.50

−25 −33 −8 31 25 41 32 34 54 47 −60

6 10 6 22 −3 −3 −3 7 30 20 14

65 56 68 57 70 53 51 62 −12 11 10

54361

5.37 4.76 4.60 4.40 4.20 3.39 3.55

56 53 50 43 45 −52 −42

−57 −58 −32 −35 −35 −34 −30

28 37 38 44 16 34 46

28023

94 202 274 40 41 65 19

Intraparietal sulcus

L L

5.82 5.07 4.98 4.72 4.65 3.84 3.98

Temporal lobe Middle/inf temporal g

R

4.01

3.64

63

−60

−1

46

Occipital lobe Inf occipital g

L

4.18

3.88

−52

−78

0

80

Subcortical regions Caudate nucleus Thalamus

R L

3.97 3.74

3.60 3.30

15 −1

7 −6

20 13

35 22

R

(r = −0.43; p = 0.01; Table 3). No relations, positive or negative, were found between WM density and extraversion. Finally, relations between regional GM and WM volume and the four other global Cattell factors (tough-mindedness, independence, anxiety and self-control) were examined for exploratory purposes. No positive or negative correlations were found in any brain regions in VBM analyses. Relations between total GM/WM volume and all global factors are summarized in Table 3. Apart from the negative relation between total WM volume and extraversion, there was a negative relation between total GM volume and independence. 4. Discussion 4.1. Extraversion and brain anatomy—general considerations The present study shows that extraversion in Cattell’s personality model is correlated with regional volume in a number of brain regions. Brain structure changes with age (Good et al., 2001), so an important issue is to what extent the present results could be confounded with aging effects. Several factors speak against an age confound. First, we control for age in all analyses. Secondly, there was no relation between age and extraversion in this sample. Finally, in males extraversion is very stable during early and middle adulthood. Rantanen et al. (2007), e.g., studied stability of

32612

81 91

extraversion between age 33 and 42 in a longitudinal study: for males, the test-retest correlation was r = 0.81, i.e. comparable in magnitude to the test-retest reliability of the test. In terms of effect size, the change in extraversion was minimal (around 0.1 SD). Extraversion was related to regional volume rather than tissue density differences. It seems reasonable to assume that also these structural differences at least in part are due to local differences in the amount of neural tissue, which in turn could reflect differences in processing capacity or in the degree of activity in neural circuits in the same regions. A general point to be noted in the present study is that all correlations were negative, i.e. in all regions more introverted individuals tended to have larger gray or white matter volume. Several studies have found positive relations between gray matter volume and cortical activity or performance in various tasks (Maguire et al., 2000; Pantev et al., 1998; Schneider et al., 2002, 2005), and so this finding seems broadly consistent with Eysenck’s theory that introverts have higher resting cortical activity than extraverts (Eysenck, 1967). The direction of the correlations is also consistent with two earlier anatomical studies (Table 1): Wright et al. (2006) studied regional cortical thickness in younger adults and found negative relations with extraversion in a number of frontal regions, but no positive correlations. Barrós-Loscertales et al. (2006) found negative but no positive relations between regional brain volume and BAS drive, a trait, which

Table 3 Correlations between total gray and white matter volume, and the global Cattell personality dimensions. Pearson r-values are given. Extraversion GM volume WM volume *

p < .01.

−.22 −.43*

Anxiety

Tough-mindedness

Independence

Self-control

−.17 −.13

.15 .05

−.45* −.18

−.01 −.12

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Table 4 White matter regions with a negative correlation between extraversion and regional volume. The coordinates in Montreal Neurological Institute (MNI) space (x, y, z) and t-values of the three most significant peak voxels of each cluster are given, except for one exceptionally large cluster (*) where data for the ten most significant peak voxels are presented. All correlations were significant (p < 0.05, FDR corrected) when controlling for age (left t-value column). When controlling for both age and total gray matter volume (right t-value column) weak trends with p-values between 0.15 and 0.2 were seen for some clusters; a ‘–’ in this column indicates that the cluster was not significant at p < 0.2. Brain region

Side

Corpus callosum Cerebellar hemisphere

L R R R R L R R R R R R R L L L L L L L R L R L L L L

t-value Age controlled

Thalamus Inf frontal g Cerebellar hemisphere Inf frontal g Angular g Internal capsule, post limb Internal capsule, post limb Fusiform g Cerebellar hemisphere Inf occipital g Hippocampus Fusiform g Hippocampus Fusiform g

Middle frontal g Sup parietal lobule Middle frontal g Cuneus Rectal g Inf temporal g Sup parietal lobule

5.36 5.33 5.26 5.14 4.97 4.91 4.88 4.85 4.83 4.81 4.76 3.78 2.37 3.12 3.04 2.88 2.72 2.70 2.34 2.24 2.53 2.44 2.31 2.29 2.28 2.28 2.25

