BRES : 43580

pp:
1212ðcol:fig: : NILÞ

Model7 brain research ] (]]]]) ]]]–]]]

121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 Q1 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180

Available online at www.sciencedirect.com

www.elsevier.com/locate/brainres

Research Report

Functional network organizations of two contrasting temperament groups in dimensions of novelty seeking and harm avoidance Sunghyon Kyeonga,b, Eunjoo Kimc, Hae-Jeong Parkb,d, Dong-Uk Hwanga,n a

Division of Computational Sciences in Mathematics, National Institute for Mathematical Sciences, Daejeon, Republic of Korea b Brain Korea 21 PLUS Project for Medical Science, Yonsei University, Seoul, Republic of Korea c Department of Psychiatry and Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea d Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea

art i cle i nfo

ab st rac t

Article history:

Novelty seeking (NS) and harm avoidance (HA) are two major dimensions of temperament in

Accepted 23 May 2014

Cloninger's neurobiological model of personality. Previous neurofunctional and biological studies on temperament dimensions of HA and NS suggested that the temperamental traits

Keywords:

have significant correlations with cortical and subcortical brain regions. However, no study to

Functional modular organization

date has investigated the functional network modular organization as a function of the

Gray matter volume

temperament dimension. The temperament dimensions were originally proposed to be

Temperament

independent of one another. However, a meta-analysis based on 16 published articles found

Harm avoidance

a significant negative correlation between HA and NS (Miettunen et al., 2008). Based on this

Novelty seeking

negative correlation, the current study revealed the whole-brain connectivity modular architecture for two contrasting temperament groups. The k-means clustering algorithm, with the temperamental traits of HA and NS as an input, was applied to divide the 40 subjects into two temperament groups: ‘high HA and low NS’ versus ‘low HA and high NS’. Using the graph theoretical framework, we found a functional segregation of whole brain network architectures derived from resting-state functional MRI. In the ‘high HA and low NS’ group, the regulatory brain regions, such as the prefrontal cortex (PFC), are clustered together with the limbic system. In the ‘low HA and high NS’ group, however, brain regions lying on the dopaminergic pathways, such as the PFC and basal ganglia, are partitioned together. These findings suggest that the neural basis of inhibited, passive, and inactive behaviors in the ‘high HA and low NS’ group was derived from the increased network associations between the PFC and limbic clusters. In addition, supporting evidence of topological differences between the two temperament groups was found by analyzing the functional connectivity density and gray matter volume, and by computing the relationships between the morphometry and function of the brain. & 2014 Published by Elsevier B.V.

n

Corresponding author. Fax: þ82 42 717 5734. E-mail address: [email protected] (D.-U. Hwang).

http://dx.doi.org/10.1016/j.brainres.2014.05.037 0006-8993/& 2014 Published by Elsevier B.V.

Please cite this article as: Kyeong, S., et al., Functional network organizations of two contrasting temperament groups in dimensions of novelty seeking and harm avoidance. Brain Research (2014), http://dx.doi.org/10.1016/j.brainres.2014.05.037

181 182 183 184

BRES : 43580

2

185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244

brain research ] (]]]]) ]]]–]]]

1.

Introduction

The Temperament and Character Inventory (TCI), based on one of the most prevalent personality models, with four biologically based temperament dimensions and three psychosocially based character dimensions (Cloninger, 1987; Cloninger et al., 1993), has been widely used for investigating the neurobiological correlates of personality in multimodal neuroimaging and gene studies. Among the four temperamental traits, novelty seeking (NS) and harm avoidance (HA) are reported to have more evidence supporting for their psychometric validity and biological basis, compared to the validity of the traits of reward dependence and persistence, and therefore are most extensively studied (Miettunen et al., 2008). HA and NS reflect individual's automatic behavioral responses to the environmental stimuli of danger and novelty and are responsible for inhibition and activation of behaviors, respectively (Cloninger, 1987). More specifically, Cloninger describes the temperament dimension as the individual differences of the behavioral activation system (BAS) and the behavioral inhibition system (BIS). The BAS governs responses to positive and rewarding stimuli, resulting in approach behavior and is closely related to NS, whereas the BIS may give rise to inhibition of response and avoidance behavior and is related to HA in Cloninger's model (Caseras et al., 2003; Cloninger et al., 1993). NS and HA are known to be associated with distinct neurochemical substrates: NS with low basal dopaminergic activity and HA with high serotonergic activity. In addition, several genetic studies of functional polymorphisms support Cloninger's claim that the temperament dimensions of NS and HA have a genetic basis (Cloninger, 1987). The temperament dimensions in Cloninger's model were originally proposed to be independent of one another (Cloninger, 1987). However, a meta-analysis based on 16 published articles on the relationship between the dimensions of the TCI found a significant negative correlation between HA and NS (Miettunen et al., 2008). This result implies that there is an interaction between the neural systems related to each temperamental construct, and the BAS and BIS can be described as antagonistic to each other (De Fruyt and Van De Wiele, 2000; Zuckerman and Cloninger, 1996). Some studies have also shown that individuals with strong BAS-related personality traits, such as NS, show diminished processing of aversive cues, indicating inhibition from the BAS on the BIS. The BIS-related personality traits, such as HA, are expected to modulate responsiveness of the BAS, through behavioral inhibition (Newman et al., 1985; Patterson et al., 1987; Zuckerman and Cloninger, 1996; De Fruyt and Van De Wiele, 2000; Ávila, 2001; Kennis et al., 2013). This might be the reason that NS and HA are negatively correlated in subjects in the above-mentioned studies. Therefore, it is natural to divide individuals into two representative groups with combinations of two temperamental traits: high HA and low NS versus low HA and high NS, instead of just describing their HA and NS scores separately, to emphasize the interaction between temperamental phenotypes. With the development of brain imaging techniques, numerous neuroimaging studies have been conducted to investigate the neurobiological basis of human temperaments. In voxelbased morphometry (VBM) studies, researchers have found

