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7 - MEG and EEG: source estimation

Published online by Cambridge University Press:  05 October 2012

By
Seppo P. Ahlfors  and
Matti S. Hämäläinen
Edited by
Romain Brette  and
Alain Destexhe
Seppo P. Ahlfors
Affiliation:
Massachusetts General Hospital, USA
Matti S. Hämäläinen
Affiliation:
Department of Radiology, USA
Romain Brette
Affiliation:
Ecole Normale Supérieure, Paris
Alain Destexhe
Affiliation:
Centre National de la Recherche Scientifique (CNRS), Paris
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Summary

Introduction

In magnetoencephalography (MEG) and electroencephalography (EEG), scalp potentials and extracranial magnetic fields generated by electrical activity in the brain are detected non-invasively (Berger, 1929; Cohen, 1972) (for an overview of the methodology see, e.g., Hamalainen et al., 1993; Niedermeyer and Lopes da Silva, 1999; Michel et al., 2009; Hansen et al., 2010). MEG and EEG signals are superpositions of contributions from sources at different locations in the brain. Source estimation (also known as inverse modeling) refers to the problem of determining the spatiotemporal patterns of neural activity on the basis of the recorded signals (Figure 7.1). The specific goal in source estimation can be stated in two closely related ways: (a) to identify the locations of the sources of the measured signals as a function of time, or (b) to disentangle the contributions from different brain regions in the measured time-varying signals. The often used term source localization refers to the former, whereas spatiotemporal imaging emphasizes the latter, reflecting the use of MEG and EEG source estimation in the analysis of the dynamical activity in networks of brain areas.

A given source in the brain generates a characteristic spatial pattern of signals in arrays of MEG and EEG sensors. These patterns can be calculated by using a forward model (see Chapter 6). In source estimation, the measured spatial patterns of signals are analyzed in order to make inferences about the distribution of the sources in the brain.

Type
Chapter
Information
Handbook of Neural Activity Measurement , pp. 257 - 286
Publisher: Cambridge University Press
Print publication year: 2012

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References

Ahlfors, S. P. and Simpson, G.V. (2004). Geometrical interpretation of fMRI-guided MEG/EEG inverse estimates. NeuroImage, 22, 323–332.CrossRefGoogle ScholarPubMed
Ahlfors, S. P., Ilmoniemi, R. J. and Hamalainen, M. S. (1992). Estimates of visually evoked cortical currents. Electroencephalogr. Clin. Neurophysiol., 82, 225–236.CrossRefGoogle ScholarPubMed
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. J. Neurophysiol., 82, 2545–2555.CrossRefGoogle ScholarPubMed
Ahlfors, S. P., Han, J., Belliveau, J.W. and Hamalainen, M. S. (2010a). Sensitivity of MEG and EEG to source orientation. Brain Topogr., 23, 227–232.CrossRefGoogle ScholarPubMed
Ahlfors, S. P., Han, J., Lin, F.H., Witzel, T., Belliveau, J.W., Hamalainen, M. S. and Halgren, E. (2010b). Cancellation of EEG and MEG signals generated by extended and distributed sources. Human Brain Mapping, 31, 140–149.Google ScholarPubMed
Astolfi, L., Cincotti, F., Mattia, D., Babiloni, C., Carducci, F., Basilisco, A., Rossini, P. M., Salinari, S., Ding, L., Ni, Y., He, B. and Babiloni, F. (2005). Assessing cortical functional connectivity by linear inverse estimation and directed transfer function: simulations and application to real data. Clin. Neurophysiol., 116, 920–932.CrossRefGoogle ScholarPubMed
Backus, G. E. (1971). Inference from inadequate and inaccurate data. In: W. H., Reid (editor), Mathematical Problems in the Geophysical Sciences 2. Inverse Problems, Dynamo Theory, and Tides. Providence, RI: American Mathematical Society, pp. 1–105.Google Scholar
Baillet, S. and Garnero, L. (1997). A Bayesian approach to introducing anatomofunctional priors in the EEG/MEG inverse problem. IEEE Trans. Biomed. Eng., 44, 374–385.CrossRefGoogle Scholar
Baillet, S., Mosher, J.C. and Leahy, R. M. (2001). Electromagnetic brain mapping. IEEE Signal Process. Mag., 18, 14–30.CrossRefGoogle Scholar
Bar, M., Kassam, K. S., Ghuman, A. S., Boshyan, J., Schmidt, A. M., Dale, A. M., Hamalainen, M. S., Marinkovic, K., Schacter, D. L., Rosen, B. R. and Halgren, E. (2006). Top-down facilitation of visual recognition. Proc. Natl. Acad. Sci. USA, 103, 449–454.CrossRefGoogle ScholarPubMed
Baule, G. and McFee, R. (1965). Theory of magnetic detection of the heart's electrical activity. J. Appl. Phys., 36, 2066–2073.CrossRefGoogle Scholar
Berger, H. (1929). Uber das Elektrenkephalogramm des Menschen. Arch. Psychiat. Nervenkr., 87, 527–570.CrossRefGoogle Scholar
Bonmassar, G., Anami, K., Ives, J. and Belliveau, J.W. (1999). Visual evoked potential (VEP) measured by simultaneous 64-channel EEG and 3T fMRI. NeuroReport, 10, 1893–1897.CrossRefGoogle ScholarPubMed
Brenner, D., Lipton, J., Kaufman, L. and Williamson, S. J. (1978). Somatically evoked magnetic fields of the human brain. Science, 199, 81–83.CrossRefGoogle ScholarPubMed
Cheyne, D., Gaetz, W., Garnero, L., Lachaux, J. P., Ducorps, A., Schwartz, D. and Varela, F. J. (2003). Neuromagnetic imaging of cortical oscillations accompanying tactile stimulation. Brain Res. Cogn. Brain. Res., 17, 599–611.CrossRefGoogle ScholarPubMed
Ciesielski, K. T., Hamalainen, M. S., Lesnik, P. G., Geller, D. A. and Ahlfors, S. P. (2005). Increased MEG activation in OCD reflects a compensatory mechanism specific to the phase of a visual working memory task. NeuroImage, 24, 1180–1191.CrossRefGoogle ScholarPubMed
