Research articleFlexible head-casts for high spatial precision MEG

•We propose a method for constructing flexible head-casts to stabilize the head during MEG scanning.•Co-registration error is minimized by using MRI images to pre-define fiducial coil locations.•Within- and between-session movement is <0.25 and <1 mm respectively.•This enables high reproducibility of source level results.

BackgroundIn combination with magnetoencephalographic (MEG) data, accurate knowledge of the brain's structure and location provide a principled way of reconstructing neural activity with high temporal resolution. However, measuring the brain's location is compromised by head movement during scanning, and by fiducial-based co-registration with magnetic resonance imaging (MRI) data. The uncertainty from these two factors introduces errors into the forward model and limit the spatial resolution of the data.New methodWe present a method for stabilizing and reliably repositioning the head during scanning, and for co-registering MRI and MEG data with low error.ResultsUsing this new flexible and comfortable subject-specific head-cast prototype, we find within-session movements of <0.25 mm and between-session repositioning errors around 1 mm.Comparison with existing method(s)This method is an improvement over existing methods for stabilizing the head or correcting for location shifts on- or off-line, which still introduce approximately 5 mm of uncertainty at best (Adjamian et al., 2004; Stolk et al., 2013; Whalen et al., 2008). Further, the head-cast design presented here is more comfortable, safer, and easier to use than the earlier 3D printed prototype, and give slightly lower co-registration errors (Troebinger et al., 2014b).ConclusionsWe provide an empirical example of how these head-casts impact on source level reproducibility. Employment of the individual flexible head-casts for MEG recordings provide a reliable method of safely stabilizing the head during MEG recordings, and for co-registering MRI anatomical images to MEG functional data.