ORIGINAL ARTICLE

Linking novelty seeking and harm avoidance personality traitsto basal ganglia: volumetry and mean diffusivity

Daniela Laricchiuta • Laura Petrosini • Fabrizio Piras • Debora Cutuli •

Enrica Macci • Eleonora Picerni • Chiara Chiapponi • Carlo Caltagirone •

Gianfranco Spalletta

Received: 4 December 2012 / Accepted: 26 February 2013

� Springer-Verlag Berlin Heidelberg 2013

Abstract Novelty Seeking (NS) and Harm Avoidance

(HA) temperamental traits are related to approaching or

avoiding motivational circuits relying on the integrity and

functionality of distributed brain areas implicated in

arousal and action. The present study verified whether and

how macro- and micro-structural variations of basal gan-

glia are correlated with scores obtained in the NS and HA

temperamental scales of the Temperament and Character

Inventory by Cloninger. To this aim, 125 healthy adults

aged 18–67 years of both sexes completed the Tempera-

ment and Character Inventory and underwent a high-reso-

lution T1-weighted magnetic resonance imaging and a

diffusion tensor imaging using a 3T scanner. The scores

obtained in the temperamental scales were associated with

volumes, mean diffusivity and fractional anisotropy mea-

sures of basal ganglia of both hemispheres separately, by

using linear regression analyses. We found increased

bilateral caudate and pallidum volumes associated with

higher NS scores, as well as increased mean diffusivity in

the bilateral putamen associated with higher HA scores.

Macro- and micro-structural variations of basal ganglia

regions contribute to explain the biological variance asso-

ciated with NS or HA personality phenotype. The present

findings evidencing some brain-temperament relationships

highlight the importance of obtaining macro- and micro-

structural measures in relation to individual differences.

Keywords Individual differences � Brain volumes �Mean

diffusivity � Fractional anisotropy � Grey matter

Introduction

The most qualified personality theories follow different

approaches, as the trait adjective (Eysenck and Eysenck

1985), the affective disposition (Tellegen 1985; Watson and

Clark 1993) and the motivational system (Gray 1987; Lang

1995) approaches. Elliot and Thrash (2002) showed that two

latent factors, approach and avoidance temperaments,

account for the shared variance among these approaches.

Namely, approach/avoidance temperament is defined as a

general neurobiological sensitivity to positive/negative

stimuli respectively, accompanied by a perceptual vigilance

for, an affective reactivity to, and a behavioral predisposition

towards such stimuli (Elliot 2008). Temperamental traits

determine approaching or avoiding disposition to attachment

and to the early emotions of fear and anger as well as allow

emitting different automatic responses to the stimuli of

novelty, danger and reward. Namely, temperamental traits of

Novelty Seeking (NS) and Harm Avoidance (HA), as

defined in the Temperament and Character Inventory (TCI)

by Cloninger (1986), are retained to be related to

approaching or avoiding motivational circuits relying on the

functionality of distributed areas implicated in arousal and

Electronic supplementary material The online version of thisarticle (doi:10.1007/s00429-013-0535-5) contains supplementarymaterial, which is available to authorized users.

D. Laricchiuta � L. Petrosini � F. Piras � D. Cutuli � E. Macci �E. Picerni � C. Chiapponi � C. Caltagirone � G. Spalletta

I.R.C.C.S. Santa Lucia Foundation, Via Ardeatina 306,

00142 Rome, Italy

D. Laricchiuta (&) � L. Petrosini � D. Cutuli � E. Picerni

Department of Psychology, Faculty of Medicine and

Psychology, University ‘‘Sapienza’’ of Rome,

Via dei Marsi 78, 00185 Rome, Italy

e-mail: daniela.laricchiuta@uniroma1.it

C. Caltagirone

Department of Neuroscience, Tor Vergata University,

Via Montpellier 1, 00135 Rome, Italy

123

Brain Struct Funct

DOI 10.1007/s00429-013-0535-5

action (LeDoux 2000). Excessive tendency to NS or HA

predicts vulnerability to psychiatric disorders (Richter and

Brandstrom 2009). In particular, high levels of behavioral

inhibition, as in high-scored HA subjects, determine

increased risk for developing anxiety disorders and depres-

sion (Biederman et al. 2001; Muris et al. 2001) and con-

versely, high levels of impulsive behavior, as in high-scored

NS subjects, determine increased risk of exhibiting sub-

stance abuse and antisocial behavior (Meyer et al. 1999;

Mitchell and Nelson-Gray 2006). However, apart from

individuals with neuropsychiatric symptoms whose scores

fall at the extreme ends of the normal distribution for each

personality trait, NS and HA are part of not-dysfunctional

behaviors and contribute to adaptive functioning. In fact,

even in not-abnormal situations, the variance in the normal

range of expression of personality traits appears to be linked

to structural variance in specific brain structures. In partic-

ular, it has been demonstrated that NS scores positively

correlate with volumes of frontal and posterior cingulate

cortices, while HA scores negatively correlate with volumes

of orbito-frontal, occipital and parietal cortices (Gardini

et al. 2009). The strength of fiber tracts from hippocampus

and amygdala to striatum predicts individual differences in

NS (Cohen et al. 2009), while decreased micro-structural

integrity of white matter (WM) in cortico-limbic circuit is

associated with high HA scores (Westlye et al. 2011). Fur-

thermore, striatal activity is correlated with novelty-based

choices (Wittmann et al. 2008). Subjects characterized by

relatively low striatal dopaminergic receptor density are

reported to score lowest on NS and highest on HA (Montag

et al. 2010). Very recently, we reported that NS scores are

positively and HA scores are negatively associated with

cerebellar WM and cortex volumes (Laricchiuta et al.

2012a). Since personality traits are related to the motiva-

tional reactions that imply the involvement of a variety of

deep structures concerned in arousal and action (LeDoux

2000; Cohen et al. 2009; Westlye et al. 2011), it seemed

important to test the hypothesis that NS and HA personality

traits are reflected in structural variations in bilateral deep

gray matter (GM) structures. Assuming that variability in

NS and HA is normally distributed, the present research was

performed on a large sample of subjects without psychiatric

diagnosis to minimize the influence of disease-related and

environmental confounders, as recently suggested by

Westlye et al. (2011). In fact, the methodological approach

comparing the so-called normal control subjects to individ-

uals with specific neuro-psychiatric disorders might belie

empirical evidence and suggests that many human traits are

normally distributed. The exclusion of subjects with neuro-

psychiatric disorders is a valid approach, although contro-

versial in relation to specific state-dependent phenomena

associated with pathological conditions (Cannon et al. 2007;

Reimold et al. 2008; Selvaraj et al. 2011). A recent

‘oversampling’ study demonstrated that serotonin trans-

porter density was associated with the variance in character

and not in temperament in healthy individuals selected for

high or low HA scores (Tuominen et al. 2012). Thus,

although further studies on the relationships between brain

structure and function in relation to character personality

variations are needed, studying brain-behavior relations

within healthy subjects exhibiting normal temperamental

personality variations might provide critical insight into the

neural substrate of human behavior and psychopathology.

