Statistical flattening of MEG beamformer images

Human Brain Mapping - Tập 18 Số 1 - Trang 1-12 - 2003
Gareth R. Barnes1, Arjan Hillebrand
1The Wellcome Trust Laboratory for MEG Studies, Neurosciences Research Institute, Aston University, Birmingham B4 7ET, United Kingdom. [email protected]

Tóm tắt

AbstractWe propose a method of correction for multiple comparisons in MEG beamformer based Statistical Parametric Maps (SPMs). We introduce a modification to the minimum‐variance beamformer, in which beamformer weights and SPMs of source‐power change are computed in distinct steps. This approach allows the calculation of image smoothness based on the computed weights alone. In the first instance we estimate image smoothness by looking at local spatial correlations in residual images generated using random data; we then go on to show how the smoothness of the SPM can be obtained analytically by measuring the correlations between the adjacent weight vectors. In simulations we show that the smoothness of the SPM is highly inhomogeneous and depends on the source strength. We show that, for the minimum variance beamformer, knowledge of image smoothness is sufficient to allow for correction of the multiple comparison problem. Per‐voxel threshold estimates, based on the voxels extent (or cluster size) in flattened space, provide accurate corrected false positive error rates for these highly inhomogeneously smooth images. Hum. Brain Mapping 18:1–12, 2003. © 2002 Wiley‐Liss, Inc.

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Human Brain Mapping

Tập 18 Số 1

1-12

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