Introducing axonal myelination in connectomics: A preliminary analysis of g-ratio distribution in healthy subjects

Matteo Mancini, Giovanni Giulietti, Nicholas Dowell, Barbara Spanò, Neil Harrison, Marco Bozzali, Mara Cercignani

Research output: Contribution to journalArticlepeer-review


Microstructural imaging and connectomics are two research areas that hold great potential for investigating brain structure and function. Combining these two approaches can lead to a better and more complete characterization of the brain as a network. The aim of this work is characterizing the connectome from a novel perspective using the myelination measure given by the g-ratio. The g-ratio is the ratio of the inner to the outer diameters of a myelinated axon, whose aggregated value can now be estimated in vivo using MRI. In two different datasets of healthy subjects, we reconstructed the structural connectome and then used the g-ratio estimated from diffusion and magnetization transfer data to characterize the network structure. Significant characteristics of g-ratio weighted graphs emerged. First, the g-ratio distribution across the edges of the graph did not show the power-law distribution observed using the number of streamlines as a weight. Second, connections involving regions related to motor and sensory functions were the highest in myelin content. We also observed significant differences in terms of the hub structure and the rich-club organization suggesting that connections involving hub regions present higher myelination than peripheral connections. Taken together, these findings offer a characterization of g-ratio distribution across the connectome in healthy subjects and lay the foundations for further investigating plasticity and pathology using a similar approach.

Original languageEnglish
Publication statusAccepted/In press - Jan 1 2017


  • Connectome
  • Diffusion weighted imaging
  • G-ratio
  • Graph theory
  • Microstructure
  • Myelin
  • Structural connectivity

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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