To this aim, in the same large cohort of healthy adults whose

TCI scores have been associated to cerebellar volumes

(Laricchiuta et al. 2012a), we investigated the associations

between NS and HA scores with variations in macro- (vol-

ume) and micro- (Mean Diffusivity, MD; Fractional

Anisotropy, FA) structural values in basal ganglia through a

high-resolution structural magnetic resonance imaging

(MRI) and a diffusion tensor imaging (DTI) scan protocol.

Noteworthy, in front of a very limited number of reports

describing DTI variations of WM in relation to personality

traits, the present research is the first report addressing GM

micro-structural data in relation to NS and HA individual

differences. DTI is sensitive to the direction and degree of

water displacement in biological tissues. Namely, MD is a

scalar measure of the total diffusion within a voxel and FA

measures anisotropy of water diffusion processes (Pierpaoli

et al. 1996). In physiological states, extracellular water

diffusion is influenced by different factors, such as pore

size between cells, cellular structure, density and surface

(Le Bihan 2007; Sykova and Nicholson 2008; Concha et al.

2010). In WM, water molecules are limited in the direc-

tions of diffusion, resulting in a high FA value. Conversely,

in GM water molecular diffusion exhibits significantly less

directional dependence, causing low FA values relative to

WM. Thus, although the link between information pro-

cessing and diffusion properties is not yet fully clarified,

diffusion parameter changes are suggested to affect the

efficacy of synaptic and extra-synaptic transmission (Sy-

kova 2004). Variations in water diffusion parameters could

be linked to variations in cognitive functions (Piras et al.

2010, 2011) and personality dimensions (Westlye et al.

2011; Bjørnebekk et al. 2012). In this view, DTI measures

represent a reliable research tool, supplying physiological

information not available on conventional MRI.

Methods

Participants

The same large cohort of healthy adults whose TCI scores

have been associated to cerebellar volumes in a recent study

(Laricchiuta et al. 2012a) was used in the present study. In

Brain Struct Funct

123

particular, the sample of 125 neurological intact subjects [52

males (42 %); mean age ± SD = 34.9 ± 12.4 years, range

18.2–67.4] was recruited from Universities, community

recreational centers and hospital personnel by local adver-

tisement. Education level ranged from an eighth grade

education to a post-graduate degree (mean educa-

tion ± SD = 15.5 ± 2.8 years, range 8–24). All partici-

pants were right-handed as assessed with the Edinburgh

Handedness Inventory (Oldfield 1971). The subjects were

submitted to MRI. The inclusion criteria were age between

18 and 70 years and suitability for MRI scanning. Exclusion

criteria included (1) suspicion of cognitive impairment or

dementia based on Mini Mental State Examination (Fol-

stein et al. 1975) score B24 (Measso et al. 1993), identi-

fying positive screening for cognitive deterioration in

Italian population and confirmed by clinical neuropsycho-

logical evaluation using the Mental Deterioration Battery

(Carlesimo et al. 1996) and NINCDS-ADRDA criteria for

dementia (McKhann et al. 1984); (2) subjective complaint

of memory difficulties or of any other cognitive deficits,

interfering or not with the daily living activities; (3) major

medical illnesses, e.g., diabetes (not stabilized), obstructive

pulmonary disease, or asthma; hematological and oncologic

disorders; pernicious anaemia; clinically significant and

unstable active gastrointestinal, renal, hepatic, endocrine, or

cardiovascular system disease; newly treated hypothyroid-

ism; (4) current or reported psychiatric, assessed by the

SCID-I and the SCID-II (First et al. 1997a, b) or neuro-

logical (assessed by a clinical neurological evaluation)

disorders (e.g., schizophrenia, mood disorders, anxiety

disorders, stroke, Parkinson disease, seizure disorder, head

injury with loss of consciousness, and any other significant

mental or neurological disorder), (5) known or suspected

history of alcoholism or drug dependence and abuse during

life-time, (6) MRI evidence of focal parenchymal abnor-

malities or cerebrovascular diseases: for each subject, a

trained neuroradiologist and a neuropsychologist expert in

neuroimaging co-inspected all the available clinical MRI

sequences (i.e. T1 and T2-weighted and FLAIR images) to

ensure that subjects were free from structural brain

pathology and vascular lesions (i.e. T2-weighted hyperin-

tensities or T1-weighted hypointensities). On the basis of

inclusion criteria, in a quality control previous to sample

definition we had excluded 28 subjects (the most elderly)

showing hyperintensities evident on T2-weighted MRI

sequences.

The same inclusion–exclusion criteria were used in our

previous study on cerebellar involvement in personality

differences (Laricchiuta et al. 2012a).

The study was approved by the Local Ethics Committee

of the I.R.C.C.S. Santa Lucia Foundation and written

consent was obtained from all participants after a full

explanation of study procedures.

Temperament and character inventory

Temperament and Character Inventory consists of 240

items comprising 7 dimensions, including 4 temperament

scales (NS, HA, Reward Dependence and Persistence) and

3 character scales (Self-directedness, Cooperativeness and

Self-transcendence) (Cloninger et al. 1993). We focused on

NS and HA stable traits with high heritability (Cloninger

1986; Cloninger et al. 1993; Stallings et al. 1996). NS

refers to a tendency to action behaviors and it is expressed

as the tendency to exploratory activity in response to

novelty, impulsive decision making, extravagant approach

to cues of reward and quick loss of temper. HA describes a

tendency to intensely respond to aversive stimuli, leading

to avoidance behavior. Individuals with high HA are

characterized as cautious, tense, fearful, worried, shy, and

easily fatigable. In the present sample NS mean scores

were 21.0 ± SD = 5.5, while HA mean scores were

13.4 ± SD = 6.3.

MRI acquisition and DTI analysis

All 125 participants underwent the same imaging protocol,

which included standard clinical sequences (FLAIR, DP-T2-

weighted), whole-brain T1-weighted and diffusion-weighted

scanning using a 3T Allegra MR imager (Siemens, Erlan-

gen, Germany) with a standard quadrature head coil. All

planar sequence acquisitions were obtained in the plane of

the anterior-posterior commissure line. Particular care was

taken to center the subjects in the head coil and to restrain

their movements with cushions and adhesive medical

tape. Diffusion-weighted volumes were acquired using

echo-planar imaging (TE/TR = 89/8,500 ms, bandwidth =

2,126 Hz/vx; matrix size: 128 9 128; 80 axial slices, voxel

size: 1.8 9 1.8 9 1.8 mm3) with 30 isotropically distributed

orientations for the diffusion-sensitizing gradients at a

b-value of 1,000 s/mm2 and six b = 0 images. Scanning

was repeated three times to increase the signal-to-noise ratio.

