Quantifying the performance of MEG source reconstruction using resting state data

Resting state networks measured with magnetoencephalography (MEG) form transiently stable spatio-temporal patterns in the subsecond range, and therefore fluctuate more rapidly than previously appreciated. These states populate and interact across the whole brain, are simple to record, and possess the same dynamic structure of task related changes. They therefore provide a generic, heterogeneous, and plentiful functional substrate against which to test different MEG recording and reconstruction approaches. Here we validate a non-invasive method for quantifying the resolution of different inversion assumptions under different recording regimes (with and without head-casts) based on resting state MEG. Spatio-temporally partitioning of data into self-similar periods confirmed a rich and rapidly dynamic temporal structure with a small number of regularly reoccurring states. To test the anatomical precision that could be resolved through these transient states we then inverted these data onto libraries of systematically distorted, subject specific, cortical meshes and compared the quality of the fit using Cross Validation and a Free Energy metric. This revealed which inversion scheme was able to best support the least distorted (most accurate) anatomical models. Both datasets showed an increase in model fit as anatomical models moved towards the true cortical surface. In the head-cast MEG data, the Empirical Bayesian Beamformer (EBB) algorithm showed the best mean anatomical discrimination (3.7 mm) compared with Minimum Norm / LORETA (6.0 mm) and Multiple Sparse priors (9.4 mm). This pattern was replicated in the second (conventional dataset) although with a marginally poorer prediction of the missing (cross-validated) data. Our findings suggest that the abundant resting state data now commonly available could be used to refine and validate MEG source reconstruction methods or recording paradigms.