Clarke, C. J. S. (1989). Probabilistic methods in a biomagnetic inverse problem. Inverse Problems, 5, 999–1012.CrossRefGoogle Scholar
Clarke, J., Hatridge, M. and Mossle, M. (2007). SQUID-detected magnetic resonance imaging in microtesla fields. Annu. Rev. Biomed. Eng., 9, 389–413.CrossRefGoogle ScholarPubMed
Cohen, D. (1972). Magnetoencephalography: detection of the brain's electrical activity with a superconducting magnetometer. Science, 175, 664–666.CrossRefGoogle ScholarPubMed
Cohen, D. and Cuffin, B. N. (1983). Demonstration of useful differences between magnetoencephalogram and electroencephalogram. Electroencephalogr. Clin. Neurophysiol., 56, 38–51.CrossRefGoogle ScholarPubMed
Cohen, D. and Halgren, E. (2003). Magnetoencephalogaphy. In: G., Adelman (editor), Encyclopedia of Neuroscience (2nd edition). Boston, MA: MIT Press.Google Scholar
Cohen, D., Cuffin, B. N., Yunokuchi, K., Maniewski, R., Purcell, C., Cosgrove, G. R., Ives, J., Kennedy, J. G. and Schomer, D. L. (1990). MEG versus EEG localization test using implanted sources in the human brain. Ann. Neurol., 28, 811–817.CrossRefGoogle ScholarPubMed
Dalal, S. S., Baillet, S., Adam, C., Ducorps, A., Schwartz, D., Jerbi, K., Bertrand, O., Garnero, L., Martinerie, J. and Lachaux, J. P. (2009). Simultaneous MEG and intracranial EEG recordings during attentive reading. NeuroImage, 45, 1289–1304.CrossRefGoogle ScholarPubMed
Dale, A.M. and Sereno, M. I. (1993). Improved localization of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: a linear approach. J. Cogn. Neurosci., 5, 162–176.CrossRefGoogle Scholar
Dale, A. M., Liu, A. K., Fischl, B.R., Buckner, R. L., Belliveau, J.W., Lewine, J. D. and Halgren, E. (2000). Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity. Neuron, 26, 55–67.CrossRefGoogle ScholarPubMed
Darvas, F., Rautiainen, M., Pantazis, D., Baillet, S., Benali, H., Mosher, J.C., Garnero, L. and Leahy, R. M. (2005). Investigations of dipole localization accuracy in MEG using the bootstrap. NeuroImage, 25, 355–368.CrossRefGoogle ScholarPubMed
David, O., Kiebel, S. J., Harrison, L. M., Mattout, J., Kilner, J. M. and Friston, K. J. (2006). Dynamic causal modeling of evoked responses in EEG and MEG. NeuroImage, 30, 1255–1272.CrossRefGoogle ScholarPubMed
de Jongh, A., De Munck, J. C., Goncalves, S. I. and Ossenblok, P. (2005). Differences in MEG/EEG epileptic spike yields explained by regional differences in signal-to-noise ratios. J. Clin. Neurophysiol., 22, 153–158.CrossRefGoogle ScholarPubMed
De Munck, J. C. (1990). The estimation of time varying dipoles on the basis of evoked potentials. Electroencephalogr. Clin. Neurophysiol., 77, 156–160.CrossRefGoogle ScholarPubMed
Ding, L. and He, B. (2008). Sparse source imaging in electroencephalography with accurate field modeling. Human Brain Mapping, 29, 1053–1067.CrossRefGoogle ScholarPubMed
Ebersole, J. S. and Hawes-Ebersole, S. (2007). Clinical application of dipole models in the localization of epileptiform activity. J. Clin. Neurophysiol., 24, 120–129.CrossRefGoogle ScholarPubMed
Elbert, T., Pantev, C., Wienbruch, C., Rockstroh, B. and Taub, E. (1995). Increased cortical representation of the fingers of the left hand in string players. Science, 270, 305–307.CrossRefGoogle ScholarPubMed
Eulitz, C., Eulitz, H. and Elbert, T. (1997). Differential outcomes from magneto- and electroencephalography for the analysis of human cognition. Neurosci. Lett., 227, 185–188.CrossRefGoogle ScholarPubMed
Fawcett, I. P., Barnes, G.R., Hillebrand, A. and Singh, K. D. (2004). The temporal frequency tuning of human visual cortex investigated using synthetic aperture magnetometry. NeuroImage, 21, 1542–1553.CrossRefGoogle ScholarPubMed
Fender, D. (1987). Source localization of brain electrical activity. In: A., Gevins and A., Remond (editors), Handbook of Electroencephalography and Clinical Neurophysiology, Amsterdam: Elsevier, pp. 355–403.Google Scholar
Freeman, W. J., Ahlfors, S. P. and Menon, V. (2009). Combining fMRI with EEG and MEG in order to relate patterns of brain activity to cognition. Int. J. Psychophysiol., 73, 43–52.CrossRefGoogle Scholar
Fuchs, M., Wagner, M., Kohler, T. and Wischmann, H. A. (1999). Linear and nonlinear current density reconstructions. J. Clin. Neurophysiol., 16, 267–295.CrossRefGoogle ScholarPubMed
Fuchs, M., Wagner, M. and Kastner, J. (2004). Confidence limits of dipole source reconstruction results. Clin. Neurophysiol., 115, 1442–1451.CrossRefGoogle ScholarPubMed
Goldenholz, D. M., Ahlfors, S. P., Hämäläinen, M. S., Sharon, D., Ishitobi, M., Vaina, L. M. and Stufflebeam, S.M. (2009). Mapping the signal-to-noise-ratios of cortical sources in magnetoencephalography and electroencephalography. Human Brain Mapping, 30, 1077–1086.CrossRefGoogle ScholarPubMed
Goncalves, S., de Munck, J. C., Heethaar, R.M., Lopes da Silva, F. H. and van Dijk, B. W. (2000). The application of electrical impedance tomography to reduce systematic errors in the EEG inverse problem – a simulation study. Physiol. Meas., 21, 379–393.CrossRefGoogle ScholarPubMed
Gorodnitsky, I. F., George, J. S. and Rao, B. D. (1995). Neuromagnetic source imaging with FOCUSS: a recursive weighted minimum norm algorithm. Electroencephalogr. Clin. Neurophysiol., 95, 231–251.CrossRefGoogle ScholarPubMed
Gotman, J., Kobayashi, E., Bagshaw, A. P., Benar, C. G. and Dubeau, F. (2006). Combining EEG and fMRI: a multimodal tool for epilepsy research. J. Magn. Reson. Imaging, 23, 906–920.CrossRefGoogle ScholarPubMed
Gow, D.W. Jr., Segawa, J. A., Ahlfors, S. P. and Lin, F. H. (2008). Lexical influences on speech perception: a Granger causality analysis of MEG and EEG source estimates. NeuroImage, 43, 614–623.CrossRefGoogle ScholarPubMed
