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Multimodal Imaging from Neuroelectromagnetic and Functional Magnetic Resonance Recordings

  • Fabio Babiloni
  • Febo Cincotti
Part of the Bioelectric Engineering book series (BEEG)

Abstract

Human neocortical processes involve temporal and spatial scales spanning several orders of magnitude, from the rapidly shifting somatosensory processes characterized by a temporal scale of milliseconds and a spatial scales of few square millimeters to the memory processes, involving time periods of seconds and spatial scale of square centimeters. Information about the brain activity can be obtained by measuring different physical variables arising from the brain processes, such as the increase in consumption of oxygen by the neural tissues or a variation of the electric potential over the scalp surface. All these variables are connected in direct or indirect way to the neural ongoing processes, and each variable has its own spatial and temporal resolution. The different neuroimaging techniques are then confined to the spatiotemporal resolution offered by the monitored variables. For instance, it is known from physiology that the temporal resolution of the hemodynamic deoxyhemoglobin increase/decrease lies in the range of 1–2 seconds, while its spatial resolution is generally observable with the current imaging techniques at few mm scale. Today, no neuroimaging method allows a spatial resolution on a mm scale and a temporal resolution on a msec scale. Hence, it is of interest to study the possibility to integrate the information offered by the different physiological variables in a unique mathematical context. This operation is called the “multimodal integration” of variable X and Y, when the X variable has typically particular appealing spatial resolution property (mm scale) and the Y variable has particular attractive temporal properties (on a ms scale). Nevertheless, the issue of several temporal and spatial domains is critical in the study of the brain functions, since different properties could become observable, depending on the spatio-temporal scales at which the brain processes are measured.

Keywords

Blood Oxygen Level Dependence Head Model fMRI Signal Current Dipole Equivalent Current Dipole 
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. Ahlfors, S.P., Simpson, G.V., Dale, A.M., Belliveau J.W., Liu, A.K., Korvenoja, A., Virtanen, J., Huotilainen, M., Tootell, R.B., Aronen, H.J., and Ilmoniemi, R.J., 1999, Spatiotemporal activity of a cortical network for processing visual motion revealed by MEG and fMRI, Journal of Neurophysiology, 82(5):2545–55.Google Scholar
  2. Allison, T., McCarthy, G., Wood, C.C., Darcey, T.M., Spencer, D.D., and Williamson, P.D., 1989, Human cortical potentials evoked by stimulation of the median nerve. I. Cytoarchitectonic areas generating short-latency activity, Journal of Neurophysiology 62(3):694–710.Google Scholar
  3. Allison, T., McCarthy, G., Luby, M., Puce, A., and Spencer, D.D., 1996, Localization of functional regions of human mesial cortex by somatosensory evoked potential recording and by cortical stimulation, Electroencephalography & Clinical Neurophysiology 100(2):126–40.CrossRefGoogle Scholar
  4. Arieli, A., Sterkin, A., Grinvald, A., Aertsen, A.D., 1996, Dynamics of ongoing activity: Explanation of the large variability in evoked cortical responses, Science 273:1868–71.CrossRefGoogle Scholar
  5. Babiloni, F., Babiloni, C., Carducci, F., Fattorini L. et al., 1997, A high resolution EEG: a new model-dependent spatial deblurring method using a realistically shaped MR-constructed subjects head model, Electroenceph. clin. Neurophysiol. 102: 69–80.CrossRefGoogle Scholar
