Rich Club Analysis of Structural Brain Connectivity at 7 Tesla Versus 3 Tesla

  • Emily L. Dennis
  • Liang Zhan
  • Neda Jahanshad
  • Bryon A. Mueller
  • Yan Jin
  • Christophe Lenglet
  • Essa Yacoub
  • Guillermo Sapiro
  • Kamil Ugurbil
  • Noam Harel
  • Arthur W. Toga
  • Kelvin O. Lim
  • Paul M. Thompson
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

The ‘rich club’ is a relatively new concept in brain connectivity analysis, which identifies a core of densely interconnected high-degree nodes. Establishing normative measures for rich club organization is vital, as is understanding how scanning parameters affect it. We compared the rich club organization in 23 subjects scanned at both 7 and 3 T, with 128-gradient high angular resolution diffusion imaging (HARDI). The rich club coefficient (RCC) did not differ significantly between low and high field scans, but the field strength did affect which nodes were included in the rich club. We also examined 3 subjects with Alzheimer’s disease and 3 healthy elderly controls to see how field strength affected the statistical comparison. RCC did not differ with field strength, but again, which nodes differed between groups did. These results illustrate how one key parameter, scanner field strength, impacts rich club organization – a promising concept in brain connectomics research.

Keywords

Rich club Connectivity Field strength Alzheimer's, DTI Tractography Connectome 7T 

References

  1. 1.
    Colizza, V., et al.: Detecting rich-club ordering in complex networks. Nat. Phys. 2, 110–115 (2006)CrossRefGoogle Scholar
  2. 2.
    Van den Heuvel, M., Sporns, O.: Rich-club organization of the human connectome. J. Neurosci. 31(44), 15775–15786 (2011)CrossRefGoogle Scholar
  3. 3.
    Dennis, E.L., et al.: Development of the “rich club” in brain connectivity networks from adolescents and adults aged 12 to 30. In: Proceedings of the 10th IEEE ISBI, pp. 624–627 (2013)Google Scholar
  4. 4.
    Zhan, L., et al.: Magnetic resonance field strength effects on diffusion measures and brain connectivity networks. Brain Connectivity 3(1), 72–86 (2013)CrossRefGoogle Scholar
  5. 5.
    Stanisz, G.J., et al.: T1, T2 relaxation and magnetization transfer in tissue at 3T. Magn. Reson. Med. 54, 507–512 (2005)CrossRefGoogle Scholar
  6. 6.
    Yacoub, E., et al.: Spin-echo fMRI in humans using high spatial resolutions and high magnetic fields. Magn. Reson. Med. 49, 655–664 (2003)CrossRefGoogle Scholar
  7. 7.
    Gallichan, D., et al.: Addressing a systematic vibration artifact in diffusion-weighted MRI. Hum. Brain Mapp. 31, 193–202 (2010)Google Scholar
  8. 8.
    Smith, S.M., et al.: Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23, 208–219 (2004)CrossRefGoogle Scholar
  9. 9.
    Woolrich, M.W., et al.: Bayesian analysis of neuroimaging data in FSL. Neuroimage 45, S173–S186 (2009)CrossRefGoogle Scholar
  10. 10.
    Wang, R., Benner, T., Sorensen, A.G., Wedeen, V.J.: Diffusion toolkit: a software package for diffusion imaging data processing and tractography. Proc. Intl. Soc. Magn. Reson. Med. 15, 3720 (2007)Google Scholar
  11. 11.
    Basser, P.J., et al.: In vivo fiber tractography using DT-MRI data. Magn. Reson. Med. 44, 625–632 (2000)CrossRefGoogle Scholar
  12. 12.
    Duarte-Carvajalino, J.M., et al.: Hierarchical topological network analysis of anatomical human brain connectivity and differences related to sex and kinship. Neuroimage 59(4), 3784–3804 (2012)CrossRefGoogle Scholar
  13. 13.
    Desikan, R.S., et al.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006)CrossRefGoogle Scholar
  14. 14.
    Morgan, V.L., et al.: Integrating functional and diffusion magnetic resonance imaging for analysis of structure-function relationship in the human language network. PLoS One 4, e6660 (2009)CrossRefGoogle Scholar
  15. 15.
    Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059–1069 (2010)CrossRefGoogle Scholar
  16. 16.
    Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat Soc B 57(1), 289–300 (1995)MATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Emily L. Dennis
    • 1
    • 2
  • Liang Zhan
    • 1
    • 2
  • Neda Jahanshad
    • 1
    • 2
  • Bryon A. Mueller
    • 3
  • Yan Jin
    • 1
    • 2
  • Christophe Lenglet
    • 4
  • Essa Yacoub
    • 4
  • Guillermo Sapiro
    • 5
  • Kamil Ugurbil
    • 4
  • Noam Harel
    • 1
    • 2
  • Arthur W. Toga
    • 1
  • Kelvin O. Lim
    • 3
  • Paul M. Thompson
    • 1
    • 2
  1. 1.Imaging Genetics Center, Institute for Neuroimaging and InformaticsUSCLos AngelesUSA
  2. 2.Department of NeurologyUCLA School of MedicineLos AngelesUSA
  3. 3.Department of PsychiatryUniversity of Minn.MinneapolisUSA
  4. 4.Center for Magnetic Resonance ResearchUniversity of Minn.MinneapolisUSA
  5. 5.Department of Electrical and Computer EngineeringDuke UniversityDurhamUSA

Personalised recommendations