Grave de Peralta-Menendez, R. and Gonzalez-Andino, S. L. (1998). A critical analysis of linear inverse solutions to the neuroelectromagnetic inverse problem. IEEE Trans. Biomed. Eng., 45, 440–448.Google ScholarPubMed
Greenblatt, R. E., Ossadtchi, A. and Pflieger, M. E. (2005). Local linear estimators for the bioelectromagnetic inverse problem. IEEE Trans. Signal Process., 53, 3403–3412.CrossRefGoogle Scholar
Gross, J., Kujala, J., Hamalainen, M., Timmermann, L., Schnitzler, A. and Salmelin, R. (2001). Dynamic imaging of coherent sources: studying neural interactions in the human brain. Proc. Natl. Acad. Sci. USA, 98, 694–699.CrossRefGoogle ScholarPubMed
Grynszpan, F. and Geselowitz, D.B. (1973). Model studies of the magnetocardiogram. Biophys. J., 13, 911–925.CrossRefGoogle ScholarPubMed
Hamalainen, M. S. and Ilmoniemi, R. J. (1984). Interpreting measured magnetic fields of the brain: estimates of current distributions. Helsinki University of Technology.Google Scholar
Hamalainen, M. S. and Ilmoniemi, R. J. (1994). Interpreting magnetic fields of the brain: minimum norm estimates. Med. Biol. Eng. Comput., 32, 35–42.CrossRefGoogle ScholarPubMed
Hamalainen, M. S. and Sarvas, J. (1989). Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data. IEEE Trans. Biomed. Eng., 36, 165–171.CrossRefGoogle ScholarPubMed
Hamalainen, M., Hari, R., Ilmoniemi, R. J., Knuutila, J. and Lounasmaa, O.V. (1993). Magnetoencephalography – theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev. Mod. Phys., 65, 413–497.CrossRefGoogle Scholar
Hamalainen, M., Hari, R., Lounasmaa, O.V. and Williamson, S. J. (1995). Do auditory stimuli activate human parietal brain regions?NeurolReport, 6, 1712–1714.Google ScholarPubMed
Hansen, P., Kringelbach, M. and Salmelin, R. (2010). MEG: An Introduction to Methods. New York: Oxford University Press.CrossRefGoogle Scholar
Hari, R. and Forss, N. (1999). Magnetoencephalography in the study of human somatosensory cortical processing. Philos. Trans. R. Soc. London Ser. B, 354, 1145–1154.CrossRefGoogle Scholar
Hari, R. and Lounasmaa, O.V. (1989). Recording and interpretation of cerebral magnetic fields. Science, 244, 432–436.CrossRefGoogle ScholarPubMed
Hari, R., Joutsiniemi, S. and Sarvas, J. (1988). Spatial resolution of neuromagnetic records: theoretical calculations in a spherical model. Electroencephalogr. Clin. Neurophysiol., 71, 64–72.CrossRefGoogle Scholar
Haueisen, J., Ramon, C., Czapski, P. and Eiselt, M. (1995). On the influence of volume currents and extended sources on neuromagnetic fields: a simulation study. Ann. Biomed. Eng., 23, 728–739.CrossRefGoogle ScholarPubMed
Heinze, H. J., Mangun, G. R., Burchert, W., Hinrichs, H., Scholz, M., Munte, T. F., Gos, A., Scherg, M., Johannes, S., Hundeshagen, H., et al. (1994). Combined spatial and temporal imaging of brain activity during visual selective attention in humans. Nature, 372, 543–546.CrossRefGoogle ScholarPubMed
Helmholtz, H. (1853). Ueber einige Gesetze der Vertheilung elektrischer Strome in korperlichen Leitern, mit Anwendung auf die thierisch-elektrischen Versuche. Ann. Phys. Chem., 89, 211–233, 353–377.Google Scholar
Herdman, A. T., Wollbrink, A., Chau, W., Ishii, R. and Pantev, C. (2004). Localization of transient and steady-state auditory evoked responses using synthetic aperture magnetometry. Brain Cogn., 54, 149–151.Google ScholarPubMed
Hillebrand, A. and Barnes, G.R. (2002). A quantitative assessment of the sensitivity of whole-head MEG to activity in the adult human cortex. NeuroImage, 16, 638–650.CrossRefGoogle ScholarPubMed
Hillebrand, A., Singh, K. D., Holliday, I. E., Furlong, P. L. and Barnes, G.R. (2005). A new approach to neuroimaging with magnetoencephalography. Human Brain Mapping, 25, 199–211.CrossRefGoogle ScholarPubMed
Huang, M., Aine, C. J., Supek, S., Best, E., Ranken, D. and Flynn, E. R. (1998). Multi-start downhill simplex method for spatio-temporal source localization in magnetoencephalography. Electroencephalogr. Clin. Neurophysiol., 108, 32–44.CrossRefGoogle ScholarPubMed
Huang, M. X., Dale, A. M., Song, T., Halgren, E., Harrington, D. L., Podgorny, I., Canive, J. M., Lewis, S. and Lee, R. R. (2006). Vector-based spatial-temporal minimum L1-norm solution for MEG. NeuroImage, 31, 1025–1037.CrossRefGoogle ScholarPubMed
Ilmoniemi, R. J., Virtanen, J., Ruohonen, J., Karhu, J., Aronen, H. J., Naatanen, R. and Katila, T. (1997). Neuronal responses to magnetic stimulation reveal cortical reactivity and connectivity. NeuroReport, 8, 3537–3540.CrossRefGoogle ScholarPubMed
Ioannides, A.A., Bolton, J. P. R., Hasson, R. and Clarke, C. J. S. (1990). Localised and distributed source solutions for the biomagnetic inverse problem. II. In: S. J., Williamson et al. (editors), Advances in Biomagnetism, New York: Plenum, pp. 591–594.Google Scholar
Ioannides, A. A., Hellstrand, E. and Abraham-Fuchs, K. (1993). Point and distributed current density analysis of interictal epileptic activity recorded by magnetoencephalography. Physiol. Meas., 14, 121–130.CrossRefGoogle ScholarPubMed
Itier, R. J., Herdman, A. T., George, N., Cheyne, D. and Taylor, M. J. (2006). Inversion and contrast-reversal effects on face processing assessed by MEG. Brain Res, 1115, 108–120.CrossRefGoogle ScholarPubMed
Jeffs, B., Leahy, R. and Singh, M. (1987). An evaluation of methods for neuromagnetic image reconstruction. IEEE Trans. Biomed. Eng., 34, 713–723.Google ScholarPubMed
Jerbi, K., Baillet, S., Mosher, J.C., Nolte, G., Garnero, L. and Leahy, R. M. (2004). Localization of realistic cortical activity in MEG using current multipoles. NeuroImage, 22, 779–793.CrossRefGoogle ScholarPubMed
