State space methods for MEG source reconstruction

M. Fukushima; O. Yamashita; M. Sato

doi:10.1017/CBO9781139941433.004

State space methods for MEG source reconstruction

from Part I - State space methods for neural data

Published online by Cambridge University Press: 05 October 2015

O. Yamashita and

Edited by

M. Fukushima: Affiliation:
ATR Neural Information Analysis Laboratories
O. Yamashita: Affiliation:
ATR Neural Information Analysis Laboratories
M. Sato: Affiliation:
ATR Neural Information Analysis Laboratories
Zhe Chen: Affiliation:
New York University

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Summary

Introduction and problem formulation

Elucidating how the human brain is structured and how it functions is a fundamental aim of human neuroscience. To achieve such an aim, the activity of the human brain has been measured using noninvasive neuroimaging techniques, the most popular of which is functional magnetic resonance imaging (fMRI) (Ogawa et al. 1990). The fMRI signals are obtained at a spatial resolution of typically 3mm and measure changes of blood flow and blood oxygen consumption whose temporal dynamics are slower than that of neuronal electrical activities, resulting in a poor temporal resolution of the order of seconds. In contrast, magnetoencephalography (MEG) and electroencephalography (EEG) can detect changes of neuronal activities by the millisecond measurement of magnetic and electric fields, respectively, outside the skull (Hämäläinen et al. 1993; Nunez & Srinivasan 2006). The high temporal resolution of MEG (and EEG) is useful, especially for studying the dynamic integration of functionally specialized brain regions, which is a subject of growing interest in human neuroscience (de Pasquale et al. 2012).

The major problem of MEG is that spatial brain activity patterns are not easily understandable from sensor measurements. This is because the magnetic fields produced by neuronal current sources are superimposed to form rather uninterpretable spatial patterns of signals on sensors. Estimating the position and intensity of these current sources from the sensor measurements is called source reconstruction, or source localization. Solving the source reconstruction problem allows the mapping of temporally dynamic electrical activities in the human brain (Baillet et al. 2001). Since how brain regions are dynamically integrated to produce a variety of functions is of great interest in human neuroscience research, the mission of MEG source reconstruction is not only to localize position of the current sources, but also to identify directed interactions between these sources. A possible approach to this involves constructing a dynamic model of brain electrical activities, as well as developing an estimation algorithm for the source positions and interactions that are parametrized in this model.

Type: Chapter
Information: Advanced State Space Methods for Neural and Clinical Data , pp. 53 - 78

DOI: https://doi.org/10.1017/CBO9781139941433.004 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2015

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References

Attias, H. (1999). Inferring parameters and structure of latent variable models by variational Bayes. In Proceedings of 15th Conference on Uncertainty in Artificial Intelligence, pp. 21–30.Google Scholar

Baillet, S., Mosher, J. C. & Leahy, R.M. (2001). Electromagnetic brain mapping. IEEE Signal Processing Magazine 18(6), 14–30.Google Scholar

Beal, M. J. (2003), Variational algorithm for approximate Bayesian inference, PhD thesis, University College London, London, UK.

Bishop, C. M. (2006). Pattern Recognition and Machine Learning, New York: Springer.

Dale, A. M., Liu, A.K., Fischl, B.R., Buckner, R. L., Belliveau, J.W., Lewine, J.D. & Halgren, E. (2000). Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity. Neuron 26(1), 55–67.Google Scholar

David, O. & Friston, K. J. (2003). A neural mass model for MEG/EEG: coupling and neuronal dynamics. NeuroImage 20(3), 1743–1755.Google Scholar

David, O., Harrison, L. & Friston, K. J. (2005). Modelling event-related responses in the brain. NeuroImage 25(3), 756–770.Google Scholar

David, O., Kiebel, S., Harrison, L., Mattout, J., Kilner, J. & Friston, K.J. (2006). Dynamic causal modeling of evoked responses in EEG and MEG. NeuroImage 30(4), 1255–1272.Google Scholar

