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Multi-scale community organization of the human structural connectome and its relationship with resting-state functional connectivity

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  • BETZEL, RICHARD F.
  • GRIFFA, ALESSANDRA
  • AVENA-KOENIGSBERGER, ANDREA
  • GOÑI, JOAQUÍN
  • THIRAN, JEAN-PHILIPPE
  • HAGMANN, PATRIC
  • SPORNS, OLAF

Abstract

The human connectome has been widely studied over the past decade. A principal finding is that it can be decomposed into communities of densely interconnected brain regions. Past studies have often used single-scale modularity measures in order to infer the connectome's community structure, possibly overlooking interesting structure at other organizational scales. In this report, we used the partition stability framework, which defines communities in terms of a Markov process (random walk), to infer the connectome's multi-scale community structure. Comparing the community structure to observed resting-state functional connectivity revealed communities across a broad range of scales that were closely related to functional connectivity. This result suggests a mapping between communities in structural networks, models of influence-spreading and diffusion, and brain function. It further suggests that the spread of influence among brain regions may not be limited to a single characteristic scale.

Suggested Citation

  • Betzel, Richard F. & Griffa, Alessandra & Avena-Koenigsberger, Andrea & Goñi, Joaquín & Thiran, Jean-Philippe & Hagmann, Patric & Sporns, Olaf, 2013. "Multi-scale community organization of the human structural connectome and its relationship with resting-state functional connectivity," Network Science, Cambridge University Press, vol. 1(3), pages 353-373, December.
  • Handle: RePEc:cup:netsci:v:1:y:2013:i:03:p:353-373_00
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    Cited by:

    1. Kang Wu & Jingxian Tang & Ying Long, 2019. "Delineating the Regional Economic Geography of China by the Approach of Community Detection," Sustainability, MDPI, vol. 11(21), pages 1-18, October.
    2. Farnaz Zamani Esfahlani & Joshua Faskowitz & Jonah Slack & Bratislav Mišić & Richard F. Betzel, 2022. "Local structure-function relationships in human brain networks across the lifespan," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    3. Raghavendra Singh & Seema Nagar & Amit A Nanavati, 2015. "Analysing Local Sparseness in the Macaque Brain Network," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
    4. Disheng Tang & Joel Zylberberg & Xiaoxuan Jia & Hannah Choi, 2024. "Stimulus type shapes the topology of cellular functional networks in mouse visual cortex," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    5. Marcelo G Mattar & Michael W Cole & Sharon L Thompson-Schill & Danielle S Bassett, 2015. "A Functional Cartography of Cognitive Systems," PLOS Computational Biology, Public Library of Science, vol. 11(12), pages 1-26, December.
    6. Banerjee, Sayantan & Akbani, Rehan & Baladandayuthapani, Veerabhadran, 2019. "Spectral clustering via sparse graph structure learning with application to proteomic signaling networks in cancer," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 46-69.

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