Jones, S. R., Pritchett, D. L., Stufflebeam, S. M., Hamalainen, M. and Moore, C. I. (2007). Neural correlates of tactile detection: a combined magnetoencephalography and biophysically based computational modeling study. J. Neurosci., 27, 10751–10764.CrossRefGoogle ScholarPubMed
Kaipio, J. and Somersalo, E. (2004). Statistical and Computational Inverse Problems. New York: Springer.Google Scholar
Knowlton, R. C., Laxer, K. D., Aminoff, M. J., Roberts, T. P., Wong, S. T. and Rowley, H. A. (1997). Magnetoencephalography in partial epilepsy: clinical yield and localization accuracy. Ann. Neurol., 42, 622–631.CrossRefGoogle ScholarPubMed
Leahy, R. M., Mosher, J.C., Spencer, M. E., Huang, M. X. and Lewine, J. D. (1998). A study of dipole localization accuracy for MEG and EEG using a human skull phantom. Electroencephalogr. Clin. Neurophysiol., 107, 159–173.CrossRefGoogle ScholarPubMed
Lin, F. H., Witzel, T., Hamalainen, M. S., Dale, A. M., Belliveau, J.W. and Stufflebeam, S. M. (2004). Spectral spatiotemporal imaging of cortical oscillations and interactions in the human brain. NeuroImage, 23, 582–595.CrossRefGoogle ScholarPubMed
Lin, F. H., Belliveau, J.W., Dale, A. M. and Hamalainen, M. S. (2006a). Distributed current estimates using cortical orientation constraints. Human Brain Mapping, 27, 1–13.CrossRefGoogle ScholarPubMed
Lin, F. H., Wald, L. L., Ahlfors, S. P., Hamalainen, M. S., Kwong, K. K. and Belliveau, J.W. (2006b). Dynamic magnetic resonance inverse imaging of human brain function. Magn. Reson. Med., 56, 787–802.CrossRefGoogle ScholarPubMed
Lin, F. H., Witzel, T., Ahlfors, S. P., Stufflebeam, S. M., Belliveau, J.W. and Hamalainen, M. S. (2006c). Assessing and improving the spatial accuracy in MEG source localization by depth-weighted minimum-norm estimates. NeuroImage, 31, 160–171.CrossRefGoogle ScholarPubMed
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. Natl. Acad. Sci. USA, 95, 8945–8950.CrossRefGoogle ScholarPubMed
Lorente de No, R. (1947). A Study of Nerve Physiology. Studies of the Rockefeller Institute 132, Chapter 16.Google ScholarPubMed
Lounasmaa, O.V., Hamalainen, M., Hari, R. and Salmelin, R. (1996). Information processing in the human brain: magnetoencephalographic approach. Proc. Natl. Acad. Sci. USA, 93, 8809–8815.CrossRefGoogle ScholarPubMed
Lu, Z. L. and Williamson, S. J. (1991). Spatial extent of coherent sensory-evoked cortical activity. Exp. Brain Res., 84, 411–416.CrossRefGoogle ScholarPubMed
Marinkovic, K., Cox, B., Reid, K. and Halgren, E. (2004). Head position in the MEG helmet affects the sensitivity to anterior sources. Neurol. Clin. Neurophysiol., 30, 1–6.Google Scholar
Martinez, A., Anllo-Vento, L., Sereno, M. I., Frank, L. R., Buxton, R. B., Dubowitz, D. J., Wong, E. C., Hinrichs, H., Heinze, H. J. and Hillyard, S. A. (1999). Involvement of striate and extrastriate visual cortical areas in spatial attention. Nature Neurosci., 2, 364–369.CrossRefGoogle ScholarPubMed
Matsuura, K. and Okabe, Y. (1997). A robust reconstruction of sparse biomagnetic sources. IEEE Trans. Biomed. Eng., 44, 720–726.CrossRefGoogle ScholarPubMed
Melcher, J. R. and Cohen, D. (1988). Dependence of the MEG on dipole orientation in the rabbit head. Electroencephalogr. Clin. Neurophysiol., 70, 460–472.CrossRefGoogle ScholarPubMed
Michel, C. M., Koenig, T., Brandeis, D., Gianotti, L.R.R. and Wackermann, J. (2009). Electrical Neuroimaging. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Mosher, J.C. and Leahy, R. M. (1999). Source localization using recursively applied and projected (RAP) MUSIC. IEEE Trans. Signal Process., 47, 332–340.CrossRefGoogle Scholar
Mosher, J.C., Lewis, P. S. and Leahy, R. M. (1992). Multiple dipole modeling and localization from spatio-temporal MEG data. IEEE Trans. Biomed. Eng., 39, 541–557.CrossRefGoogle ScholarPubMed
Mosher, J.C., Spencer, M. E., Leahy, R.M. and Lewis, P. S. (1993). Error bounds for EEG and MEG dipole source localization. Electroencephalogr. Clin. Neurophysiol., 86, 303–321.CrossRefGoogle ScholarPubMed
Mosher, J.C., Baillet, S. and Leahy, R. M. (1999). EEG source localization and imaging using multiple signal classification approaches. J. Clin. Neurophysiol., 16, 225–238.CrossRefGoogle ScholarPubMed
Murakami, S., Hirose, A. and Okada, Y. C. (2003). Contribution of ionic currents to magnetoencephalography (MEG) and electroencephalography (EEG) signals generated by guinea-pig CA3 slices. J. Physiol., 553, 975–985.CrossRefGoogle ScholarPubMed
Nakamura, A., Yamada, T., Goto, A., Kato, T., Ito, K., Abe, Y., Kachi, T. and Kakigi, R. (1998). Somatosensory homunculus as drawn by MEG. NeuroImage, 7, 377–386.CrossRefGoogle ScholarPubMed
Nakasato, N. and Yoshimoto, T. (2000). Somatosensory, auditory, and visual evoked magnetic fields in patients with brain diseases. J. Clin. Neurophysiol., 17, 201–211.CrossRefGoogle ScholarPubMed
Niedermeyer, E. and Lopes da Silva, F. (1999). Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Philadelphia, PA: Lippincott, Williams and Wilkins.Google Scholar
Nolte, G. and Curio, G. (2000). Current multipole expansion to estimate lateral extent of neuronal activity: a theoretical analysis. IEEE Trans. Biomed. Eng., 47, 1347–1355.CrossRefGoogle ScholarPubMed
Nummenmaa, A., Auranen, T., Hamalainen, M. S., Jaaskelainen, I. P., Lampinen, J., Sams, M. and Vehtari, A. (2007). Hierarchical Bayesian estimates of distributed MEG sources: theoretical aspects and comparison of variational and MCMC methods. NeuroImage, 35, 669–685.CrossRefGoogle ScholarPubMed
Ou, W., Hamalainen, M. S. and Golland, P. (2009a). A distributed spatio-temporal EEG/MEG inverse solver. NeuroImage, 44, 932–946.CrossRefGoogle ScholarPubMed
