Statistical flattening of MEG beamformer images

Gareth R Barnes, Arjan Hillebrand

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

We 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.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalHuman Brain Mapping
Volume18
Issue number1
DOIs
Publication statusPublished - Jan 2003
Externally publishedYes

Cite this

Barnes, Gareth R ; Hillebrand, Arjan. / Statistical flattening of MEG beamformer images. In: Human Brain Mapping. 2003 ; Vol. 18, No. 1. pp. 1-12.
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Statistical flattening of MEG beamformer images. / Barnes, Gareth R; Hillebrand, Arjan.

In: Human Brain Mapping, Vol. 18, No. 1, 01.2003, p. 1-12.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Barnes, Gareth R

AU - Hillebrand, Arjan

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N2 - We 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.

AB - We 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.

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