Size (mm3 )

Peak coordinate Age and total WM volume controlled 4.38 4.23 4.16 4.12 4.10 3.80 3.88 3.91 3.70 3.94 3.92 2.68 – 2.24 1.79 1.85 1.75 1.78 – 1.85 – – – – – – 1.99

x

y

z

−18 15 13 15 16 −38 15 45 36 18 16 31 21 −24 −33 −35 −35 −33 −34 −35 37 −18 27 −18 −11 −47 −15

−26 −48 −63 −56 −8 3 −58 12 −50 −10 −7 −70 −81 −103 −12 −15 −19 −8 −7 −24 21 −78 11 −82 51 −14 −65

34 −39 −31 −41 4 33 −35 22 37 8 11 −12 −12 −4 −21 −23 −19 −26 −32 −17 40 47 53 42 −17 −28 64

283,225

555 826 614

122 26 15 3 8 38 9

in turn is positively correlated with extraversion (Caseras et al., 2003; Jorm et al., 1998). However, positive relations have also been reported between extraversion and anatomical measures (Table 1): regional cortical thickness in elderly adults (Wright et al., 2007);

orbitofrontal cortical thickness in younger adults (Rauch et al., 2005); and gray matter density in the amygdala and frontal, temporal, and occipital cortical regions in younger adults (Omura et al., 2005). Like introversion, neuroticism is also thought to be related to a strong BIS. However, neuroticism has consistently been related to reductions in cortical volume and thickness (Knutson et al., 2001; Rauch et al., 2005; Wright et al., 2007, 2006). The crucial difference could be that neuroticism is characterized by both a strong BIS and a strong BAS, which gives a higher reactivity to stressful events (Gray and McNaughton, 2000). Also pathological conditions that include negative affect and high stress reactivity, e.g. post-traumatic stress disorder and mood disorders, are accompanied by reductions in brain volume (Bremner et al., 2008; Friedman et al., 1998). One can note that in the present study Anxiety showed non-significant trends for negative relations to both total WM volume and total GM volume (see Table 3). With regard to functional studies of resting blood flow, our finding of larger regional volumes in introverts would appear to be in line with one early study that found higher blood flow in frontal regions of introverts (Johnson et al., 1999). Also here, however, the picture is complicated by other studies that report patterns of both positive and negative correlations between blood flow in subcortical and cortical regions and extraversion (Fischer et al., 1997; O’Gorman et al., 2006) or extraversion-correlated Cloninger traits (Sugiura et al., 2000). Further studies will be required to understand the role of demographical and morphometrical variables for these discrepancies.

Fig. 2. White matter regions in which regional volume correlated negatively with extraversion. Clusters, significant at 0.05 (corrected), are projected on coronal, axial and sagittal sections. Coordinates in standard space are given above each section. The color scale represents t-values. Detailed data for all clusters is given in Table 4. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

4.2. Extraversion and regional gray matter volume Extraversion was uncorrelated with total gray matter volume, and strong trends for correlations between extraversion and regional volume remained when controlling for total gray

L.J. Forsman et al. / Neuroscience Research 72 (2012) 59–67

matter volume. These correlations thus appear to represent largely regional rather than global effects. Negative correlations between extraversion and gray matter volume are summarized in Table 2. The majority of these clusters are located in the frontal, parietal and temporal lobes of the right hemisphere. This is in line with a large literature on functional cerebral asymmetry, which shows that regions in the right hemisphere are more involved in different components of the BIS, i.e. withdrawal behavior, negative emotional valence and individual-oriented rather than affiliative emotions (Craig, 2005; Davidson, 2004; Demaree et al., 2005; Wright et al., 2006). The findings are consistent with earlier studies that have found, for the right middle frontal gyrus, negative relations between extraversion and cortical thickness (Wright et al., 2006), as well as been between BAS drive and regional volume (Barrós-Loscertales et al., 2006). Interestingly, several of the observed regions have been reported to be involved in various aspects of social cognition (Ochsner, 2008). This suggests that one aspect of personality differences may be individual variation in the organization of brain networks for evaluation of socially relevant stimuli, including regions involved in recognition of social-affective stimuli and social judgment. Inferior parietal regions have thus been found to be activated when politically active participants view pictures of politicians from an opposing party (Kaplan et al., 2007). The right middle frontal gyrus as well as cortical regions around the right parieto–temporal junction are deactivated when participants view pictures of faces of individuals that arouse feelings of either romantic love (Bartels and Zeki, 2000, 2004) or maternal love (Bartels and Zeki, 2004). Saxe and co-workers have in an important set of studies (see e.g. Saxe and Powell, 2006; Saxe and Wexler, 2005) argued that cortical regions around the right temporo–parietal junction are essential for reasoning about the contents of other people’s minds, and the attribution of mental states to other individuals. The social withdrawal of introverts includes both low affiliation, i.e. a tendency to form less warm and close interpersonal bonds, and low agency – a tendency to be less dominant and assertive in social situations (Depue and Collins, 1999). One could speculate that these behavioral patterns are related to functional differences in networks involved in social reasoning and negative social judgment, which, in turn, are reflected in regional volume differences in the involved brain regions. The present findings can be also interpreted within the framework of intrinsic and extrinsic neural systems, as proposed e.g. by Malach and co-workers (Goldberg et al., 2006; Golland et al., 2007). The intrinsic system is involved in introspection and self-reflection; it is inhibited during active motor behavior. One could speculate that individual differences on the extraversion–introversion dimension are reflected in these neural systems. The finding of a negative association between extraversion and regional volume in the left superior frontal gyrus, which is known to be involved in selfreferential thought and imagery (Szpunar et al., 2007), appears to be in line with this idea, and could possibly reflect a higher tendency for introspective cognition in introverts. 4.3. Extraversion and regional white matter volume We found low extraversion scores, i.e. introversion, to be related to larger WM volume in widespread areas, extending from the upper brain stem to the frontal and parietal lobes. This may to a large extent be due to a larger overall white matter volume in introverts: total white matter volume showed a negative relation to extraversion, and correlations with regional white matter volume were not significant when controlling for total white matter volume. Eysenck (1967) originally suggested that one mechanism underlying introversion is a higher baseline level of arousal, due to a stronger influence of ascending reticular pathways that have