  6. Babiloni, F., Carducci, F., Cincotti, F., Del Gratta, C., Roberti, G.M., Romani, G.L., Rossini, P.M., and Babiloni, C., 2000, Integration of High Resolution EEG and Functional Magnetic Resonance in the Study of Human Movement-Related Potentials, Methods of Information in Medicine 39(2):179–82.Google Scholar
  7. Babiloni, F., Carducci, F., Cincotti, F., Del Gratta, C., Pizzella, V, Romani, G.L., Rossini, P.M., Tecchio F., and Babiloni, C., 2001, Linear inverse source estimate of combined EEG and MEG data related to voluntary movements, Human Brain Mapping, 14(4):197–210.CrossRefGoogle Scholar
  8. Baillet, S. and Garnero, L., 1997, A bayesian framework to introducing anatomo-functional priors in the EEG/MEG inverse problem, IEEE Trans. Biom. Eng. 44:374–85.CrossRefGoogle Scholar
  9. Baillet, S., Garnero, L., Marin, G., and Hugonin, P., 1999, J Combined MEG and EEG source imaging by minimization of mutual information, IEEE Trans. Biom. Eng. 46:522–34.CrossRefGoogle Scholar
  10. Baillet, S., Leahy, R., Singh, M., Shattuck D., and Mosher, J., 2001, Supplementary Motor Area Activation Preceding Voluntary Finger Movements as Evidenced by Magnetoencephalography and fMRI, International Journal of Bioelectromagnetism, 1(3).Google Scholar
  11. Bandettini, P. A. (1993) Functional MRI of the Brain, Soc. Magnetic Resonance in Medicine, Berkeley, CA.Google Scholar
  12. Blamire, A. M., Ogawa, S., Ugurbil, K., Rothman, D., McCarthy, G., Ellerman, J. M., Hyder, F., Rattner, Z., and Shulman, R. G., 1992, Proc. Natl. Acad. Sci. USA 89:11069–73.CrossRefGoogle Scholar
  13. Braitemberg, V. and Schuz, A., 1991. Anatomy of the cortex. Statistics and Geometry. New York: Springer-Verlag.Google Scholar
  14. Belliveau J. W. 1993. MRI techniques for functional mapping of the human brain: integration with PET, EEG/MEG and infrared spectroscopy. In: Quantification of Brain Function. Elsevier Science Publishers (Excerpta Medica), Amsterdam. 639–67.Google Scholar
  15. Beisteiner, R., Erdler, M., Teichtmeister, C., Diemling, M., Moser, E., Edward, V., and Deecke L., 1997, Magnetoencephalography may help to improve functional MRI brain mapping, European Journal of Neuroscience 9(5):1072–7.CrossRefGoogle Scholar
  16. Bonmassar, G., Van Der Moortele, P., Purdon, P., Jaaskelainen, I., Ives, J., Vaughan, T., Ugurbil K., and Belliveau J., 2001, 7 Tesla interleaved EEG and fMRI recordings: BOLD measurements, Neurolmage 13(6):S6.CrossRefGoogle Scholar
  17. Cohen, D., Cuffin B., Yunokuchi, K., Manieski, R., Purcell, C., Cosgrove, G.R., Ives, J., Kennedy, J., and Schomer D., 1990, MEG versus EEG localization test using implanted sources in the human brain, Ann. Neurol., 28:811–817.CrossRefGoogle Scholar
  18. Dale, A.M., and Sereno, M., 1993, Improved localization of cortical activity by combining EEG anf MEG with MRI cortical surface reconstruction: a linear approach, J. Cognitive Neuroscience, 5:162–76.CrossRefGoogle Scholar
  19. Dale, A. M., Fischl, B., Sereno, M. I., 1999, Cortical surface-based analysis. I. Segmentation and surface reconstruction, Neuroimage 9(2):179–94.CrossRefGoogle Scholar
  20. Dale, A., Liu, A., Fischl, B., Buckner, R., Belliveau, J. W., Lewine, J., and Halgren, E., 2000, Dynamic Statistical Parametric Mapping: Combining fMRI and MEG for High-Resolution Imaging of Cortical Activity, Neuron 26:55–67.CrossRefGoogle Scholar
  21. Ebersole, J. Defining epileptogenic foci: past, present, future., 1997, Journal of Clinical Neurophysiology 14:470–483.CrossRefGoogle Scholar
  22. Ebersole, J., 1999, The last word, Journal of Clinical Neurophysiology 16:297–302.CrossRefGoogle Scholar
  23. Edlinger, G., Wach, P., and Pfurtscheller, G., 1998, On the realization of an analytic high-resolution EEG, IEEE Trans. Biomed. Eng. 45:736–45.CrossRefGoogle Scholar
