A wavelet-based method for measuring the oscillatory dynamics of resting-state functional connectivity in MEG.

Determining the dynamics of functional connectivity is critical for understanding the brain. Recent functional magnetic resonance imaging (fMRI) studies demonstrate that measuring correlations between brain regions in resting-state activity can be used to reveal intrinsic neural networks. To study the oscillatory dynamics that underlie intrinsic functional connectivity between regions requires high temporal resolution measures of electrophysiological brain activity, such as magnetoencephalography (MEG). However, there is a lack of consensus as to the best method for examining connectivity in resting-state MEG data. Here we adapted a wavelet-based method for measuring phase-locking with respect to the frequency of neural oscillations. This method employs anatomical MRI information combined with MEG data using the minimum norm estimate inverse solution to produce functional connectivity maps from a "seed" region to all other locations on the cortical surface at any and all frequencies of interest. We test this method by simulating phase-locked oscillations at various points on the cortical surface, which illustrates a substantial artifact that results from imperfections in the inverse solution. We demonstrate that normalizing resting-state MEG data using phase-locking values computed on empty room data reduces much of the effects of this artifact. We then use this method with eight subjects to reveal intrinsic interhemispheric connectivity in the auditory network in the alpha frequency band in a silent environment. This spectral resting-state functional connectivity imaging method may allow us to better understand the oscillatory dynamics underlying intrinsic functional connectivity in the human brain.