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widespread, diffuse projections to the neocortex. In line with this general idea, one could speculate that the present findings partly reflect a larger volume of ascending reticulo–cortical projections in introverts, which could imply a stronger influence of ascending brainstem pathways on the cortex. It has to be noted, however, that the classical view of a single, non-specific ascending reticular “arousal” system has been known to be oversimplistic for a long time (Robins, 1997). In reality, a number of monoaminergic and cholinergic systems with different functional roles project to widespread cortical regions from different sets of nuclei in the brainstem. Among these systems, ascending dopaminergic projections have in particular been coupled to extraversion, through their modulatory control of brain regions involved in the BAS system and positive affect (for extensive reviews see Depue and Collins (1999), DeYoung et al. (2005)). Whether the differences in white matter volume found in the present study are specifically related to dopaminergic fibres is obviously impossible to tell.

5. General conclusion The present findings suggest that individual differences in extraversion are related to individual differences in brain anatomy, i.e. regional volume, in a number of gray and white matter regions. Several of these regions are known from previous work to be involved in social cognition and the regulation of behavioral approach and withdrawal. The fact that most regions that showed a larger regional volume in introverts were located in the right hemisphere is consistent with known, well-established hemispheric asymmetries in the control of approach and withdrawal behavior. Widespread increases in white matter volume in regions from the brain stem to the fronto–parietal cortex in introverts may partly reflect a larger volume of the ascending reticulo–cortical systems. The findings can thus be said to be broadly consistent with an earlier literature suggesting that extraversion is related to individual differences in the BAS and the BIS, including their ascending modulatory systems. Correlations with cortical regions around the temporo–parietal junction suggests the possibility that individual differences on the introversion–extraversion dimension may include differences in neural networks involved in theory of mind and the processing of socially relevant stimuli. It appears likely that the inclusion of personality data in social cognition paradigms could shed further light on the neural underpinnings of personality differences (Ochsner, 2008). We would like to emphasize, however, that conclusions about relations between extraversion and regional brain structure still have to be made with caution, given the partly inconsistent picture provided by the literature in toto (see Table 1). This picture could depend on many factors, including differences between studies in demographical variables and morphometrical measurements. Secondly, it should be emphasized that the present sample consisted of males only, and further studies are thus required to see whether the findings generalize also to the female population. A third important issue is presumably that self-report measures of extraversion are far from perfect measures of actual, real-life behavior on the extraversion–introversion dimension. A recent meta-analysis (Connolly et al., 2007), thus found the mean sampleweighted correlation between self-ratings and observer ratings of extraversion, across 50 studies, to be a modest r = 0.45 (r = 0.62 after correcting for unreliability in the measures). It may well be, therefore, that objective measures of extravert behavior would show more consistent relation to brain anatomy. The exploratory analyses of total brain volume suggested that also other global Cattell dimensions may be related to individual differences in brain anatomy. Further studies on large samples will be required to analyse these associations on a regional level.

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Acknowledgments We are thankful to Jacqueline Borg and Andreas Olsson for comments on an earlier version of the manuscript. This work was supported by the Swedish Research Council, the Freemasons in Sweden Foundation for Children’s Welfare, and the Söderberg Foundation.

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