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한국 아동 집단의 구조 뇌연결지도

Anatomical Brain Connectivity Map of Korean Children

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참고문헌

주제분류방사선과학

초록

목적 : 본 연구의 목적은 확산텐서영상에 기반하여 한국 아동 집단의 해부학적 뇌연결성 지도를확립하고 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 해부학적뇌연결망 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.

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.

참고문헌 (60)

  1. Bullmore E, "Complex brain networks: graph theoretical analysis of structural and functional systems", Nat Rev Neurosci, 10, pp.186~198 (2009)
  2. Sporns O, "The human connectome: a complex network", Ann N Y Acad Sci (2011)
  3. Achard S, "A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs", J Neurosci, 26, pp.63~72 (2006)
  4. Bassett DS, "Small-world brain networks", Neuroscientist, 12, pp.512~523 (2006)
  5. Raichle ME, "A default mode of brain function", Proc Natl Acad Sci U S A, 98, pp.676~682 (2001)
  6. Smit DJ, "Heritability of“ small-world”networks in the brain: a graph theoretical analysis of resting-state EEG functional connectivity", Hum Brain Mapp, 29, pp.1368~1378 (2008)
  7. Valencia M, "Dynamic small-world behavior in functional brain networks unveiled by an event-related networks approach", Phys Rev E Stat Nonlin Soft Matter Phys, 77, pp.050905~ (2008)
  8. Zhou C, "Hierarchical organization unveiled by functional connectivity in complex brain networks", Phys Rev Lett, 97, pp.238103~ (2006)
  9. Gong G, "Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography", Cereb Cortex, 19, pp.524~536 (2009)
  10. Greicius MD, "Resting-state functional connectivity reflects structural connectivity in the default mode network", Cereb Cortex, 19, pp.72~78 (2009)
  11. Eguiluz VM, "Scale-free brain functional networks", Phys Rev Lett, 94, pp.018102~ (2005)
  12. Salvador R, "Neurophysiological architecture of functional magnetic resonance images of human brain", Cereb Cortex, 15, pp.1332~1342 (2005)
  13. Ferrarini L VI, "Hierarchical functional modularity in the resting-state human brain", Hum Brain Mapp, 30, pp.2220~2231 (2009)
  14. Meunier D, "Age-related changes in modular organization of human brain functional networks", Neuroimage, 44, pp.715~723 (2009)
  15. Stam CJ, "Functional connectivity patterns of human magnetoencephalographic recordings: a ‘small-world’ network", Neurosci Lett, 355, pp.25~38 (2004)
  16. Achard S, "Fractal connectivity of long-memory networks", Phys Rev E Stat Nonlin Soft Matter Phys, 77, pp.036104~ (2008)
  17. Klaus Linkenkaer-Hansen VVN, "Long-Range Temporal Correlations and Scaling Behavior in Human Brain Oscillations", The Journal of Neuroscience, 21, pp.1370~1377 (2001)
  18. Maxim V, "Fractional Gaussian noise, functional MRI and Alzheimer’s disease", Neuroimage, 25, pp.141~158 (2005)
  19. Felleman DJ, "Distributed hierarchical processing in the primate cerebral cortex", Cereb Cortex, 1, pp.1~47 (1991)
  20. Hilgetag CC, "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, 355, pp.91~110 (2000)
  21. Latora V, "Economic small-world behavior in weighted networks", European Physical Journal B, 32, pp.249~263 (2003)
  22. Scannell JW, "The connectional organization of the cortico-thalamic system of the cat", Cereb Cortex, 9, pp.277~299 (1999)
  23. Young MP, "Objective analysis of the topological organization of the primate cortical visual system", Nature, 358, pp.152~155 (1992)
  24. Lee JD, "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, 66, pp.167~212 (2005)
  26. Bullmore ET, "Brain graphs: graphical models of the human brain connectome", Annu Rev Clin Psychol, 7, pp.113~140 (2011)
  27. Patric Hagmann MK, "Mapping Human Whole-Brain Structural Networks with Diffusion MRI", PLoS ONE, 7, pp.e597~ (2007)
  28. Counsell SJ, "Thalamo-cortical connectivity in children born preterm mapped using probabilistic magnetic resonance tractography", Neuroimage, 34, pp.896~904 (2007)
  29. Lenroot RK, "Brain development in children and adolescents: insights from anatomical magnetic resonance imaging", Neurosci Biobehav Rev, 30, pp.718~729 (2006)
  30. Fair DA, "The maturing architecture of the brain’s default network", Proc Natl Acad Sci U S A, 105, pp.4028~4032 (2008)
