Forward Beamforming and Inferring Functional Connectivity with MEG Data

The conventional beamformers that reconstruct the cerebral origin of brain activity measured outside the head via electro- and magnetoencephalography (EEG/MEG) suffer from depth bias and smearing of nearby sources.

Here, to meet these methodological challenges, we propose a depth-invariant and forward beamformer for magnetoencephalography (MEG) data. Based on the new proposal, we further develop a two-step approach for inferring functional connectivity in the brain.

The proposed methodology has nice features including its invariance with respect to source depths in the brain, nulling smearing of nearby sources and allowing for time-varying source orientations. We illustrate the new approach with MEG data derived from a face-perception experiment, revealing patterns of functional connectivity for face perception. We identify a set of brain regions where their responses and connectivity are significantly varying when stimuli alter between faces and scrambled faces.

By simulation studies, we show that the proposed forward beamformer can outperform the forward methods based on conventional beamformers in terms of localization bias.