Linking novelty seeking and harm avoidance personality traits to basal ganglia: volumetry and mean...

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 / Published online: 14 March 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 inarousal 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 HAtemperamental 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 scoresobtained in the temperamental scales were associated with

volumes, mean diffusivity and fractional anisotropy mea-

sures of basal ganglia of both hemispheres separately, byusing 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 presentfindings 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 differentapproaches, as the trait adjective (Eysenck and Eysenck

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

Clark 1993) and the motivational system (Gray 1987; Lang1995) 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/negativestimuli respectively, accompanied by a perceptual vigilance

for, an affective reactivity to, and a behavioral predisposition

towards such stimuli (Elliot 2008). Temperamental traitsdetermine 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 ofnovelty, 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. SpallettaI.R.C.C.S. Santa Lucia Foundation, Via Ardeatina 306,00142 Rome, Italy

D. Laricchiuta (&) ! L. Petrosini ! D. Cutuli ! E. PicerniDepartment of Psychology, Faculty of Medicine andPsychology, University ‘‘Sapienza’’ of Rome,Via dei Marsi 78, 00185 Rome, Italye-mail: [email protected]

C. CaltagironeDepartment of Neuroscience, Tor Vergata University,Via Montpellier 1, 00135 Rome, Italy

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Brain Struct Funct (2014) 219:793–803

DOI 10.1007/s00429-013-0535-5

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

predicts vulnerability to psychiatric disorders (Richter andBrandstrom 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 fromindividuals 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-dysfunctionalbehaviors 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 linkedto structural variance in specific brain structures. In partic-

ular, it has been demonstrated that NS scores positively

correlate with volumes of frontal and posterior cingulatecortices, while HA scores negatively correlate with volumes

of orbito-frontal, occipital and parietal cortices (Gardini

et al. 2009). The strength of fiber tracts from hippocampusand 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 isassociated with high HA scores (Westlye et al. 2011). Fur-

thermore, striatal activity is correlated with novelty-based

choices (Wittmann et al. 2008). Subjects characterized byrelatively 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 arepositively 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 seemedimportant 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 inNS 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 andenvironmental 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 characterand 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 brainstructure and function in relation to character personality

variations are needed, studying brain-behavior relations

within healthy subjects exhibiting normal temperamentalpersonality 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 associationsbetween NS and HA scores with variations in macro- (vol-

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

Anisotropy, FA) structural values in basal ganglia through ahigh-resolution structural magnetic resonance imaging

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

Noteworthy, in front of a very limited number of reportsdescribing 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 individualdifferences. 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 FAmeasures anisotropy of water diffusion processes (Pierpaoli

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

diffusion is influenced by different factors, such as poresize 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 toWM. Thus, although the link between information pro-

cessing and diffusion properties is not yet fully clarified,

diffusion parameter changes are suggested to affect theefficacy 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 physiologicalinformation 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

794 Brain Struct Funct (2014) 219:793–803

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particular, the sample of 125 neurological intact subjects [52

males (42 %); mean age ± SD = 34.9 ± 12.4 years, range18.2–67.4] was recruited from Universities, community

recreational centers and hospital personnel by local adver-

tisement. Education level ranged from an eighth gradeeducation 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 EdinburghHandedness Inventory (Oldfield 1971). The subjects were

submitted to MRI. The inclusion criteria were age between18 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 complaintof 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), obstructivepulmonary disease, or asthma; hematological and oncologic

disorders; pernicious anaemia; clinically significant and

unstable active gastrointestinal, renal, hepatic, endocrine, orcardiovascular 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, headinjury with loss of consciousness, and any other significant

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

history of alcoholism or drug dependence and abuse duringlife-time, (6) MRI evidence of focal parenchymal abnor-

malities or cerebrovascular diseases: for each subject, a

trained neuroradiologist and a neuropsychologist expert inneuroimaging 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 brainpathology 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 sampledefinition 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 fullexplanation of study procedures.

