Download - Functional network organizations of two contrasting temperament groups in dimensions of novelty seeking and harm avoidance

Functional Network Organisations of two contrasting temperament groups in dimensions of novelty seeking and harm avoidance

Sunghyon KyeongNational Institute for Mathematical Science (NIMS), Daejeon, Republic of Korea

Published at Brain ResearchDOI: 10.1016/j.brainres.2014.05.037

Joint work with Eunjoo Kim, Hae-Jeong Park, and Dong-Uk Hwang

Application of Graph Theoretical Methodology "

to"

Behavioural Neuroscience

In behavioural neuroscience,"behavioural characteristics of individuals originating from the different patterns of functional activity and morphometric variation in the brain

In psychology,"patterns of an individuals’s emotion, thoughts and behaviours"generally stable throughout his or her life and across situations.

Personality

S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain |

TCI, a measure of personality

• Temperament and character Inventory (TCI) was developed by Cloninger (1994).

• TCI traits were originally proposed to be independent of one another.

• However, meta-study found a significant negative correlation between Harm Avoidance (HA) and Novelty Seeking (NS) (Miettunen et al. 2008).

4


HA and NS• Harm avoidance (HA) is a personality trait

characterised by excessive worrying, shyness, and being fearful, doubtful, and easily fatigued.

• Novelty seeking (NS) is a personality trait associated with exploratory activity in response to novel stimulation, impulsive decision making, and quick loss of temper and avoidance of frustration.

5


Think about your friends

6

• Novelty seeking is positively related to active, energetic activity."

• A high novelty seeking trait has been suggested to be related to high dopaminergic activity.

• Harm avoidance is positively related to passive, avoiding, hesitating behaviours."

• A high harm avoidance trait has been suggested to be related to avoid high risky (or harmful) activity.


• Our goal is to identify the characteristics of the functional network modular organisations that make two contrasting temperament groups different."

• Existing studies didn’t show how the brain networks are organised across personality groups."

• Recently, brain modular organisations of three different impulsivity groups (i.e. low, medium, and high) were revealed (F. Caroline Davis et al. (2012)).

Objective

7


Key Regions associated with Temperament

Prefrontalprefrontal cortex, orbitofrontal cortex,

anterior cinculate cortex

Basal Ganglia Caudate,Putamen,Pallidum,Thalamus

LimbicAmygdala,Hippocampus,Parahippocampal gyrus

Cremers H et al. 2011, Omura K et al. 2005, Yamasue H et al. 2008 Deckersbach T et al. 2006

Yamasue H et al. 2008, Iidaka T et al. 2006, Omura K et al. 2005

Haier RJ T et al. 1987, O’Gorman RL et al. 2006

8

Prefrontal

Limbic

Temperament traits & Regional association

Personality & Local brain

activity

GLM approach

“connectome” approach

Materials and Methods


10 20 30 40 50 60 700

10

20

10 20 30 40 50 60 700

5

10

15

90 100 110 120 130 1400

5

10

Subjects and Materials

...

high-resolution T1 resting state fMRI (404 scans with TR=2s)

• Brain Images Acquisition at Severance:

• 40 healthy male subjects (25.2 ± 3.3 years)

• TCI with 140 items & K-WAIS at Severance

NS HA IQ

11


the mean of points in Si

The goal of k-means clustering is to minimise the within-cluster sum of squares.

V =2X

i=1

X

xj2Si

||xj

� µ

i

||2 S = {S1, S2}

p=0.0024 p<0.0001 p=0.0004 p=0.0115 p<0.0001 p=0.0436 p=0.0122

20 30 40 50 60 700

10

20

30

40

50

60

70

Novelty Seeking

Har

m A

void

ance

IntrovertsExtravertsCentroids

20 30 40 50 60 70 Novelty Seeking

Harm

Avo

idan

ce

70

60

50

40

30

20

10

0

Subject Clustering

A. k-means Clustering B. Group comparison of TCI traits

high HA & low NS low HA & high NSCentroids×

high HA & low NS group

low HA & high NS group

Trai

t Sco

re

010203040506070

NS HA P SD C STRD

12


Mechanism of BOLD fMRI

Time

Signal

Mo sinθ

T2* (task)

T2* (control)

TEoptimum

Stask

Scontrol ΔS

↑ Neural Activity ↑ Blood Flow ↑ Oxyhemoglobin

↑ T2*

↑ MR Signal

13


Network Construction

...

...

Spatial Preprocessingrealignment, co-registration, normalisation, and smoothing

Parcellation into 116 brain regions

Adjacent Matrix

AAL atlas

Akij

Individual functional network for k-th subject

• Network extraction in individual level

FN `ij =

1

nl

X

k2G`

Akij

G`

FN `ij

Adjacency matrices in a group

Group Averaged FN

where is a set of subjects within group l

• Group averaged functional network (FN)

14


Community Detection

15

Functional network communities of each group was detected by the Louvain method which maximises the modularity, Q.

where si 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.

A community is a dense subnetwork within a larger network.

