Hierarchical complexity of the adult human structural connectome

Smith, Keith and Bastin, Mark E. and Cox, Simon R. and Valdes Hernandez, Maria and Wiseman, Stewart and Escudero, Javier and Sudlow, Catherine (2019) Hierarchical complexity of the adult human structural connectome. NeuroImage, 191. pp. 205-215. ISSN 1053-8119 (https://doi.org/10.1016/j.neuroimage.2019.02.028)

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Abstract

The structural network of the human brain has a rich topology which many have sought to characterise using standard network science measures and concepts. However, this characterisation remains incomplete and the non-obvious features of this topology have largely confounded attempts towards comprehensive constructive modelling. This calls for new perspectives. Hierarchical complexity is an emerging paradigm of complex network topology based on the observation that complex systems are composed of hierarchies within which the roles of hierarchically equivalent nodes display highly variable connectivity patterns. Here we test the hierarchical complexity of the human structural connectomes of a group of seventy-nine healthy adults. Binary connectomes are found to be more hierarchically complex than three benchmark random network models. This provides a new key description of brain structure, revealing a rich diversity of connectivity patterns within hierarchically equivalent nodes. Dividing the connectomes into four tiers based on degree magnitudes indicates that the most complex nodes are neither those with the highest nor lowest degrees but are instead found in the middle tiers. Spatial mapping of the brain regions in each hierarchical tier reveals consistency with the current anatomical, functional and neuropsychological knowledge of the human brain. The most complex tier (Tier 3) involves regions believed to bridge high-order cognitive (Tier 1) and low-order sensorimotor processing (Tier 2). We then show that such diversity of connectivity patterns aligns with the diversity of functional roles played out across the brain, demonstrating that hierarchical complexity can characterise functional diversity strictly from the network topology.

ORCID iDs

Smith, Keith ORCID logoORCID: https://orcid.org/0000-0002-4615-9020, Bastin, Mark E., Cox, Simon R., Valdes Hernandez, Maria, Wiseman, Stewart, Escudero, Javier and Sudlow, Catherine;
  • Item type:Article
    ID code:87346
    Dates:
    Date
    Event
    1 May 2019
    Published
    14 February 2019
    Published Online
    11 February 2019
    Accepted
    8 January 2019
    Submitted
    Notes:Funding Information This work is dedicated to the memory of Prof. John M. Starr. This work was supported by Health Data Research UK (MRC ref Mr/S004122/1), which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, National Institute for Health Research, Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division, Public Health Agency, British Heart Foundation and Wellcome. MCVH is funded by the Row Fogo Charitable Trust (Grant BRO-D.FID3668413). Data collection was funded by NIH grant R01 EB004155. Funding Information: This work is dedicated to the memory of Prof. John M. Starr. This work was supported by Health Data Research UK (MRC ref Mr/S004122/1 ), which is funded by the UK Medical Research Council , Engineering and Physical Sciences Research Council , Economic and Social Research Council , National Institute for Health Research , Chief Scientist Office of the Scottish Government Health and Social Care Directorates , Health and Social Care Research and Development Division , Public Health Agency , British Heart Foundation and Wellcome. MCVH is funded by the Row Fogo Charitable Trust (Grant BRO- D.FID3668413 ). Data collection was funded by NIH grant R01 EB004155 . Publisher Copyright: © 2019 The Author(s) Keith Smith, Mark E. Bastin, Simon R. Cox, Maria C. Valdés Hernández, Stewart Wiseman, Javier Escudero, Catherine Sudlow, Hierarchical complexity of the adult human structural connectome, NeuroImage, Volume 191, 2019, Pages 205-215, https://doi.org/10.1016/j.neuroimage.2019.02.028
    Subjects:Medicine
    Department:Faculty of Science > Computer and Information Sciences
    Depositing user: Pure Administrator
    Date deposited:16 Nov 2023 11:05
    Last modified:11 Nov 2024 14:08
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/87346
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