anatomical regions that are significantly correlated with the temperamental traits using a multiple regression method. For example, the HA was found to be positively correlated with the gray matter (GM) volume in the left orbitofrontal cortex (OFC), right angular gyrus, left amygdala, and right middle temporal gyrus (Iidaka et al., 2006) and negatively correlated with the GM volume in the left prefrontal cortex (PFC) and right hippocampus (Yamasue et al., 2008), and the NS was found to be positively correlated with the GM volume in the left middle frontal gyrus (MFG) (Iidaka et al., 2006). In a diffusion tensor imaging study, the fiber connections among the striatum, hippocampus, and amygdala could predict individual differences in the NS (Cohen et al., 2009). In addition, NS was positively correlated with fiber connections from the OFC and amygdala to the striatum, and HA was positively associated with the fiber connectivity from the PFC to the striatum (Lei et al., 2013). Furthermore, several neurofunctional studies have reported that several brain regions were associated with the traits of HA and NS. For example, the HA score was correlated with the connectivity between the PFC and the insula (Markett et al., 2013), and between centromedial amygdala subregions and frontal cortices associated with emotional processes (Li et al., 2012). According to Haier et al. (1987), the neurobiological basis of the BAS includes the PFC, amygdala, and basal ganglia (BG), while the neurobiological basis of the BIS consists of the OFC, septo-hippocampal system, and hypothalamus. Taken together, we can summarize that the important brain regions for the neural correlates underlying the temperaments are the PFC, limbic structure, and BG territories. Until recently, personality studies using neuroimaging data were performed within the framework of a linear regression model and focused on finding brain regions that were linearly correlated with temperamental dimensions. However, since the human personality consists of multidimensional traits composed of various cognitive, emotional, and behavioral characteristics interconnecting with each other, it may be postulated that a wide array of cerebral circuits mediate the individual variability, and the study of human brain networks related to personality traits has been seen to be necessary. Under this background, modular analysis of neuroimaging data within a graph theoretical framework has been growing rapidly and could potentially provide new insights into a better understanding of whole human brain network organizations for various personality groups (Boccaletti et al., 2006; Bullmore and Sporns, 2009). For example, Davis et al. (2013) investigated modular architecture in a functional network (FN) for different impulsivity groups, i.e., low, intermediate, and high impulsivity. Because the ‘connectome’ is one of the encouraging frameworks for brain researchers to understand how the brain works, studying the patterns of regional association under the graph theoretical framework, rather than focusing on the local brain activity under the general linear model framework, can advance our understanding of the neural correlates of personality. The aim of the present study was to identify the characteristics of the modular organization of FN that make each temperamental group different: ‘high HA and low NS’ versus ‘low HA and high NS’ groups. In the current study, we hypothesized that different patterns of functional connections (i.e., modular organizations) among the PFC, BG, and limbic regions during the brain's resting-state constitute the neural basis of personality that characterizes the ‘high HA and low NS’ and ‘low

Please cite this article as: Kyeong, S., et al., Functional network organizations of two contrasting temperament groups in dimensions of novelty seeking and harm avoidance. Brain Research (2014), http://dx.doi.org/10.1016/j.brainres.2014.05.037

245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304

BRES : 43580

3

brain research ] (]]]]) ]]]–]]]

305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364

HA and high NS’ groups. Analyses of the individual differences in the density of functional connectivity and the variation of GM volumes, along with the temperament traits of NS and HA, were performed to find supporting evidence of topological differences between the two temperament groups. We first divided the subjects into two groups, ‘high HA and low NS’ and ‘low HA and high NS’ individuals, based on the k-means clustering technique, using the temperament traits of HA and NS as the input data. Next, the functional network modular architectures were examined for each temperament group. Finally, we performed statistical comparisons of the functional connectivity density and GM volume between the two temperament groups.

2.

Results

2.1.

Subject clustering

Fig. 1). The resulting groups have the opposite temperament characteristics since significant negative correlation between the HA and the NS (r ¼
0:56 and p ¼0.0002) was found in our data. Nineteen subjects with high HA and low NS were grouped together and denoted as ‘high HA and low NS’ individuals, while the remaining subjects were denoted as ‘low HA and high NS’ individuals. Table 1 shows a summary of the statistical comparisons of phenotypic information. Age and full-scale intelligence quotient (FSIQ) were not significantly different between the two groups, as follows: age, p¼ 0.1039; FSIQ, p ¼0.0635. In the ‘high HA and low NS’ individuals, however, a significantly high HA score (po0:0001) and low NS score (po0:0001) were found compared to the ‘low HA and high NS’ individuals.

2.2.

The k-means clustering algorithm categorized 40 subjects into two personality groups by minimizing the within-group variance of the two temperament traits of HA and NS (see

Fig. 1 – Scatter plot for k-means clustering results with two temperament traits of novelty seeking and harm avoidance as input data.

Patterns of FN modular structure

The best partitions of the FNs, which have a maximum averaged normalized mutual information (NMI) value over all optimization results, were computed to find the representative FN modular structures. As described in the supplementary data (SI Table 2) and Fig. 2, five distinct functional communities were found. The overall patterns of the FN community structures between temperament groups were almost similar (NMI¼ 0.8879). Among them, the brain regions falling within Module 1 (frontal regions and temporal regions), Module 2 (motor regions and insula), and Module 3 (visual and parietal regions) showed indistinguishable modular organization across the two temperament groups. However, FN Modules 4 and 5 of each group demonstrated different patterns of regional association among three territorial clusters. They were (i) PFC cluster, including the superior/medial OFC, olfactory, anterior cingulate cortex (ACC), and rectus gyrus, (ii) BG and thalamus (BG/THL) cluster including the caudate nucleus, putamen, pallidum, and thalamus, and (iii) limbic cluster including the amygdala, hippocampus, and parahippocampal gyrus. In the ‘high HA and low NS’ group, the PFC and limbic clusters were partitioned into Module 5, and the BG/THL cluster was partitioned into Module 4, while in the ‘low HA and high NS’ group, the PFC cluster and BG/THL clusters were partitioned into Module 4, and the limbic cluster was partitioned into Module 5. To assess the stability of the module assignments for each of the two groups, we performed a jackknife analysis (Davis et al.,

Table 1 – Summary of phenotypic information and TCI traits for the two temperament groups. The number in the table represents the mean value within the group and the number in the parentheses represents the standard deviation. Variable

High HA and low NS group (n ¼19, male, right-handed)

Low HA and high NS group (n ¼21, male, right-handed)

Group comparison (p value)

Age Full-scale IQ Temperament dimensions Novelty seeking (NS) Harm avoidance (HA) Reward dependence Persistence Character dimensions Self-directedness Cooperativeness Self-transcendence

26.05 (3.68) 114.26 (11.87)

24.33 (2.82) 106.05 (14.94)

0.1039 0.0635

34.53 46.79 41.53 42.26

(9.48) (8.11) (8.73) (12.65)

38.63 (11.17) 53.68 (8.15) 21.47 (12.60)

44.29 26.00 50.71 52.19

(9.48) (8.26) (6.26) (10.98)

0.0024 o0:0001 0.0004 0.0115

52.38 (7.93) 59.86 (10.29) 32.14 (12.97)

o0:0001 0.0436 0.0122

Please cite this article as: Kyeong, S., et al., Functional network organizations of two contrasting temperament groups in dimensions of novelty seeking and harm avoidance. Brain Research (2014), http://dx.doi.org/10.1016/j.brainres.2014.05.037

365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424

BRES : 43580

4

425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484

brain research ] (]]]]) ]]]–]]]

Fig. 2 – Graph visualization of the FN modular organization for the ‘high HA and low NS’ group (A) and the ‘low HA and high NS’ group (B). The FN modular structures were overlaid on a template brain and color-coded by the corresponding module partitions for the ‘high HA and low NS’ group (C) and the ‘low HA and high NS’ group (D): purple¼ Module 1; green¼ Module 2; yellow¼Module 3; magenta¼Module 4; cyan¼Module 5. See the supplementary data (SI Table 1) for ROI abbreviations.