Whole-brain T1-weighted images were obtained in the

sagittal plane using a modified driven equilibrium Fourier

transform (MDEFT) sequence (TE/TR = 2.4/7.92 ms, flip

angle: 15�, voxel-size: 1 9 1 9 1 mm3).

Image processing was performed using FSL 4.1 (www.

fmrib.ox.ac.uk/fsl/). Image distortions induced by eddy

currents and head motion in the DTI data were corrected by

applying a 3D full affine (mutual information cost func-

tion) alignment of each image to the mean no-diffusion

weighting (b0) image. After these corrections, DTI data

were averaged and concatenated into 31 (1 b0 ? 30 b1000)

volumes. A diffusion tensor model was fitted at each voxel,

generating FA and MD maps. The FA maps were used to

obtain a better co-registration with T1-weighted images

because the spatial distribution of signal intensities was

Brain Struct Funct

123

similar in both image modalities, and MD values were used

as index of micro-structural integrity within the deep GM

nuclei. The FA maps created were registered to brain-

extracted whole-brain volumes from T1-weighted images

using a full affine (correlation ratio cost function) align-

ment with nearest-neighbor resampling. The calculated

transformation matrix was applied to the MD maps with

identical resampling options.

Anatomical T1-weighted images were processed with

the segmentation tool FIRST 1.1 integrated in the FSL

software. This is a model-based segmentation/registration

tool. The shape/appearance models used in FIRST are

constructed from manually segmented images provided by

the Center for Morphometric Analysis (CMA), MGH,

Boston, MA. The manual labels are parameterized as sur-

face meshes and modeled as a point distribution model.

Deformable surfaces are used to automatically parameterize

the volumetric labels in terms of meshes; the deformable

surfaces are constrained to preserve vertex correspondence

across the training data. Furthermore, normalized intensities

along the surface are sampled and modeled. The shape and

appearance model is based on multivariate Gaussian

assumptions. Shape is then expressed as a mean with modes

of variation (principal components). On the basis of the

learned models, FIRST searches through linear combina-

tions of shape modes of variation for the most probable

shape instance given the intensities observed in the T1

image. In other words, this tool is optimized to find the

optimal border and extent of the structures considered,

modeling these structures as surfaces.

This method of segmentation is particularly useful for

structures with a low contrast-to-noise ratio. For each sub-

ject and each hemisphere, the caudate (body), the putamen,

and the pallidum were segmented. For each subject, the

results of region of interest (ROI) segmentation and the co-

registered FA map were superimposed on the original T1-

weighted volume and the resulting images were visually

assessed to exclude misregistration or erroneous ROI

identification. For each subject and each hemisphere, we

calculated the volumes of the above-mentioned ROIs. Prior

to statistical analyses, to account for individual differences

in head size, we used the integrated tool SIENAX part of the

FSL software library (Smith et al. 2004) for automatic

evaluation of brain size, atrophy, and GM and WM vol-

umes. It corrects each volume with a multiplicative scaling

factor derived from an affine transform. This Atlas Scaling

Factor (ASF) was computed as the determinant of the affine

transform connecting each individual to the MNI standard

template. The ASF represents the whole-brain volume

expansion (or contraction) required to register each indi-

vidual to the template. In other words, we used for statistical

analyses the normalized volumes calculated as follows:

VolNormalized = ASF*VolReal.

The segmented ROIs defined the binary masks where

mean values of FA and MD were calculated for each

individual. For each subject, all available clinical MRI

sequences (i.e. T1- and T2-weighted and FLAIR images)

were visually assessed also to ensure that subjects were

free from structural brain pathology and vascular lesions.

Statistical analyses

Parametric associations between NS scores, HA scores,

volumes of bilateral structures (i.e., caudate, putamen and

pallidum), DTI (MD and FA) values of the same structures,

age and years of education were tested by using Pearson

product moment correlation (Fisher r to z). The effect of

sex was assessed by using independent-samples t test for

NS scores, HA scores, volumes or DTI values of bilateral

structures. Associations between NS or HA scores and

volumes or DTI values of bilateral structures were tested

by using linear regression analyses with the NS or HA

scores as dependent variable and age, sex, total GM vol-

ume and volumes (or MD or FA values) of bilateral

structures as independent variables. Sex was considered a

‘‘dummy variable’’ given its dichotomic nature.

As in the present study a large number of tests was run,

controlling for the alpha inflation was needed. The pro-

portion of type I errors among all rejected null hypotheses

was controlled by setting the False Discovery Rate (FDR)

to 0.05. The FDR was estimated through the procedure

described by Storey and Tibshirani (2003). The bootstrap

procedure was used to estimate the p0 parameter (Storey

et al. 2004). In our results, the 0.05 level of significance

corresponded to an FDR 0.04. Power analysis calculated on

the multiple linear regressions with 125 participants

showed high statistical power = 1.

Results

Relationships between NS, HA, macro- (volume)

or micro- (MD and FA) structural variations in deep

GM structures and years of education, sex or age

A negative correlation between age and years of education

(r = -0.32, p = 0.0005) was found.

No correlation between HA or NS scores and years of

education (NS: r = 0.08, p = 0.37; HA: r = -0.05,

p = 0.56) was found. Only the bilateral putamen was

positively correlated, as regard the volumes (right:

r = 0.22, p = 0.01; left: r = 0.20, p = 0.02), and nega-

tively correlated, as regard MD values (right: r = -0.32,

p = 0.0001; left: r = -0.29, p = 0.001) with years of

education. No correlation between FA values and years of

education was found in any basal structure.

Brain Struct Funct

123

Male and female participants had similar NS scores

(t = -0.52, p = 0.60). On the contrary, female partici-

pants showed higher HA scores than male participants

(t = 4.01, p = 0.0001). Furthermore, while female partic-

ipants showed smaller volumes for all bilateral structures

than male participants (caudate: right: t = 5.32, p =

0.0001; left: t = 5.02, p = 0.0001; putamen: right:

t = 5.37, p = 0.0001; left: t = 5.53, p = 0.0001; palli-

dum: right: t = 2.57, p = 0.01; left: t = 4.36, p =

0.0001), both females and males showed similar MD and

FA values for all bilateral structures.

No correlation between HA scores and age (r = 0.15,

p = 0.08) was found, while significant negative correla-

tions between NS scores and age (r = -0.33, p = 0.0002),

and between NS and HA scores (r = -0.37, p = 0.0001)

were found.

Negative correlations were found between age and

caudate (right: r = -0.27, p = 0.002; left: r = -0.31,

p = 0.0001), putamen (right: r = -0.39, p = 0.0001; left:

r = -0.40, p = 0.0001) and pallidum (right: r = -0.27,

p = 0.002; left: r = -0.30, p = 0.0001) volumes. Positive

correlations were found between age and bilateral caudate

(right: r = 0.49, p = 0.00001; left: r = 0.27, p = 0.002)

as well as right putamen (r = 0.22, p = 0.01) MD values.

