Accuracy and applications of group MEG studies using cortical source locations estimated from participants' scalp surfaces

Abstract

We contend that powerful group studies can be conducted using magnetoencephalography (MEG), which can provide useful insights into the approximate distribution of the neural activity detected with MEG without requiring magnetic resonance imaging (MRI) for each participant. Instead, a participant's MRI is approximated with one chosen as a best match on the basis of the scalp surface from a database of available MRIs. Because large inter-individual variability in sulcal and gyral patterns is an inherent source of blurring in studies using grouped functional activity, the additional error introduced by this approximation procedure has little effect on the group results, and offers a sufficiently close approximation to that of the participants to yield a good indication of the true distribution of the grouped neural activity. T1-weighted MRIs of 28 adults were acquired in a variety of MR systems. An artificial functional image was prepared for each person in which eight 5 x 5 x 5 mm regions of brain activation were simulated. Spatial normalisation was applied to each image using transformations calculated using SPM99 with (1) the participant's actual MRI, and (2) the best matched MRI substituted from those of the other 27 participants. The distribution of distances between the locations of points using real and substituted MRIs had a modal value of 6 mm with 90% of cases falling below 12.5 mm. The effects of this approach on real grouped SAM source imaging of MEG data in a verbal fluency task are also shown. The distribution of MEG activity in the estimated average response is very similar to that produced when using the real MRIs.

Figure 1

A: Sagittal and coronal sections of the results of spatial normalisation with the MNI template brain using a participant's own MRI with the simulated functional activations overlaid. B: Performing the spatial normalisation using an MRI with the best matching scalp surface extracted from a set of 27 MRIs.

Figure 2

Histogram of the distribution of distances between the centroids of each of eight corresponding regions of simulated brain activation in each of two artificial functional images produced using 28 MRIs. For each MRI the synthetic activations were subject to two transformations to bring them into registration with the MNI template, the first using the participants MRI, and the second using the best matched head shape from the other 27 MRIs to approximate the correct transformation.

Figure 3

The results of averaging the MEG responses of six participants in a verbal fluency task using: (A) the participants MRIs [Singh et al., 2002] and (B) MRIs selected as the best match from a pool of 27 other MRIs.