Anatomical Brain Connectivity Map of Korean Children

한국 아동 집단의 구조 뇌연결지도

  • Um, Min-Hee (BK21 Project for Medical Science, Yonsei University College of Medicine) ;
  • Park, Bum-Hee (BK21 Project for Medical Science, Yonsei University College of Medicine) ;
  • Park, Hae-Jeong (BK21 Project for Medical Science, Yonsei University College of Medicine)
  • 엄민희 (연세대학교 의과대학 BK21 연세의과학사업단) ;
  • 박범희 (연세대학교 의과대학 BK21 연세의과학사업단) ;
  • 박해정 (연세대학교 의과대학 BK21 연세의과학사업단)
  • Received : 2011.04.27
  • Accepted : 2011.07.13
  • Published : 2011.08.31

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.

목적 : 본 연구의 목적은 확산텐서영상에 기반하여 한국 아동 집단의 해부학적 뇌연결성 지도를 확립하고 뇌신경망의 효율성을 평가하는 기법을 개발하는 것이다. 대상 및 방법 : 건강한 아동 12명에서 얻은 확산텐서영상과 뇌구획영상을 바탕으로 구조 연결 행렬을 구하여 집단의 구조 연결성을 평가하였다. 일표본 t-검정을 시행하여 평균적인 구조 연결성을 파악하였고 이 때 얻은 각 피험자의 백질 다발을 표준공간으로 정규화하여 집단의 해부학적 뇌연결망 지도를 확립했다. 뇌신경망의 군집정도(clustering coefficient), 평균이동거리(characteristic path length), 전체/부분 연결망 효율성(global/local efficiency) 등 연결망 속성을 계산한 후 시각화 하였다. 결과 : 연결망 측면에서 한국 아동 집단의 뇌연결성이 작은세상속성을 가짐을 밝혔다. 또한 해부학적 뇌연결망 지도를 얻었는데 대뇌 반구 내의 연결성이 높게 나타남과 뇌간과 운동/감각 영역간에 많은 신경 연결이 집중되어 있음을 확인하였다. 결론 : 한국 아동 집단의 해부학적 뇌연결망 지도를 작성하는 방법론을 제시하여 뇌를 연결성 측면에서 이해하고 발달 장애와 성인 뇌신경망의 효율성을 평가할 수 있는 기본 도구를 확립하게되었다.