Positive correlations were found between age and bilateral

putamen (right: r = 0.4, p = 0.0001; left: r = 0.4, p = 0.

0001), left caudate (r = 0.21, p = 0.02), as well as left

pallidum (r = 0.20, p = 0.04) FA values.

No correlation between volumes and MD or FA values

as well as between MD and FA values in any bilateral basal

structure was found.

Relationships between NS, HA and macro- (volume)

or micro- (MD and FA) structural variations in deep

GM structures

Results of linear regression analyses used to evaluate the

associations between NS or HA scores and macro- or

micro-structural variations of bilateral basal ganglia are

reported in Table 1 (volume values) and in Table 2 (MD

values). In particular, significant positive associations

emerged between NS scores and left and right caudate as

well as left and right pallidum volumes (Fig. 1). No asso-

ciation was found between NS scores and bilateral putamen

volumes. NS scores resulted to be associated to total GM

values for both caudate nuclei and for left pallidum. Fur-

thermore, no significant association emerged between NS

scores and MD measures in all GM assemblies.

No significant associations were observed between HA

scores and bilateral basal ganglia volumes. Interestingly,

a significant positive association emerged between

HA scores and left and right putamen MD measures

(Fig. 2).

For both volumes and MD of bilateral basal structures,

NS and HA scores resulted to be associated to age and sex,

respectively.

As for FA analyses, no significant association between

NS or HA scores and bilateral basal structures was found

(details are reported in Online Resource Table 1).

Discussion

Novelty Seeking trait is defined as a heritable tendency to

exhibit exploratory activity in pursuit of reward and

avoidance of monotony and it is characterized by a pro-

clivity to impulsivity and risk-taking behaviors, linked to

approach tendencies and arousal regulation. HA trait is

defined as a heritable tendency to withdrawal and inhibi-

tion of behavior and it is characterized by sensitivity to

aversive and non-rewarding stimuli that evoke negative

emotions such as anticipatory worry, fear and anxiety as

well as avoidance behavior. Individuals with high NS

scores are exploratory, impulsive, fickle, excitable, quick-

tempered and extravagant, whereas those with high HA

scores are cautious, passive, fearful of uncertainty, shy and

easily fatigued (Cloninger 1986; Cloninger et al. 1993).

Individuals with neuropsychiatric symptoms such as

depression (Ono et al. 2002), suicidal behavior (Pompili

et al. 2008), bipolar mania (Loftus et al. 2008), schizo-

phrenia (Fresan et al. 2007), substance use disorders

(Conway et al. 2003), pathological gambling (Martinotti

et al. 2006) and anxiety disorders (Kashdan and Hofmann

2008) have scores which fall at the extreme tails of the

normal distribution for each personality trait. However, NS

and HA traits support the adaptive functioning to envi-

ronmental stimuli even in not-dysfunctional situations. In

fact, the associations between macro- and micro-structural

data of GM structures and NS and HA scores indeed

reflected the normal variability in personality traits and not

pathological states. In accord with other studies (Cloninger

et al. 1993; Fresan et al. 2011; Westlye et al. 2011), we

indicated that female participants had HA scores higher

than male participants and younger participants had NS

scores higher than older participants. Furthermore, we

found that the bilateral putamen was positively correlated

as regard the volumes, and negatively correlated as regard

MD values, with years of education.

Specific associations between NS and HA scores and

basal ganglia structure were demonstrated: bilateral cau-

date and pallidum volumes correlated positively with NS

trait, while bilateral putamen MD measures correlated

positively with HA trait. No association was found between

temperamental traits and FA measures. Thus, it may be

advanced that macro- (volume) and micro- (MD) structural

integrity of basal structures contributes to explain the

Brain Struct Funct

123

biological variance which leads to personality phenotypes,

as NS or HA.

Although some data have already suggested that

structural variability might be related to personality traits

(Cohen et al. 2009; Gardini et al. 2009; Westlye et al.

2011; Bjørnebekk et al. 2012; Laricchiuta et al. 2012a),

the fields of personality research and neurosciences have

developed with very little overlap and the nature of brain-

temperament relationship in healthy individuals is far to be

clarified. The present findings indicate that specific basal

regions supply the substrate to develop interest in the

specific domains featuring NS and HA personality traits.

Since these multidimensional traits are associated with

motivational and emotional processing, attentional focus,

inhibitory control and reward sensitivity (Goldsmith et al.

2000, 2008), all functions mediated by cortex, basal gan-

glia and limbic system, the involvement of basal regions

in the neuro-geography of NS and HA traits appears

necessary.

Through the cerebral cortex and limbic structures

information about emotionally significant stimuli is con-

veyed to basal ganglia that mediate the autonomic and

somatic components of arousal and action (Cain and Le-

Doux 2008). Concerning this, the basal ganglia, in partic-

ular the dorsal striatum (caudate-putamen), play a crucial

role in generating consumatory conditioned actions (Ber-

ridge and Robinson 1998; Everitt et al. 1999; Ikemoto and

Panksepp 1999; Cardinal et al. 2002; Pezze and Feldon

2004) and in mediating behaviors related to approach or

avoidance motivation (Haring et al. 2011; Laricchiuta et al.

2012b). Furthermore, the striatum plays a key role in

reward-based learning as well as in social and non-social

decision-making (Balleine et al. 2007; Wickens et al. 2007;

