A systematic evaluation of source reconstruction of resting MEG of the human brain with a new high-resolution atlas: Performance, precision, and parcellation

Noninvasive functional neuroimaging of the human brain can give crucial insight into the mechanisms that underpin healthy cognition and neurological disorders. Magnetoencephalography (MEG) measures extracranial magnetic fields originating from neuronal activity with high temporal resolution, but requires source reconstruction to make neuroanatomical inferences from these signals. Many source reconstruction algorithms are available, and have been widely evaluated in the context of localizing task-evoked activities. However, no consensus yet exists on the optimum algorithm for resting-state data. Here, we evaluated the performance of six commonly-used source reconstruction algorithms based on minimum-norm and beamforming estimates. Using human resting-state MEG, we compared the algorithms using quantitative metrics, including resolution properties of inverse solutions and explained variance in sensor-level data. Next, we proposed a data-driven approach to reduce the atlas from the Human Connectome Project's multi-modal parcellation of the human cortex based on metrics such as MEG signal-to-noise-ratio and resting-state functional connectivity gradients. This procedure produced a reduced cortical atlas with 230 regions, optimized to match the spatial resolution and the rank of MEG data from the current generation of MEG scanners. Our results show that there is no one size fits all algorithm, and make recommendations on the appropriate algorithms depending on the data and aimed analyses. Our comprehensive comparisons and recommendations can serve as a guide for choosing appropriate methodologies in future studies of resting-state MEG.

Automated summary

Noninvasive functional neuroimaging techniques like magnetoencephalography (MEG) provide important insights into healthy cognition and neurological disorders. However, determining the optimal source reconstruction algorithm for resting-state data remains a challenge. This study evaluated common algorithms and proposed a data-driven approach to optimize a reduced cortical atlas for MEG data analysis. Our results emphasize the importance of selecting appropriate algorithms based on the specific dataset and research goals.