Keywords

References

  1. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 2009;10:186-198 https://doi.org/10.1038/nrn2575
  2. Sporns O. The human connectome: a complex network. Ann N Y Acad Sci 2011
  3. Achard S, Salvador R, Whitcher B, Suckling J, Bullmore E. A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J Neurosci 2006;26:63-72 https://doi.org/10.1523/JNEUROSCI.3874-05.2006
  4. Bassett DS, Bullmore E. Small-world brain networks. Neuroscientist 2006;12:512-523 https://doi.org/10.1177/1073858406293182
  5. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci U S A 2001;98:676-682 https://doi.org/10.1073/pnas.98.2.676
  6. Smit DJ, Stam CJ, Posthuma D, Boomsma DI, de Geus EJ. Heritability of "small-world" networks in the brain: a graph theoretical analysis of resting-state EEG functional connectivity. Hum Brain Mapp 2008;29:1368-1378 https://doi.org/10.1002/hbm.20468
  7. Valencia M, Martinerie J, Dupont S, Chavez M. Dynamic small- world behavior in functional brain networks unveiled by an event-related networks approach. Phys Rev E Stat Nonlin Soft Matter Phys 2008;77:050905
  8. Zhou C, Zemanova L, Zamora G, Hilgetag CC, Kurths J. Hierarchical organization unveiled by functional connectivity in complex brain networks. Phys Rev Lett 2006;97:238103
  9. Gong G, He Y, Concha L, Lebel C, Gross DW, Evans AC, et al. Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cereb Cortex 2009;19:524-536 https://doi.org/10.1093/cercor/bhn102
  10. Greicius MD, Supekar K, Menon V, Dougherty RF. Resting-state functional connectivity reflects structural connectivity in the default mode network. Cereb Cortex 2009;19:72-78
  11. Eguiluz VM, Chialvo DR, Cecchi GA, Baliki M, Apkarian AV. Scale-free brain functional networks. Phys Rev Lett 2005;94:018102
  12. Salvador R, Suckling J, Coleman MR, Pickard JD, Menon D, Bullmore E. Neurophysiological architecture of functional magnetic resonance images of human brain. Cereb Cortex 2005;15:1332-1342
  13. Ferrarini L VI, Baerends E, van Tol MJ, Renken RJ, van der Wee NJ, Veltman DJ, et al. Hierarchical functional modularity in the resting-state human brain. Hum Brain Mapp 2009;30:2220-2231 https://doi.org/10.1002/hbm.20663
  14. Meunier D, Achard S, Morcom A, Bullmore E. Age-related changes in modular organization of human brain functional networks. Neuroimage 2009;44:715-723 https://doi.org/10.1016/j.neuroimage.2008.09.062
  15. Stam CJ. Functional connectivity patterns of human magnetoencephalographic recordings: a 'small-world' network? Neurosci Lett 2004;355:25-28 https://doi.org/10.1016/j.neulet.2003.10.063
  16. Achard S, Bassett DS, Meyer-Lindenberg A, Bullmore E. Fractal connectivity of long-memory networks. Phys Rev E Stat Nonlin Soft Matter Phys 2008;77:036104
  17. Klaus Linkenkaer-Hansen VVN, J. Matias Palva, Risto J. Ilmoniemi. Long-Range Temporal Correlations and Scaling Behavior in Human Brain Oscillations. The Journal of Neuroscience 2001;21:1370-1377
  18. Maxim V, Sendur L, Fadili J, Suckling J, Gould R, Howard R, et al. Fractional Gaussian noise, functional MRI and Alzheimer's disease. Neuroimage 2005;25:141-158 https://doi.org/10.1016/j.neuroimage.2004.10.044
  19. Felleman DJ, Van Essen DC. Distributed hierarchical processing in the primate cerebral cortex. Cereb Cortex 1991;1:1-47
  20. Hilgetag CC, Burns GA, O'Neill MA, Scannell JW, Young MP. Anatomical connectivity defines the organization of clusters of cortical areas in the macaque monkey and the cat. Philos Trans R Soc Lond B Biol Sci 2000;355:91-110 https://doi.org/10.1098/rstb.2000.0551
  21. Latora V, Marchiori M. Economic small-world behavior in weighted networks. European Physical Journal B 2003;32:249-263 https://doi.org/10.1140/epjb/e2003-00095-5
  22. Scannell JW, Burns GA, Hilgetag CC, O'Neil MA, Young MP. The connectional organization of the cortico-thalamic system of the cat. Cereb Cortex 1999;9:277-299 https://doi.org/10.1093/cercor/9.3.277
  23. Young MP. Objective analysis of the topological organization of the primate cortical visual system. Nature 1992;358:152-155 https://doi.org/10.1038/358152a0
  24. Lee JD, Park HJ, Park ES, Oh MK, Park B, Rha DW, et al. Motor pathway injury in patients with periventricular leucomalacia and spastic diplegia. Brain : a journal of neurology 2011
  25. Park HJ. Quantification of white matter using diffusion-tensor imaging. Int Rev Neurobiol 2005;66:167-212
  26. Bullmore ET, Bassett DS. Brain graphs: graphical models of the human brain connectome. Annu Rev Clin Psychol 2011;7:113-140 https://doi.org/10.1146/annurev-clinpsy-040510-143934
  27. Patric Hagmann MK, Xavier Gigandet, Patrick Thiran, Van J. Wedeen, Reto Meuli, Jean-Philippe Thiran. Mapping Human Whole-Brain Structural Networks with Diffusion MRI. PLoS ONE 2007;7:e597
  28. Counsell SJ, Dyet LE, Larkman DJ, Nunes RG, Boardman JP, Allsop JM, et al. Thalamo-cortical connectivity in children born preterm mapped using probabilistic magnetic resonance tractography. Neuroimage 2007;34:896-904 https://doi.org/10.1016/j.neuroimage.2006.09.036
  29. Lenroot RK, Giedd JN. Brain development in children and adolescents: insights from anatomical magnetic resonance imaging. Neurosci Biobehav Rev 2006;30:718-729 https://doi.org/10.1016/j.neubiorev.2006.06.001
  30. Fair DA, Cohen AL, Dosenbach NU, Church JA, Miezin FM, Barch DM, et al. The maturing architecture of the brain's default network. Proc Natl Acad Sci U S A 2008;105:4028-4032 https://doi.org/10.1073/pnas.0800376105