Ou, W., Nissila, I., Radhakrishnan, H., Boas, D.A., Hamalainen, M. S. and Franceschini, M. A. (2009b). Study of neurovascular coupling in humans via simultaneous magnetoencephalography and diffuse optical imaging acquisition. NeuroImage, 46, 624–632.CrossRefGoogle ScholarPubMed
Pantazis, D., Simpson, G.V., Weber, D. L., Dale, C. L., Nichols, T. E. and Leahy, R. M. (2009). A novel ANCOVA design for analysis of MEG data with application to a visual attention study. NeuroImage, 44, 164–174.CrossRefGoogle ScholarPubMed
Papanicolaou, A. C., Simos, P.G., Breier, J. I., Zouridakis, G., Willmore, L. J., Wheless, J. W., Constantinou, J. E., Maggio, W.W. and Gormley, W. B. (1999). Magnetoencephalographic mapping of the language-specific cortex. J. Neurosurg, 90, 85–93.CrossRefGoogle ScholarPubMed
Pascual-Marqui, R.D. (2002). Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find. Exp. Clin. Pharmacol., 24D, 5–12.Google Scholar
Pascual-Marqui, R.D., Michel, C. M. and Lehmann, D. (1994). Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. Int. J. Psychophysiol., 18, 49–65.CrossRefGoogle Scholar
Paus, T. (1999). Imaging the brain before, during, and after transcranial magnetic stimulation. Neuropsychologia, 37, 219–224.Google ScholarPubMed
Petridou, N., Plenz, D., Silva, A. C., Loew, M., Bodurka, J. and Bandettini, P. A. (2006). Direct magnetic resonance detection of neuronal electrical activity. Proc. Natl. Acad. Sci. USA., 103, 16015–16020.CrossRefGoogle ScholarPubMed
Phillips, C., Rugg, M. D. and Friston, K. J. (2002). Systematic regularization of linear inverse solutions of the EEG source localization problem. NeuroImage, 17, 287–301.CrossRefGoogle ScholarPubMed
Plonsey, R. (1969). Bioelectric Phenomena. New York: McGraw-Hill.Google Scholar
Ritter, P. and Villringer, A. (2006). Simultaneous EEG-fMRI. Neurosci. Biobehav. Rev., 30:823–838.CrossRefGoogle ScholarPubMed
Salmelin, R. and Kujala, J. (2006). Neural representation of language: activation versus long-range connectivity. Trends Cogn. Sci., 10, 519–525.CrossRefGoogle ScholarPubMed
Salmelin, R., Hari, R., Lounasmaa, O.V. and Sams, M. (1994). Dynamics of brain activation during picture naming. Nature, 368, 463–465.CrossRefGoogle ScholarPubMed
Sander, T.H., Liebert, A., Mackert, B. M., Wabnitz, H., Leistner, S., Curio, G., Burghoff, M., Macdonald, R. and Trahms, L. (2007). DC-magnetoencephalography and time-resolved near-infrared spectroscopy combined to study neuronal and vascular brain responses. Physiol. Meas., 28, 651–664.CrossRefGoogle ScholarPubMed
Santiuste, M., Nowak, R., Russi, A., Tarancon, T., Oliver, B., Ayats, E., Scheler, G. and Graetz, G. (2008). Simultaneous magnetoencephalography and Intracranial EEG registration: technical and clinical aspects. J. Clin. Neurophysiol., 25, 331–339.CrossRefGoogle ScholarPubMed
Sarvas, J. (1987). Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem. Phys. Med. Biol., 32, 11–22.CrossRefGoogle ScholarPubMed
Scherg, M. and Ebersole, J. S. (1994). Brain source imaging of focal and multifocal epileptiform EEG activity. Neurophysiol. Clin., 24, 51–60.CrossRefGoogle ScholarPubMed
Scherg, M. and Von Cramon, D. (1986). Evoked dipole source potentials of the human auditory cortex. Electroencephalogr. Clin. Neurophysiol., 65, 344–360.CrossRefGoogle ScholarPubMed
Schmidt, D. M., George, J. S. and Wood, C. C. (1999). Bayesian inference applied to the electromagnetic inverse problem. Human Brain Mapping, 7, 195–212.3.0.CO;2-F>CrossRefGoogle ScholarPubMed
Sekihara, K. and Nagarajan, S. S. (2008). Adaptive Spatial Filters for Electromagnetic Brain Imaging. Berlin: Springer.Google Scholar
Sekihara, K., Poeppel, D., Marantz, A., Koizumi, H. and Miyashita, Y. (1997). Noise covariance incorporated MEG-MUSIC algorithm: a method for multiple-dipole estimation tolerant of the influence of background brain activity. IEEE Trans. Biomed. Eng., 44, 839–847.CrossRefGoogle ScholarPubMed
Sekihara, K., Nagarajan, S. S., Poeppel, D. and Marantz, A. (2002). Performance of an MEG adaptive-beamformer technique in the presence of correlated neural activities: effects on signal intensity and time-course estimates. IEEE Trans. Biomed. Eng., 49, 1534–1546.CrossRefGoogle ScholarPubMed
Simos, P. G., Breier, J. I., Fletcher, J. M., Foorman, B. R., Castillo, E. M. and Papanicolaou, A. C. (2002). Brain mechanisms for reading words and pseudowords: an integrated approach. Cereb. Cortex, 12, 297–305.CrossRefGoogle ScholarPubMed
Simpson, G.V., Pflieger, M. E., Foxe, J. J., Ahlfors, S. P., Vaughan, H. G. Jr., Hrabe, J., Ilmoniemi, R. J. and Lantos, G. (1995). Dynamic neuroimaging of brain function. J. Clin. Neurophysiol., 12, 432–449.CrossRefGoogle ScholarPubMed
Stufflebeam, S.M., Tanaka, N. and Ahlfors, S. P. (2009). Clinical applications of magnetoencephalography. Human Brain Mapping, 30, 1813–1823.CrossRefGoogle ScholarPubMed
Suk, J., Ribary, U., Cappell, J., Yamamoto, T. and Llinas, R. (1991). Anatomical localization revealed by MEG recordings of the human somatosensory system. Electroencephalogr. Clin. Neurophysiol., 78, 185–196.CrossRefGoogle ScholarPubMed
Supek, S. and Aine, C. J. (1993). Simulation studies of multiple dipole neuromagnetic source localization: model order and limits of source resolution. IEEE Trans. Biomed. Eng., 40, 529–540.CrossRefGoogle ScholarPubMed
Supp, G. G., Schlogl, A., Trujillo-Barreto, N., Muller, M. M. and Gruber, T. (2007). Directed cortical information flow during human object recognition: analyzing induced EEG gamma-band responses in brain's source space. PLoS ONE 2:e684.Google ScholarPubMed
Tagamets, M. A. and Horwitz, B. (1998). Integrating electrophysiological and anatomical experimental data to create a large-scale model that simulates a delayed match-to sample human brain imaging study. Cereb. Cortex, 8, 310–320.CrossRefGoogle Scholar