2013) by re-computing module partitions for subsamples of the two groups, leaving out one participant at a time. Firstly, we computed the similarity of the partition between each of the subsamples and the original partition for the two temperament groups. Similarity between two partitions was quantified by NMI, with the maximum value of 1 indicating identical partitions. Average partition similarities for the ‘high HA and low NS’ and ‘low HA and high NS’ groups were 0.9229 (s ¼ 0:0585) and 0.9571 (s ¼ 0:0278), respectively. For comparison, the mean partition similarity for between-group subsamples was 0.8491 (s ¼ 0:0595). These comparisons against the jackknife subsamples indicate that the two temperament group partitions show a high degree of consistency. Testing 1000 random permutations, in which 40 subjects are randomly assigned into two groups with keeping the size of groups, we found that the topologies of the two temperament groups were significantly different from the random permutation groups: ‘high HA and low NS’ (p¼0.0231) and ‘low HA and high NS’ (p¼0.0112). The permutation testing indicates that the partitions of the two temperament groups are not by chance but by meaningful neurobiological choice.

2.3.

Functional connectivity (FC) density

FC density analysis was performed to support the differences in functional modular organizations between two temperament groups. Three territorial clusters, the PFC, limbic, and BG/THL, were identified as key regions in characterizing the

two temperament groups in the FN module analysis because they associated and modularized differently across the groups (Table 2 and SI Table 2). In the analysis of the group comparisons for between-cluster FC density across the three territorial clusters (Table 3), the between-cluster FC density between the PFC and limbic territorial clusters was significantly increased in the ‘high HA and low NS’ individuals (p ¼0.0089). This difference was not observed if we divided subjects into two random groups. In the partial correlation analysis, correlation coefficients between the temperamental traits and the between-cluster FC density were computed while controlling for the effect of age (SI Table 3 and Fig. 3). The between-cluster FC density between the PFC and limbic territorial clusters was found to be significantly and negatively correlated with NS (r ¼
0:52 and p¼ 0.0006), while it was marginally positively correlated with HA (r¼0.30 and p ¼0.0588).

2.4.

GM volume

To provide anatomical evidence for difference of functional network modular architectures between two groups, we performed a voxel-based morphometry (VBM) analysis. In the comparison of GM volume between the two temperament groups, we found that the GM volumes in the limbic (p ¼0.0087) and BG/THL (p¼ 0.0125) clusters were significantly larger in the ‘high HA and low NS’ group (Table 3). In the analysis of partial correlation while controlling for the effect

Please cite this article as: Kyeong, S., et al., Functional network organizations of two contrasting temperament groups in dimensions of novelty seeking and harm avoidance. Brain Research (2014), http://dx.doi.org/10.1016/j.brainres.2014.05.037

485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544

BRES : 43580

5

brain research ] (]]]]) ]]]–]]]

545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604

Table 2 – Summary of the six territorial clusters from the functional module analysis results. PFC¼ prefrontal cortex, BG/ THL¼ basal ganglia/thalamus. Cluster name

Brian regions

High HA & low NS

Low HA & high NS

Frontotemporal

Superior frontal gyrus, middle frontal gyrus, orbitofrontal cortex, inferior frontal gyrus, temporal pole, middle temporal gyrus, inferior temporal gyrus Precentral gyrus, postcentral gyrus, supplementary motor area, insular, middler cingulate cortex, supramarginal gyrus, paracentral lobule, Heschl gyrus, superior temporal gyrus Superior occipital gyrus, calcarine cortex, lingual gyrus, and cuneus Orbitofrontal cortex (superior), orbitofrontal cortex (medial), olfactory, anterior cingulate cortex, and rectus gyrus Amygdala, hippocampus, and parahippocampal gyrus Caudate nucleus, putamen, pallidum, and thalamus

Module 1

Module 1

Module 2

Module 2

Module 3

Module 3

Module 5

Module 4

Module 5

Module 5

Module 4

Module 4

Sensorimotor

Visual PFC

Limbic BG/THL

Table 3 – Functional connectivity density (FCD), gray matter volume, and statistical comparison between the temperament groups for the three regional clusters. The number in the table represents the mean value within the group and the number in the parentheses represents the standard deviation. PFC¼prefrontal cortex, BG/THL¼basal ganglia/thalamus. Variable

High HA and low NS

Low HA and high NS

p-value

Within-cluster FCD PFC Limbic BG/THL

0.43 (0.10) 0.38 (0.08) 0.40 (0.07)

0.37 (0.09) 0.42 (0.11) 0.39 (0.09)

0.0655 0.1780 0.6718

Between-cluster FCD PFC2Limbic PFC2BG=THL Limbic2BG=THL

0.14 (0.06) 0.10 (0.08) 0.08 (0.07)

0.07 (0.09) 0.11 (0.09) 0.12 (0.07)

0.0089 0.8193 0.0681

Gray matter volume (cm3) FCD Limbic BG/THL

8.75 (0.77) 4.79 (0.35) 4.01 (0.28)

8.39 (0.70) 4.54 (0.22) 3.77 (0.28)

0.1379 0.0087 0.0125

Fig. 3 – Graphical illustrations of the average FC density for the ‘high HA and low NS’ group (A) and the ‘low HA and high NS’ group (B). Regional cluster abbreviations: F ¼prefrontal cortex cluster; B¼ basal ganglia and thalamus cluster; L¼ limbic cluster. Two-dimensional scatter plots: the between-cluster FC density between PFC and limbic versus novelty seeking (C) and harm avoidance (D), respectively. Please cite this article as: Kyeong, S., et al., Functional network organizations of two contrasting temperament groups in dimensions of novelty seeking and harm avoidance. Brain Research (2014), http://dx.doi.org/10.1016/j.brainres.2014.05.037

605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664

BRES : 43580

6

665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724

brain research ] (]]]]) ]]]–]]]

of age, positive correlations between the HA score and the GM volume in the limbic region (r¼0.37 and p ¼0.0188) and between the HA score and the GM volume in the BG/THL region (r¼ 0.50 and p¼ 0.0013) were found (SI Table 4), whereas a significant negative correlation between the NS score and the GM volume in the limbic region was found (r ¼
0:32 and p¼ 0.0466).

3.

