Improving the interpretability of all-to-all pairwise source connectivity analysis in MEG with nonhomogeneous smoothing.

Studying the interaction between brain regions is important to increase our understanding of brain function. Magnetoencephalography (MEG) is well suited to investigate brain connectivity, because it provides measurements of activity of the whole brain at very high temporal resolution. Typically, brain activity is reconstructed from the sensor recordings with an inverse method such as a beamformer, and subsequently a connectivity metric is estimated between predefined reference regions-of-interest (ROIs) and the rest of the source space. Unfortunately, this approach relies on a robust estimate of the relevant reference regions and on a robust estimate of the activity in those reference regions, and is not generally applicable to a wide variety of cognitive paradigms. Here, we investigate the possibility to perform all-to-all pairwise connectivity analysis, thus removing the need to define ROIs. Particularly, we evaluate the effect of nonhomogeneous spatial smoothing of differential connectivity maps. This approach is inspired by the fact that the spatial resolution of source reconstructions is typically spatially nonhomogeneous. We use this property to reduce the spatial noise in the cerebro-cerebral connectivity map, thus improving interpretability. Using extensive data simulations we show a superior detection rate and a substantial reduction in the number of spurious connections. We conclude that nonhomogeneous spatial smoothing of cerebro-cerebral connectivity maps could be an important improvement of the existing analysis tools to study neuronal interactions noninvasively.