Taniguchi, M., Kato, A., Fujita, N., Hirata, M., Tanaka, H., Kihara, T., Ninomiya, H., Hirabuki, N., Nakamura, H., Robinson, S. E., Cheyne, D. and Yoshimine, T. (2000). Movement-related desynchronization of the cerebral cortex studied with spatially filtered magnetoencephalography. NeuroImage, 12, 298–306.CrossRefGoogle ScholarPubMed
Tarantola, A. (1987). Inverse Problem Theory. Methods for Data Fitting and Model Parameter Estimation. Amsterdam: Elsevier.Google Scholar
Tarkiainen, A., Liljestrom, M., Seppa, M. and Salmelin, R. (2003). The 3D topography of MEG source localization accuracy: effects of conductor model and noise. Clin. Neurophysiol., 114, 1977–1992.CrossRefGoogle ScholarPubMed
Taulu, S. and Simola, J. (2006). Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements. Phys. Med. Biol., 51, 1759–1768.CrossRefGoogle ScholarPubMed
Tripp, J. H. (1983). Physical concepts and mathematical models. In: S. J., Williamson, G-L., Romani, L., Kaufman and I., Modena (editors), Biomagnetism: An Interdisciplinary Approach, New York: Plenum Press, pp. 101–139.Google Scholar
Uusitalo, M.A. and Ilmoniemi, R. J. (1997). Signal-space projection method for separating MEG or EEG into components. Med. Biol. Eng. Comput., 35, 135–140.CrossRefGoogle ScholarPubMed
Uutela, K., Hamalainen, M. and Salmelin, R. (1998). Global optimization in the localization of neuromagnetic sources. IEEE Trans. Biomed. Eng., 45, 716–723.CrossRefGoogle ScholarPubMed
Uutela, K., Hamalainen, M. and Somersalo, E. (1999). Visualization of magnetoencephalographic data using minimum current estimates. NeuroImage, 10, 173–180.CrossRefGoogle ScholarPubMed
Van Veen, B. D., van Drongelen, W., Yuchtman, M. and Suzuki, A. (1997). Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Trans. Biomed. Eng., 44, 867–880.CrossRefGoogle ScholarPubMed
Vaughan, H. G. Jr., (1974). The analysis of scalp-recorded brain potentials. In: R. F., Thompson and M. M., Patterson (editors). Bioelectric Recording Techniques, New York: Academic Press, pp. 157–207.Google Scholar
Volegov, P., Matlachov, A. N., Espy, M. A., George, J. S. and Kraus, R.H. Jr., (2004). Simultaneous magnetoencephalography and SQUID detected nuclear MR in microtesla magnetic fields. Magn. Reson. Med., 52, 467–470.CrossRefGoogle ScholarPubMed
Vrba, J. and Robinson, S. E. (2001). Signal processing in magnetoencephalography. Methods, 25, 249–271.CrossRefGoogle ScholarPubMed
Wagner, M., Fuchs, M. and Kastner, J. (2004). Evaluation of sLORETA in the presence of noise and multiple sources. Brain Topogr., 16, 277–280.Google ScholarPubMed
Wehner, D. T., Ahlfors, S. P. and Mody, M. (2007). Effects of phonological contrast on auditory word discrimination in children with and without reading disability: a magnetoencephalography (MEG) study. Neuropsychologia, 45, 3251–3262.CrossRefGoogle ScholarPubMed
Wehner, D. T., Hamalainen, M. S., Mody, M. and Ahlfors, S. P. (2008). Head movements of children in MEG: quantification, effects on source estimation, and compensation. NeuroImage, 40, 541–550.CrossRefGoogle ScholarPubMed
Witzel, T., Lin, F. H., Rosen, B. R. and Wald, L. L. (2008). Stimulus-induced rotary saturation (SIRS): a potential method for the detection of neuronal currents with MRI. NeuroImage, 42, 1357–1365.CrossRefGoogle ScholarPubMed
Bathellier, B., Van De Ville, D., Blu, T., Unser, M. and Carleton, A. (2007). Wavelet-based multi-resolution statistics for optical imaging signals: Application to automated detection of odour activated glomeruli in the mouse olfactory bulb. NeuroImage, 34, 1020–1035.CrossRefGoogle ScholarPubMed
Berger, T., Borgdorff, A., Crochet, S., Neubauer, F. B., Lefort, S., Fauvet, B., Ferezou, I., Carleton, A., Luscher, H.R. and Petersen, C.C. (2007). Combined voltage and calcium epifluorescence imaging in vitro and in vivo reveals subthreshold and suprathreshold dynamics of mouse barrel cortex. J. Neurophysio., 97, 3751–3762.CrossRefGoogle ScholarPubMed
Berwick, J., Johnston, D., Jones, M., Martindale, J., Martin, C., Kennerley, A.J., Redgrave, P., and Mayhew, J.E. (2008). Fine detail of neurovascular coupling revealed by spatiotemporal analysis of the hemodynamic response to single whisker stimulation in rat barrel cortex. J. Neurophysio., 99, 787–798.CrossRefGoogle ScholarPubMed
Bringuier, V., Chavane, F., Glaeser, L., and Fregnac, Y. (1999). Horizontal propagation of visual activity in the synaptic integration field of area 17 neurons. Science, 283, 695–699.CrossRefGoogle ScholarPubMed
Carmona, R.A., Hwang, W.L. and Frostig, R.D. (1995). Wavelet analysis for brain-function imaging. IEEE Trans. Med. Imaging, 14, 556–564.CrossRefGoogle ScholarPubMed
Chen-Bee, C. H., Kwon, M.C., Masino, S.A. and Frostig, R.D. (1996). Areal extent quantification of functional representations using intrinsic signal optical imaging. J. Neurosci. Methods, 68, 27–37.CrossRefGoogle ScholarPubMed
Chen-Bee, C. H., Polley, D.B., Brett-Green, B., Prakash, N., Kwon, M.C. and Frostig, R. D. (2000). Visualizing and quantifying evoked cortical activity assessed with intrinsic signal imaging. J. Neurosci. Methods, 97, 157–173.CrossRefGoogle ScholarPubMed
Chen-Bee, C. H., Agoncillo, T., Xiong, Y. and Frostig, R.D. (2007). The triphasic intrinsic signal: implications for functional imaging. J. Neurosci., 27, 572–4586.CrossRefGoogle ScholarPubMed
Chen, L.M., Friedman, R.M. and Roe, A.W. (2005). Optical imaging of SI topography in anesthetized and awake squirrel monkeys. J. Neurosci., 25, 7648–7659.CrossRefGoogle ScholarPubMed
Das, A. and Gilbert, C.D. (1995). Long-range horizontal connections and their role in cortical reorganization revealed by optical recording of cat primary visual cortex [see comments]. Nature, 375, 780–784.CrossRefGoogle Scholar
