Brain Topography

, Volume 16, Issue 3, pp 139–158 | Cite as

Commonalities and Differences Among Vectorized Beamformers in Electromagnetic Source Imaging

  • M-X Huang
  • J.J. Shih
  • R.R. Lee
  • D.L. Harrington
  • R.J. Thoma
  • M.P. Weisend
  • F. Hanlon
  • K.M. Paulson
  • T. Li
  • K. Martin
  • G.A. Miller
  • J.M. Canive
Article

Abstract

A number of beamformers have been introduced to localize neuronal activity using magnetoencephalography (MEG) and electroencephalography (EEG). However, currently available information about the major aspects of existing beamformers is incomplete. In the present study, detailed analyses are performed to study the commonalities and differences among vectorized versions of existing beamformers in both theory and practice. In addition, a novel beamformer based on higher-order covariance analysis is introduced. Theoretical formulas are provided on all major aspects of each beamformer; to examine their performance, computer simulations with different levels of correlation and signal-to-noise ratio are studied. Then, an empirical data set of human MEG median-nerve responses with a large number of neuronal generators is analyzed using the different beamformers. The results show substantial differences among existing MEG/EEG beamformers in their ways of describing the spatial map of neuronal activity. Differences in performance are observed among existing beamformers in terms of their spatial resolution, false-positive background activity, and robustness to highly correlated signals. Superior performance is obtained using our novel beamformer with higher-order covariance analysis in simulated data. Excellent agreement is also found between the results of our beamformer and the known neurophysiology of the median-nerve MEG response.