  24. Fuchs, M., Wischmann, H.A., Wagner, M., and Krüger, J., 1995, Coordinate System Matching for Neuromagnetic and Morphological Reconstruction Overlay, IEEE Transactions on Biomedical Engineering. 42:416–420.CrossRefGoogle Scholar
  25. Fuchs M., Wagner M., Wischmann H.A., Kohler T., Theissen A., Drenckhahn R., Buchner H., 1998, Improving source reconstruction by combining bioelectrical and biomagnetic data. Electroenceph clin Neurophysiol 107:69–80.CrossRefGoogle Scholar
  26. George J. S., Aine, C. J., Mosher, J. C., Schmidt D. M., Ranken D. M., Schlitt H. A., Wood, C. C., Lewine J. D., Sanders, J. A., and Belliveau, J. W., 1995, Mapping function in the human brain with MEG, anatomical MRI and functional MRI. J. Clin. Neurophysiol. 12:406–431.CrossRefGoogle Scholar
  27. Gevins, A., 1989, Dynamic functional topography of cognitive task, Brain Topogr., 2:37–56.CrossRefGoogle Scholar
  28. Gevins, A., Brickett, P., Reutter, B., and Desmond, J., 1991, Seeing through the skull: advanced EEGs use MRIs to accurately measure cortical activity from the scalp, Brain Topogr. 4:125–131.CrossRefGoogle Scholar
  29. Gevins, A., Le, J., Leong, H., McEvoy, L.K., Smith, M.E., 1999, Deblurring, J Clin Neurophysiol, 16(3):204–13.CrossRefGoogle Scholar
  30. Gevins, A., Le, J., Martin, N., Brickett, P., Desmond, J., and Reutter, B., 1994, High resolution EEG: 124-channel recording, spatial deblurring and MRI integration methods, Electroenceph. clin. Neurophysiol. 39:337–358.CrossRefGoogle Scholar
  31. Grave de Peralta Menendez, R., Gonzalez Andino S., and Lutkenhoner B., 1996, Figures of merit to compare linear distributed inverse solutions, Brain Topograph 9(2):117–24.CrossRefGoogle Scholar
  32. Grave de Peralta, R., Hauk, O., Gonzalez Andino, S., Vogt, H., and Michel, C.M., 1997, Linear inverse solution with optimal resolution kernels applied to the electromagnetic tomography, Human Brain Mapping 5, 454–67.CrossRefGoogle Scholar
  33. Grave de Peralta Menendez, R., and Gonzalez Andino S.L., 1998, Distributed source models: standard solutions and new developments. In: Uhl, C. (ed): Analysis of neurophysiological brain functioning. Springer Verlag, pp. 176–201.Google Scholar
  34. Grinvald, A., Lieke, E., Frostig, R.D., Gilbert, C.D., and Wiesel, T.N., 1986, Functional architecture of cortex revealed by optical imaging of intrinsic signals, Nature 324(6095):361–4.CrossRefGoogle Scholar
  35. Hämäläinen, M., and Ilmoniemi, R., 1984, Interpreting measured magnetic field of the brain: Estimates of the current distributions. Technical report TKK-F-A559, Helsinki Univesity of Technology.Google Scholar
  36. He, B., Wang, Y., Pak, S., and Ling, Y., 1996, Cortical source imaging from scalp electroencephalograms, Med. & Biol. Eng. & Comput., 34 Suppl, part 2, 257–8.CrossRefGoogle Scholar
  37. He, B., 1999, Brain Electrical Source Imaging: Scalp Laplacian mapping and cortical imaging, Critical Reviews in Biomedical Engineering, 27, 149–188.Google Scholar
  38. He B., Wang Y., Wu D., 1999, Estimating cortical potentials from scalp EEG’s in a realistically shaped inhomogeneous head model by means of the boundary element method. IEEE Trans Biomed Eng 46:1264–8.CrossRefGoogle Scholar
  39. He, B., Lian, J., Li, G., 2001, High-resolution EEG: a new realistic geometry spline Laplacian estimation technique, Clinical Neurophysiology 112(5):845–52.CrossRefGoogle Scholar
  40. He, B., Zhang, Lian, J., Sasaki, H., Wu, D., Towle, V.L. 2002. Boundary Element Method Based Cortical Potential Imaging of Somatosensory Evoked Potentials Using Subjects’ Magnetic Resonance Images, Neuroimage, in press.Google Scholar