Discussion

The graph theoretical analyses of the resting-state fMRI data revealed that the behavioral characteristics of the two temperament groups, which are the ‘high HA and low NS’ and ‘low HA and high NS’ groups, resulted from different patterns of functional modular organization (Fig. 2 and SI Table 2). According to network module analysis results (Table 2), the key regions that have an important role in identifying the temperament groups are the PFC, BG/THL, and limbic territorial clusters. In the FN module analysis, the PFC and limbic territories were partitioned into Module 5 in the ‘high HA and low NS’ group, whereas the PFC and BG/THL clusters were partitioned into Module 4 in the ‘low HA and high NS’ group (Table 2). This result indicates that the different patterns of network associations constitute the neural substrates of personality that makes the two contrasting temperament groups different. According to Gray's reinforcement sensitivity theory (Gray, 1970), the PFC, limbic structure, and BG/THL clusters play an important role in the human behavioral system. In addition, several studies support the idea that the different patterns of brain activation are associated with behavioral systems. For example, high BAS individuals are expected to more easily activate the ventral tegmental area, ventral pallidum, ventral striatum, and the PFC in response to positive stimuli (McNaughton and Corr, 2004). On the other hand, high BIS individuals are expected to more easily activate (1) the parahippocampal gyrus, medial hypothalamus, and amygdala when threat intensity is high (McNaughton and Corr, 2004) and (2) the septo-hippocampal system, PCC, and dorsal PFC when threat intensity is low (Gray and McNaughton, 2000; McNaughton and Corr, 2004). Thus, the results of the FN module analysis suggest that the neurofunctional basis of inhibited, passive, avoidant, and inactive behaviors in the ‘high HA and low NS’ group is derived from the increased network associations between the regulatory PFC and the limbic system, whereas the neurofunctional basis of active, executive, approaching, and highly motivated behaviors for internal goals in the ‘low HA and high NS’ group is derived from the increased neurochemical and neurofunctional interactions between the PFC and BG regions (Masterman and Cummings, 1997; Whittle et al., 2006; De Young, 2010). The other regions, such as the frontotemporal (Module 1), sensorimotor (Module 2), and visual (Module 3) clusters, show the identical modular organization in both temperament groups (Table 2).

3.1.

Network organizations and behavioral characteristics

We found that the increased between-cluster FC density between the PFC and limbic regions in ‘high HA and low

NS’ individuals resulted in FN Module 5 (Table 3). How should this result be interpreted? First, we suggest that an individual's anxiety, which is a source of inhibited and avoidant behavior in the ‘high HA and low NS’ group, is regulated by the connectivity strength between the PFC and limbic territories because it is well known that PFC regions such as the ACC and OFC play an important role in the regulation of mental, cognitive, and emotional process (Morgan et al., 1993; Bush et al., 2000; Bremner et al., 2002). Second, the increased resting-state functional association between the PFC and limbic territories may be the basis of inhibited behavior in ‘high HA and low NS’ individuals, as they have the characteristics of a strong BIS and weak BAS (Forsman et al., 2012). Q2 Third, the significant correlations between HA and the GM volume in frontal and limbic structures (Johnson et al., 1999; Iidaka et al., 2006; Yamasue et al., 2008) can be a source of increased functional connectivity between frontal and limbic regions. Taken together, the functional and structural characteristics in ‘high HA and low NS’ individuals may be the neural basis of inhibited, passive, and avoidant behaviors because HA describes the tendency to respond to signals of aversive stimuli (Cloninger, 1987; Cloninger et al., 1993). Lastly, studies regarding on neuroticism and trait anxiety, which are similar concepts to HA, have reported positive correlations with activation of the PFC and amygdala when fearful and angry faces were presented (Etkin et al., 2006; Williams et al., 2006). Negative correlations were found between trait anxiety and HA and the resting-state functional connectivity between the ventromedial PFC and amygdala (Buckholtz et al., 2008; Kim et al., 2011).

3.2. Correlation between FC density and temperament dimensions The analysis of partial correlation between the betweencluster FC density and temperamental traits supported the FN module analyses results (Fig. 3). The between-cluster FC density between the PFC and limbic territories was significantly negatively correlated with NS (r ¼
0:52 and p ¼0.0006), and marginally positively correlated with HA (r¼ 0.30 and p¼ 0.0588). This implies that the stronger the between-cluster FC density between the PFC and limbic territories in individuals is, the more inhibited the behavior probably is; this is because NS and HA as measures of temperament dimensions describe the individual differences of behavior based on the BAS and BIS, respectively (Cloninger, 1987). According to LeDoux (2000), a brain network covering the frontal and limbic regions, including the PFC and amygdala is involved in generating autonomic responses to threatening stimuli from the environment. Additionally, the amygdala response is controlled and modulated through connections from the ACC and the PFC.

3.3. GM density, temperament, and behavioral characteristics A significant negative correlation between NS and the GM volume of the limbic structure (r ¼
0:32, p¼ 0.0466) was found. The amygdala, as a part of limbic structure, has been particularly associated with extraversion, which is a similar

Please cite this article as: Kyeong, S., et al., Functional network organizations of two contrasting temperament groups in dimensions of novelty seeking and harm avoidance. Brain Research (2014), http://dx.doi.org/10.1016/j.brainres.2014.05.037

725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784

BRES : 43580 brain research ] (]]]]) ]]]–]]]

785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844

concept to that of NS in Cloninger's model. Extraversion has been reported to have negative correlations with amygdala activity (Cohen et al., 2005; Mobbs et al., 2005; Hooker et al., 2008). However, several studies have also reported the opposite results. For example, extraversion and BAS have been reported to correlate positively with amygdala activity when positive stimuli were presented (Canli et al., 2001; Cohen et al., 2005; Beaver et al., 2006), as well as during the restingstate (Kunisato et al., 2011). Another remarkable finding was the existence of a significant positive correlation between HA and the GM volume of the limbic structure (r¼0.37, p¼ 0.0188). Neuroticism, anxiety, and negative affectivity as well as HA were found to have positive associations with amygdala reactivity to negative stimuli (Schaefer et al., 2002; Reuter et al., 2004; Cools et al., 2005; Etkin et al., 2006; Baeken et al., 2009; Ewbank et al., 2009; Cunningham et al., 2010; Frühholz et al., 2010; Coen et al., 2011). In addition, we found

7

a significant positive correlation between HA and the GM volume of the BG/THL including the caudate, putamen, pallidum, and thalamus (r¼ 0.50, p¼ 0.0013) (Fig. 4).

3.4. Association between the structure and function of the brain The current study analyzed multimodal neuroimaging data and performed two types of analyses that included functional network modular organizations and structural variations. We further analyzed the relationship between the function and structure of the brain because both are the neural substrates of human temperaments. However, the question about which anatomical regions constrain or predict brain functions is still unfolding (Honey et al., 2010). In the combined analysis of the functional and anatomical brain imaging data, we found a significant positive correlation (r¼0.45 and p¼ 0.0033) between

Fig. 4 – Two-dimensional scatter plots for temperamental traits versus gray matter (GM) volume: harm avoidance versus GM volume in limbic cluster (A) and GM volume in BG/THL cluster (B); novelty seeking versus limbic cluster (C) and GM volume in BG/THL cluster (D). Please cite this article as: Kyeong, S., et al., Functional network organizations of two contrasting temperament groups in dimensions of novelty seeking and harm avoidance. Brain Research (2014), http://dx.doi.org/10.1016/j.brainres.2014.05.037

845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904

BRES : 43580

8

905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964

brain research ] (]]]]) ]]]–]]]

the GM volume of the limbic structure and the between-cluster FC density of the PFC and limbic cluster (Fig. 5). Those results suggest the existence of a relationship between the morphometry and the function of the brain. Regarding the relationship between brain morphometry and functional activity, several studies have claimed that the cell numbers in a structure reflected by a particular volume measure should also be important for capillary recruitment with brain activity (Makris et al., 2004; Barrós-Loscertales et al., 2006); i.e., the greater the volume, the greater the probability of functional activation.