Davies-Thompson, J. & Andrews, T. J. (2012). Intra- and interhemispheric connectivity between face-selective regions in the human brain. Journal of Neurophysiology 108(11), 3087–3095.Google Scholar

de Pasquale, F., Della Penna, S., Snyder, A. Z., Marzetti, L., Pizzella, V., Romani, G. L. & Corbetta, M. (2012). A cortical core for dynamic integration of functional networks in the resting human brain. Neuron 74(4), 753–764.Google Scholar

Fairhall, S. L. & Ishai, A. (2007). Effective connectivity within the distributed cortical network for face perception. Cerebral Cortex 17(10), 2400–2406.Google Scholar

Friston, K. J., Harrison, L. & Penny, W. (2003). Dynamic causal modelling. NeuroImage 19(4), 1273–1302.Google Scholar

Fukushima, M., Yamashita, O., Kanemura, A., Ishii, S., Kawato, M. & Sato, M. (2012). A state-space modeling approach for localization of focal current sources from MEG. IEEE Transactions on Biomedical Engineering 59(6), 1561–1571.Google Scholar

Fukushima, M., Yamashita, O., Knösche, T. R. & Sato, M. (2015). MEG source reconstruction based on identification of directed source interactions on whole-brain anatomical networks. NeuroImage, 105, 408–427.Google Scholar

Gabriel, D., Veuillet, E., Ragot, R., Schwartz, D., Ducorps, A., Norena, A., Durrant, J.D., Bonmartin, A., Cotton, F. & Collet, L. (2004). Effect of stimulus frequency and stimulation site on the N1m response of the human auditory cortex. Hearing Research 197(1–2), 55–64.Google Scholar

Galka, A., Yamashita, O., Ozaki, T., Biscay, R. & Valdés-Sosa, P. (2004). A solution to the dynamical inverse problem of EEG generation using spatiotemporal Kalman filtering. NeuroImage 23(2), 435–453.Google Scholar

Ghosh, A., Rho, Y., McIntosh, A. R., Kötter, R. & Jirsa, V. K. (2008). Noise during rest enables the exploration of the brain's dynamic repertoire. PLoS Computational Biology 4(10), e1000196.Google Scholar

Godey, B., Schwartz, D., de Graaf, J. B., Chauvel, P. & Liégeois-Chauvel, C. (2001). Neuromagnetic source localization of auditory evoked fields and intracerebral evoked potentials: a comparison of data in the same patients. Clinical Neurophysiology 112(10), 1850–1859.Google Scholar

Goodale, M.A. & Milner, A. D. (1992). Separate visual pathways for perception and action. Trends in Neuroscience 15(1), 20–25.Google Scholar

Gschwind, M., Pourtois, G., Schwartz, S., Van De Ville, D. & Vuilleumier, P. (2012). Whitematter connectivity between face-responsive regions in the human brain. Cerebral Cortex 22(7), 1564–1576.Google Scholar

Hämäläinen, M., Hari, R., Ilmoniemi, R. J., Knuutila, J. & Lounasmaa, O.V. (1993). Magnetoencephalography – theory, instrumentation, and applications to noninvasive studies of the working human brain. Reviews of Modern Physics 65(2), 413–497.Google Scholar

Hämäläinen, M. S. & Ilmoniemi, R. J. (1994). Interpreting magnetic fields of the brain: minimum norm estimates. Medical & Biological Engineering & Computing 32(1), 35–42.Google Scholar

Hari, R., Aittoniemi, K., Järvinen, M. L., Katila, T. & Varpula, T. (1980). Auditory evoked transient and sustained magnetic fields of the human brain localization of neural generators. Experimental Brain Research 40(2), 237–240.Google Scholar

Haxby, J.V., Hoffman, E. & Gobbini, M. I. (2000). The distributed human neural system for face perception. Trends in Cognitive Science 4(6), 223–233.Google Scholar

Henson, R. N., Wakeman, D.G., Litvak, V. & Friston, K. J. (2011). A parametric empirical Bayesian framework for the EEG/MEG inverse problem: Generative models for multi-subject and multi-modal integration. Frontiers in Human Neuroscience 5(76), 1–16.Google Scholar

