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

Graph Theoretical Methodology "


to"
Behavioural Neuroscience
In behavioural neuroscience,"
behavioural characteristics of individuals
originating from the different patterns of
functi...
S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain |
TCI, a measure of personality
• Tem...
S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain |
HA and NS
• Harm avoidance (HA) is ...
S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain |
Think about your friends
6
• Novelt...
S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain |
• Our goal is to identify the chara...
S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain |
Key Regions
associated with 

Tempe...
Prefrontal
Limbic
Temperament traits &
Regional association
Personality &
Local brain
activity
GLM
approach
“connectome” a...
Materials and Methods
S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain |
10 20 30 40 50 60 70
0
10
20
10 20 ...
S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain |
the mean of points in Si
The goal o...
S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain |
Mechanism of BOLD fMRI
Time
Signal
...
S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain |
Network Construction
...
...
Spatia...
S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain |
Community Detection
15
Functional n...
Results
S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain |
Functional Network (FN)
A. high HA ...
S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain |
PFC Limbic BG/THL
0.5
0.4
0.3
0.2
0...
S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain |
20 30 40 50 60
−0.2
−0.1
0
0.1
0.2
...
S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain |
3.5 4 4.5
20
30
40
50
60
BG/THL Vol...
S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain |
4 4.5 5 5.5 6
−0.3
−0.2
−0.1
0
0.1
...
Discussion
S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain |
• Female subjects should be include...
S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain |
24
Neural Substrates for 

high HA ...
S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain |
25
Neural Substrates for 

low HA a...
“I am my connectome"
by Sebastian Seung
S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain |
• We classified the 40 subjects into...
Thank you 8-)
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Functional network organizations of two contrasting temperament groups in dimensions of novelty seeking and harm avoidance

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This material was presented at international workshop in neurodynamics 2014, Spain.

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

  1. 1. Functional Network Organisations of two contrasting temperament groups in dimensions of novelty seeking and harm avoidance Sunghyon Kyeong National Institute for Mathematical Science (NIMS), 
 Daejeon, Republic of Korea Published at Brain Research
 DOI: 10.1016/j.brainres.2014.05.037 Joint work with Eunjoo Kim, Hae-Jeong Park, and Dong-Uk Hwang
  2. 2. Application of 
 Graph Theoretical Methodology " 
 to" Behavioural Neuroscience
  3. 3. 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
  4. 4. 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
  5. 5. S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain | 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
  6. 6. S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain | 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.
  7. 7. S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain | • 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
  8. 8. S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain | Key Regions associated with 
 Temperament Prefrontal
 prefrontal cortex, orbitofrontal cortex, 
 anterior cinculate cortex Basal Ganglia
 Caudate,Putamen,Pallidum,Thalamus Limbic
 Amygdala,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
  9. 9. Prefrontal Limbic Temperament traits & Regional association Personality & Local brain activity GLM approach “connectome” approach
  10. 10. Materials and Methods
  11. 11. S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain | 10 20 30 40 50 60 70 0 10 20 10 20 30 40 50 60 70 0 5 10 15 90 100 110 120 130 140 0 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
  12. 12. S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain | 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 xj 2Si ||xj µi||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 70 0 10 20 30 40 50 60 70 Novelty Seeking HarmAvoidance Introverts Extraverts Centroids 20 30 40 50 60 70 Novelty Seeking HarmAvoidance 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 NS Centroids× high HA & low NS group low HA & high NS group TraitScore 0 10 20 30 40 50 60 70 NS HA P SD C STRD 12
  13. 13. S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain | 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
  14. 14. S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain | Network Construction ... ... Spatial Preprocessing
 realignment, co-registration, normalisation, and smoothing Parcellation 
 into 116 brain regions Adjacent Matrix AAL atlas Ak ij Individual functional network for k-th subject • Network extraction in individual level FN` ij = 1 nl X k2G` Ak ij 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
  15. 15. S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain | 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.
  16. 16. Results
  17. 17. S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain | 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
  18. 18. S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain | 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/THL PFC 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
  19. 19. S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain | 20 30 40 50 60 −0.2 −0.1 0 0.1 0.2 Novelty Seeking 20 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 Seeking 20 30 40 50 60 Densityoffunctionalconnectivity
 ofthePFCandLimbicClusters 0.2 0.1 0 C0.1 C0.2 Harm Avoidance 20 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 NS Low HA and high NS Fitting Curve High HA and low NS Low HA and high NS Fitting Curve 19
  20. 20. S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain | 3.5 4 4.5 20 30 40 50 60 BG/THL Volume (cm 3 ) 4.2 4.6 5 5.4 5.8 20 30 40 50 60 Limbic Volume (cm 3 ) HarmAvoidance 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 (cm 3 ) 4.2 4.6 5 5.4 5.8 20 30 40 50 60 Limbic Volume (cm 3 ) NoveltySeeking 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 HarmAvoidanceNoveltySeeking 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.
  21. 21. S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain | 4 4.5 5 5.5 6 −0.3 −0.2 −0.1 0 0.1 0.2 0.3 Volume of Limbic (cm3 ) FCDensity(betweenPFCandLimbic) 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$high$NS Fitting$Curve 21 Coupling (FCD, VBM)
  22. 22. Discussion
  23. 23. S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain | • 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
  24. 24. 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
  25. 25. 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
  26. 26. “I am my connectome" by Sebastian Seung
  27. 27. S. Kyeong (NIMS) | Functional Modular Organisations | NDy14 @ Castro-Urdiales, Spain | • 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
  28. 28. Thank you 8-)

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