  41. Heinze H.J., Mangun, G.R., Burchert, W., Hinrichs, H., Scholz, M., Munte, T.F., Gos, A., Scherg, M., Johannes, S., and Hundeshagen, H., 1994, Combined spatial and temporal imaging of brain activity during visual selective attention in humans, Nature 372:543–46.CrossRefGoogle Scholar
  42. Heinze H.J., Hinrichs H., Scholz M., Burchert W., and Mangun G.R., 1998, Neural mechanisms of global and local processing. A combined PET and ERP study. J. Cogn. Neurosci. 10:485–98.CrossRefGoogle Scholar
  43. Huang-Hellinger F.R., Breiter H.C., McCormak G., Cohen M.S., Kwong K.K., Sutton J.P., Savoy R.L., Weisskoff R.M., Davis T.L., Baker J.R., Belliveau J.W., and Rosen B.R. 1995. Simultaneous functional magnetic resonance imaging and electrophysiological recording. Hum. Brain Map. 3:13–23.CrossRefGoogle Scholar
  44. Ives, J.R., Warach, S., Schmitt, F., Edelman, R.R., and Schomer, D.L., 1993, Monitoring the patient’s EEG during echo-planar MRI, Electroenceph. clin. Neurophysiol. 87:417–420.CrossRefGoogle Scholar
  45. Kampe, K.K., Jones, R.A., and Auer, D.P., 2000, Frequency dependence of the functional MRI response after electrical median nerve stimulation, Human Brain Mapping, 9(2):106–14CrossRefGoogle Scholar
  46. Kim, S., Ashe, J., Hendrich, K., Ellermann, J., Merkle, H., Ugurbil, K., and Georgopulos, A. 1993, Functional magnetic resonance imaging of motor cortex: hemispheric asymmetry and handedness, Science 261: 615–7.CrossRefGoogle Scholar
  47. Kim, D.S., Duong T.Q., Kim S.G., 2000, High-resolution mapping of iso-orientation columns by fMRI, Nature Neuroscience 3(2):164–9.CrossRefGoogle Scholar
  48. Krakow, K., Woermann, F.G., Symms, M.R., Allen, P.J., Barker, G.J., Duncan, J.S., and Fish, D.R., 1999, EEG-triggered functional MRI of intertictal epileptiform activity in patients with partial seizures, Brain 122:1679–88.CrossRefGoogle Scholar
  49. Korvenoja, A., Huttunen, J., Salli, E., Pohjonen, H., Martinkauppi, S., Palva, J.M., Lauronen, L., Virtanen, J., Ilmoniemi, R.J., and Aronen, H.J., 1999, Activation of multiple cortical areas in response to somatosensory stimulation: combined magnetoencephalographic and functional magnetic resonance imaging, Human Brain Mapping, 8(1):13–27.CrossRefGoogle Scholar
  50. Lamusuo, S., Forss, N., Ruottinen, H.M., Bergman, J., Makela, J.P., Mervaala, E., Solin, O., Rinne, J.K., Ruotsalainen, U., Ylinen, A., Vapalahti, M., Hari, R., and Rinne, J.O., 1999, [18F]FDG-PET and whole-scalp MEG localization of epileptogenic cortex, Epilepsia 40:921–30.CrossRefGoogle Scholar
  51. Lawson, C.L., and Hanson, R., J. 1974, Solving least squares problems. Prentice Hall, Englewood Cliff, New Jersey.MATHGoogle Scholar
  52. Le, J., and Gevins, A., 1993, A method to reduce blur distortion from EEG’s using a realistic head model. IEEE Trans. Biomed. Eng. 40:517–528.CrossRefGoogle Scholar
  53. Lemieux, L., Krakow, K., Fish, D.R., 2001, Comparison of spike-triggered functional MRI BOLD activation and EEG dipole model localization, Neuroimage, 14(5):1097–104.CrossRefGoogle Scholar
  54. Liu, A.K., Belliveau, J.W., and Dale, A.M., 1998, Spatiotemporal imaging of human brain activity using functional MRI constrained magnetoencephalography data: Monte Carlo simulations, Proc. Nat. Acad. Sc., 95(15):8945–50.CrossRefGoogle Scholar
  55. Liu, A.K., 2000, Spatiotemporal brain imaging, PhD dissertation, Massachusetts Institute of Technology, Cambridge, MA.Google Scholar
  56. Logothetis N.K., Pauls J., Augath M., Trinath T., Oeltermann A., 2001, Neurophysiological investigation of the basis of the fMRI signal. Nature. 412(6843):150–7.CrossRefGoogle Scholar
  57. Luck S.J. 1999. Direct and indirect integration of event-related potentials, functional magnetic resonance images, and single-unit recordings, Hum. Brain Map. 8:115–201.CrossRefGoogle Scholar
