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

Abstract

A number of recent studies have begun to show the promise of magnetoencephalography (MEG) as a means to non-invasively measure functional connectivity within distributed networks in the human brain. However, a number of problems with the methodology still remain — the biggest of these being how to deal with the non-independence of voxels in source space, often termed signal leakage. In this paper we demonstrate a method by which non-zero lag cortico-cortical interactions between the power envelopes of neural oscillatory processes can be reliably identified within a multivariate statistical framework. The method is spatially unbiased, moderately conservative in false positive rate and removes linear signal leakage between seed and target voxels. We demonstrate this methodology in simulation and in real MEG data. The multivariate method offers a powerful means to capture the high dimensionality and rich information content of MEG signals in a single imaging statistic. Given a significant interaction between two areas, we go on to show how classical statistical tests can be used to quantify the importance of the data features driving the interaction.

Highlights

► We demonstrate a method to image cortico-cortical interactions in MEG data. ► The method employs a multivariate statistical framework in MEG source space. ► The technique has reduced sensitivity to signal leakage across voxels. ► In simulation we show spatial accuracy and slightly conservative false positive rate. ► In real data we show cortico-cortical interaction between left and right motor cortex.