Brain Topography

, Volume 27, Issue 5, pp 620–634 | Cite as

Inter- and Intra-Subject Variability of Neuromagnetic Resting State Networks

  • Vincent Wens
  • Mathieu Bourguignon
  • Serge Goldman
  • Brice Marty
  • Marc Op de Beeck
  • Catherine Clumeck
  • Alison Mary
  • Philippe Peigneux
  • Patrick Van Bogaert
  • Matthew J. Brookes
  • Xavier De Tiège
Original Paper

Abstract

Functional connectivity studies conducted at the group level using magnetoencephalography (MEG) suggest that resting state networks (RSNs) emerge from the large-scale envelope correlation structure within spontaneous oscillatory brain activity. However, little is known about the consistency of MEG RSNs at the individual level. This paper investigates the inter- and intra-subject variability of three MEG RSNs (sensorimotor, auditory and visual) using seed-based source space envelope correlation analysis applied to 5 min of resting state MEG data acquired from a 306-channel whole-scalp neuromagnetometer (Elekta Oy, Helsinki, Finland) and source projected with minimum norm estimation. The main finding is that these three MEG RSNs exhibit substantial variability at the single-subject level across and within individuals, which depends on the RSN type, but can be reduced after averaging over subjects or sessions. Over- and under-estimations of true RSNs variability are respectively obtained using template seeds, which are potentially mislocated due to inter-subject variations, and a seed optimization method minimizing variability. In particular, bounds on the minimal number of subjects or sessions required to obtain highly consistent between- or within-subject averages of MEG RSNs are derived. Furthermore, MEG RSN topography positively correlates with their mean connectivity at the inter-subject level. These results indicate that MEG RSNs associated with primary cortices can be robustly extracted from seed-based envelope correlation and adequate averaging. MEG thus appears to be a valid technique to compare RSNs across subjects or conditions, at least when using the current methods.

Keywords

Resting state networks Functional connectivity Brain oscillations MEG Variability 

Supplementary material

10548_2014_364_MOESM1_ESM.pdf (10.1 mb)
Supplementary material 1 (PDF 10378 kb)

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Vincent Wens
    • 1
  • Mathieu Bourguignon
    • 1
    • 2
  • Serge Goldman
    • 1
  • Brice Marty
    • 1
  • Marc Op de Beeck
    • 1
  • Catherine Clumeck
    • 1
  • Alison Mary
    • 3
  • Philippe Peigneux
    • 3
  • Patrick Van Bogaert
    • 1
  • Matthew J. Brookes
    • 4
  • Xavier De Tiège
    • 1
  1. 1.Laboratoire de Cartographie fonctionnelle du Cerveau, UNI – ULB Neurosciences InstituteUniversité libre de Bruxelles (ULB)BrusselsBelgium
  2. 2.Brain Research Unit, O.V. Lounasmaa Laboratory, Aalto NeuroImaging, School of ScienceAalto UniversityAalto, EspooFinland
  3. 3.UR2NF – Neuropsychology and Functional Neuroimaging Research Unit at CRCN – Centre de Recherches Cognition et Neurosciences, and UNI – ULB Neurosciences InstituteUniversité libre de Bruxelles (ULB)BrusselsBelgium
  4. 4.Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and AstronomyUniversity of NottinghamNottinghamUK

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