title: Spatiotemporal localization of significant activation in MEG using permutation tests.
abstract: 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.
authors.affiliation: Signal & Image Processing Institute, University of Southern California, Los Angeles, CA 90089-2564, USA. pantazis@sipi.usc.edu
authors.forename: Dimitrios
authors.initials: D
authors.lastname: Pantazis
authors.forename: Thomas E
authors.initials: TE
authors.lastname: Nichols
authors.forename: Sylvain
authors.initials: S
authors.lastname: Baillet
authors.forename: Richard M
authors.initials: RM
authors.lastname: Leahy
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D005073:Evoked Potentials, Somatosensory / Q000502:physiology; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D015225:Magnetoencephalography / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013269:Stochastic Processes; D013382:Subtraction Technique