High precision anatomy for MEG

Abstract

Precise MEG estimates of neuronal current flow are undermined by uncertain knowledge of the head location with respect to the MEG sensors. This is either due to head movements within the scanning session or systematic errors in co-registration to anatomy. Here we show how such errors can be minimized using subject-specific head-casts produced using 3D printing technology. The casts fit the scalp of the subject internally and the inside of the MEG dewar externally, reducing within session and between session head movements. Systematic errors in matching to MRI coordinate system are also reduced through the use of MRI-visible fiducial markers placed on the same cast. Bootstrap estimates of absolute co-registration error were of the order of 1 mm. Estimates of relative co-registration error were < 1.5 mm between sessions. We corroborated these scalp based estimates by looking at the MEG data recorded over a 6 month period. We found that the between session sensor variability of the subject's evoked response was of the order of the within session noise, showing no appreciable noise due to between-session movement. Simulations suggest that the between-session sensor level amplitude SNR improved by a factor of 5 over conventional strategies. We show that at this level of coregistration accuracy there is strong evidence for anatomical models based on the individual rather than canonical anatomy; but that this advantage disappears for errors of greater than 5 mm. This work paves the way for source reconstruction methods which can exploit very high SNR signals and accurate anatomical models; and also significantly increases the sensitivity of longitudinal studies with MEG.

Optimising experimental design for MEG resting state functional connectivity measurement

“…Note the significant increase in voxel count when using the headcast (significance tested using a permutation test). These data confirm previous reports Troebinger et al, 2014) in demonstrating the utility of head-cast technology to improve the within subject consistence of MEG reconstructions. Also noteworthy is the effect of recording duration, with a lower angle error with longer recordings; this is particularly noticeable for the data recorded with a head-cast.…”

“…Specifically we assessed the effects of coregistration method and data recording duration on the between session reliability of network estimation. Our results showed that the use of a foam head-cast, which is known to improve coregistration accuracy Troebinger et al, 2014), increased significantly the between session repeatability of both MEG reconstruction accuracy and connectivity estimation. This was the case for all MEG connectivity metrics, but was strongest for phase based methods.…”

“…The head-cast has been shown as a useful methodology to minimise coregistration error Troebinger et al, 2014).…”

Quantifying the performance of MEG source reconstruction using resting state data

“…The MSP algorithm, although perhaps the most elegant and comprehensive method 413 we tested, is also computationally disadvantaged by the need to search over a large space of 414 possible patch/prior combinations and the inherent pitfalls of local extrema in this optimization. A 415 more robust way to implement this algorithm would have been to select the best model from many 416 random patch choices (Troebinger et al, 2014b). 417…”

“…http://dx.doi.org/10.1101/248252 doi: bioRxiv preprint first posted online Jan. 15, 2018; Troebinger et al, 2014aTroebinger et al, , 2014b, where the forward model is more precisely known, and those 78 collected without. 79…”