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We describe the use of non-parametric permutation tests to detect activation in cortically-constrained maps of current density computed from MEG data. The methods are applicable to any inverse imaging method that maps event-related MEG to a coregistered cortical surface. To determine an appropriate threshold to apply to statistics computed from these maps, it is important to control for the multiple testing problem associated with testing 10's of thousands of hypotheses (one per surface element). By randomly permuting pre- and post-stimuius data from the collection of individual epochs in an event related study, we develop thresholds that control the familywise (type 1) error rate. These thresholds are based on the distribution of the maximum intensity, which implicitly accounts for spatial and temporal correlation in the cortical maps. We demonstrate the method in application to simulated data and experimental data from a somatosensory evoked response study.

Type

Journal article

Journal

Information processing in medical imaging : proceedings of the ... conference

Publication Date

07/2003

Volume

18

Pages

512 - 523

Addresses

Signal & Image Processing Institute, University of Southern California, Los Angeles, CA 90089-2564, USA. pantazis@sipi.usc.edu

Keywords

Brain, Humans, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Magnetic Resonance Imaging, Image Enhancement, Subtraction Technique, Magnetoencephalography, Brain Mapping, Models, Statistical, Sensitivity and Specificity, Stochastic Processes, Reproducibility of Results, Evoked Potentials, Somatosensory, Algorithms, Models, Biological, Computer Simulation, Pattern Recognition, Automated