Temperament and character inventory

Temperament and Character Inventory consists of 240items comprising 7 dimensions, including 4 temperament

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

3 character scales (Self-directedness, Cooperativeness andSelf-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). NSrefers to a tendency to action behaviors and it is expressed

as the tendency to exploratory activity in response to

novelty, impulsive decision making, extravagant approachto 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 arecharacterized 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 were13.4 ± 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. Allplanar 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 restraintheir 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 ab-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 thesagittal 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 byapplying 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 datawere 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 (2014) 219:793–803 795

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similar in both image modalities, and MD values were used

as index of micro-structural integrity within the deep GMnuclei. 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 FSLsoftware. This is a model-based segmentation/registration

tool. The shape/appearance models used in FIRST are

constructed from manually segmented images provided bythe 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 correspondenceacross the training data. Furthermore, normalized intensities

along the surface are sampled and modeled. The shape and

appearance model is based on multivariate Gaussianassumptions. 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 theoptimal border and extent of the structures considered,

modeling these structures as surfaces.

This method of segmentation is particularly useful forstructures 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, theresults 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 visuallyassessed to exclude misregistration or erroneous ROI

identification. For each subject and each hemisphere, we

calculated the volumes of the above-mentioned ROIs. Priorto 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 automaticevaluation 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 ScalingFactor (ASF) was computed as the determinant of the affine

transform connecting each individual to the MNI standard

template. The ASF represents the whole-brain volumeexpansion (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 eachindividual. 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 werefree 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 Pearsonproduct 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 bilateralstructures. 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 HAscores 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 proceduredescribed 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 significancecorresponded 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 deepGM 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 ofeducation was found in any basal structure.


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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 structuresthan 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 andFA 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 bilateralputamen (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 deepGM structures

Results of linear regression analyses used to evaluate theassociations 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 (MDvalues). 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 HAscores 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 aswell as avoidance behavior. Individuals with high NS

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

tempered and extravagant, whereas those with high HAscores are cautious, passive, fearful of uncertainty, shy and

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

Individuals with neuropsychiatric symptoms such asdepression (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 thenormal distribution for each personality trait. However, NS

and HA traits support the adaptive functioning to envi-

ronmental stimuli even in not-dysfunctional situations. Infact, 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 notpathological 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 higherthan male participants and younger participants had NS

scores higher than older participants. Furthermore, we

found that the bilateral putamen was positively correlatedas regard the volumes, and negatively correlated as regard

MD values, with years of education.

Specific associations between NS and HA scores andbasal ganglia structure were demonstrated: bilateral cau-

date and pallidum volumes correlated positively with NS

trait, while bilateral putamen MD measures correlatedpositively with HA trait. No association was found between

temperamental traits and FA measures. Thus, it may be

advanced that macro- (volume) and micro- (MD) structuralintegrity of basal structures contributes to explain the


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biological variance which leads to personality phenotypes,

as NS or HA.

Although some data have already suggested thatstructural 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 thespecific 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 regionsin 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 andsomatic 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 crucialrole in generating consumatory conditioned actions (Ber-

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


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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 andKlingberg 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 activatingbehavior (Seger 2008; Utter and Basso 2008). This gating

system applies to a large range of decisions made on thebasis of motivation to approach what we find rewarding,

and/or to avoid what we find negative.

Interestingly, highly significant positive correlationsemerged between Extraversion (personality trait belonging

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

and Eysenck 1985) and perfusion measures in the basalganglia, 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 thepresent 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-wideset 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 basalganglia 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, theinformation processing related to motivational valence

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

Palmiter 2008) and even personality style (O’Gormanet 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 mayexplain 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


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), thepresent 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 tractsoriginating 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


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 putamencharacterized by higher MD values will be more vulnerable

in experiencing negative emotional states and tendencies to

withdrawal and inhibition. In contrast, individuals withlarger 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. FabioFerlazzo for his kind and expert support in statistical analyses.

Conflict of interest The authors declare that they have no conflictof interest.

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