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Spatiotemporal Localization of Significant Activation in MEG Using Permutation Tests

  • Dimitrios Pantazis
  • Thomas E. Nichols
  • Sylvain Baillet
  • Richard M. Leahy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2732)

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-stimulus 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.

Keywords

Permutation Test Surface Element Maximum Statistic Global Threshold Spatiotemporal Localization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Phillips, J.W., Leahy, R.M., Mosher, J.C.: MEG-Based Imaging of Focal Neuronal Current Sources. IEEE Transactions of Medical Imaging 163, 338–348 (1997)CrossRefGoogle Scholar
  2. 2.
    Dale, A.M., Liu, A.K., Fischi, R.B., Buckner, R.L., Belliveau, J.W., Lewine, J.D., Halgren, E.: Dynamic Statistical Parametric Mapping: Combining fMRI and MEG for High- Resolution Imaging of Cortical Activity. Neuron 26, 55–67 (2000)CrossRefGoogle Scholar
  3. 3.
    Worsley, K.J., Andermann, M., Koulis, T., MacDonald, D., Evans, A.C.: Detecting Changes in Nonisotropic Images. Human Brain Mapping 8, 98–101 (1999)CrossRefGoogle Scholar
  4. 4.
    Barnes, G.R., Hillebrand, A.: Statistical Flattening of MEG Beamformer Images. Human Brain Mapping 18, 1–12 (2003)CrossRefGoogle Scholar
  5. 5.
    Nichols, T.E., Holmes, A.P.: Nonparametric Permutation Tests For Functional Neuroimaging: A Primer with Examples. Human Brain Mapping 15, 1–25 (2001)CrossRefGoogle Scholar
  6. 6.
    Blair, R.C., Karnisky, W.: Distribution-Free Statistical Analyses of Surface and Volumetric Maps. In: Thatcher, R.W., Hallett, M., Roy, J.E., Huerta, M. (eds.) Functional Neuroimaging: Technical Foundations, Academic Press, San Diego (1994)Google Scholar
  7. 7.
    Arndt, S., Cizadlo, T., Andreasen, N.C., Heckel, D., Gold, S., O’Leary, D.S.: Tests for comparing images based on randomization and permutation methods. Journal of Cerebral Blood Flow and Metabolism 16, 1271–1279 (1996)Google Scholar
  8. 8.
    Holmes, A.P., Blair, R.C., Watson, J.D.G., Ford, I.: Nonparametric analysis of statistic images from functional mapping experiements. Journal of Cerebral Blood Flow and Metabolism 16, 7–22 (1996)Google Scholar
  9. 9.
    Shattuck, D.W., Leahy, R.M.: BrainSuite: An Automated Cortical Surface Identification Tool. Medical Image Analysis 6(2), 129–142 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Dimitrios Pantazis
    • 1
  • Thomas E. Nichols
    • 2
  • Sylvain Baillet
    • 3
  • Richard M. Leahy
    • 1
  1. 1.Signal & Image Processing InstituteUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Department of BiostatisticsUniversity of MichiganAnn ArborUSA
  3. 3.Neurosciences Cognitives & Imagerie Cerebrale CNRS UPR640-LENAHospital de la SalpetriereParisFrance

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