Abstract：
:Beamforming has been widely adopted as a source reconstruction technique in the analysis of magnetoencephalography data. Most beamforming implementations incorporate a spatially-varying rescaling (which we term weights normalisation) to correct for the inherent depth bias in raw beamformer estimates. Here, we demonstrate that such rescaling can cause critical problems whenever analyses are performed over multiple sessions of separately beamformed data, for example when comparing effect sizes between different populations. Importantly, we show that the weights-normalised beamformer estimates of neural activity can even lead to a reversal in the inferred sign of the effect being measured. We instead recommend that no weights normalisation be carried out; any depth bias is instead accounted for in the calculation of multi-session (e.g. group) statistics. We demonstrate the severity of the weights normalisation confound with a 2-D simulation, and in real MEG data by performing a group statistical analysis to detect differences in alpha power in eyes-closed rest compared with continuous visual stimulation.
journal_name
Neuroimagejournal_title
NeuroImageauthors
Luckhoo HT,Brookes MJ,Woolrich MWdoi
10.1016/j.neuroimage.2013.12.026subject
Has Abstractpub_date
2014-07-15 00:00:00pages
330-5eissn
1053-8119issn
1095-9572pii
S1053-8119(13)01230-5journal_volume
95pub_type
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