  31. Park HJ, Kim JJ, Lee SK, Seok JH, Chun J, Kim DI, et al. Corpus callosal connection mapping using cortical gray matter parcellation and DT-MRI. Hum Brain Mapp 2008;29:503-516 https://doi.org/10.1002/hbm.20314
  32. Friston KJ. Commentary and opinion: II. Statistical parametric mapping: ontology and current issues. J Cereb Blood Flow Metab 1995;15:361-370 https://doi.org/10.1038/jcbfm.1995.45
  33. Kim DJ, Park HJ, Kang KW, Shin YW, Kim JJ, Moon WJ, et al. How does distortion correction correlate with anisotropic indices? A diffusion tensor imaging study. Magn Reson Imaging 2006;24:1369-1376 https://doi.org/10.1016/j.mri.2006.07.014
  34. Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 1999;9:179-194 https://doi.org/10.1006/nimg.1998.0395
  35. Fischl B, Sereno MI, Dale AM. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 1999;9:195-207 https://doi.org/10.1006/nimg.1998.0396
  36. Fischl B, van der Kouwe A, Destrieux C, Halgren E, Segonne F, Salat DH, et al. Automatically parcellating the human cerebral cortex. Cereb Cortex 2004;14:11-22 https://doi.org/10.1093/cercor/bhg087
  37. Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 2010;52:1059-1069 https://doi.org/10.1016/j.neuroimage.2009.10.003
  38. Zalesky A, Fornito A, Harding IH, Cocchi L, Yucel M, Pantelis C, et al. Whole-brain anatomical networks: does the choice of nodes matter? Neuroimage 2010;50:970-983 https://doi.org/10.1016/j.neuroimage.2009.12.027
  39. Sporns O, Tononi G, Edelman GM. Theoretical neuroanatomy and the connectivity of the cerebral cortex. Behav Brain Res 2002;135:69-74 https://doi.org/10.1016/S0166-4328(02)00157-2
  40. Rubinov M, Sporns O. Weight-conserving characterization of complex functional brain networks. Neuroimage 2011
  41. Jeff W. Lichtman JL, Joshua R. Sanes. A technicolour approach to the connectome. Nature 2008;9:417-422
  42. Michael D. Greicius BK, Allan L. Reiss , and Vinod Menon. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis Proceedings of the National Academy of Sciences of the United States of America 2002;100:253-258
  43. Honey CJ, Sporns O, Cammoun L, Gigandet X, Thiran JP, Meuli R, et al. Predicting human resting-state functional connectivity from structural connectivity. Proc Natl Acad Sci U S A 2009;106:2035-2040 https://doi.org/10.1073/pnas.0811168106
  44. Koch MA, Norris DG, Hund-Georgiadis M. An investigation of functional and anatomical connectivity using magnetic resonance imaging. Neuroimage 2002;16:241-250 https://doi.org/10.1006/nimg.2001.1052
  45. Iturria-Medina Y, Sotero RC, Canales-Rodriguez EJ, Aleman-Gomez Y, Melie-Garcia L. Studying the human brain anatomical network via diffusion-weighted MRI and Graph Theory. Neuroimage 2008;40:1064-1076 https://doi.org/10.1016/j.neuroimage.2007.10.060
  46. Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 2006;31:968-980 https://doi.org/10.1016/j.neuroimage.2006.01.021
  47. Hofer S, Frahm J. Topography of the human corpus callosum revisited - Comprehensive fiber tractography using diffusion tensor magnetic resonance imaging. Neuroimage 2006;32:989-994 https://doi.org/10.1016/j.neuroimage.2006.05.044
  48. Huang H, Zhang J, Jiang H, Wakana S, Poetscher L, Miller MI, et al. DTI tractography based parcellation of white matter: application to the mid-sagittal morphology of corpus callosum. Neuroimage 2005;26:195-205 https://doi.org/10.1016/j.neuroimage.2005.01.019
  49. Kim M, Ronen I, Ugurbil K, Kim DS. Spatial resolution dependence of DTI tractography in human occipito-callosal region. Neuroimage 2006;32:1243-1249 https://doi.org/10.1016/j.neuroimage.2006.06.006
  50. Catani M, Thiebaut de Schotten M. A diffusion tensor imaging tractography atlas for virtual in vivo dissections. Cortex 2008;44:1105-1132 https://doi.org/10.1016/j.cortex.2008.05.004
  51. Latora V, Marchiori M. Efficient behavior of small-world networks. Phys Rev Lett 2001;87:198701
  52. He Y, Chen ZJ, Evans AC. Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cereb Cortex 2007;17:2407-2419
  53. Gabriela Kalna DJH. Clustering Coefficients for Weighted Networks. Symposium on Network Analysis in Natural Sciences and Engineering 2006
  54. Fransson P, Aden U, Blennow M, Lagercrantz H. The functional architecture of the infant brain as revealed by resting-state FMRI. Cereb Cortex 2011;21:145-154 https://doi.org/10.1093/cercor/bhq071
  55. Fransson P, Skiold B, Engstrom M, Hallberg B, Mosskin M, Aden U, et al. Spontaneous brain activity in the newborn brain during natural sleep--an fMRI study in infants born at full term. Pediatr Res 2009;66:301-305 https://doi.org/10.1203/PDR.0b013e3181b1bd84
  56. Fransson P, Skiold B, Horsch S, Nordell A, Blennow M, Lagercrantz H, et al. Resting-state networks in the infant brain. Proc Natl Acad Sci U S A 2007;104:15531-15536 https://doi.org/10.1073/pnas.0704380104
  57. Lin W, Zhu Q, Gao W, Chen Y, Toh CH, Styner M, et al. Functional connectivity MR imaging reveals cortical functional connectivity in the developing brain. AJNR Am J Neuroradiol 2008;29:1883-1889 https://doi.org/10.3174/ajnr.A1256
  58. Zhang S, Correia S, Laidlaw DH. Identifying white-matter fiber bundles in DTI data using an automated proximity-based fiberclustering method. IEEE Trans Vis Comput Graph 2008;14:1044-1053
  59. Ashburner J, Friston KJ. Nonlinear spatial normalization using basis functions. Human Brain Mapping 1999;7:254-266 https://doi.org/10.1002/(SICI)1097-0193(1999)7:4<254::AID-HBM4>3.0.CO;2-G
  60. Wedeen VJ, Hagmann P, Tseng WY, Reese TG, Weisskoff RM. Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging. Magn Reson Med 2005;54:1377-1386 https://doi.org/10.1002/mrm.20642