3.5. Interpretation of temperament dimensions as extraversion–introversion Previous studies revealed that frontal and subcortical regions were known to be associated with the measure of extraversion. For example, cerebral perfusion in brain regions including the BG and inferior frontal gyrus (O'Gorman et al., 2006) and ACC (Johnson et al., 1999) was positively associated with extraversion. In addition, regional cerebral glucose metabolism in the OFC was associated with the measure of extraversion (Deckersbach et al., 2006). According to Mardaga and Hansenne (Mardaga and Hansenne, 2007), extraversion is the association of a strong BAS and a weak BIS, while introversion is the association of a weak BAS and a strong BIS. In addition, several studies (Zuckerman and Cloninger, 1996; De Fruyt and Van De Wiele, 2000) have reported significant relationships between Cloninger's temperamental traits and extraversion; i.e., HA is negatively correlated with extraversion, and NS is positively associated with extraversion. Thus, it is postulated that the ‘high HA and low NS’ group describes individuals with introvert-like behavior and the ‘low HA and high NS’ group describes individuals with extravert-like behavior.

3.6.

Limitations

Some limitations exist in this current study, which should be considered for future directions of research in the field. First, further multimodal neuroimaging studies (e.g., structural

Fig. 5 – Combined analysis of the multimodal neuroimaging data: a significant positive correlation between the GM volume of the limbic cluster and the density of functional connectivity of the PFC and limbic clusters was found (r¼ 0.45 and p ¼0.0033).

networks from diffusion tensor imaging data) will be necessary to promote deeper insight into the neural correlates of personality. Second, our findings were limited to males. One reason for focusing on males was that some temperament dimensions have shown gender differences at both behavioral and neurobiological levels (Yamasue et al., 2008; Raine et al., 2011; Whittle et al., 2011). Further studies should include female subjects to allow the results to be generalized regardless of gender.

3.7.

Conclusion

The present study used a resting-state fMRI data comprising functional networks to investigate the patterns of modular organization across the personality groups. We note that our study was the first attempt to divide temperament groups using the k-means clustering algorithm with HA and NS as input variables and to reveal the neural substrates of two contrasting temperament groups. Using the graph theoretical framework, we found that the patterns of functional connectivity among the PFC, BG/THL, and limbic regions play an important role in identifying temperament groups. In addition, we found supporting evidence of topological differences between the two groups by analyzing the FC density and GM volume, and by computing the associations between the morphometry and function of the brain.

4.

Experimental procedures

4.1.

Subjects and TCI questionnaire

A total of 40 healthy male volunteers (mean age¼ 25.273.3 years) were recruited from universities by local advertisement. Exclusion criteria for the current study were (1) any psychiatric disorder, as defined by the Structured Clinical Interview for DSM-IV (SCID-IV) (First et al., 1997) and the SCID-II, (2) history of traumatic brain injury, epilepsy, or other neurological and medical conditions, and (3) known history of substance abuse or dependence during lifetime. This study was performed in accordance with a protocol approved by the Institutional Review Board at Severance Hospital, and all volunteers provided informed consent at the beginning of the study. We used the Korean version of the TCI with 140 items (Cloninger et al., 1993; Sung et al., 2002) to assess the temperamental traits of all of the participants and the Korean-Wechsler Adult Intelligence Scale (K-WAIS) to assess intellectual ability; this was administered by clinical psychology graduate students who had been trained in standardized assessment and scoring procedures. The k-means clustering technique, which is one of the unsupervised learning algorithms, was used with the HA and NS scores as input data to classify the 40 subjects into two contrasting temperament groups. Basically the k-means clustering technique is finding a partition which minimizing variance in each group such as 2

arg min ∑ G

∑ ‖xi
μl ‖2 ;

ð1Þ

l ¼ 1 xi A Gl

where l is the group index; μl is the mean of the values in Gl; and G ¼ fG1 ; G2 g. In this paper, xi is two dimensional vector of NS and HA scores.

Please cite this article as: Kyeong, S., et al., Functional network organizations of two contrasting temperament groups in dimensions of novelty seeking and harm avoidance. Brain Research (2014), http://dx.doi.org/10.1016/j.brainres.2014.05.037

965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024

BRES : 43580

9

brain research ] (]]]]) ]]]–]]]

1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084

4.2. High-resolution magnetic resonance imaging acquisition

4.5.

Among the 40 participants, we obtained high-resolution T1weighted magnetic resonance imaging (MRI) volume data sets using a Philips 3 Tesla MRI scanner (Intera Achieva, Philips Medical System, Best, The Netherlands) with a SENSE head coil. The scanning parameters of the 3D 1T-TFE sequence were a coronal acquisition with a 256  256 matrix, field of view (FOV)¼ 220 mm, voxel size¼ 0.98  0.98  1.2 mm3, echo time (TE)¼ 4.6 ms, repetition time (TR)¼9.7 ms, flip angle¼81.

4.3.

Resting-state functional MRI acquisition

The participants underwent the resting-state functional MRI (fMRI) scanning (404 volumes with TR¼ 2000 ms) with a 3T MRI scanner to obtain T2-weighted single shot echo planar imaging (EPI) sequences. Each participant was axially scanned using the following parameters: voxel size¼ 2.75  2.75  3.5 mm3, slice number¼36 (interleaved), matrix¼80  80, slice thickness ¼3.5 mm, slice gap ¼0.5 mm, TR¼ 2000 ms, TE¼30 ms, FOV¼ 220  220. During each scan, subjects were instructed to keep their eyes closed and rest without moving, sleeping, and focusing on a specific thought for  13 min.

4.4.

Resting-state functional network construction

Image preprocessing of all resting-state fMRI data was performed using statistical parametric mapping software (SPM8) (Friston et al., 1995). Because of the magnetization equilibrium, the first 4 images from the fMRI data were excluded. The remaining 400 EPI data were corrected for head motion by realigning all consecutive images to the first. The realigned images were co-registered to the T1-weighted images, which were used to spatially normalize the EPI data into a template space using nonlinear transformation. Then, all of the normalized images were smoothed using a 4 mm full width at half maximum (FWHM) Gaussian kernel. The smoothed images were subsequently subjected to temporal band-pass filtering (0.009–0.08 Hz), and regressing out the effects of individual head motion and global signal changes in white matter, cerebrospinal fluid, and whole brain. After the preprocessing steps, the cortical and sub-cortical brain areas were divided into 90 regions of interests (ROIs) using an automated anatomical labeling (AAL) atlas (TzourioMazoyer et al., 2002), and the mean time series for each ROI were obtained. The adjacency matrix (Akij) for the k-th subject was obtained with Pearson's correlation coefficients between the i- and j-th mean time series. Finally, an average adjacency matrix, computed as the mean functional network (FN) for each group, showed the representative group FN. FNlij ¼

1 ∑ Ak ; nl k A Gl ij

ð2Þ

where l is the group index; Gl is a set of subjects within group l; and nl is the number of subjects in group l.