  31. Park HJ, "Corpus callosal connection mapping using cortical gray matter parcellation and DT-MRI", Hum Brain Mapp, 29, pp.503~516 (2008)
  32. Friston KJ, "Commentary and opinion: II. Statistical parametric mapping: ontology and current issues", J Cereb Blood Flow Metab, 15, pp.361~370 (1995)
  33. Kim DJ, "How does distortion correction correlate with anisotropic indices? A diffusion tensor imaging study", Magn Reson Imaging, 24, pp.1369~1376 (2006)
  34. Dale AM, "Cortical surface-based analysis. I. Segmentation and surface reconstruction", Neuroimage, 9, pp.179~194 (1999)
  35. Fischl B, "Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system", Neuroimage, 9, pp.195~207 (1999)
  36. Fischl B, "Automatically parcellating the human cerebral cortex", Cereb Cortex, 14, pp.11~22 (2004)
  37. Rubinov M, "Complex network measures of brain connectivity: uses and interpretations", Neuroimage, 52, pp.1059~1069 (2010)
  38. Zalesky A, "Whole-brain anatomical networks: does the choice of nodes matter", Neuroimage, 50, pp.970~983 (2010)
  39. Sporns O, "Theoretical neuroanatomy and the connectivity of the cerebral cortex", Behav Brain Res, 135, pp.69~74 (2002)
  40. Rubinov M, "Weight-conserving characterization of complex functional brain networks", Neuroimage (2011)
  41. Jeff W. Lichtman, "A technicolour approach to the connectome", Nature, 9, pp.417~422 (2008)
  42. Michael D. Greicius, "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, 100, pp.253~258 (2002)
  43. Honey CJ, "Predicting human resting-state functional connectivity from structural connectivity", Proc Natl Acad Sci U S A, 106, pp.2035~2040 (2009)
  44. Koch MA, "An investigation of functional and anatomical connectivity using magnetic resonance imaging", Neuroimage, 16, pp.241~250 (2002)
  45. Iturria-Medina Y, "Studying the human brain anatomical network via diffusion-weighted MRI and Graph Theory", Neuroimage, 40, pp.1064~1076 (2008)
  46. Desikan RS, "An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest", Neuroimage, 31, pp.968~980 (2006)
  47. Hofer S, "Topography of the human corpus callosum revisited - Comprehensive fiber tractography using diffusion tensor magnetic resonance imaging", Neuroimage, 32, pp.989~994 (2006)
  48. Huang H, "DTI tractography based parcellation of white matter: application to the mid-sagittal morphology of corpus callosum", Neuroimage, 26, pp.195~205 (2005)
  49. Kim M, "Spatial resolution dependence of DTI tractography in human occipito-callosal region", NEUROIMAGE, 32(3), pp.1243~1249, ACADEMIC PRESS INC ELSEVIER SCIENCE
  50. Catani M, "A diffusion tensor imaging tractography atlas for virtual in vivo dissections", Cortex, 44, pp.1105~1132 (2008)
  51. Latora V, "Efficient behavior of small-world networks", Phys Rev Lett, 87, pp.198701~ (2001)
  52. He Y, "Small-world anatomical networks in the human brain revealed by cortical thickness from MRI", Cereb Cortex, 17, pp.2407~2419 (2007)
  53. Gabriela Kalna DJH, "Clustering Coefficients for Weighted Networks", Symposium on Network Analysis in Natural Sciences and Engineering 2006
  54. Fransson P, "The functional architecture of the infant brain as revealed by resting-state FMRI", Cereb Cortex, 21, pp.145~154 (2011)
  55. Fransson P, "Spontaneous brain activity in the newborn brain during natural sleep--an fMRI study in infants born at full term", Pediatr Res, 66, pp.301~305 (2009)
  56. Fransson P, "Resting-state networks in the infant brain", Proc Natl Acad Sci U S A, 104, pp.15531~15536 (2007)
  57. Lin W, "Functional connectivity MR imaging reveals cortical functional connectivity in the developing brain", AJNR Am J Neuroradiol, 29, pp.1883~1889 (2008)
  58. Zhang S, "Identifying white-matter fiber bundles in DTI data using an automated proximity-based fiberclustering method", IEEE Trans Vis Comput Graph, 14, pp.1044~1053 (2008)
  59. Ashburner J, "Nonlinear spatial normalization using basis functions", Human Brain Mapping, 7, pp.254~266 (1999)
  60. Wedeen VJ, "Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging", Magn Reson Med, 54, pp.1377~1386 (2005)

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