Fekete, T., Omer, D.B., Naaman, S. and Grinvald, A. (2009). Removal of spatial biological artifacts in functional maps by local similarity minimization. J. Neurosci. Methods, 178, 31–39.CrossRefGoogle ScholarPubMed
Feldman, D.E. and Brecht, M. (2005). Map plasticity in somatosensory cortex. Science, 310, 810–815.CrossRefGoogle ScholarPubMed
Ferezou, I., Bolea, S. and Petersen, C.C. (2006). Visualizing the cortical representation of whisker touch: voltage-sensitive dye imaging in freely moving mice. Neuron, 50, 617–629.CrossRefGoogle ScholarPubMed
Ferezou, I., Haiss, F., Gentet, L.J., Aronoff, R., Weber, B. and Petersen, C.C. (2007). Spatiotemporal dynamics of cortical sensorimotor integration in behaving mice. Neuron, 56, 907–923.CrossRefGoogle ScholarPubMed
Ferezou, I., Matyas, F. and Petersen, C.C.H. (2009). Imaging the brain in action – real-time voltage-sensitive dye imaging of sensorimotor cortex of awake behaving mice. In: R.D., Frostig (editor), In Vivo Optical Imaging of Brain Function (2nd edition). Boca Raton, FL: CRC Press.Google ScholarPubMed
Fox, M.D. and Raichle, M.E. (2007). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Revi., 8, 700–711.Google ScholarPubMed
Frostig, R.D., Lieke, E.E., Ts'o, D. Y. and Grinvald, A. (1990). Cortical functional architecture and local coupling between neuronal activity and the microcirculation revealed by in vivo high-resolution optical imaging of intrinsic signals. Proc. Nati. Acad. Sci. USA, 87, 6082–6086.CrossRefGoogle ScholarPubMed
Frostig, R.D., Xiong, Y., Chen-Bee, C.H., Kvasnak, E. and Stehberg, J. (2008). Large-scale organization of rat sensorimotor cortex based on a motif of large activation spreads. J. Neurosci., 28, 13274–13284.CrossRefGoogle ScholarPubMed
Gabbay, M., Brennan, C., Kaplan, E. and Sirovich, L. (2000). A principal components-based method for the detection of neuronal activity maps: application to optical imaging. NeuroImage, 11, 313–325.CrossRefGoogle ScholarPubMed
Grinvald, A. and Hildesheim, R. (2004). VSDI: a new era in functional imaging of cortical dynamics. Nature Revi, 5, 874–885.Google ScholarPubMed
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, 361–364.CrossRefGoogle ScholarPubMed
Grinvald, A., Lieke, E.E., Frostig, R.D. and Hildesheim, R. (1994). Cortical point-spread function and long-range lateral interactions revealed by real-time optical imaging of macaque monkey primary visual cortex. J. Neurosci., 14, 2545–2568.CrossRefGoogle ScholarPubMed
Gurden, H., Uchida, N. and Mainen, Z.F. (2006). Sensory-evoked intrinsic optical signals in the olfactory bulb are coupled to glutamate release and uptake. Neuron, 52, 335–345.CrossRefGoogle ScholarPubMed
Hillman, E.M., Devor, A., Bouchard, M.B., Dunn, A.K., Krauss, G.W., Skoch, J., Bacskai, B.J., Dale, A.M. and Boas, D.A. (2007). Depth-resolved optical imaging and microscopy of vascular compartment dynamics during somatosensory stimulation. NeuroImage, 35, 89–104.CrossRefGoogle ScholarPubMed
Hofer, S.B., Mrsic-Flogel, T.D., Bonhoeffer, T. and Hubener, M. (2006). Prior experience enhances plasticity in adult visual cortex. Nature neurosci., 9, 127–132.CrossRefGoogle ScholarPubMed
Husson, R.T. and Issa, N.P. (2009). Functional imaging with mitochondrial flavoprotein autofluorescence: theory, practice, and applications. In: R. D., Frostig (editor), In Vivo Optical Imaging of Brain Function (2nd edition). Boca Raton, FL: CRC Press.Google ScholarPubMed
Kalatsky, V.A. (2009). Fourier approach to optical imaging. In: R. D., Frostig (editor), In Vivo Optical Imaging of Brain Function (2nd edition). Boca Raton, FL: CRC Press.Google ScholarPubMed
Kalatsky, V.A. and Stryker, M.P. (2003). New paradigm for optical imaging: temporally encoded maps of intrinsic signal. Neuron, 38, 529–545.CrossRefGoogle ScholarPubMed
Kaur, S., Lazar, R. and Metherate, R. (2004). Intracortical pathways determine breadth of subthreshold frequency receptive fields in primary auditory cortex. J. Neurophysio., 91, 2551–2567.CrossRefGoogle ScholarPubMed
Keck, T., Mrsic-Flogel, T.D., Vaz Afonso, M., Eysel, U.T., Bonhoeffer, T. and Hubener, M. (2008). Massive restructuring of neuronal circuits during functional reorganization of adult visual cortex. Nature Neurosci., 11, 1162–1167.CrossRefGoogle ScholarPubMed
Logothetis, N.K. (2008). What we can do and what we cannot do with fMRI. Nature, 453, 869–878.CrossRefGoogle Scholar
Logothetis, N.K. and Pfeuffer, J. (2004). On the nature of the BOLD fMRI contrast mechanism. Magn. Reson. Imaging, 22, 1517–1531.CrossRefGoogle ScholarPubMed
Malonek, D. and Grinvald, A. (1996). Interactions between electrical activity and cortical microcirculation revealed by imaging spectroscopy: implications for functional brain mapping. Science, 272, 551–554.CrossRefGoogle ScholarPubMed
Masino, S.A., Kwon, M.C., Dory, Y. and Frostig, R.D. (1993). Characterization of functional organization within rat barrel cortex using intrinsic signal optical imaging through a thinned skull. Proc. Nati. Acad. Sci. USA, 90, 9998–10002.CrossRefGoogle ScholarPubMed
Mathiesen, C., Caesar, K., Akgoren, N. and Lauritzen, M. (1998). Modification of activity-dependent increases of cerebral blood flow by excitatory synaptic activity and spikes in rat cerebellar cortex. J. Physiol., 512 (2), 555–566.CrossRefGoogle ScholarPubMed
Mcloughlin, N.P. and Blasdel, G.G. (1998). Wavelength-dependent differences between optically determined functions maps from macaque striate cortex. NeuroImage, 7, 326–336.Google Scholar
