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MEG: An Introduction to Methods$
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Peter Hansen, Morten Kringelbach, and Riitta Salmelin

Print publication date: 2010

Print ISBN-13: 9780195307238

Published to Oxford Scholarship Online: September 2010

DOI: 10.1093/acprof:oso/9780195307238.001.0001

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The Dowser in the Fields: Searching for MEG Sources

The Dowser in the Fields: Searching for MEG Sources

Chapter:
(p.83) 5 The Dowser in the Fields: Searching for MEG Sources
Source:
MEG: An Introduction to Methods
Author(s):

Sylvain Baillet

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780195307238.003.0005

This chapter reviews the statistical tools available for the analysis of distributed activation maps defined either on the 2D cortical surface or throughout the 3D brain volume. Statistical analysis of MEG data bears a great resemblance to the analysis of functional magnetic resonance imaging (fMRI) or positron emission tomography (PET) activation maps, therefore much of the methodology can be borrowed or adapted from the functional neuroimaging literature. In particular, the General Linear Modeling (GLM) approach, where the MEG data are first mapped into brain space, and then fitted to a univariate or multivariate model at each surface or volume element, is described. A desired contrast of the estimated parameters produces a statistical map, which is then thresholded for evidence of an experimental effect. The chapter also describes several approaches that can produce corrected thresholds and control for false positives: Bonferroni, Random Field Theory (RFT), permutation tests, and False Discovery error Rate (FDR).

Keywords:   statistical tools, statistical map, neuroimaging, statistical analysis, activation maps, General Linear Modeling

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