Consistency of magnetoencephalographic functional connectivity and network reconstruction using a template versus native MRI for co-registration

Linda Douw, Dagmar Nieboer*, Cornelis J. Stam, Prejaas Tewarie, Arjan Hillebrand

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Introduction: Studies using functional connectivity and network analyses based on magnetoencephalography (MEG) with source localization are rapidly emerging in neuroscientific literature. However, these analyses currently depend on the availability of costly and sometimes burdensome individual MR scans for co-registration. We evaluated the consistency of these measures when using a template MRI, instead of native MRI, for the analysis of functional connectivity and network topology. Methods: Seventeen healthy participants underwent resting-state eyes-closed MEG and anatomical MRI. These data were projected into source space using an atlas-based peak voxel and a centroid beamforming approach either using (1) participants’ native MRIs or (2) the Montreal Neurological Institute's template. For both methods, time series were reconstructed from 78 cortical atlas regions. Relative power was determined in six classical frequency bands per region and globally averaged. Functional connectivity (phase lag index) between each pair of regions was calculated. The adjacency matrices were then used to reconstruct functional networks, of which regional and global metrics were determined. Intraclass correlation coefficients were calculated and Bland–Altman plots were made to quantify the consistency and potential bias of the use of template versus native MRI. Results: Co-registration with the template yielded largely consistent relative power, connectivity, and network estimates compared to native MRI. Discussion: These findings indicate that there is no (systematic) bias or inconsistency between template and native MRI co-registration of MEG. They open up possibilities for retrospective and prospective analyses to MEG datasets in the general population that have no native MRIs available. Hum Brain Mapp, 2017.

Original languageEnglish
Pages (from-to)104-119
Number of pages16
JournalHuman Brain Mapping
Volume39
Issue number1
Early online date8 Oct 2017
DOIs
Publication statusPublished - Jan 2018

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Keywords

  • beamforming
  • co-registration
  • consistency
  • functional connectivity
  • magnetoencephalography
  • MNI template
  • MRI
  • network analyses

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