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Brain Topography

, Volume 27, Issue 5, pp 635–647 | Cite as

Accuracy of Estimating Strengths of Dipole Moments from a Small Number of Magnetoencephalographic Trial Data

  • Norio Fujimaki
  • Yasushi Terazono
  • Aya S. Ihara
  • Tomoe Hayakawa
  • Ayumu Matani
  • Hiroaki Umehara
Original Paper
  • 92 Downloads

Abstract

The conventional analysis estimates both the locations and strengths of neural source activations from event-related magnetoencephalography data that are averaged across about a hundred trials. In the present report, we propose a new method based on a minimum modified-l 1-norm to obtain the dependence of strengths on the presented stimuli from a limited number of trial data. It estimates the strengths from 10-trial average data and the locations from 100-trial average data. The method can be applied to neural activations whose strengths, but not locations, depend on the presented stimuli. For instance, it can be used in experiments in which the activation in the anterior temporal area (aT) is measured by varying semantic relatedness between stimuli in linguistic experiments. We conducted a realistic simulation, following previous experiments on lexico-semantic processing, in which five neural sources were simultaneously activated. The results showed that when the signal-to-noise ratio was one for non-averaged data, the proposed method had standard deviations of 13 % for the normalized strengths in the aT. It is inferred on the basis of the general linear model in which the strength has a linear dependence on the stimulus parameters that the proposed method can detect the dependence at a significance level of 1 % if the peak-to-peak change in normalized strength is more than 49 %. It is smaller than 66 % for the conventional method, which estimated locations and strengths from 10-trial data for each point. Thus, the proposed method can plot an activation-strength versus stimulus-parameter curve with better sensitivity.

Keywords

Magnetoencephalography Source estimation Few trials Moment strength Semantic processing General linear model 

Notes

Acknowledgments

The authors would like to thank Professor Toshio Yanagida of Osaka University and Dr. Shinro Mashiko, Dr. Kazuhiro Oiwa, and Dr. Takahisa Taguchi of the National Institute of Information and Communications Technology for their support. This work was supported by a Grant from the Japan Society for the Promotion of Science (KAKENHI 24500401).

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Norio Fujimaki
    • 1
  • Yasushi Terazono
    • 2
  • Aya S. Ihara
    • 1
  • Tomoe Hayakawa
    • 3
  • Ayumu Matani
    • 2
  • Hiroaki Umehara
    • 1
  1. 1.Center for Information and Neural NetworksNational Institute of Information and Communications TechnologySuitaJapan
  2. 2.Graduate School of Information Science and TechnologyThe University of TokyoTokyoJapan
  3. 3.Department of PsychologyTeikyo UniversityHachiojiJapan

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