van der Meer et al. 2012). Paradigms in which a subject has

to cooperate with a partner recruit striatum and the recip-

rocated trust activates striatal regions while the unrecip-

rocated trust deactivates them (Rilling et al. 2004, 2008). It

is noteworthy that these latter paradigms engaging striatal

Table 1 Associations between NS or HA scores and bilateral basal ganglia volumes

Structure Variable NS HA

Beta t (1,120) p Beta t (1,120) p

Putamen Age -0.37 -3.68 0.0003 0.15 1.55 0.30

NS R2 = 0.12

HA R2 = 0.14

Tot GM volume -0.16 -1.18 0.24 -0.07 -0.55 0.58

Sex -0.10 -0.94 0.35 0.34 3.2 0.002

Right 0.06 0.50 0.62 0.08 0.72 0.47

NS R2 = 0.12

HA R2 = 0.14

Age -0.37 -3.65 0.0004 0.13 1.32 0.19

Tot GM volume -0.16 -1.22 0.22 -0.01 -0.04 0.97

Sex -0.10 -0.92 0.36 0.32 2.98 0.004

Left 0.07 0.59 0.55 -0.05 -0.39 0.70

Caudate Age -0.38 -3.89 0.0002 0.14 1.43 0.16

NS R2 = 0.15

HA R2 = 0.14

Tot GM volume -0.27 -2.08 0.04 0.0001 0.0000 1.00

Sex -0.08 -0.76 0.45 0.32 3.03 0.003

Right 0.26 2.28 0.02 -0.05 -0.43 0.67

NS R2 = 0.15

HA R2 = 0.14

Age -0.37 -3.77 0.0003 0.14 1.38 0.17

Tot GM volume -0.28 -2.10 0.04 0.02 0.16 0.87

Sex -0.08 -0.82 0.42 0.32 3.02 0.003

Left 0.26 2.28 0.02 -0.09 -0.75 0.46

Pallidum Age -0.36 -3.75 0.0003 0.14 1.40 0.16

NS R2 = 0.15

HA R2 = 0.14

Tot GM volume -0.22 -1.84 0.07 -0.01 -0.10 0.92

Sex -0.11 -1.10 0.27 0.32 3.12 0.002

Right 0.22 2.31 0.02 -0.04 -0.38 0.71

NS R2 = 0.18

HA R2 = 0.15

Age -0.35 -3.65 0.0004 0.13 1.32 0.19

Tot GM volume -0.27 -2.20 0.03 0.02 0.17 0.87

Sex -0.07 -0.73 0.46 0.31 2.99 0.003

Left 0.31 3.0 0.003 -0.11 -1.01 0.31

Linear regressions analyses are reported for NS or HA scores and bilateral deep nuclei volumes

Significant results are reported in bold italics

NS novelty seeking, HA harm avoidance, Tot GM total grey matter

Brain Struct Funct

123

areas encompass a social component similar to the

behavior of shyness with strangers (sub-scale HA3) fea-

turing HA trait. Therefore, the network involving basal

ganglia is retained to serve as gating system allowing to

select among a variety of available behaviors (McNab and

Klingberg 2008; Koziol et al. 2010). The basal ganglia

allow cortical actions to be boosted or released by way of

pallidum over the thalamus, rather than directly activating

behavior (Seger 2008; Utter and Basso 2008). This gating

system applies to a large range of decisions made on the

basis of motivation to approach what we find rewarding,

and/or to avoid what we find negative.

Interestingly, highly significant positive correlations

emerged between Extraversion (personality trait belonging

to Eysenck’s model of personality similar to NS) (Eysenck

and Eysenck 1985) and perfusion measures in the basal

ganglia, thalamus, inferior frontal gyrus and cerebellum

(O’Gorman et al. 2006). A very recent study from our lab

performed on the same sample of healthy adults of the

present research demonstrated that even cerebellar WM

and cortex volumes are associated positively with NS and

negatively with HA scores (Laricchiuta et al. 2012a).

These data are nicely aligned with the associations

between NS and HA scores and macro- and micro-struc-

tural measures of the basal ganglia we found in the present

research. Taken together these results support the evidence

that basal ganglia and cerebellum are parts of a brain-wide

set of adaptive neural systems and indicate that their

functions might be more interconnected than previously

retained (Centonze et al. 2008; Cutuli et al. 2011). Actu-

ally, several imaging studies have reported robust cere-

bellar activation along with activation in the dorsal and

ventral striatum in models of reward-related learning

(O’Doherty et al. 2003; Seymour et al. 2004). In this

regard, the direct reciprocal connections between basal

ganglia and cerebellum (Cotterill 2001; Hoshi et al. 2005;

Bostan et al. 2010) allow clarifying and extending the

neuro-anatomical basis of the reward-driven behavior, the

information processing related to motivational valence

(Robbins and Everitt 1996; Wise 2006; Delgado 2007;

Palmiter 2008) and even personality style (O’Gorman

et al. 2006; Laricchiuta et al. 2012a). Thus, it is teasing to

advance that the looped, architectural connection as the

cortico-striatal-cerebellar thalamic-cortical circuit criti-

cally sustain the processes linked to temperamental indi-

vidual differences. The model that emerges (but needs to

be refined) emphasizing these sub-cortical structures may

explain how the ability to form intentions and to bring

them to fruition results in building of normal or abnormal

personality.

Table 2 Associations between NS or HA scores and bilateral basal ganglia mean diffusivity values

Structure Variable NS HA

Beta t (1,121) p Beta t (1,120) p

Putamen Age -0.31 -3.60 0.0004 0.11 1.33 0.18

NS R2 = 0.11

HA R2 = 0.17

Sex -0.05 -0.57 0.56 0.35 4.25 0.00001

Right -0.05 -0.59 0.55 0.17 2.07 0.04

NS R2 = 0.12

HA R2 = 0.17

Age -0.32 -3.71 0.0003 0.12 1.50 0.13

Sex -0.04 -0.46 0.64 0.31 3.83 0.0002

Left -0.05 -0.57 0.56 0.18 2.20 0.03

Caudate Age -0.34 -3.53 0.0006 0.21 1.80 0.06

NS R2 = 0.11

HA R2 = 0.15

Sex -0.04 -0.49 0.62 0.33 3.92 0.0001

Right 0.03 0.37 0.70 -0.12 -1.24 0.21

NS R2 = 0.11

HA R2 = 0.16

Age -0.32 -3.63 0.0004 0.19 1.95 0.06

Sex -0.04 -0.54 0.58 0.32 3.80 0.0001

Left -0.01 -0.18 0.85 -0.15 -1.78 0.07

Pallidum Age -0.37 -3.82 0.0002 0.14 1.78 0.07

NS R2 = 0.11

HA R2 = 0.15

Sex -0.04 -0.55 0.57 0.34 4.13 0.00001

Right -0.04 -0.50 0.61 0.12 1.43 0.15

NS R2 = 0.11

HA R2 = 0.15

Age -0.32 -3.83 0.0002 0.15 1.86 0.06

Sex -0.04 -0.51 0.60 0.32 3.90 0.0001

Left -0.004 -0.004 0.96 0.11 1.39 0.16

Linear regressions analyses are reported for NS or HA scores and bilateral deep nuclei MD values

Significant results are reported in bold italics

NS novelty seeking, HA harm avoidance, Tot MD total mean diffusivity

Brain Struct Funct

123

Recently, increased HA scores have been associated

with increased MD and decreased FA measures in WM

cortico-limbic tracts (Westlye et al. 2011). To our knowl-

edge the current research is the first large-scale study

demonstrating association between increased HA scores

and increased MD in GM basal region of putamen. Inter-

estingly, both studies (Westlye et al. 2011 and the present

research) emphasize the relationship between anxiety-

related personality trait and DTI-derived indices of WM

and GM integrity. Given the described involvement of

putamen in inhibitory control (Rubia et al. 2006), the

present findings support the notion that a defective micro-

structural integrity of putamen may lead to dysfunctional

HA trait. Several studies have described the relationship

between repetitive behavior or inhibitory control deficits

and striatal abnormalities in individuals with various neu-

ropsychiatric disorders, such as obsessive compulsive dis-

order, Tourette’s syndrome and autism (Albin and Mink

2006; Langen et al. 2009; van den Heuvel et al. 2010).

