Depth-invariant beamforming for 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 is invariant with respect to source depths in the brain. It nulls smearing of nearby sources and allows 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.