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
In recent years, neuroscience and neuroimaging have been revolutionised by the discovery of large distributed brain networks, some associated with simple sensory processing and others associated with higher level function
We address some of the limitations currently associated with MEG source space functional connectivity analysis by application of a multivariate statistical framework
3) We present a method for addressing the leakage problem via a simple linear subtraction prior to multivariate analysis
Summary
Measuring functional connectivity in MEG: A multivariate approach insensitive to linear source leakage. 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. 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. 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. 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
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