Beamformer MEG Dipole Somatosensory Median nerve Inverse problem 

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References

  1. Baldissera, F. and Leocani, L. Afferent excitation of human motor cortex as revealed by enhancement of direct cortico-spinal action on motoneurones. Electroenceph. Clin. Neurophysiol., 1995, 97: 394-401.Google Scholar
  2. Barba, C., Frot, M., Guenot, M. and Mauguiere, F. Stereotactic recordings of median nerve somatosensory-evoked potentials in the human pre-supplementary motor area. Eur. J. Neurosci., 2001, 13(2): 347-356.Google Scholar
  3. Barnes, G.R. and Hillebrand, A. Statistical flattening of MEG beamformer images. Hum. Brain. Mapp., 2003, 18: 1-12.Google Scholar
  4. Bergeron, J.W. and Braddom, R.L. Palmar cutaneous nerve recording and clarification of median premotor potential generators. Am. J. Phys. Med. Rehabil., 1998, 77(5): 399-406.Google Scholar
  5. Boakye, M., Huckins, S.C., Szeverenyi, N.M., Taskey, B.I. and Hodge, C.J.Jr. Functional magnetic resonance imaging of somatosensory cortex activity produced by electrical stimulation of the median nerve or tactile stimulation of the index finger. J. Neurosurg., 2000, 93(5): 774-783.Google Scholar
  6. Borgiotti, G. and Kaplan, L.J. Super resolution of uncorrelated interference sources by using adaptive array technique. IEEE Trans. Antennas Propagat., 1979, 27: 842-845.Google Scholar
  7. Chang, L. and Yeh, C.C. Performance of DMI and eigenspace-based beamformers. IEEE Trans. Antennas Propagat., 1992, 40: 1336-1347.Google Scholar
  8. Davidoff, R.A. The pyramidal tract. Neurology, 1990, 40: 332-339.Google Scholar
  9. Forss, N., Hari, R., Salmelin, R., Ahonen, A., Hamalainen, M., Kajola, M., Knuutila, J. and Simola, J. Activation of the human posterior parietal cortex by median nerve stimulation. Exp. Brain. Res., 1994, 99(2): 309-315.Google Scholar
  10. Forss, N. and Jousmaki, V. Sensorimotor integration in human primary and secondary somatosensory cortices. Brain Res., 1998, 781(1-2): 259-267.Google Scholar
  11. Fujiwara, N., Imai, M., Nagamine, T., Mima, T., Oga, T., Takeshita, K., Toma, K. and Shibasaki, H. Second somatosensory area (SII) plays a significant role in selective somatosensory attention. Brain Res. Cogn. Brain Res., 2002, 14(3): 389-397.Google Scholar
  12. Golub, G.H. and Van Loan, C.E. Matrix Computations. Baltimore, Maryland: The Johns Hopkins University Press, 1983.Google Scholar
  13. Gross, J. and Ioannides, A.A. Linear transformations of data space in MEG. Phys. Med. Biol., 1999, 44: 2081-2097.Google Scholar
  14. Gross, J., Kujala, J., Hamalainen, M., Timmermann, L., Schnitzler, A. and Salmelin, R. Dynamic imaging of coher-ent sources: studying neural interactions in the human brain. Proc. Natl. Acad. Sci. U.S.A., 2001, 98: 694-699.Google Scholar
  15. Hari, R., Karhu, J., Hämäläinen, M., Knuutila, J., Salonen, O., Sams, M. and Vilkman, V. Functional organization of the human first and second somatosensory cortices: a neuromagnetic study. Eur. J. Neurosci., 1993, 5(6): 724-734.Google Scholar
  16. Hari, R. and Forss, N. Magnetoencephalography in the study of human somatosensory cortical processing. Philos. Trans. R. Soc. Lond. B Biol. Sci., 1999, 354(1387): 1145-1154.Google Scholar
  17. Huang, M., Aine, C.J., Supek, S., Best, E., Ranken, D. and Flynn, E.R. Multi-start downhill simplex method for spatio-temporal source localization in Magnetoencephalography. Electroenceph. Clin. Neurophysiol., 1998, 108: 32-44.Google Scholar
  18. Huang, M., Aine, C., Davis, L., Butman, J., Christner, R., Weisend, W., Stephen, J., Meyer, J., Silveri, J., Herman, M. and Lee, R.R. Sources on the Anterior and Posterior Banks of the Central Sulcus Identified from Magnetic Somatosensory Evoked Responses using Multi-Start Spatio-Temporal Localization. Human Brain Mapping, 2000, 11: 59-76.Google Scholar
  19. Jones, E.G., Coulter, J.D. and Hendry, S.H.C. Intracortical conectivity of architectonic field in somatic sensory, motor and parietalcortex of monkey. J. Comp. Neurol., 1978, 181: 291-348.Google Scholar
  20. Jones, E.G., Wise, S.P. and Coulter, J.D. Differential thalamic relationships of sensory-motor and parietal cortical fields in monkeys. J. Comp. Neurol., 1979, 183: 833-892.Google Scholar
  21. Jousmaki, V. and Forss, N. Effect of stimulus intensity on signals from human somatosensory cortices. Neuroreport, 1998, 9(15): 3427-3431.Google Scholar
  22. Kawamura, T., Nakasato, N., Seki, K., Kanno, A., Fujita, S., Fujiwara, S. and Yoshimoto, T. Neuromagnetic evidence of pre-and post-central cortical sources of somatosensory evoked responses. Electroenceph. Clin. Neurophysiol., 1996, 100: 44-50.Google Scholar
  23. Lemon, R.N. and Porter, R. Afferent input to movement-related precentral neurones in conscious monkey. Proc. R. Soc. Lond. [Biol]., 1976, 194: 313-339.Google Scholar
  24. Lemon, R.N. and van der Burg, J. Short-latency peripheral inputs to thalamic neurones projecting to the motor cortex in the monkey. Exp. Brain. Res., 1979, 36: 445-462.Google Scholar