Significant micro-structural abnormalities of WM tracts

originating from putamen and nucleus accumbens have

been described in adults with autism (Langen et al. 2012).

Furthermore, it has been reported that HA scores nega-

tively correlate with dopaminergic receptor availability in

the dorsal caudate and putamen (Kim et al. 2011).

Recently, a link between social reward dependency and

WM microstructure in a large healthy sample has been

described (Bjørnebekk et al. 2012).

Fig. 1 Relationship between caudate (upper panel, red color) and pallidum (lower panel, blue color) volumes and Novelty Seeking scores.

Scatterplots are separated for left and right volumes. Linear fit (solid black line) is also reported

Fig. 2 Relationship between putamen (green color) MD values and Harm Avoidance scores. Scatterplots are separated for left and right side.

Linear fit (solid black line) is also reported

Brain Struct Funct

123

The temperamental differences and their neural sub-

strates in healthy subjects may be relevant for under-

standing individual differences in resilience and

vulnerability to clinical psychiatric disorders. Our data

suggest that individuals with a micro-structure of putamen

characterized by higher MD values will be more vulnerable

in experiencing negative emotional states and tendencies to

withdrawal and inhibition. In contrast, individuals with

larger volumes of caudate and pallidum will be more vul-

nerable in experiencing positive emotional states and ten-

dencies to approach. The present findings concerning the

regional specificity of brain-temperament relationships

highlight on the importance of obtaining macro- and micro-

structural measures in the sub-cortical regions related to

motivational and emotional processing. Studies on healthy

and clinical subjects may establish whether the brain-

temperament relationship is maintained across non-patho-

logical variability and full-blown psychiatric disorders.

Acknowledgments The authors would like to thank Prof. Fabio

Ferlazzo for his kind and expert support in statistical analyses.

Conflict of interest The authors declare that they have no conflict

of interest.

References

Albin RL, Mink JW (2006) Recent advances in Tourette syndrome

research. Trends Neurosci 29:175–182

Balleine BW, Delgado MR, Hikosaka O (2007) The role of the dorsal

striatum in reward and decision making. J Neurosci 27:8161–8165

Berridge KC, Robinson TE (1998) What is the role of dopamine in

reward: hedonic impact reward learning or incentive salience?

Brain Res Rev 28:309–369

Biederman J, Hirshfeld-Becker DR, Rosenbaum JF, Herot C,

Friedman D, Snidman N, Kagan J, Faraone SV (2001) Further

evidence of association between behavioral inhibition and social

anxiety in children. Am J Psychiatry 158:1673–1679

Bjørnebekk A, Westlye LT, Fjell AM, Grydeland H, Walhovd KB

(2012) Social reward dependence and brain white matter

microstructure. Cereb Cortex 22:2672–2679

Bostan AC, Dum RP, Strick PL (2010) The basal ganglia commu-

nicate with the cerebellum. Proc Natl Acad Sci USA

107:8452–8456

Cain CK, LeDoux JE (2008) Emotional and motivational processing:

in search of brain mechanisms. In: Elliot AJ (ed) Handbook of

approach and avoidance motivation. Psychology Press Taylor &

Francis Group, New York, pp 17–34

Cannon DM, Ichise M, Rollis D, Klaver JM, Gandhi SK, Charney DS,

Manji HK, Drevets WC (2007) Elevated serotonin transporter

binding in major depressive disorder assessed using positron

emission tomography and [11C]DASB; comparison with bipolar

disorder. Biol Psychiatry 62:870–877

Cardinal RN, Parkinson JA, Hall J, Everitt BJ (2002) Emotion and

motivation: the role of the amygdala ventral striatum and

prefrontal cortex. Neurosci Biobehav Rev 26:321–352

Carlesimo G, Caltagirone C, Gainotti G (1996) The mental deteri-

oration battery: normative data diagnostic reliability and

qualitative analyses of cognitive impairment. The group for the

standardization of the mental deterioration battery. Eur Neurol

36:378–384

Centonze D, Rossi S, De Bartolo P, De Chiara V, Foti F, Musella A,

Mataluni G, Rossi S, Bernardi G, Koch G, Petrosini L (2008)

Adaptations of glutamatergic synapses in the striatum contribute

to recovery from cerebellar damage. Eur J Neurosci

27:2188–2196

Cloninger CR, Svrakic DM, Przybeck TR (1993) A psychobiological

model of temperament and character. Arch Gen Psychiatry

50:975–990

Cloninger CR (1986) A unified biosocial theory of personality and its

role in the development of anxiety states. Psychiatr Dev 4:167–226

Cohen MX, Schoene-Bake JC, Elger CE, Weber B (2009) Connec-

tivity-based segregation of the human striatum predicts person-

ality characteristics. Nat Neurosci 12:32–34

Concha L, Livy DJ, Beaulieu C, Wheatley BM, Gross DW (2010)

In vivo diffusion tensor imaging and histopathology of the

fimbria-fornix in temporal lobe epilepsy. J Neurosci 30:996–1002

Conway KP, Kane RJ, Ball SA, Poling JC, Rounsaville BJ (2003)

Personality substance of choice and polysubstance involvement

among substance dependent patients. Drug Alcohol Depend

71:65–75

Cotterill RM (2001) Cooperation of the basal ganglia cerebellum

sensory cerebrum and hippocampus: possible implications for

cognition consciousness intelligence and creativity. Prog Neu-

robiol 64:1–33

Cutuli D, Rossi S, Burello L, Laricchiuta D, De Chiara V, Foti F, De

Bartolo P, Musella A, Gelfo F, Centonze D, Petrosini L (2011)

Before or after does it matter? Different protocols of environ-

mental enrichment differently influence motor synaptic and

structural deficits of cerebellar origin. Neurobiol Dis 42:9–20

Delgado MR (2007) Reward-related responses in the human striatum.

Ann N Y Acad Sci 1104:70–88

Elliot AJ, Thrash TM (2002) Approach-avoidance motivation in

personality: approach and avoidance temperaments and goals.

J Pers Soc Psychol 82:804–818

Elliot AJ (2008) Approach and Avoidance Motivation. In: Elliot AJ

(ed) Handbook of approach and avoidance motivation. Psychol-

ogy Press Taylor & Francis Group, New York, pp 3–14

Everitt BJ, Parkinson JA, Olmstead MC, Arroyo M, Robledo P,

Robbins TW (1999) Associative processes in addiction and

reward. The role of amygdala-ventral striatal subsystems. Ann N

Y Acad Sci 877:412–438

Eysenck HJ, Eysenck MW (1985) Personality and individual

differences: a natural science approach. Plenum, New York

First MB, Gibbon M, Spitzer RL, Williams JB, Benjamin LS (1997a)

structured clinical interview for DSM-IV Axis II personality

disorders (SCID-II). American Psychiatric Press, Washington

DC

First MB, Spitzer RL, Gibbon M, Williams JB (1997b) Structured

clinical interview for DSM-IV axis I disorders (SCID-I).

