Advertisement

Brain Topography

, Volume 32, Issue 1, pp 1–16 | Cite as

Robust Identification of Rich-Club Organization in Weighted and Dense Structural Connectomes

  • Xiaoyun LiangEmail author
  • Chun-Hung Yeh
  • Alan Connelly
  • Fernando Calamante
Original Paper

Abstract

The human brain is a complex network, in which some brain regions, denoted as ‘hub’ regions, play critically important roles. Some of these hubs are highly interconnected forming a rich-club organization, which has been identified based on the degree metric from structural connectomes constructed using diffusion tensor imaging (DTI)-based fiber tractography. However, given the limitations of DTI, the yielded structural connectomes are largely compromised, possibly affecting the characterization of rich-club organizations. Recent progress in diffusion MRI and fiber tractography now enable more reliable but also very dense structural connectomes to be achieved. However, while the existing rich-club analysis method is based on weighted networks, it is essentially built upon degree metric and, therefore, not suitable for identifying rich-club organizations from such dense networks, as it yields nodes with indistinguishably high degrees. Therefore, we propose a novel method, i.e. Rich-club organization Identification using Combined H-degree and Effective strength to h-degree Ratio (RICHER), to identify rich-club organizations from dense weighted networks. Overall, it is shown that more robust rich-club organizations can be achieved using our proposed framework (i.e., state-of-the-art fiber tractography approaches and our proposed RICHER method) in comparison to the previous method focusing on weighted networks based on degree, i.e., RC-degree. Furthermore, by simulating network attacks in 3 ways, i.e., attack to non-rich-club/non-rich-club edges (NRC2NRC), rich-club/non-rich-club edges (RC2NRC), and rich-club/rich-club edges (RC2RC), brain network damage consequences have been evaluated in terms of global efficiency (GE) reductions. As expected, significant GE reductions have been detected using our proposed framework among conditions, i.e., NRC2NRC < RC2NRC, NRC2NRC < RC2RC and RC2NRC < RC2RC, which however have not been detected otherwise.

Keywords

Rich club h-Degree Diffusion MRI Fiber tractography Structural connectome 

Notes

Acknowledgements

We thank Dr. Robert Elton Smith (Florey Institute of Neuroscience and Mental Health) for very helpful discussions and advice. We are grateful to the National Health and Medical Research Council (NHMRC) of Australia, the Australian Research Council (ARC), and the Victorian Government’s Operational Infrastructure Support Program for their support. The authors also acknowledge the facilities, and the scientific and technical assistance of the National Imaging Facility at the Florey Node.

Funding

Funding was provided by National Health and Medical Research Council (Grant Nos. 1091593 and APP1117724).

Supplementary material

10548_2018_661_MOESM1_ESM.docx (2.3 mb)
Supplementary material 1 (DOCX 2370 KB)