  58. Magistretti, P.J., Pellerin, L., Rothman, D.L., and Shulman, R.G., 1999, Energy on demand, Science 283(5401):496–7.CrossRefGoogle Scholar
  59. Malonek D., Grinvald A., 1996, Interactions between electrical activity and cortical microcirculation revealed by imaging spectroscopy: implications for functional brain mapping. Science, 272(5261):551–4.CrossRefGoogle Scholar
  60. Menke W: Geophysical Data Analysis: Discrete Inverse Theory. San Diego, CA Academic Press, 1989.MATHGoogle Scholar
  61. Menon, V., Ford, J.M., Lim, K.O., Glover, G.H., and Pfefferbaum, A., 1997, Combined Event-Related fMRI and EEG Evidence For Temporal-Parietal Cortex Activation During Target Detection, NeuroReport 8: 3029–37.CrossRefGoogle Scholar
  62. Morioka, T., Mizushima, A., Yamamoto, T., Tobimatsu, S., Matsumoto, S., Hasuo, K., Fujii, K., and Fukui, M., 1995, Functional mapping of the sensorimotor cortex: combined use of magnetoencephalography, functional MRI, and motor evoked potentials, Neuroradiology 37:526–30.CrossRefGoogle Scholar
  63. Nunez, P.L., Silberstein, R., 2000, On the relationship of synaptic activity to macroscopic measurements: does co-registration of EEG with fMRI make sense? Brain Topogr. 13(2):79–96.CrossRefGoogle Scholar
  64. Nunez, P. Electric fields of the brain. Oxford University Press, New York, 1981.Google Scholar
  65. Nunez, P. L., 1995, Neocortical dynamics and human EEG rhythms, Oxford University Press, New York.Google Scholar
  66. Opitz, B., Mecklinger, A., Von Cramon, D.Y., and Kruggel, F., 1999, Combining electrophysiological and hemodynamic measures of the auditory oddball. Psychophysiology 36:142–7.CrossRefGoogle Scholar
  67. Oostendorp, T.F., Delbeke, J., Stegeman, D.F., 2000, The conductivity of the human skull: results of in vivo and in vitro measurements, IEEE Trans. Biom. Eng. 47(11):1487–92.CrossRefGoogle Scholar
  68. Pascual-Marqui, R.D. (1995) Reply to comments by Hamalainen, Ilmoniemi and Nunez. In ISBET Newsletter N.6, December 1995. Ed: W. Skrandies., 16–28.Google Scholar
  69. Phillips, J.W., Leahy, R., and Mosher, J.C., 1997, MEG-based imaging of focal neuronal current sources, IEEE Trans. Med. Imag., vol. 16., n. 3, pp. 338–348.CrossRefGoogle Scholar
  70. Puce, A., Allison, T., Spencer, S.S., Spencer, D.D., and McCarthy, G., 1997, Comparison of cortical activation evoked by faces measured by intracranial field potentials and functional MRI: two case studies, Hum Brain Mapp 5(4):298–305.CrossRefGoogle Scholar
  71. Rao, C.R., and Mitra, S.K., Generalized inverse of matrices and its applications. Wiley, New York, 1977.Google Scholar
  72. Rosen, B., Buckner, R., and Dale, A., 1998, Event-related fMRI: past, present and future. PNAS, 95:773–780.CrossRefGoogle Scholar
  73. Rush S., and Driscoll, D.A., 1968, Current distribution in the brain from surface electrodes, Anesthesia Analgesia, 47:717–23.CrossRefGoogle Scholar
  74. Salmelin, R., Forss, N., Knuutila, J., and Hari, R., 1995, Bilateral activation of the human somatomotor cortex by distal hand movements, Electroenceph. clin. Neurophysiol. 95:444–52.CrossRefGoogle Scholar
  75. Sanders, J.A., Lewine, J.D., Orrison, W.W., 1996, Comparison of primary motor localization using functional magnetic resonance imaging and magnetoencephalography, Human Brain Mapping, 4:47–57.CrossRefGoogle Scholar
  76. Savoy, R.L., Bandettini, P.A., O’Craven, K.M., Kwong, K.K., Davis, T.L., Baker, J.R., Weisskoff, R.M., and Rosen, B.R., 1995, Proc. Soc. Magn. Reson. Med. Third Sci. Meeting Exhib. 2:450.Google Scholar
  77. Scherg, M., von Cramon, D., and Elton, M., 1984, Brain-stem auditory-evoked potentials in post-comatose patients after severe closed head trauma, J Neurol 231(1):1–5.CrossRefGoogle Scholar