Clinician version. American Psychiatric Press, Washington DC

Folstein M, Folstein S, McHugh P (1975) ‘‘Mini-mental state’’. A

practical method for grading the cognitive state of patients for

the clinician. J Psychiatr Res 12:189–198

Fresan A, Apiquian R, Nicolini H, Cervantes JJ (2007) Temperament

and character in violent schizophrenic patients. Schizophr Res

94:74–80

Fresan A, Robles-Garcıa R, Lopez-Avila A, Cloninger CR (2011)

Personality differences according to age and sex in a Mexican

sample using the temperament and character inventory-revised.

Compr Psychiatry 52:774–779

Gardini S, Cloninger CR, Venneri A (2009) Individual differences in

personality traits reflect structural variance in specific brain

regions. Brain Res Bull 79:265–270

Brain Struct Funct

123

Goldsmith HH, Lemery KS, Aksan N, Buss KA (2000) Tempera-

mental substrates of personality development. In: Molfese V,

Molfese D (eds) Temperamental substrates of personality

development. Lawrence Erlbaum, Mahwah NJ, pp 1–32

Goldsmith HH, Pollak SD, Davidson RJ (2008) Developmental

neuroscience perspectives on emotion regulation. Child Dev

Perspect 2:132–140

Gray JA (1987) The psychology of fear and stress, 2nd edn.

Cambridge University Press, New York

Haring M, Kaiser N, Monory K, Lutz B (2011) Circuit specific

functions of cannabinoid CB1 receptor in the balance of

investigatory drive and exploration. PLoS ONE 6:e26617

Hoshi E, Tremblay L, Feger J, Carras PL, Strick PL (2005) The

cerebellum communicates with the basal ganglia. Nat Neurosci

8:1491–1493

Ikemoto S, Panksepp J (1999) The role of nucleus accumbens

dopamine in motivated behavior: a unifying interpretation with

special reference to reward-seeking. Brain Res Rev 31:6–41

Kashdan TB, Hofmann SG (2008) The high-novelty-seeking impul-

sive subtype of generalized social anxiety disorder. Depress

Anxiety 25:535–541

Kim JH, Son YD, Kim HK, Lee SY, Cho SE, Kim YB, Cho ZH

(2011) Association of harm avoidance with dopamine D2/3

receptor availability in striatal subdivisions: a high resolution

PET study. Biol Psychol 87:164–167

Koziol LF, Budding DE, Chidekel D (2010) Adaptation expertise and

giftedness: towards an understanding of cortical subcortical and

cerebellar network contributions. Cerebellum 9:499–529

Lang PJ (1995) The emotion probe: studies of motivation and

attention. Am Psychol 50:372–385

Langen M, Leemans A, Johnston P, Ecker C, Daly E, Murphy CM,

Dell’acqua F, Durston S, AIMS Consortium, Murphy DG (2012)

Fronto-striatal circuitry and inhibitory control in autism: findings

from diffusion tensor imaging tractography. Cortex 48:183–193

Langen M, Schnack HG, Nederveen H, Bos D, Lahuis BE, de Jonge

MV, van Engeland H, Durston S (2009) Changes in the

developmental trajectories of striatum in autism. Biol Psychiatry

66:327–333

Laricchiuta D, Petrosini L, Piras F, Macci E, Cutuli D, Chiapponi C,

Cerasa A, Piperni E, Caltagirone C, Girardi P, Tamorri SM,

Spalletta G (2012a) Linking novelty seeking and harm avoidance

personality traits to cerebellar volumes. Hum Brain Mapp. doi:

10.1002/hbm.22174

Laricchiuta D, Rossi S, Musella A, De Chiara V, Cutuli D, Centonze

D, Petrosini L (2012b) Differences in spontaneously avoiding or

approaching mice reflect Differences in CB1-mediated signaling

of dorsal striatal transmission. PLoS ONE 7:e33260

Le Bihan D (2007) The ‘wet mind’: water and functional neuroim-

aging. Phys Med Biol 52:R57–R90

LeDoux JE (2000) Emotion circuits in the brain. Annu Rev Neurosci

23:155–184

Loftus ST, Garno JL, Jaeger J, Malhotra AK (2008) Temperament

and character dimensions in bipolar I disorder: a comparison to

healthy controls. J Psychiatr Res 42:1131–1136

Martinotti G, Andreoli S, Giametta E, Poli V, Bria P, Janiri L (2006)

The dimensional assessment of personality in pathologic and

social gamblers: the role of novelty seeking and self-transcen-

dence. Compr Psychiatry 47:350–356

McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan

E (1984) Clinical diagnosis of Alzheimer’s disease: report of the

NINCDS-ADRDA. Work Group under the auspices of Depart-

ment of Health and Human Services Task Force on Alzheimer’s

disease. Neurology 34:939–944

McNab F, Klingberg T (2008) Prefrontal cortex and basal ganglia

control access to working memory. Nat Neurosci 11:103–107

Measso G, Cavarzeran F, Zappala‘ G, Lebowitz B, Crook T,

Pirozzolo F, Amaducci L, Massari D, Grigoletto F (1993) The

mini-mental state examination: normative study of an Italian

random sample. Dev Neuropsychol 9:77–85

Meyer B, Johnson SL, Carver CS (1999) Exploring behavioral

activation and inhibition sensitivities among college students at

risk for bipolar spectrum symptomatology. J Psychopathol

Behav Assess 21:275–292

Mitchell JT, Nelson-Gray RO (2006) Attention-deficit/hyperactivity

disorder symptoms in adults: relationship to Gray’s behavioral

approach system. Pers Individ Differ 40:749–760

Montag C, Markett S, Basten U, Stelzel C, Fiebach C, Canli T, Reuter

M (2010) Epistasis of the DRD2/ANKK1 Taq Ia and the BDNF

Val66Met polymorphism impacts novelty seeking and harm

avoidance. Neuropsychopharmacology 35:1860–1867

Muris P, Merckelbach H, Schmidt H, Gadet BB, Bogie N (2001)

Anxiety and depression as correlates of self-reported behavioural

inhibition in normal adolescents. Behav Res Ther 39:1051–1061

O’Doherty JP, Dayan P, Friston K, Critchley H, Dolan RJ (2003)

Temporal difference models and reward-related learning in the

human brain. Neuron 38:329–337

O’Gorman RL, Kumari V, Williams SC, Zelaya FO, Connor SE,

Alsop DC, Gray JA (2006) Personality factors correlate with

regional cerebral perfusion. Neuroimage 31:489–495

Oldfield RC (1971) The assessment and analysis of handedness: the

Edinburgh inventory. Neuropsychologia 9:97–113

Ono Y, Ando J, Onoda N, Yoshimura K, Momose T, Hirano M,

Kanba S (2002) Dimensions of temperament as vulnerability

factors in depression. Mol Psychiatry 7:948–953

Palmiter RD (2008) Dopamine signaling in the dorsal striatum is

essential for motivated behaviors: lessons from dopamine-

deficient mice. Ann NY Acad Sci 1129:35–46

Pezze MA, Feldon J (2004) Mesolimbic dopaminergic pathways in

fear conditioning. Prog Neurobiol 74:301–320

Pierpaoli C, Jezzard P, Basser PJ, Barnett A, Di Chiro G (1996)

Diffusion tensor MR imaging of the human brain. Radiology

201:637–648

Piras F, Caltagirone C, Spalletta G (2010) Working memory

performance and thalamus microstructure in healthy subjects.

