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ORIGINAL RESEARCH article

Front. Netw. Physiol.
Sec. Networks in the Brain System
Volume 4 - 2024 | doi: 10.3389/fnetp.2024.1457486
This article is part of the Research Topic Network Physiology: Insights into the Brain System, Vol II View all articles

Constructing representative group networks from tractography: lessons from a dynamical approach

Provisionally accepted
Eleanna Kritikaki Eleanna Kritikaki 1Matteo Mancini Matteo Mancini 2,3Diana Kyriazis Diana Kyriazis 4Natasha Sigala Natasha Sigala 4Simon F. Farmer Simon F. Farmer 5,6Luc Berthouze Luc Berthouze 1*
  • 1 University of Sussex, Brighton, West Sussex, United Kingdom
  • 2 National Institute of Health (ISS), Rome, Lazio, Italy
  • 3 Cardiff University, Cardiff, United Kingdom
  • 4 Brighton and Sussex Medical School, Brighton, England, United Kingdom
  • 5 National Hospital for Neurology and Neurosurgery (NHNN), London, United Kingdom
  • 6 University College London, London, England, United Kingdom

The final, formatted version of the article will be published soon.

    Human group connectome analysis relies on combining individual connectome data to construct a single representative network, which can be used to describe brain organisation and identify differences between subject groups. Existing methods adopt different strategies to select which network structural features to retain or optimise at group level. In the absence of ground truth, however, it is unclear which structural features are the most suitable, and how to evaluate the consequences on the group network of applying any given strategy. In this investigation, we considered the impact of de@ining a connectome as representative if it can recapitulate not just the structure of the individual networks in the cohort tested but also their dynamical behaviour, which we measured using a model of coupled oscillators. We applied the widely used approach of consensus thresholding to a dataset of individual structural connectomes from a healthy adult cohort to construct group networks for a range of thresholds and then identi@ied the most dynamically-representative group connectome as that with the least deviation from the individual connectomes given a dynamical measure of the system.We found that our dynamically-representative network recaptured aspects of structure that it did not speci@ically optimise for, with no signi@icant difference to other group connectomes constructed via methods which did optimise for those metrics.Additionally, these other group connectomes were either as dynamically representative as our chosen network, or less so. While we suggest that dynamics should be at least one of the criteria for representativeness, given that the brain has evolved under the pressure to carry out speci@ic functions, our results suggest that the question regarding which of these criteria are valid and testable persists.

    Keywords: Consensus network algorithms, Metastability, synchronization, tractography, Connectome Analysis, dynamics

    Received: 30 Jun 2024; Accepted: 14 Oct 2024.

    Copyright: © 2024 Kritikaki, Mancini, Kyriazis, Sigala, Farmer and Berthouze. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Luc Berthouze, University of Sussex, Brighton, BN1 9RH, West Sussex, United Kingdom

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