Network modular structure

The positive edge weights (Shin et al., 2013) of the groupaveraged functional network matrix FNlij formed by Pearson's correlations were subjected to the graph theoretical modularity estimation algorithm, which characterizes the segregation of a network (Newman, 2006; Rubinov and Sporns, 2010). Modularity was computed from the weighted functional networks using a Louvain community detection algorithm (Blondel et al., 2008). Q¼

 
si sj 1 ∑ Aij
δ Ci ; Cj ; 2m i;j 2m

ð3Þ

where Aij represents the weight of the edge between i and j; si ¼ ∑j Aij is the sum of the weights of the edges attached to node i; Ci is the modular (community) structure to which vertex i is assigned; the delta function δðu; vÞ is 1 if u¼v and 0 otherwise; and m ¼ 12 ∑ij Aij . All reported modularity and corresponding modular structures represent optimal solutions obtained from 1000 independent optimizations. Since the Louvain method uses a heuristic strategy to maximize network modularity for a given initial condition, every independent optimization process produces a different modular organization (Blondel et al., 2008; Davis et al., 2013). NMI, which is a measure of similarity of partitions borrowed from information theory, was used to determine which community had the most similar structure to the others (Pluim et al., 2003; Lancichinetti et al., 2008). The community structure having the highest average value of NMI over all other optimization results was called ‘best partition’ of the network.

4.6.

FC density

The group comparison of FC density and partial correlations of FC density with temperament traits of HA and NS was necessary because the individual variations in the groupaveraged brain network were ignored. Three distinct territories of the PFC cluster, BG/THL cluster, and limbic system were selected for the FC density analysis because these territories were identified as the key regions involved in human temperaments (Haier et al., 1987; Deckersbach et al., 2006; O'Gorman et al., 2006). The FC density (i.e., edge density of the functional network) was computed within and between territorial clusters for each individual subject. FCDkαβ ¼

1 ∑ ∑ Ak ; N i A CLα j A CLβ ij

ð4Þ

iaj

where k is the subject index; α and β are the cluster indices (α and β represents the PFC, BG/THL, and limbic clusters); CLα is a set of nodes within a cluster α; N is the number of edges between clusters (i.e., N ¼ nα nβ if αaβ) or within a cluster (i.e., N ¼ n2α
nα if α ¼ β); and nα is the number of nodes in a cluster α. A statistical comparison was then performed to test for group differences in the FC density after converting the correlation values to z-statistics.

Please cite this article as: Kyeong, S., et al., Functional network organizations of two contrasting temperament groups in dimensions of novelty seeking and harm avoidance. Brain Research (2014), http://dx.doi.org/10.1016/j.brainres.2014.05.037

1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144

BRES : 43580

10

1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204

4.7.

brain research ] (]]]]) ]]]–]]]

Gray matter volume

High-resolution T1-weighted images were processed through the optimized VBM protocol (Good et al., 2001) using DARTEL (Ashburner, 2007) implemented in SPM8. We generated a group GM template to which the entire individual GM segmentation map was spatially normalized. To adjust the volume changes during the non-linear transformations, spatially normalized GM segmentation maps were modulated by the amount of expansion or shrinkage during the spatial normalization process by applying the Jacobian determinant of the deformation field. These modulated GM maps were smoothed using an isotropic Gaussian kernel with a FWHM of 8 mm. To avoid edge effects around the border between the gray and white matter, voxels with a value of less than 0.1 were excluded for the next step (Mühlau et al., 2006). The significant GM map was parcellated into 90 ROIs based on the AAL atlas, which is the same atlas used in the FN construction. We obtained the regional GM volume for three distinct territorial clusters, which are the PFC, BG/THL, and limbic structures. A statistical comparison was then performed to test for group differences in GM volume. In addition, we computed partial correlations between temperamental traits such as NS and HA and the GM volume while controlling for the effect of age.

Appendix A.

Supplementary data

Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.brainres. 2014.05.037.

references

Ashburner, J., 2007. A fast diffeomorphic image registration algorithm. NeuroImage 38, 95–113. ´ vila, C., 2001. Distinguishing BIS-mediated and BAS-mediated A disinhibition mechanisms: a comparison of disinhibition models of Gray (1981, 1987) and of Patterson and Newman (1993). J. Personal. Soc. Psychol. 80 (2), 311–324. Baeken, C., De Raedt, R., Ramsey, N., Van Schuerbeek, P., Hermes, D., Bossuyt, A., Leyman, L., Vanderhasselt, M.-A., DeMey, J., Luypaert, R., 2009. Amygdala responses to positively and negatively valenced baby faces in healthy female volunteers: influences of individual differences in harm avoidance. Brain Res. 1296 (November), 94–103. Barro´s-Loscertales, A., Meseguer, V., Sanjua´n, A., Belloch, V., ´ vila, C., 2006. Behavioral Inhibition Parcet, M.A., Torrubia, R., A System activity is associated with increased amygdala and hippocampal gray matter volume: a voxel-based morphometry study. NeuroImage 33 (November (3)), 1011–1015. Beaver, J.D., Lawrence, A.D., van Ditzhuijzen, J., Davis, M.H., Woods, A., Calder, A.J., 2006. Individual differences in reward drive predict neural responses to images of food. J. Neurosci. 26 (May (19)), 5160–5166. Blondel, V.D., Guillaume, J.L., Lambiotte, R., 2008. Fast unfolding of communities in large networks. J. Stat. Mech.: Theory Exp. 2008, 10008.

Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.-U., 2006. Complex networks: structure and dynamics. Phys. Rep. 424 (4–5), 175–308 〈http://www.sciencedirect.com/science/ article/pii/S037015730500462X〉. Bremner, J.D., Vythilingam, M., Vermetten, E., Nazeer, A., Adil, J., Khan, S., Staib, L.H., Charney, D.S., 2002. Reduced volume of orbitofrontal cortex in major depression. Biol. Psychiatry 51 (February (4)), 273–279. Buckholtz, J.W., Callicott, J.H., Kolachana, B., Hariri, A.R., Goldberg, T.E., Genderson, M., Egan, M.F., Mattay, V.S., Weinberger, D.R., Meyer-Lindenberg, A., 2008. Genetic variation in MAOA modulates ventromedial prefrontal circuitry mediating individual differences in human personality. Mol. Psychiatry 13 (March (3)), 313–324. Bullmore, E., Sporns, O., 2009. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10 (3), 186–198. Bush, G., Luu, P., Posner, M., 2000. Cognitive and emotional influences in anterior cingulate cortex. Trends Cognit. Sci. 4 (June (6)), 215–222. Canli, T., Zhao, Z., Desmond, J.E., Kang, E., Gross, J., Gabrieli, J.D.E., 2001. An fMRI study of personality influences on brain reactivity to emotional stimuli. Behav. Neurosci. 115, 33–42. ´ vila, C., Torrubia, R., 2003. The measurement of Caseras, X., A individual differences in Behavioural Inhibition and Behavioural Activation Systems: a comparison of personality scales. Personal. Individ. Differ. 34 (April (6)), 999–1013. Cloninger, C.R., 1987. A systematic method for clinical description and classification of personality variants: a proposal. Arch. Gen. Psychiatry 44 (June (6)), 573–588. Cloninger, C.R., Svrakic, D.M., Przybeck, T.R., 1993. A psychobiological model of temperament and character. Arch. Gen. Psychiatry 50 (12), 975–990. Coen, S.J., Kano, M., Farmer, A.D., Kumari, V., Giampietro, V., Brammer, M., Williams, S.C.R., Aziz, Q., 2011. Neuroticism influences brain activity during the experience of visceral pain. Gastroenterology 141 (September (3)), 909–917 e1. Cohen, M.X., Schoene-Bake, J.C., Elger, C.E., 2009. Connectivitybased segregation of the human striatum predicts personality characteristics. Nat. Neurosci. 12, 32–34. Cohen, M.X., Young, J., Baek, J.-M., Kessler, C., Ranganath, C., 2005. Individual differences in extraversion and dopamine genetics predict neural reward responses. Cognit. Brain Res. 25 (3), 851–861. Cools, R., Blackwell, A., Clark, L., Menzies, L., Cox, S., Robbins, T.W., 2005. Tryptophan depletion disrupts the motivational guidance of goal-directed behavior as a function of trait impulsivity. Neuropsychopharmacology 30 (July(7)), 1362–1373. Cunningham, W.A., Arbuckle, N.L., Jahn, A., Mowrer, S.M., Abduljalil, A.M., 2010. Aspects of neuroticism and the amygdala: chronic tuning from motivational styles. Neuropsychologia 48 (12), 3399–3404. Davis, F.C., Knodt, A.R., Sporns, O., Lahey, B.B., Zald, D.H., Brigidi, B.D., Hariri, A.R., 2013. Impulsivity and the modular organization of resting-state neural networks. Cereb. Cortex 23 (6), 1444–1452. De Fruyt, F., Van De Wiele, L., 2000. Cloninger’s psychobiological model of temperament and character and the five-factor model of personality. Personal. Individ. Differ. 29, 441–452. Deckersbach, T., Miller, K.K., Klibanski, A., 2006. Regional cerebral brain metabolism correlates of neuroticism and extraversion. Depress. Anxiety 23, 133–138. De Young, C.G., 2010. Personality neuroscience and the biology of traits. Soc. Personal. Psychol. Compass. 4 (12), 1165–1180. Etkin, A., Egner, T., Peraza, D.M., Kandel, E.R., Hirsch, J., 2006. Resolving emotional conflict: a role for the rostral anterior

Please cite this article as: Kyeong, S., et al., Functional network organizations of two contrasting temperament groups in dimensions of novelty seeking and harm avoidance. Brain Research (2014), http://dx.doi.org/10.1016/j.brainres.2014.05.037

1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264

BRES : 43580 brain research ] (]]]]) ]]]–]]]

1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324

cingulate cortex in modulating activity in the amygdala. Neuron 51 (6), 871–882. Ewbank, M.P., Lawrence, A.D., Passamonti, L., Keane, J., Peers, P.V., Calder, A.J., 2009. Anxiety predicts a differential neural response to attended and unattended facial signals of anger and fear. NeuroImage 44 (3), 1144–1151. First, M.B., Gibbon, M., Spitzer, R.L., Williams, J.B.W., Benjamin, L. S., 1997. User’s Guide for the Structured Clinical Interview for DSM-IV Axis II Personality Disorders (SCID-II). American Psychiatric Press, Inc. Friston, K.J., Holmes, A.P., Worsley, K.J., Poline, J.P., Frith, C.D., Frackowiak, R.S.J., 1995. Statistical parametric maps in functional imaging: a general linear approach. Human Brain Mapp. 2 (4), 189–210. Fru¨hholz, S., Prinz, M., Herrmann, M., 2010. Affect-related personality traits and contextual interference processing during perception of facial affect. Neurosci. Lett. 469 (January (2)), 260–264. Good, C.D., Johnsrude, I.S., Ashburner, J., Henson, R.N., Friston, K.J., Frackowiak, R.S., 2001. A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage 14, 21–36. Gray, J.A., 1970. The psychophysiological basis of introversionextraversion. Behav. Res. Therapy 8, 249–266. Gray, J.A., McNaughton, N., 2000. The Neuropsychology of Anxiety: An Enquiry into the Functions of the SeptoHippocampal System, Second ed. Oxford University Press, Q4 Inc.. Haier, R.J., Sokolski, K., Katz, M., Buchsbaum, M.S., 1987. The study of personality with positron emission tomography. In: Strelau, J., Eysenck, H.J. (Eds.), Personality Dimensions and Arousal. Plenum, New York. Honey, C.J., Thivierge, J.-P., Sporns, O., 2010. Can structure predict function in the human brain?. NeuroImage 52 (3), 766–776. Hooker, C.I., Verosky, S.C., Miyakawa, A., Knight, R.T., D’Esposito, M., 2008. The influence of personality on neural mechanisms of observational fear and reward learning. Neuropsychologia 46 (11), 2709–2724. Iidaka, T., Matsumoto, A., Ozaki, N., Suzuki, T., Iwata, N., Yamamoto, Y., Okada, T., Sadato, N., 2006. Volume of left amygdala subregion predicted temperamental trait of harm avoidance in female young subjects. A voxel-based morphometry study. Brain Res. 1125 (1), 85–93. Johnson, D.L., Wiebe, J.S., Gold, S.M., Andreasen, N.C., Hichwa, R.D., Watkins, L., Ponto, L.L.B., 1999. Cerebral blood flw and personality: a positron emission tomography study. Am. J. Psychiatry 156 (2), 252–257. Kennis, M., Rademaker, A.R., Geuze, E., 2013. Neural correlates of personality: an integrative review. Neurosci. Biobehav. Rev. 37 (1), 73–95. Kim, M.J., Gee, D.G., Loucks, R.A., Davis, F.C., Whalen, P.J., 2011. Anxiety dissociates dorsal and ventral medial prefrontal cortex functional connectivity with the amygdala at rest. Cereb. Cortex 21 (7), 1667–1673. Kunisato, Y., Okamoto, Y., Okada, G., Aoyama, S., Nishiyama, Y., Onoda, K., Yamawaki, S., 2011. Personality traits and the amplitude of spontaneous low-frequency oscillations during resting state. Neurosci. Lett. 492 (2), 109–113. Lancichinetti, A., Fortunato, S., Radicchi, F., 2008. Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78 (4), 046110. LeDoux, J.E., 2000. Emotion circuits in the brain. Annu. Rev. Neurosci. 23 (1), 155–184. Lei, X., Chen, C., Xue, F., He, Q., Chen, C., Liu, Q., Moyzis, R.K., Xue, G., Cao, Z., Li, J., Li, H., Zhu, B., Liu, Y., Hsu, A.S.C., Li, J., Dong, Q., 2013. Fiber connectivity between the striatum and cortical and subcortical regions is associated with Q3 temperaments in Chinese males. NeuroImage.