  78. Scherg, M., Bast T., and Berg, P., 1999, Multiple source analysis of interictal spikes: goals, requirements, and clinical value, Journal of Neurophysiology 16:214–224.CrossRefGoogle Scholar
  79. Seeck, M., Lazeyras, F., Michel, C.M., Blamke, O., Gericke, C.A., Ives, J., Delavelle, J., Golay, X., Haenggeli, C.A., De Tribolet, N., and Landis, T., 1998, Non-invasive epileptic focus localization using EEG-triggered functional MRI and electromagnetic tomography, Electroenceph. and Clin. Neurophysiol. 106:508–12.CrossRefGoogle Scholar
  80. Shoham, D., Glaser, D.E., Arieli, A., Kenet, T., Wijnbergen, C., Toledo, Y., Hildesheim, R., and Grinvald, A., 1999, Imaging cortical dynamics at high spatial and temporal resolution with novel blue voltage-sensitive dyes, Neuron 24:791–802.CrossRefGoogle Scholar
  81. Sidman, R., Vincent, D., Smith, D., and Lu, L., 1992, Experimental tests of the cortical imaging technique-applications to the response to median nerve stimulation and the localization of epileptiform discharges, IEEE Trans. Biomed. Eng. 39:437–444.CrossRefGoogle Scholar
  82. Spiegel, M. Theory and problems of vector analysis and an introduction to tensor analysis. Mc Graw Hill, New York, 1978.Google Scholar
  83. Srebro, R., Oguz, R.M., Hughlett, K., and Purdy, P.D., 1993, Estimating regional brain activity from evoked potential field on the scalp, IEEE Trans. Biom. Eng. 40:509–516.CrossRefGoogle Scholar
  84. Srebro, R., and Oguz, R.M., 1997, Estimating cortical activity from VEPS with the shrinking ellipsoid inverse, Electroenceph. & clin. Neurophysi.; 102:343–355.CrossRefGoogle Scholar
  85. Snyder, A.Z., Abdullaev, Y.G., Posner, M.I., and Raichle, M.E., 1995, Scalp electrical potentials reflect regional cerebral blood flow responses during processing of written words, Proc. Natl. Acad. Sci. USA. 92:1689–93.CrossRefGoogle Scholar
  86. Stok, C.J., Meijs, J.W., and Peters M.J., 1987, Inverse solutions based on MEG and EEG applied to volume conductor analysis. Phys Med Biol 32:99–104.CrossRefGoogle Scholar
  87. Tikhonov, A.N., and Arsenin, V.Y., Solutions of ill-posed problems. Washington D.C., Winston, 1977MATHGoogle Scholar
  88. Uutela, K., Hämäläinen, M., and Somersalo, E., 1999, Visualization of magnetoencephalographic data using minimum current estimates, Neuroimage, 10(2):173–80.CrossRefGoogle Scholar
  89. van den Elsen, P.A., Pol, E.J., Viergever M., 1993, Medical image matching — A review with classification, IEEE Engineering in Medicine and Biology, 12:26–39.CrossRefGoogle Scholar
  90. Wagner, M., and Fuchs, M. 2001, Integration of Functional MRI, Structural MRI, EEG, and MEG, International Journal of Bioelectromagnetism, 1(3).Google Scholar
  91. Warach, S., Ives, J.R., Schlaug, G., Patel, M.R., Darby, D.G., Thangaraj, V., Edelman, R.R., and Schomer, D.L., 1996, EEG-triggered echo-planar functional MRI in epilepsy. Neurology 47:89–93.Google Scholar
  92. Wells W.M., Viola P., Atsumi H., Nakajima S., Kikinis R., 1997, Multi-modal volume registration by maximization of mutual information, Medical Image Analysis 1:35–51.CrossRefGoogle Scholar
  93. Wikström H., Huttunen J., Korvenoja A., Virtanen J., Salonen O., Aronen H., Ilmoniemi R.J. 1996, Effects of interstimulus interval on somatosensory evoked magnetic fields (SEFs): a hypothesis concerning SEF generation at the primary sensorimotor cortex, Electroencephalography and Clinical Neurophysiology 100(6):479–87.Google Scholar

Copyright information

© Kluwer Academic/Plenum Publishers, New York 2004

Authors and Affiliations

  • Fabio Babiloni
    • 1
  • Febo Cincotti
    • 1
  1. 1.Dipartimento di Fisiologia Umana e FarmacologiaUniversità di Roma “La Sapienza”RomaItaly

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