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MEG-SIM Web Portal: A Database of Realistic Simulated and Empirical MEG Data for Testing Algorithms

  • Lori Sanfratello
  • Julia Stephen
  • Elaine Best
  • Doug Ranken
  • Cheryl Aine
Chapter

Abstract

MEG is a noninvasive measure of electrophysiological brain activity which provides excellent temporal and high spatial resolution. Because of its uniquely high temporal resolution relative to the more commonly used hemodynamic-based measures (fMRI, PET), the usefulness of MEG as a complementary neuroimaging method is becoming more widely recognized, particularly in the investigation of functional connectivity within and between large-scale brain networks. However, the available analysis methods for solving the inverse problem for MEG have yet to be compared and standardized. A comparison of analysis methods is further complicated by the fact that the different MEG systems have different data formats, noise cancellation methods, and sensor configurations. In order to facilitate this process, we established a website containing an extensive series of realistic simulated data for testing purposes (http://cobre.mrn.org/megsim/). In addition, we assert the usefulness of these datasets for training purposes, as they will provide an unambiguous answer to whether a trainee is correctly carrying out analyses. Here we present a brief rationale and description of the testbed created, including cases emphasizing functional connectivity (e.g., oscillatory activity) and the Default Mode Network (DMN). They are suitable for use with a wide assortment of analyses including equivalent current dipole (ECD), minimum norm, beamformers, independent component analysis (ICA), Granger causality/directed transfer function, and single-trial methods.

Keywords

MEG Simulations/simulated data Algorithms Minimum norm Beamformer Dipole modeling 

Notes

Acknowledgment

This work was funded by NIH grantsR21MH080141-02, 1P20 RR021938-04, and R01AG029495-04. It was also supported in part by the Department of Energy under Award Number DE-FG02-99ER62764 to the Mind Research Network. We thank M. Weisend, S. Ahlfors, M. Hämäläinen, J. Mosher, A. Leuthold, and A. Georgopoulos for their help when the initial partnership between institutions was established which permitted the acquisition of these data. We also wish to thank J. A. MacArthur, T. Wallace, K. Gilliam, C. H. Donahue, R. Montaño, J. E. Bryant, and A. Scott who aided in the construction of the earlier datasets. The content of this study is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Lori Sanfratello
    • 1
  • Julia Stephen
    • 2
  • Elaine Best
    • 2
  • Doug Ranken
    • 3
  • Cheryl Aine
    • 1
  1. 1.Department of RadiologyUniversity of New Mexico School of MedicineAlbuquerqueUSA
  2. 2.The Mind Research NetworkAlbuquerqueUSA
  3. 3.Los Alamos National LaboratoryLos AlamosUSA

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