Neuroscience 171:496–505

Piras F, Cherubini A, Caltagirone C, Spalletta G (2011) Education

mediates microstructural changes in bilateral hippocampus. Hum

Brain Mapp 32:282–289

Pompili M, Rihmer Z, Akiskal HS, Innamorati M, Iliceto P, Akiskal

KK, Lester D, Narciso V, Ferracuti S, Tatarelli R, De Pisa E,

Girardi P (2008) Temperament and personality dimensions in

suicidal and nonsuicidal psychiatric inpatients. Psychopathology

41:313–321

Reimold M, Batra A, Knobel A, Smolka MN, Zimmer A, Mann K,

Solbach C, Reischl G, Schwarzler F, Grunder G, Machulla HJ,

Bares R, Heinz A (2008) Anxiety is associated with reduced

central serotonin transporter availability in unmedicated patients

with unipolar major depression : a [11C]DASB PET study. Mol

Psychiatry 13(606–613):557

Richter J, Brandstrom S (2009) Personality disorder diagnosis by

means of the temperament and character inventory. Compr

Psychiatry 50:347–352

Rilling JK, Goldsmith DR, Glenn AL, Jairam MR, Elfenbein HA,

Dagenais JE, Murdock CD, Pagnoni G (2008) The neural

correlates of the affective response to unreciprocated coopera-

tion. Neuropsychologia 46:1256–1266

Rilling JK, Sanfey AG, Aronson JA, Nystrom LE, Cohen JD (2004)

Opposing BOLD responses to reciprocated and unreciprocated

altruism in putative reward pathways. NeuroReport

15:2539–2543

Brain Struct Funct

123

Robbins TW, Everitt BJ (1996) Neurobehavioural mechanisms of

reward and motivation. Curr Opin Neurobiol 6:228–236

Rubia K, Smith AB, Woolley J, Nosarti C, Heyman I, Taylor E,

Brammer M (2006) Progressive increase of frontostriatal brain

activation from childhood to adulthood during event-related

tasks of cognitive control. Hum Brain Mapp 27:973–993

Seger CA (2008) How do the basal ganglia contribute to categori-

zation? Their roles in generalization response selection and

learning via feedback. Neurosci Biobehav Rev 32:265–278

Selvaraj S, Murthy NV, Bhagwagar Z, Bose SK, Hinz R, Grasby PM,

Cowen PJ (2011) Diminished brain 5-HT transporter binding in

major depression: a positron emission tomography study with

[11C]DASB. Psychopharmacology 213:555–562

Seymour B, O’Doherty JP, Dayan P, Koltzenburg M, Jones AK,

Dolan RJ, Friston KJ, Frackowiak RS (2004) Temporal differ-

ence models describe higher-order learning in humans. Nature

429:664–667

Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE,

Johansen-Berg H, Bannister PR, De Luca M, Drobnjak I, Flitney

DE, Niazy RK, Saunders J, Vickers J, Zhang Y, De Stefano N,

Brady JM, Matthews PM (2004) Advances in functional and

structural MR image analysis and implementation as FSL.

Neuroimage 23:S208–S219

Stallings MC, Hewitt JK, Cloninger CR, Heath AC, Eaves LJ (1996)

Genetic and environmental structure of the tridimensional

personality questionnaire: three or four temperament dimen-

sions? J Pers Soc Psychol 70:127–140

Storey JD, Taylor JE, Siegmund D (2004) Strong control, conserva-

tive point estimation and simultaneous conservative consistency

of false discovery rates: a unified approach. J R Stat Soc

B66:187–205

Storey JD, Tibshirani RJ (2003) Statistical significance for genome-

wide studies. Proc Natl Acad Sci USA 100:9440–9445

Sykova E, Nicholson C (2008) Diffusion in brain extracellular space.

Physiol Rev 88:1277–1340

Sykova E (2004) Diffusion properties of the brain in health and

disease. Neurochem Int 45:453–466

Tellegen A (1985) Structures of mood and personality and their

relevance to assessing anxiety with an emphasis on self-report.

In: Tuma A, Maser J (eds) Anxiety and the anxiety disorders.

Erlbaum, Hillsdale NJ

Tuominen L, Salo J, Hirvonen J, Nagren K, Laine P, Melartin T,

Isometsa E, Viikari J, Cloninger CR, Raitakari O, Hietala J,

Keltikangas-Jarvinen L (2012) Temperament, character and

serotonin activity in the human brain: a positron emission

tomography study based on a general population cohort. Psychol

Med 31:1–14

Utter AA, Basso MA (2008) The basal ganglia: an overview of

circuits and function. Neurosci Biobehav Rev 32:333–342

van den Heuvel OA, van der Werf YD, Verhoef KM, de Wit S,

Berendse HW, Wolters EC, Veltman DJ, Groenewegen HJ

(2010) Frontal-striatal abnormalities underlying behaviours in

the compulsive-impulsive spectrum. J Neurol Sci 289:55–59

van der Meer M, Kurth-Nelson Z, Redish AD (2012) Information

processing in decision-making systems. Neuroscientist 18:

342–359

Watson D, Clark LA (1993) Behavioral disinhibition versus con-

straint: a dispositional perspective. In: Wegner D, Pennebaker J

(eds) Handbook of mental control. Prentice Hall, New York,

pp 506–527

Westlye LT, Bjørnebekk A, Grydeland H, Fjell AM, Walhovd KB

(2011) Linking an anxiety-related personality trait to brain white

matter microstructure: diffusion tensor imaging and harm

avoidance. Arch Gen Psychiatry 68:369–377

Wickens JR, Budd CS, Hyland BI, Arbuthnott GW (2007) Striatal

contributions to reward and decision making: making sense of

regional variations in a reiterated processing matrix. Ann NY

Acad Sci 1104:192–212

Wise RA (2006) Role of brain dopamine in food reward and

reinforcement. Philos Trans R Soc Lond B Biol Sci

361:1149–1158

Wittmann BC, Daw ND, Seymour B, Dolan RJ (2008) Striatal

activity underlies novelty-based choice in humans. Neuron

58:967–973

Brain Struct Funct

123