Elsevier

NeuroImage

Volume 117, 15 August 2015, Pages 439-448
NeuroImage

A symmetric multivariate leakage correction for MEG connectomes

https://doi.org/10.1016/j.neuroimage.2015.03.071Get rights and content
Under a Creative Commons license
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Highlights

  • A method for removing source leakage from multivariate network analyses in MEG.

  • Network inference performed using regularised partial correlations between ROIs.

  • Artificial correlations are removed using a symmetric orthogonalisation step.

  • Simulations show accurate false-positive rates for network edge detection.

  • Resting-state networks show increased bilateral connectivity after correction.

Abstract

Ambiguities in the source reconstruction of magnetoencephalographic (MEG) measurements can cause spurious correlations between estimated source time-courses. In this paper, we propose a symmetric orthogonalisation method to correct for these artificial correlations between a set of multiple regions of interest (ROIs). This process enables the straightforward application of network modelling methods, including partial correlation or multivariate autoregressive modelling, to infer connectomes, or functional networks, from the corrected ROIs. Here, we apply the correction to simulated MEG recordings of simple networks and to a resting-state dataset collected from eight subjects, before computing the partial correlations between power envelopes of the corrected ROItime-courses. We show accurate reconstruction of our simulated networks, and in the analysis of real MEGresting-state connectivity, we find dense bilateral connections within the motor and visual networks, together with longer-range direct fronto-parietal connections.

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