The impact of MEG source reconstruction method on source-space connectivity estimation: A comparison between minimum-norm solution and beamforming

The effect of the choice of the inverse method on the cortico-cortical coupling analysis has been largely overlooked in the literature. Here, we set out to investigate the impact of three inverse methods on source coherence detection using simulated MEG data. To this end, we created thousands of randomly located pairs of sources and varied their inter- and intra-source correlation strength, source size and spatial configuration. Then, we used the simulated pairs of sources to generate sensor-level MEG measurements at varying signal-to-noise ratios (SNR). Next, we reconstructed the sources using L2-Minimum-Norm Estimate (MNE), Linearly Constrained Minimum Variance (LCMV) beamforming, and Dynamic Imaging of Coherent Sources (DICS) beamforming; and calculated source level power and coherence maps. We evaluated the performance of the methods using the Receiver Operating Characteristic (ROC) curves. The results indicate that beamformers perform better than MNE for coherence reconstructions of interacting point-like sources; but MNE provides better connectivity estimation than beamformers of interacting extended cortical patches, if each patch consists of dipoles with identical time series (high intra-patch coherence). However, the performance of the beamformers for interacting patches improves substantially if each cortical patch is simulated with partly coherent time series (partial intra-patch coherence). These results demonstrate that the choice of the inverse method impacts the results of MEG source-space coherence analysis, and that the optimal choice of the inverse solution depends on the spatial and synchronization profile of the interacting cortical sources. Our conclusions can guide method selection and help improve data interpretation regarding MEG connectivity estimation.