References

  1. Alstott J, Panzarasa P, Rubinov M, Bullmore ET, Vertes PE (2014) A unifying framework for measuring weighted rich clubs. Sci Rep 4:7258CrossRefGoogle Scholar
  2. Andersson JL, Skare S, Ashburner J (2003) How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage 20:870–888CrossRefGoogle Scholar
  3. Barrat A, Barthelemy M, Pastor-Satorras R, Vespignani A (2004) The architecture of complex weighted networks. Proc Natl Acad Sci USA 101:3747–3752CrossRefGoogle Scholar
  4. Basser PJ, Mattiello J, Lebihan D (1994) Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 103:247–254CrossRefGoogle Scholar
  5. Bassett DS, Bullmore ET (2016) Small-world brain networks revisited. Neuroscientist 23:499–516CrossRefGoogle Scholar
  6. Bastiani M, Shah NJ, Goebel R, Roebroeck A (2012) Human cortical connectome reconstruction from diffusion weighted MRI: the effect of tractography algorithm. Neuroimage 62:1732–1749CrossRefGoogle Scholar
  7. Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10:186–198CrossRefGoogle Scholar
  8. Bullmore E, Sporns O (2012) The economy of brain network organization. Nat Rev Neurosci 13:336–349CrossRefGoogle Scholar
  9. Colizza V, Flammini A, Serrano MA, Vespignani A (2006) Detecting rich-club ordering in complex networks. Nat Phys 2:110–115CrossRefGoogle Scholar
  10. Crossley NA, Mechelli A, Vertes PE, Winton-Brown TT, Patel AX, Ginestet CE, Mcguire P, Bullmore ET (2013) Cognitive relevance of the community structure of the human brain functional coactivation network. Proc Natl Acad Sci USA 110:11583–11588CrossRefGoogle Scholar
  11. Daducci A, Dal Palu A, Lemkaddem A, Thiran JP (2015) COMMIT: Convex optimization modeling for microstructure informed tractography. IEEE Trans Med Imaging 34:246–257CrossRefGoogle Scholar
  12. Dale AM, Fischl B, Sereno MI (1999) Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9:179–194CrossRefGoogle Scholar
  13. Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31:968–980CrossRefGoogle Scholar
  14. Drakesmith M, Caeyenberghs K, Dutt A, Lewis G, David AS, Jones DK (2015) Overcoming the effects of false positives and threshold bias in graph theoretical analyses of neuroimaging data. Neuroimage 118:313–333CrossRefGoogle Scholar
  15. Girard G, Whittingstall K, Deriche R, Descoteaux M (2014) Towards quantitative connectivity analysis: reducing tractography biases. Neuroimage 98:266–278CrossRefGoogle Scholar
  16. Goulas A, Bastiani M, Bezgin G, Uylings HB, Roebroeck A, Stiers P (2014) Comparative analysis of the macroscale structural connectivity in the macaque and human brain. PLoS Comput Biol 10:e1003529CrossRefGoogle Scholar
  17. Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, Sporns O (2008) Mapping the structural core of human cerebral cortex. PLoS Biol 6:e159CrossRefGoogle Scholar
  18. Harriger L, Van Den Heuvel MP, Sporns O (2012) Rich club organization of macaque cerebral cortex and its role in network communication. PLoS ONE 7:e46497CrossRefGoogle Scholar
  19. Hirsch JE (2005) An index to quantify an individual’s scientific research output. Proc Natl Acad Sci USA 102:16569–16572CrossRefGoogle Scholar
  20. Hutchison RM, Gallivan JP, Culham JC, Gati JS, Menon RS, Everling S (2012) Functional connectivity of the frontal eye fields in humans and macaque monkeys investigated with resting-state fMRI. J Neurophysiol 107:2463–2474CrossRefGoogle Scholar
  21. Jones DK, Knosche TR, Turner R (2013) White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. Neuroimage 73:239–254CrossRefGoogle Scholar
  22. Kennedy H, Knoblauch K, Toroczkai Z (2013) Why data coherence and quality is critical for understanding interareal cortical networks. Neuroimage 80:37–45CrossRefGoogle Scholar
  23. Latora V, Marchiori M (2001) Efficient behavior of small-world networks. Phys Rev Lett 87:198701CrossRefGoogle Scholar
  24. Latora V, Marchiori M (2003) Economic small-world behavior in weighted networks. Eur Phys J B 32:249–263CrossRefGoogle Scholar
  25. Markov NT, Misery P, Falchier A, Lamy C, Vezoli J, Quilodran R, Gariel MA, Giroud P, Ercsey-Ravasz M, Pilaz LJ, Huissoud C, Barone P, Dehay C, Toroczkai Z, Essen V, Kennedy DC, Knoblauch K (2011) Weight consistency specifies regularities of macaque cortical networks. Cereb Cortex 21:1254–1272CrossRefGoogle Scholar
  26. Markov NT, Ercsey-Ravasz M, Lamy C, Ribeiro Gomes AR, Magrou L, Misery P, Giroud P, Barone P, Dehay C, Toroczkai Z, Knoblauch K, Van Essen DC, Kennedy H (2013a) The role of long-range connections on the specificity of the macaque interareal cortical network. Proc Natl Acad Sci USA 110:5187–5192CrossRefGoogle Scholar
  27. Markov NT, Ercsey-Ravasz M, Van Essen DC, Knoblauch K, Toroczkai Z, Kennedy H (2013b) Cortical high-density counterstream architectures. Science 342:1238406CrossRefGoogle Scholar
  28. Mccolgan P, Seunarine KK, Raz i A, Cole JH, Gregory S, Durr A, Roos RA, Stout JC, Landwehrmeyer B, Scahill RI, Clark CA, Rees G, Tabrizi SJ, Track HD I (2015) Selective vulnerability of Rich Club brain regions is an organizational principle of structural connectivity loss in Huntington’s disease. Brain 138:3327–3344CrossRefGoogle Scholar