Merzenich, M.M., Nelson, R.J., Stryker, M.P., Cynader, M.S., Schoppmann, A. and Zook, J. M. (1984). Somatosensory cortical map changes following digit amputation in adult monkeys. J. Comp. Neurol., 224, 591–605.CrossRefGoogle ScholarPubMed
Mukamel, R., Gelbard, H., Arieli, A., Hasson, U., Fried, I. and Malach, R. (2005). Coupling between neuronal firing, field potentials, and FMRI in human auditory cortex. Science, 309, 951–954.CrossRefGoogle ScholarPubMed
Nicolelis, M.A. and Ribeiro, S. (2002). Multielectrode recordings: the next steps. Curr. Opinion Neurobiol., 12, 602–606.CrossRefGoogle ScholarPubMed
Niessing, J., Ebisch, B., Schmidt, K.E., Niessing, M., Singer, W. and Galuske, R.A. (2005). Hemodynamic signals correlate tightly with synchronized gamma oscillations. Science, 309, 948–951.CrossRefGoogle ScholarPubMed
Petersen, C.C. (2007). The functional organization of the barrel cortex. Neuron, 56, 339–355.CrossRefGoogle ScholarPubMed
Prakash, N., Vanderhaeghen, P., Cohen-Cory, S., Frisen, J., Flanagan, J.G. and Frostig, R. D. (2000). Malformation of the functional organization of somatosensory cortex in adult ephrin-A5 knock-out mice revealed by in vivo functional imaging. J. Neurosci., 20, 5841–5847.CrossRefGoogle ScholarPubMed
Rauch, A., Rainer, G. and Logothetis, N.K. (2008). The effect of a serotonin-induced dissociation between spiking and perisynaptic activity on BOLD functional MRI. Proc. Nati. Acad. Sci. USA, 105, 6759–6764.CrossRefGoogle ScholarPubMed
Rector, D.M., Yao, X., Harper, R.M. and George, J.S. (2009). In-vivo observations of rapid scattered light changes associated with neurophysiological activity. In: R. D., Frostig (editor), In Vivo Optical Imaging of Brain Function (2nd edition). Boca Raton, FL: CRC Press.Google ScholarPubMed
Reidl, J., Starke, J., Omer, D.B., Grinvald, A. and Spors, H. (2007). Independent component analysis of high-resolution imaging data identifies distinct functional domains. NeuroImage, 34, 94–108.CrossRefGoogle ScholarPubMed
Roland, P.E., Hanazawa, A., Undeman, C., Eriksson, D., Tompa, T., Nakamura, H., Valentiniene, S. and Ahmed, B. (2006). Cortical feedback depolarization waves: a mechanism of top-down influence on early visual areas. Proc. Nati. Acad. Sci. USA, 103, 12586–12591.CrossRefGoogle ScholarPubMed
Schiessl, I., Stetter, M., Mayhew, J.E., McLoughlin, N., Lund, J.S. and Obermayer, K. (2000). Blind signal separation from optical imaging recordings with extended spatial decorrelation. IEEE Trans. Biomed. Eng., 47, 573–577.CrossRefGoogle ScholarPubMed
Schiessl, I., Wang, W. and McLoughlin, N. (2008). Independent components of the haemodynamic response in intrinsic optical imaging. NeuroImage, 39, 634–646.CrossRefGoogle ScholarPubMed
Schummers, J., Yu, H. and Sur, M. (2008). Tuned responses of astrocytes and their influence on hemodynamic signals in the visual cortex. Science, 320, 1638–1643.CrossRefGoogle ScholarPubMed
Sharon, D., Jancke, D., Chavane, F., Na'aman, S. and Grinvald, A. (2007). Cortical response field dynamics in cat visual cortex. Cereb. Cortex, 17, 2866–2877.CrossRefGoogle ScholarPubMed
Sheth, S.A., Nemoto, M., Guiou, M., Walker, M., Pouratian, N., Hageman, N. and Toga, A.W. (2004). Columnar specificity of microvascular oxygenation and volume responses: implications for functional brain mapping. J. Neurosci., 24, 634–641.CrossRefGoogle ScholarPubMed
Shibuki, K., Hishida, R., Tohmi, M., Takahashi, K., Kitaura, H. and Kubota, Y. (2009). Flavoprotein fluorescence imaging of experience-dependent cortical plasticity in rodents. In: R. D., Frostig (editor), In Vivo Optical Imaging of Brain Function (2nd edition). Boca Raton, FL: CRC Press.Google ScholarPubMed
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 ScholarPubMed
Siegel, R.M., Duann, J.R., Jung, T.P. and Sejnowski, T. (2007). Spatiotemporal dynamics of the functional architecture for gain fields in inferior parietal lobule of behaving monkey. Cereb. Cortex, 17, 378–390.Google ScholarPubMed
Slovin, H., Arieli, A., Hildesheim, R. and Grinvald, A. (2002). Long-term voltage-sensitive dye imaging reveals cortical dynamics in behaving monkeys. J. Neurophysio., 88, 3421–3438.CrossRefGoogle ScholarPubMed
Stetter, M., Schiessl, I., Otto, T., Sengpiel, F., Hubener, M., Bonhoeffer, T. and Obermayer, K. (2000). Principal component analysis and blind separation of sources for optical imaging of intrinsic signals. NeuroImage, 11, 482–490.CrossRefGoogle ScholarPubMed
Toth, L.J., Rao, S.C., Kim, D.S., Somers, D. and Sur, M. (1996). Subthreshold facilitation and suppression in primary visual cortex revealed by intrinsic signal imaging. Proc. Nati. Acad. Sci. USA, 93, 9869–9874.CrossRefGoogle ScholarPubMed
Ts'o, D.Y., Frostig, R.D., Lieke, E.E. and Grinvald, A. (1990). Functional organization of primate visual cortex revealed by high resolution optical imaging. Science, 249, 417–420.CrossRefGoogle ScholarPubMed
Vanzetta, I., Hildesheim, R. and Grinvald, A. (2005). Compartment-resolved imaging of activity-dependent dynamics of cortical blood volume and oximetry. J. Neurosci., 25, 2233–2244.CrossRefGoogle ScholarPubMed
Vanzetta, I. and Grinvald, A. (2008). Coupling between neuronal activity and microcirculation: implications for functional brain imaging. HFSP J., 2, 79–98.CrossRefGoogle ScholarPubMed
Viswanathan, A. and Freeman, R.D. (2007). Neurometabolic coupling in cerebral cortex reflects synaptic more than spiking activity. Nature neurosci., 10, 1308–1312.CrossRefGoogle ScholarPubMed
Xerri, C. (2008). Imprinting of idyosyncratic experience in cortical sensory maps: Neural substrates of representational remodeling and correlative perceptual changes. Behavi. Brain Res., 192, 26–41.CrossRefGoogle ScholarPubMed

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