Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-669899f699-qzcqf Total loading time: 0 Render date: 2025-04-25T12:58:08.803Z Has data issue: false hasContentIssue false

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

By
M. Fukushima ,
O. Yamashita  and
M. Sato
Edited by
Zhe Chen
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
Get access

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
Publisher: Cambridge University Press
Print publication year: 2015

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Book purchase

Temporarily unavailable

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

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×