  29. Mori S, Crain BJ, Chacko VP, Van Zijl PC (1999) Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol 45:265–269CrossRefGoogle Scholar
  30. Mugler JP, 3RD and Brookeman JR (1990) Three-dimensional magnetization-prepared rapid gradient-echo imaging (3D MP RAGE). Magn Reson Med 15:152–157CrossRefGoogle Scholar
  31. Newman MEJ (2004) Analysis of weighted networks. Phys Rev E 70:056131CrossRefGoogle Scholar
  32. Nigam S, Shimono M, Ito S, Yeh FC, Timme N, Myroshnychenko M, Lapish CC, Tosi Z, Hottowy P, Smith WC, Masmanidis SC, Litke AM, Sporns O, Beggs JM (2016) Rich-club organization in effective connectivity among cortical neurons. J Neurosci 36:670–684CrossRefGoogle Scholar
  33. Opsahl T, Colizza V, Panzarasa P, Ramasco JJ (2008) Prominence and control: the weighted rich-club effect. Phys Rev Lett, 101:168702CrossRefGoogle Scholar
  34. Patenaude B, Smith SM, Kennedy DN, Jenkinson M (2011) A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage 56:907–922CrossRefGoogle Scholar
  35. Pestilli F, Yeatman JD, Rokem A, Kay KN, Wandell BA (2014) Evaluation and statistical inference for human connectomes. Nat Methods 11:1058–1063CrossRefGoogle Scholar
  36. Reese TG, Heid O, Weisskoff RM, Wedeen VJ (2003) Reduction of eddy-current-induced distortion in diffusion MRI using a twice-refocused spin echo. Magn Reson Med 49:177–182CrossRefGoogle Scholar
  37. Roberts JA, Perry A, Lord AR, Roberts G, Mitchell PB, Smith RE, Calamante F, Breakspear M (2016) The contribution of geometry to the human connectome. Neuroimage 124:379–393CrossRefGoogle Scholar
  38. Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52:1059–1069CrossRefGoogle Scholar
  39. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H, Bannister PR, De Luca M, Drobnjak I, Flitney DE, Niazy RK, Saunders J, Vickers J, De Stefano Zhang Y, Brady N, Matthews PM (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23 Suppl 1, S208-19Google Scholar
  40. Smith RE, Tournier JD, Calamante F, Connelly A (2012) Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information. Neuroimage 62:1924–1938CrossRefGoogle Scholar
  41. Smith RE, Tournier JD, Calamante F, Connelly A (2013) SIFT: Spherical-deconvolution informed filtering of tractograms. Neuroimage 67:298–312CrossRefGoogle Scholar
  42. Smith RE, Tournier JD, Calamante F, Connelly A (2015) The effects of SIFT on the reproducibility and biological accuracy of the structural connectome. Neuroimage 104:253–265CrossRefGoogle Scholar
  43. Sporns O, Honey CJ, Kotter R (2007) Identification and classification of hubs in brain networks. PLoS ONE 2:e1049CrossRefGoogle Scholar
  44. Tournier JD, Calamante F, Connelly A (2007) Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. Neuroimage 35:1459–1472CrossRefGoogle Scholar
  45. Tournier JD, Calamante F, Connelly A (2010) Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions. Proc ISMRM 18:1670Google Scholar
  46. Tournier JD, Mori S, Leemans A (2011) Diffusion tensor imaging and beyond. Magn Reson Med 65:1532–1556CrossRefGoogle Scholar
  47. Tournier JD, Calamante F, Connelly A (2012) MRtrix: Diffusion tractography in crossing fiber regions. Int J Imaging Syst Technol 22:53–66CrossRefGoogle Scholar
  48. Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29:1310–1320CrossRefGoogle Scholar
  49. Van Den Heuvel MP, Sporns O (2011) Rich-club organization of the human connectome. J Neurosci 31:15775–15786CrossRefGoogle Scholar
  50. Van Den Heuvel MP, Sporns O, Collin G, Scheewe T, Mandl RC, Cahn W, Goni J, Pol H, Kahn RS (2013) Abnormal rich club organization and functional brain dynamics in schizophrenia. JAMA Psychiatry 70:783–792CrossRefGoogle Scholar
  51. Wirsich J, Perry A, Ridley B, Proix T, Golos M, Benar C, Ranjeva J-P, Bartolomei F, Breakspear M, Jirsa V, Guye M (2016) Whole-brain analytic measures of network communication reveal increased structure-function correlation in right temporal lobe epilepsy. Neuroimage  https://doi.org/10.1016/j.nicl.2016.05.010 Google Scholar
  52. Yeh CH, Smith RE, Liang X, Calamante F, Connelly A (2016) Correction for diffusion MRI fibre tracking biases: The consequences for structural connectomic metrics. Neuroimage  https://doi.org/10.1016/j.neuroimage.2016.05.047 Google Scholar
  53. Zalesky A, Fornito A, Harding IH, Cocchi L, Yucel M, Pantelis C, Bullmore ET (2010) Whole-brain anatomical networks: does the choice of nodes matter? Neuroimage 50:970–983CrossRefGoogle Scholar
  54. Zhao SX, Rousseau R, Ye FY (2011) h-Degree as a basic measure in weighted networks. J Inform 5:668–677CrossRefGoogle Scholar
  55. Zhou S, Mondragon RJ (2004) The rich-club phenomenon in the Internet topology. IEEE Commun Lett 8:180–182CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.The Florey Institute of Neuroscience and Mental HealthHeidelbergAustralia
  2. 2.The Florey Department of Neuroscience and Mental Health MedicineUniversity of MelbourneMelbourneAustralia
  3. 3.Department of Medicine, Austin Health and Northern HealthUniversity of MelbourneMelbourneAustralia
  4. 4.Sydney Imaging and School of Aerospace, Mechanical and Mechatronic Engineering (Faculty of Engineering & Information Technologies)University of SydneySydneyAustralia

Personalised recommendations