Imig, T. J. & Adrián, H. O. (1977). Binaural columns in the primary field (A1) of cat auditory cortex. Brain Research 138(2), 241–257.Google Scholar

Lamus, C., Hämäläinen, M. S., Temereanca, S., Brown, E. N. & Purdon, P. L. (2012). A spatiotemporal dynamic distributed solution to the MEG inverse problem. NeuroImage 63(2), 894–909.Google Scholar

Matsuura, K. & Okabe, Y. (1995). Selective minimum-norm solution of the biomagnetic inverse problem. IEEE Transactions on Biomedical Engineering 42(6), 608–615.Google Scholar

Mosher, J.C., Leahy, R. M. & Lewis, P. S. (1999). EEG and MEG: forward solutions for inverse methods. IEEE Transactions on Biomedical Engineering 46(3), 245–259.Google Scholar

Neal, R. M. (1996). Bayesian Learning for Neural Networks, New York: Springer.

Nummenmaa, A., Auranen, T., Hämäläinen, M. S., Jääskeläinen, I. P., Lampinen, J., Sams, M. & Vehtari, A. (2007). Hierarchical Bayesian estimates of distributed MEG sources: theoretical aspects and comparison of variational and MCMC methods. NeuroImage 35(2), 669–685.Google Scholar

Nunez, P. L. & Srinivasan, R. (2006). Electric Fields of the Brain: The Neurophysics of EEG, New York: Oxford University Press.

Ogawa, S., Lee, T. M., Kay, A.R. & Tank, D.W. (1990). Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proceedings of National Academy of Science USA 87(24), 9868–9872.Google Scholar

Olier, I., Trujillo-Barreto, N. J. & El-Deredy, W. (2013). A switching multi-scale dynamical network model of EEG/MEG. NeuroImage 83, 262–287.Google Scholar

Ou, W., Hämäläinen, M. S. & Golland, P. (2009). A distributed spatio-temporal EEG/MEG inverse solver. NeuroImage 44(3), 932–946.Google Scholar

Pantev, C., Ross, B., Berg, P., Elbert, T. & Rockstroh, B. (1998). Study of the human auditory cortices using a whole-head magnetometer: left vs. right hemisphere and ipsilateral vs. contralateral stimulation. Audiology and Neurotology 3, 183–190.Google Scholar

Pascual-Marqui, R. D., Michel, C. M. & Lehmann, D. (1994). Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. International Journal of Psychophysiology 18(1), 49–65.Google Scholar

Portin, K., Vanni, S., Virsu, V. & Hari, R. (1999). Stronger occipital cortical activation to lower than upper visual field stimuli Neuromagnetic recordings. Experimental Brain Research 124(3), 287–294.Google Scholar

Sato, M. (2001). Online model selection based on the variational Bayes. Neural Computation 13(7), 1649–1681.Google Scholar

Sato, M., 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.Google Scholar

Schmitt, U., Louis, A. K., Darvas, F., Buchner, H. & Fuchs, M. (2001). Numerical aspects of spatio-temporal current density reconstruction from EEG-/MEG-data. IEEE Transactions on Medical Imaging 20(4), 314–324.Google Scholar

Tournier, J.D., Calamante, F. & Connelly, A. (2007). Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. NeuroImage 35(4), 1459–1472.Google Scholar

Yamashita, O., Galka, A., Ozaki, T., Biscay, R. & Valdés-Sosa, P. (2004). Recursive penalized least squares solution for dynamical inverse problems of EEG generation. Human Brain Mapping 21(4), 221–235.Google Scholar

Yoshioka, T., Toyama, K., Kawato, M., Yamashita, O., Nishina, S., Yamagishi, N. & Sato, M. (2008). Evaluation of hierarchical Bayesian method through retinotopic brain activities reconstruction from fMRI and MEG signals. NeuroImage 42(4), 1397–1413.Google Scholar

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State space methods for MEG source reconstruction

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