11

Li, Y., Qin, W., Jiang, T., Zhang, Y., Yu, C., 2012. Sex-dependent correlations between the personality dimension of harm avoidance and the resting-state functional connectivity of amygdala subregions. PLoS One 7 (4), e35925. Makris, N., Gasic, G.P., Seidman, L.J., Goldstein, J.M., Gastfriend, D.R., Elman, I., Albaugh, M.D., Hodge, S.M., Ziegler, D.A., Sheahan, F.S., Caviness, V.S., Tsuang, M.T., Kennedy, D.N., Hyman, S.E., Rosen, B.R., Breiter, H.C., 2004. Decreased absolute amygdala volume in cocaine addicts. Neuron 44 (November (4)), 729–740. Mardaga, S., Hansenne, M., 2007. Relationships between Cloninger’s biosocial model of personality and the behavioral inhibition/approach systems (BIS/BAS). Personal. Individ. Differ. 42, 715–722. Markett, S., Weber, B., Voigt, G., Montag, C., Felten, A., Elger, C., Reuter, M., 2013. Intrinsic connectivity networks and personality: the temperament dimension harm avoidance moderates functional connectivity in the resting brain. Neuroscience 240, 98–105. Masterman, D.L., Cummings, J.L., 1997. Frontal-subcortical circuits: the anatomic basis of executive, social and motivated behaviors. J. Psychopharmacol. 11 (2), 107–114. McNaughton, N., Corr, P.J., 2004. A two-dimensional neuropsychology of defense: fear/anxiety and defensive distance. Neurosci. Biobehav. Rev. 28 (3), 285–305. Miettunen, J., Lauronen, E., Kantoja¨rvi, L., Veijola, J., 2008. Intercorrelations between Cloninger’s temperament dimensions— a meta-analysis. Psychiatry Res. 160, 106–114. Mobbs, D., Hagan, C.C., Azim, E., Menon, V., Reiss, A.L., 2005. Personality predicts activity in reward and emotional regions associated with humor. Proc. Natl. Acad. Sci. U. S. A. 102 (November (45)), 16502–16506. Morgan, M.A., Romanski, L.M., LeDoux, J.E., 1993. Extinction of emotional learning: contribution of medial prefrontal cortex. Neurosci. Lett. 163 (1), 109–113. Mu¨hlau, M., Rauschecker, J.P., Oestreicher, E., Gaser, C., Ro¨ttinger, M., Wohlschla¨ger, A.M., Simon, F., Etgen, T., Conrad, B., Sander, D., 2006. Structural brain changes in tinnitus. Cereb. Cortex 16 (September (9)), 1283–1288. Newman, J.P., Widom, C.S., Nathan, S., 1985. Passive avoidance in syndromes of disinhibition: psychopathy and extraversion. J. Personal. Soc. Psychol. 48 (5), 1316–1327. Newman, M.E.J., 2006. Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74, 036104. O’Gorman, R.L., Kumari, V., Williams, S.C.R., Zelaya, F.O., Connor, S.E.J., Alsop, D.C., Gray, J.A., 2006. Personality factors correlate with regional cerebral perfusion. NeuroImage 31 (2), 489–495. Patterson, C.M., Kosson, D.S., Newman, J.P., 1987. Reaction to punishment, reflectivity, and passive avoidance learning in extraverts. J. Personal. Soc. Psychol. 52 (3), 565–575. Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A., 2003. Mutualinformation-based registration of medical images: a survey. IEEE Trans. Med. Imaging 22 (August (8)), 986–1004. Raine, A., Yang, Y., Narr, K.L., Toga, A.W., 2011. Sex differences in orbitofrontal gray as a partial explanation for sex differences in antisocial personality. Mol. Psychiatry 16 (2), 227–236. Reuter, M., Stark, R., Hennig, J., Walter, B., Kirsch, P., Schienle, A., Vaitl, D., 2004. Personality and emotion: test of Gray’s personality theory by means of an fMRI study. Behav. Neurosci. 118 (3), 462–469. Rubinov, M., Sporns, O., 2010. Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52 (3), 1059–1069. Schaefer, S.M., Jackson, D.C., Davidson, R.J., Aguirre, G.K., Kimberg, D.Y., Thompson-Schill, S.L., 2002. Modulation of amygdalar activity by the conscious regulation of negative emotion. J. Cognit. Neurosci. 14 (6), 913–921.

Please cite this article as: Kyeong, S., et al., Functional network organizations of two contrasting temperament groups in dimensions of novelty seeking and harm avoidance. Brain Research (2014), http://dx.doi.org/10.1016/j.brainres.2014.05.037

1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384

BRES : 43580

12

1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400

brain research ] (]]]]) ]]]–]]]

Shin, D.-J., Jung, W.H., He, Y., Wang, J., Shim, G., Byun, M.S., Jang, J. H., Kim, S.N., Lee, T.Y., Park, H.Y., Kwon, J.S., October 2013. The effects of pharmacological treatment on functional brain connectome in obsessive-compulsive disorder. Biol. Psychiatry. Sung, S.M., Kim, J.H., Yang, E., Abrams, K.Y., Lyoo, I.K., 2002. Reliability and validity of the Korean version of the Temperament and Character Inventory. Compr. Psychiatry 43 (3), 235–243. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M., 2002. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15 (1), 273–289. Whittle, S., Allen, N.B., Lubman, D.I., Yu¨cel, M., 2006. The neurobiological basis of temperament: towards a better

understanding of psychopathology. Neurosci. Biobehav. Rev. 30 (4), 511–525. Whittle, S., Yu¨cel, M., Yap, M.B.H., Allen, N.B., 2011. Sex differences in the neural correlates of emotion: evidence from neuroimaging. Biol. Psychol. 87 (3), 319–333. Williams, L.M., Brown, K.J., Palmer, D., Liddell, B.J., Kemp, A.H., Olivieri, G., Peduto, A., Gordon, E., 2006. The mellow years?: neural basis of improving emotional stability over age. J. Neurosci. 26 (24), 6422–6430. Yamasue, H., Abe, O., Suga, M., Yamada, H., Inoue, H., Tochigi, M., Rogers, M., Aoki, S., Kato, N., Kasai, K., 2008. Gender-common and -specific neuroanatomical basis of human anxiety-related personality traits. Cereb. Cortex 18, 46–52. Zuckerman, M., Cloninger, C.R., 1996. Relationships between Cloninger’s, Zuckerman’s, and Eysenck’s dimensions of personality. Personal. Individ. Differ. 21 (2), 283–285.

Please cite this article as: Kyeong, S., et al., Functional network organizations of two contrasting temperament groups in dimensions of novelty seeking and harm avoidance. Brain Research (2014), http://dx.doi.org/10.1016/j.brainres.2014.05.037

1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415