Results


Functional Network (FN)A. high HA and low NS group B. low HA and high NS group

Modules:$Visual,$Motor,$Frontal,$BG/THL,$PFC+Limbic Modules:$Visual,$Motor,$Frontal,$Limbic,$PFC+BG/THL

17


PFC Limbic BG/THL

0.5

0.4

0.3

0.2

0.1

0

FN Density (high HA and low NS)"(numbers represent group average FN density)

FN Density (low HA and high NS) (numbers represent group average FN density)

Two sample T-Test(numbers represent p-value)

0.39

0.0655 0.0089 0.8193

0.178 0.0681

0.6718

0.43 0.14 0.1

0.38 0.08

0.4

0.37 0.07 0.11

0.42 0.12

0.39

PFC Limbic BG/THLPFC Limbic BG/THL

OLF.L

ACC.R

ACC.LACC.L

RG.L

mOFC.L

OLF.R

AMYG.L AMYG.R

HIP.L

HIP.RPHG.L

PHG.R

CAU.R

CAU.L

TAL.R

TAL.L

PUT.L

SOFC.R

SOFC.LmOFC.R

0.37

0.39 0.42

0.11 0.07

0.12

PAL.R

PAL.L

PUT.R

OLF.L

ACC.R

ACC.LACC.L

RG.L

mOFC.L

OLF.R

AMYG.L AMYG.R

HIP.RPHG.L

PHG.R

CAU.R

CAU.L

TAL.R

TAL.L

PAL.R

PAL.L

PUT.R

PUT.L

SOFC.R

SOFC.LmOFC.R

0.43

0.40 0.38

0.10 0.14

0.08

HIP.L

FN Sub-Graph and FCD

18

PFC

BG/THL Limbic

PFC

BG/THL Limbic

A. high HA and low NS B. low HA and high NS


20 30 40 50 60

−0.2

−0.1

0

0.1

0.2

Novelty Seeking20 30 40 50 60

−0.2

−0.1

0

0.1

0.2

Harm Avoidance

Corr. (FCD, TCI)

0.2

0.1

0

C0.1

C0.2

Novelty Seeking20 30 40 50 60

Dens

ity o

f fun

ctio

nal c

onne

ctivi

ty

of th

e PF

C an

d Li

mbi

c Cl

uste

rs 0.2

0.1

0

C0.1

C0.2

Harm Avoidance20 30 40 50 60

r=0.30 (p=0.0588)r=-0.52 (p=0.0006)

correlation was computed while controlling age.

correlation between functional connectivity density (FCD) and temperament traits: (1) FCD between PFC and Limbic territories and NS; (2) FCD between PFC and Limbic territories and HA.

High HA and low NSLow HA and high NSFitting Curve

High HA and low NSLow HA and high NSFitting Curve

19


3.5 4 4.5

20

30

40

50

60

BG/THL Volume (cm3)4.2 4.6 5 5.4 5.8

20

30

40

50

60

Limbic Volume (cm3)

Harm

Avo

idan

ce

r=0.37 (p=0.0188) r=0.50 (p=0.0013)

60

50

40

30

20

70

3.5 4 4.5

20

30

40

50

60

BG/THL Volume (cm3)4.2 4.6 5 5.4 5.8

20

30

40

50

60

Limbic Volume (cm3)

Nove

lty S

eekin

g

r=-0.32 (p=0.0466) r=-0.30 (p=0.0659)

60

50

40

30

20

70

Volume of Limbic (cm3)4.2 4.6 5.0 5.4 5.8

Volume of BG/THL (cm3)4.6 5.0 5.4

Harm

Avo

idan

ceNo

velty

See

king

60

50

40

30

20

70

60

50

40

30

20

70

Corr. (GMV, TCI)High HA and low NS Low HA and high NS Fitting Curve

20

correlation was computed while controlling age.


4 4.5 5 5.5 6−0.3

−0.2

−0.1

0

0.1

0.2

0.3

Volume of Limbic (cm3)

FC D

ensi

ty (b

etw

een

PFC

and

Lim

bic)

Volume$of$Limbic$(cm3)4.0 4.5 5.0 5.5 6.0

Density$of$Functional$Connectivity

of$PFC$and$Limbic$Clusters

0.3

0.2

0

C0.1

C0.2

0.1

C0.3

r= 0.45 (p=0.0033)

High$HA$and$low$NS

Low$HA$and$high$NSFitting$Curve

21

Coupling (FCD, VBM)

Discussion


• Female subjects should be included to generalise the results regardless of gender."

• Structural networks from diffusion tensor imaging data could be considered to promote deeper insights into the neural correlates of personality."

• Cross-cultural study would advance the understanding of personality."

• Other questionnaires (Eysenck’ personality scale) should be performed to check the extraversion score directly.

In the future study,the followings should be considered.

23

S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain |24

Neural Substrates for high HA and low NS group

PFC

BG/THL

Limbic

• The neural substrate of the individuals with ‘high HA and low NS’ group arise from the increased connectivity between PFC and Limbic. "

• Individuals with High HA and low NS showed the inhibited behaviour because the regulatory brain region such as the PFC is strongly association with fear related brain region such as the limbic system.

Designed(by( Eunha&Lim

Neural Network for Introverted Individuals

24

S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain |25

Neural Substrates for low HA and high NS group• The neural substrate of the individuals with low HA and high NS arise

from the strong connectivity between PFC and BG/THL. "

• Active and facilitating, extravert-like behaviour of the low HA and high NS group arise from the functional connection between PFC and BG/THL. The increased connectivity across the regions of dopamine pathway might related to behavioural characteristics of this group.

25

PFC

BG/THL

Limbic

“I am my connectome"by Sebastian Seung


• We classified the 40 subjects into two contrasting temperament groups: high HA and low NS vs. low HA and high NS.

• The different functional network organisation among the PFC, BG/THL, and limbic system are the neural basis of two contrasting temperament groups

• Watching your neighbours and telling them the neural basis of behaviours

• This study was recently published online at Brain Research. DOI: 10.1016/j.brainres.2014.05.037

Conclusion

27

Thank you 8-)