  25. Lemon, R.N. Functional properties of monkey motor cortex neurones receiving afferent input from the hand and fingers. J. Physiol. (Lond)., 1981, 311: 497-519.Google Scholar
  26. Mauguiere, F., Merlet, I., Forss, N., Vanni, S., Jousmaki, V., Adeleine, P. and Hari, R. Activation of a distributed somatosensory cortical network in the human brain: a dipole modeling study of magnetic fields evoked by median nerve stimulation. Part I: Location and activation timing of SEF sources. Electroenceph. Clin. Neurophysiol., 1997a, 104(4): 281-289.Google Scholar
  27. Mauguiere, F., Merlet, I., Forss, N., Vanni, S., Jousmaki, V., Adeleine, P. and Hari, R. Activation of a distributed somatosensory cortical network in the human brain: a dipole modeling study of magnetic fields evoked by median nerve stimulation. Part II: Effect of stimulus rate, attention and stimulus detection. Electroenceph. Clin. Neurophysiol., 1997, 104(4): 290-295.Google Scholar
  28. McGlone, F., Kelly, E.F., Trulsson, M., Francis, S.T., Westling, G. and Bowtell, R. Functional neuroimaging studies of human somatosensory cortex. Behav. Brain Res., 2002, 135(1-2): 147-158.Google Scholar
  29. Mosher, J.C., Lewis, P.S. and Leahy, R.M. Multiple dipole modeling and localization from spatio-temporal MEG data. IEEE Trans. Biomed. Eng., 1992, 39: 541-557.Google Scholar
  30. Park, T.A. and Del Toro, D.R. Generators of the early and late median thenar premotor potentials. Muscle Nerve, 1995, 18(9): 1000-1008.Google Scholar
  31. Robinson, S.E. and Vrba, J. Functional neuroimaging by syntheticaperture magnetometry (SAM). In: T. Yoshimoto, M. Kotani, S. Kuriki, H. Karibe and N. Nakasato (Ed.) Recent Advances in Biomagnetism, Tohoku University Press, Sendai, Japan, 1999: 302-305.Google Scholar
  32. Rosen, I. and Asanuma, H. Peripheral afferent inputs to the forelimb area of the monkey motor cortex: input-output relations. Exp. Brain Res., 1972, 14: 257-273.Google Scholar
  33. Sarvas, J. Basic mathematical and electromagnetic concepts of the bio-magnetic inverse problems. Phys. Med. Biol., 1987, 32: 11-22.Google Scholar
  34. Sekihara, K., Poeppel, D., Miyashita, Y. Application of eigenspace beamformer to virtual depth-electrode measurement using MEG. In S. Supek (Ed.) Proceedings 2nd International Symposium in Noninvasive Functional Source Imaging within the Human Brain and Heart (Biomedizinische Technik), Zagreb, Coratia, 1999:127-130.Google Scholar
  35. Sekihara, K., Nagarajan, S.S., Poeppel, D., Marantz, A. and Miyashita, Y. Reconstructing spatio-temporal activities of neural sources using an MEG vector beamformer technique. IEEE Trans. Biomed. Eng., 2001, 48(7): 760-771.Google Scholar
  36. Simoes, C., Jensen, O., Parkkonen, L. and Hari, R. Phase locking between human primary and secondary somatosensory cortices. Proc. Natl. Acad. Sci. U.S.A., 2003, 100(5): 2691-2694.Google Scholar
  37. Spiegel, J., Tintera, J., Gawehn, J., Stoeter, P. and Treede, R.D. Functional MRI of human primary somatosensory and motor cortex during median nerve stimulation. Clin. Neurophysiol. 1999, 110(1): 47-52.Google Scholar
  38. Urbano, A., Babiloni, F., Babiloni, C., Ambrosini, A., Onorati, P. and Rossini, P.M. Human short latency cortical responses to somatosensory stimulation. A high resolution EEG study. Neuroreport, 1997, 8(15): 3239-3243.Google Scholar
  39. Uutela, K., Hamalainen, M. and Salmelin, R. Global optimization in the localization of neuromagnetic sources. IEEE Trans. Biomed. Eng., 1998, 45(6): 716-723.Google Scholar
  40. Van Veen, B.D., van Drongelen, W., Yuchtman, M. and Suzuki, A. Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Trans. Biomed. Eng., 1997, 44(9): 867-880.Google Scholar
  41. Van Veen, B.D. and Buckley, K.M. Beamforming: a versatile approach to spatial filtering. IEEE ASSP Mag. 1988: 4-24.Google Scholar
  42. Vrba, J. and Robinson, S.E. SQUID sensor array configurations for magnetoencephalography applications. Supercond. Sci. Technol., 2002, 15: R51-R89.Google Scholar
  43. Waberski, T.D., Gobbele, R., Darvas, F., Schmitz, S. and Buchner, H. Spatiotemporal imaging of electrical activity related to attention to somatosensory stimulation. Neuroimage, 2002, 17(3): 1347-1357.Google Scholar
  44. Wong, Y.C., Kwan, H.C., MacKay, W.A. and Murphy, J.T. Spatial organization of precentral cortex in awake primates. I. Somato-sensory inputs. J. Neurophysiol., 1978, 41: 1107-1120.Google Scholar
  45. Wood, C.C., Cohen, D., Cuffin, B.N., Yarita, M. and Allison, T. Electrical sources in human somatosensory cortex: identification by combined magnetic and potential recording. Science, 1985, 227: 1051-1053.Google Scholar

Copyright information

© Human Sciences Press, Inc. 2004

Authors and Affiliations

  • M-X Huang
  • J.J. Shih
  • R.R. Lee
  • D.L. Harrington
  • R.J. Thoma
  • M.P. Weisend
  • F. Hanlon
  • K.M. Paulson
  • T. Li
  • K. Martin
  • G.A. Miller
  • J.M. Canive

There are no affiliations available

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