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Neighbourhood topology unveils pathological hubs in the brain networks of epilepsy-surgery patients

  • Leonardo Di Gaetano
  • , Fernando A.N. Santos
  • , Federico Battiston
  • , Ginestra Bianconi
  • , Nicolò Defenu
  • , Ida A. Nissen
  • , Elisabeth C.W. Van Straaten
  • , Arjan Hillebrand
  • , Ana P. Millán*
  • *Corresponding author for this work
  • Central European University
  • University of Amsterdam
  • Queen Mary University of London
  • Alan Turing Institute
  • Swiss Federal Institute of Technology Zurich
  • Vrije Universiteit Amsterdam
  • Kempenhaeghe Epilepsy Centre
  • Amsterdam UMC
  • University of Granada

Research output: Contribution to journalArticlepeer-review

Abstract (may include machine translation)

Pathological hubs in the brain networks of epilepsy patients are hypothesized to drive seizure generation and propagation. In epilepsy-surgery patients, these hubs have traditionally been associated with the resection area (RA): the region removed during the surgery with the goal of stopping the seizures, and which is typically used as a proxy for the epileptogenic zone. However, recent studies hypothesize that pathological hubs may extend to the vicinity of the RA, potentially complicating post-surgical seizure control. Here we propose a neighbourhood-based analysis of brain organization to investigate this hypothesis. We exploit a large dataset of pre-surgical magnetoencephalography-derived whole-brain networks from 91 epilepsy-surgery patients. Our neighbourhood focus is 2-fold. Firstly, we propose a partition of the brain regions into three sets, namely resected nodes, their neighbours and the remaining network nodes. Secondly, we introduce generalized centrality metrics that describe the neighbourhood of each node, providing a regional measure of hubness. Our analyses reveal that both the RA and its neighbourhood present large hub status, but with significant variability across patients. For some, hubs appear in the RA; for others, in its neighbourhood. Moreover, this variability does not correlate with surgical outcome. These results highlight the potential of neighbourhood-based analyses to uncover novel insights into brain connectivity in brain pathologies, and the need for individualized studies, with large enough cohorts, that account for patient-specific variability.

Original languageEnglish
Article numberfcaf431
Pages (from-to)1-16
Number of pages16
JournalBrain Communications
Volume7
Issue number6
DOIs
StatePublished - 31 Oct 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • brain hubs
  • brain networks
  • epilepsy
  • magnetoencephalography
  • topological data analysis

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