Agreement between the white matter connectivity based tensor-based

morphometry and the volumetric white matter parcellations

16 Nov 2011 Wed, 08:00~08:15

Seung-Goo KIMDepartment of Brain and Cognitive Sciences,

Seoul National University, Korea (ROK)

Nanosymposium: Data Analysis and Statistics

ACKNOWLEDGEMENT

• Hyekyoung Lee @ SNU

• Moo K. Chung, Jamie L. Hanson, Richard J. Davidson, Seth D. Pollak @ U Wisconsin-Madison

• Brain B. Avants, James C. Gee @ U Penn

INTRODUCTION

Connectivity based on correlation• Correlation of functional measures

Connectivity based on correlation• Correlation of functional measures

i j

scan 2scan 1

i j

scan 3

i j

Connectivity based on correlation• Correlation of functional measures

i j

scan 2scan 1

i j

scan 3

i j

Connectivity based on correlation• Correlation of functional measures

i j i j

subject 1 subject 2

i j

subject 3

• Correlation of anatomical measures

i j

scan 2scan 1

i j

scan 3

i j

Brain network based on cortical thickness

Worsley et al., 2005, Phil. Trans. R. Soc. B

White matter connectivity

White matter connectivity

• Cortical thickness is only defined along the cortical surface, thus the connectivity within the white matter cannot be known.

White matter connectivity

• Cortical thickness is only defined along the cortical surface, thus the connectivity within the white matter cannot be known.

• We can build the white matter connectivity using tensor-based morphometry (TBM) that quantifies local volume at all voxels.

Kim et al., 2011, IEEE International Symposium on Biomedical Imaging (ISBI), pp. 808-811.

TBM-based networks

OBJECTIVES

OBJECTIVES

• Agreements between TBM-based networks and DTI-based connectivity

OBJECTIVES

• Agreements between TBM-based networks and DTI-based connectivity

• Differences between a clinical group and a normal control group using TBM-based networks

METHODS

Subjects & images• PI (Post-Institutionalized) subjects

• 32 children who experienced maltreatment in the early stages of life (<2 yr-old) in orphanages and were adopted later

• NC (Normal Control) subjects

• 33 age & gender matched children

• T1-weighted MRIs (3 Tesla; 1mm3 voxel)

• Non-linear normalization by ANTS (U Penn)

Tensor-based morphometry

Tensor-based morphometry

13

23

Jacobian determinant

Tensor-based morphometry

Tensor-based morphometry

Partial correlation

• To factor out age and gender, first fit a GLM JD = β0 + β1 · age+ β2 · gender+ noise

Partial correlation

• To factor out age and gender, first fit a GLM JD = β0 + β1 · age+ β2 · gender+ noise

r = JD− (�β0 + �β1 · age+ �β2 · gender)• Take residuals of the fit from the observation

Partial correlation

• To factor out age and gender, first fit a GLM JD = β0 + β1 · age+ β2 · gender+ noise

r = JD− (�β0 + �β1 · age+ �β2 · gender)• Take residuals of the fit from the observation

• Pearson’s correlation between the residuals is the partial correlation

TBM-based networks

DTI-based white matter atlas(ICBM-DTI-81)

S. Mori et al., 2008, NI.

Ck(k = 1, · · · , 48)

DTI-based white matter atlas(ICBM-DTI-81)

S. Mori et al., 2008, NI.

C3C4

Connectivity Matrix

Xmn =

�i∈Cm,j∈Cn

corr(i, j)

Number of pairs

[Xmn] ∈ R48×48

C3C4

C3C4

Connectivity Matrix

Xmn =

�i∈Cm,j∈Cn

corr(i, j)

Number of pairs

[Xmn] ∈ R48×48

C4C3C4

C3

Connectivity Matrix

Xmn =

�i∈Cm,j∈Cn

corr(i, j)

Number of pairs

[Xmn] ∈ R48×48

Xmn =

�i∈Cm,j∈Cn

corr(i, j)

Number of pairs

WITHIN- vs. BETWEEN-[Xmn] ∈ R48×48

m = n

Diagonal element in

C3

[Xmn]

m �= n

C3C4

Off-diagonal element in [Xmn]

Xmn =

�i∈Cm,j∈Cn

corr(i, j)

Number of pairs

WITHIN- vs. BETWEEN-[Xmn] ∈ R48×48

m = n

Diagonal element in

C3

[Xmn]

m �= n

C3C4

Off-diagonal element in [Xmn]

WITHIN-connectivity

Xmn =

�i∈Cm,j∈Cn

corr(i, j)

Number of pairs

WITHIN- vs. BETWEEN-[Xmn] ∈ R48×48

m = n

Diagonal element in

C3

[Xmn]

m �= n

C3C4

Off-diagonal element in [Xmn]

WITHIN-connectivity

BETWEEN-connectivity

Xmn =

�i∈Cm,j∈Cn

corr(i, j)

Number of pairs

WITHIN- vs. BETWEEN-[Xmn] ∈ R48×48

m = n

Diagonal element in

C3

[Xmn]

m �= n

C3C4

Off-diagonal element in [Xmn]

WITHIN-connectivity

BETWEEN-connectivity

Xmn =

�i∈Cm,j∈Cn

corr(i, j)

Number of pairs

WITHIN- vs. BETWEEN-[Xmn] ∈ R48×48

m = n

Diagonal element in

C3

[Xmn]

m �= n

C3C4

Off-diagonal element in [Xmn]

WITHIN-connectivity

BETWEEN-connectivity

>>

Statistical inferences

• Jackknifing on NC and PI

• 500 random networks as null models against brain networks

• Willcoxon rank sum test is used

RESULTS

NC PI Random

Connectivity matrices

Within- vs. Between-conn.* p<0.001

NC PI Random

Within- vs. Between-conn.* p<0.001

NC PI Random

NC vs. PI: Global inference

NC PI

* p<0.001

NC vs. PI: Global inference

NC PI

* p<0.001

NC PI

NC vs. PI: Local inference

PI<NC, PI>NC

p<0.01, Bonferroni corrected

NC PI

NC vs. PI: Local inference

PI<NC, PI>NC

p<0.01, Bonferroni corrected

NC PI

NC vs. PI: Local inference

PI<NC, PI>NC

p<0.01, Bonferroni corrected

NC vs. PI: local inference

PI<NC PI>NCGCC: Genu of corpus callosum EC-R: External capsule, right

SCR-L: Superior corona radiata, left FX/ST-R: Fornix / Stria terminalis, right

SCP-L: Superior cerebellar peduncle, left

Conclusions

Conclusions

• The greater within-connectivity than between-connectivity in brain networks shows agreement between TBM-based network and DTI-based atlas.

Conclusions

• The greater within-connectivity than between-connectivity in brain networks shows agreement between TBM-based network and DTI-based atlas.

• Locally differences in within-connectivity may imply altered white matter integrity due to early maltreatment

THANK U