Journal
NEUROIMAGE
Volume 95, Issue -, Pages 330-335Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2013.12.026
Keywords
MEG; Group statistics; Beamforming; Source reconstruction
Funding
- Centre for Doctoral Training in Healthcare Innovation
- RCUK Digital Economy Programme
- Leverhulme Trust for fellowship
- University of Nottingham
- Wellcome Trust [092753]
- MRC/EPSRC UK MEG Partnership award
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre based at Oxford University Hospitals Trust Oxford University
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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. (C) 2014 The Authors. Published by Elsevier Inc.
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