KEYWORDS: Magnetoencephalography, Data modeling, Contrast transfer function, Data centers, Statistical analysis, Data analysis, Nerve, Magnetic resonance imaging, Brain mapping, Image segmentation
Cortical activation maps estimated from MEG data fall prey to variability across subjects, trials, runs and
potentially MEG centers. To combine MEG results across sites, we must demonstrate that inter-site variability
in activation maps is not considerably higher than other sources of variability. By demonstrating relatively
low inter-site variability with respect to inter-run variability, we establish a statistical foundation for sharing
MEG data across sites for more powerful group studies or clinical trials of pathology. In this work, we analyze
whether pooling MEG data across sites is more variable than aggregating MEG data across runs when estimating
significant cortical activity. We use data from left median nerve stimulation experiments on four subjects at each
of three sites on two runs occurring on consecutive days for each site. We estimate cortical current densities
via minimum-norm imaging. We then compare maps across machines and across runs using two metrics: the
Simpson coefficient, which admits equality if one map is equal in location to the other, and the Dice coefficient,
which admits equality if one map is equal in location and size to the other. We find that sharing MEG data
across sites does not noticeably affect group localization accuracy unless one set of data has abnormally low
signal power.
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