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Measuring functional connectivity in MEG: a multivariate approach insensitive to linear source leakage.

M J Brookes¹, M W Woolrich, G R Barnes

¹Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK. matthew.brookes@nottingham.ac.uk

|April 10, 2012

Summary

This study introduces a new multivariate method to accurately measure brain connectivity using magnetoencephalography (MEG). The technique effectively reduces signal leakage, improving the analysis of neural oscillatory processes in the human brain.

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Area of Science:

Neuroscience
Biophysics
Computational Biology

Background:

Magnetoencephalography (MEG) shows promise for non-invasive measurement of functional brain connectivity.
A significant challenge in MEG analysis is addressing signal leakage due to voxel non-independence in source space.

Purpose of the Study:

To demonstrate a novel multivariate statistical method for identifying cortico-cortical interactions.
To address and mitigate the issue of signal leakage in MEG data analysis.

Main Methods:

Development of a multivariate statistical framework to analyze non-zero lag interactions between neural oscillatory power envelopes.
The method is designed to be spatially unbiased and control the false positive rate.
Demonstration of the methodology through both simulated and real MEG data.

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Main Results:

The proposed method reliably identifies cortico-cortical interactions.
It effectively removes linear signal leakage between seed and target voxels.
The multivariate approach captures high-dimensional MEG data in a single imaging statistic.

Conclusions:

The new multivariate method offers a robust solution for analyzing functional brain connectivity with MEG.
It overcomes key limitations of previous approaches by addressing signal leakage.
Further classical statistical tests can quantify the drivers of identified neural interactions.