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


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.


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



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).


  1. Ahlfors SP, Han J, Belliveau JW, Hämäläinen MS (2010) Sensitivity of MEG and EEG to source orientation. Brain Topogr 23(3):227–232. doi: 10.1007/s10548-010-0154-x PubMedCentralPubMedCrossRefGoogle Scholar
  2. Cohen L, Dehaene S, Naccache L, Lehéricy S, Dehaene-Lambertz G, Hénaff MA, Michel F (2000) The visual word form area: spatial and temporal characterization of an initial stage of reading in normal subjects and posterior split-brain patients. Brain 123(Pt 2):291–307PubMedCrossRefGoogle Scholar
  3. Dale AM, Sereno MI (1993) Improved localization of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: a linear approach. J Cogn Neurosci 5(2):162–176PubMedCrossRefGoogle Scholar
  4. Downing PE, Chan AW-Y, Peelen MV, Dodds CM, Kanwisher N (2006) Domain specificity in visual cortex. Cereb Cortex 16(10):1453–1461. doi: 10.1093/cercor/bhj086 PubMedCrossRefGoogle Scholar
  5. Fujimaki N, Hayakawa T, Nielsen M, Knösche TR, Miyauchi S (2002) An fMRI-constrained MEG source analysis with procedures for dividing and grouping activation. Neuroimage 17(1):324–343PubMedCrossRefGoogle Scholar
  6. Fujimaki N, Hayakawa T, Ihara A, Wei Q, Munetsuna S, Terazono Y, Matani A, Murata T (2009) Early neural activation for lexico-semantic access in the left anterior temporal area analyzed by an fMRI-assisted MEG multidipole method. Neuroimage 44(3):1093–1102. doi: 10.1016/j.neuroimage.2008.10.021 PubMedCrossRefGoogle Scholar
  7. Fujimaki N, Hayakawa T, Ihara A, Matani A, Wei Q, Terazono Y, Murata T (2010) Masked immediate-repetition-priming effect on the early lexical process in the bilateral anterior temporal areas assessed by neuromagnetic responses. Neurosci Res 68(2):114–124. doi: 10.1016/j.neures.2010.06.006 PubMedCrossRefGoogle Scholar
  8. Gramfort A, Kowalski M, Hämäläinen M (2012) Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods. Phys Med Biol 57:1937–1961. doi: 10.1088/0031-9155/57/7/1937 PubMedCentralPubMedCrossRefGoogle Scholar
  9. Hämäläinen MS, Ilmoniemi RJ (1994) Interpreting magnetic fields of the brain: minimum norm estimates. Med Biol Eng Comput 32(1):35–42PubMedCrossRefGoogle Scholar
  10. Hämäläinen MS, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV (1993) Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev Mod Phys 65(2):413–497CrossRefGoogle Scholar
  11. Haufe S, Nikulin VV, Ziehe A, Müller K-R, Nolte G (2008) Combining sparsity and rotational invariance in EEG/MEG source reconstruction. Neuroimage 42:726–738. doi: 10.1010/j.neuroimage.2008.04.246 PubMedCrossRefGoogle Scholar
  12. Ihara A, Hayakawa T, Wei Q, Munetsuna S, Fujimaki N (2007) Lexical access and selection of contextually appropriate meaning for ambiguous words. Neuroimage 38(3):576–588. doi: 10.1016/j.neuroimage.2007.07.047 PubMedCrossRefGoogle Scholar
  13. Ihara A, Wei Q, Matani A, Fujimaki N, Yagura H, Nogai T, Umehara H, Murata T (2012) Language comprehension dependent on emotional context: a magnetoencephalography study. Neurosci Res 72(1):50–58. doi: 10.1016/j.neures.2011.09.011 PubMedCrossRefGoogle Scholar
  14. Matsuura K, Okabe Y (1995) Selective minimum-norm solution of the biomagnetic inverse problem. IEEE Trans Biomed Eng 42(6):608–615. doi: 10.1109/10.387200 PubMedCrossRefGoogle Scholar
  15. Matsuura K, Okabe Y (1997) A robust reconstruction of sparse biomagnetic sources. IEEE Trans Biomed Eng 44(8):720–726. doi: 10.1109/10.605428 PubMedCrossRefGoogle Scholar
  16. Mosher JC, Lewis PS, Leahy RM (1992) Multiple dipole modeling and localization from spatio-temporal MEG data. IEEE Trans Biomed Eng 39(6):541–557. doi: 10.1109/10.141192 PubMedCrossRefGoogle Scholar
  17. Ou W, Hämäläinen MS, Golland P (2009) A distributed spatio-temporal EEG/MEG inverse solver. Neuroimage 44:932–946. doi: 10.1016/j.neuroimage.2008.05.063 PubMedCentralPubMedCrossRefGoogle Scholar
  18. Robinson SE, Vrba J (1999) Functional neuroimaging by synthetic aperture magnetometry (SAM). In: Yoshimoto T et al (eds) Recent advances in biomagnetism. Tohoku University Press, Sendai, pp 302–305Google Scholar
  19. Sato MA, Yoshioka T, Kajihara S, Toyama K, Goda N, Doya K, Kawato M (2004) Hierarchical Bayesian estimation for MEG inverse problem. Neuroimage 23(3):806–826. doi: 10.1016/j.neuroimage.2004.06.037 PubMedCrossRefGoogle Scholar
  20. Sekihara K, Nagarajan SS, Poeppel D, Marantz A, Miyashita Y (2002) Application of an MEG eigenspace beamformer to reconstructing spatio-temporal activities of neural sources. Hum Brain Mapp 15(4):199–215PubMedCrossRefGoogle Scholar
  21. Solomyak O, Marantz A (2009) Lexical access in early stages of visual word processing: a single-trial correlational MEG study of heteronym recognition. Brain Lang 108(3):191–196. doi: 10.1016/j.bandl.2008.09.004 PubMedCrossRefGoogle Scholar
  22. Terazono Y, Fujimaki N, Murata T, Matani A (2010) Point source reconstruction principle of linear inverse problems. Inverse Problems 26:115016. doi: 10.1088/0266-5611/26/11/115016 CrossRefGoogle Scholar
  23. Tesche CD, Uusitalo MA, Ilmoniemi RJ, Huotiainen M, Kajola M, Salonen O (1995) Signal-space projection of MEG data characterize both distributed and well-localized neuronal sources. Electroencephalogr Clin Neurophysiol 95:189–200PubMedCrossRefGoogle Scholar
  24. Uusitalo MA, Ilmoniemi RJ (1997) Signal-space projection method for separating MEG and EEG into components. Med Biol Eng Comput 35:135–140PubMedCrossRefGoogle Scholar
  25. Wei Q, Ihara A, Hayakawa T, Murata T, Matsumoto E, Fujimaki N (2007) Phonological influences on lexicosemantic processing of kanji words. NeuroReport 18(17):1775–1780. doi: 10.1097/WNR.0b013e3282f16ddf PubMedCrossRefGoogle Scholar
  26. Winer BJ (1991) Statistical principles in experimental design, 3rd edn. McGraw-Hill Inc., New York, pp 941–954Google Scholar
  27. Zannino GD, Buccione I, Perri R, Macaluso E, Lo Gerfo E, Caltagirone C, Carlesimo GA (2009) Visual and semantic processing of living things and artifacts: an FMRI study. J Cogn Neurosci 22(3):554–570. doi: 10.1162/jocn.2009.21197 CrossRefGoogle Scholar

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

Personalised recommendations