J Korean Soc Magn Reson Med.  2011 Aug;15(2):110-122. 10.13104/jksmrm.2011.15.2.110.

Anatomical Brain Connectivity Map of Korean Children

• Um M
• Park B
• Park HJ

• KMID: 2000059
• DOI: http://doi.org/10.13104/jksmrm.2011.15.2.110

Abstract

PURPOSE
The purpose of this study is to establish the method generating human brain anatomical connectivity from Korean children and evaluating the network topological properties using small-world network analysis.
MATERIALS AND METHODS
Using diffusion tensor images (DTI) and parcellation maps of structural MRIs acquired from twelve healthy Korean children, we generated a brain structural connectivity matrix for individual. We applied one sample t-test to the connectivity maps to derive a representative anatomical connectivity for the group. By spatially normalizing the white matter bundles of participants into a template standard space, we obtained the anatomical brain network model. Network properties including clustering coefficient, characteristic path length, and global/local efficiency were also calculated.
RESULTS
We found that the structural connectivity of Korean children group preserves the small-world properties. The anatomical connectivity map obtained in this study showed that children group had higher intra-hemispheric connectivity than inter-hemispheric connectivity. We also observed that the neural connectivity of the group is high between brain stem and motorsensory areas.
CONCLUSION
We suggested a method to examine the anatomical brain network of Korean children group. The proposed method can be used to evaluate the efficiency of anatomical brain networks in people with disease.

Keyword

Structural connectivity; Small-world network; Korean children connectome

MeSH Terms

Brain
Brain Stem
Child
Diffusion
Humans

Figure

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  Fig. 1 (a) Parcellation images provided by FreeSurfer. (b) A flowchart for the construction of the anatomical network in the human brain using DTI tractography. (1) Reconstructing white matter bundles in the whole brain using DTI tractography. (2) Co-registering T1-weighted image with B0-image and generating a transformation matrix. (3) Registering a label image provided by FreeSurfer to B0-image space by applying the transformation matrix and reslicing the image. (4) Determining the white matter bundles connecting every pair of cortical and sub-cortical regions and identifying correlation matrix for each patient. (rRCgC : right rostral anterior cingulate gyrus, rMFGr : right rostral middle frontal gyrus, rSFG : right superior frontal gyrus, rSPG : right superior parietal gyrus)

• 

  Fig. 2 A process for assigning colors into white matter bundles connecting two regions. (1) Identifying colors for white matter bundles connecting each pair of all regions by averaging the RGB values of two regions. (2) Visualizing color-coded white matter bundles in the whole brain.

• 

  Fig. 3 A flow sheet for obtaining an anatomical connectivity map for a group. (1) Normalizing B0-image to T2-weighted image in MNI space and gaining the transformation matrix. (2) Reconstructing all the white matter bundles from the B0-image for each participants. (3) Normalizing the bundles to the MNI space by applying the transformation matrix. (4) Sampling bundles for all participants to obtain population-based white matter bundles.

• 

  Fig. 4 An anatomical connectivity for Korean children group in single color (a) and multi-colors which were obtained in the method averaging the RGB values of pair of all regoins (b). The connectivity shows high degree between two regions including left-superior frontal gyrus and right-superior frontal gyrus, left-post central gyrus and left-supramarginal gyrus, brain stem and left cerebellum cortex, brain stem and right cerebellum cortex. (R : right, A : anterior, P : posterior)

• 

  Fig. 5 Connectivity after conducting one sample t-test (a-c) (p<0.001, uncorrected) and measuring the top 10 percent of the average of the anatomical correlation matirx (d-f) in transverse top view (a, d), coronal anterior view (b, e), and sagittal left view (c, f). For one sample t test, the value of global efficiency is 0.134, local efficiency is 0.148, characteristic path length is 8.262, clustering coefficient is 5.078. For the top 10 percent, global efficiency is 0.264, local efficiency is 0.483, characteristic path length is 3.634, clustering coefficient is 1.107. The size of node and the width of the line indicate node degree and connectivity. Abbreviation: A : anterior, P : posterior, L : left, R : right, lCBllCx : left cerebellum, lTH : left thalamus, lPU : left putamen, BrSt : brain stem, lbankssts : left bankssts, lMFGc : left caudal middle frontal gyrus, lFuG : left fusiform gyrus, lIPG : left inferior parietal gyrus, lITL : left inferior temporal gurys, lICgG : left isthmus cingulate gyrus, lMTG : left middle temporal gyrus, lPCL : left paracentral gyrus, lPCG : left postcentral gyrus, lPCu : left precuneus, lSFG : left superior frontal gyrus, lSPG : left superior parietal gyrus, rFuG : right fusiform gyrus, rIPG : right inferior parietal gyrus, rITG :right inferior temporal gyrus, rICgG : right isthmus cingulate gyrus, rPCG : right postcentral gyrus, rPrG : right precentral gyrus, rPCu : right precuneus, rSPG : right superior parietal gyrus.

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